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  1. Principles of gene microarray data analysis.

    Science.gov (United States)

    Mocellin, Simone; Rossi, Carlo Riccardo

    2007-01-01

    The development of several gene expression profiling methods, such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE), and gene microarray, together with the sequencing of the human genome, has provided an opportunity to monitor and investigate the complex cascade of molecular events leading to tumor development and progression. The availability of such large amounts of information has shifted the attention of scientists towards a nonreductionist approach to biological phenomena. High throughput technologies can be used to follow changing patterns of gene expression over time. Among them, gene microarray has become prominent because it is easier to use, does not require large-scale DNA sequencing, and allows for the parallel quantification of thousands of genes from multiple samples. Gene microarray technology is rapidly spreading worldwide and has the potential to drastically change the therapeutic approach to patients affected with tumor. Therefore, it is of paramount importance for both researchers and clinicians to know the principles underlying the analysis of the huge amount of data generated with microarray technology.

  2. Extended -Regular Sequence for Automated Analysis of Microarray Images

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    Jin Hee-Jeong

    2006-01-01

    Full Text Available Microarray study enables us to obtain hundreds of thousands of expressions of genes or genotypes at once, and it is an indispensable technology for genome research. The first step is the analysis of scanned microarray images. This is the most important procedure for obtaining biologically reliable data. Currently most microarray image processing systems require burdensome manual block/spot indexing work. Since the amount of experimental data is increasing very quickly, automated microarray image analysis software becomes important. In this paper, we propose two automated methods for analyzing microarray images. First, we propose the extended -regular sequence to index blocks and spots, which enables a novel automatic gridding procedure. Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images. Experimental results show that the proposed methods are more reliable and convenient than the commercial tools.

  3. An Introduction to MAMA (Meta-Analysis of MicroArray data) System.

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    Zhang, Zhe; Fenstermacher, David

    2005-01-01

    Analyzing microarray data across multiple experiments has been proven advantageous. To support this kind of analysis, we are developing a software system called MAMA (Meta-Analysis of MicroArray data). MAMA utilizes a client-server architecture with a relational database on the server-side for the storage of microarray datasets collected from various resources. The client-side is an application running on the end user's computer that allows the user to manipulate microarray data and analytical results locally. MAMA implementation will integrate several analytical methods, including meta-analysis within an open-source framework offering other developers the flexibility to plug in additional statistical algorithms.

  4. Microarray Я US: a user-friendly graphical interface to Bioconductor tools that enables accurate microarray data analysis and expedites comprehensive functional analysis of microarray results.

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    Dai, Yilin; Guo, Ling; Li, Meng; Chen, Yi-Bu

    2012-06-08

    Microarray data analysis presents a significant challenge to researchers who are unable to use the powerful Bioconductor and its numerous tools due to their lack of knowledge of R language. Among the few existing software programs that offer a graphic user interface to Bioconductor packages, none have implemented a comprehensive strategy to address the accuracy and reliability issue of microarray data analysis due to the well known probe design problems associated with many widely used microarray chips. There is also a lack of tools that would expedite the functional analysis of microarray results. We present Microarray Я US, an R-based graphical user interface that implements over a dozen popular Bioconductor packages to offer researchers a streamlined workflow for routine differential microarray expression data analysis without the need to learn R language. In order to enable a more accurate analysis and interpretation of microarray data, we incorporated the latest custom probe re-definition and re-annotation for Affymetrix and Illumina chips. A versatile microarray results output utility tool was also implemented for easy and fast generation of input files for over 20 of the most widely used functional analysis software programs. Coupled with a well-designed user interface, Microarray Я US leverages cutting edge Bioconductor packages for researchers with no knowledge in R language. It also enables a more reliable and accurate microarray data analysis and expedites downstream functional analysis of microarray results.

  5. Advanced spot quality analysis in two-colour microarray experiments

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    Vetter Guillaume

    2008-09-01

    Full Text Available Abstract Background Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information. Findings We evaluated the performance of two image analysis packages MAIA and GenePix (GP using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5% than GP with default spot filtering conditions. Conclusion Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions.

  6. Tissue Microarray Analysis Applied to Bone Diagenesis

    OpenAIRE

    Barrios Mello, Rafael; Regis Silva, Maria Regina; Seixas Alves, Maria Teresa; Evison, Martin; Guimarães, Marco Aurélio; Francisco, Rafaella Arrabaça; Dias Astolphi, Rafael; Miazato Iwamura, Edna Sadayo

    2017-01-01

    Taphonomic processes affecting bone post mortem are important in forensic, archaeological and palaeontological investigations. In this study, the application of tissue microarray (TMA) analysis to a sample of femoral bone specimens from 20 exhumed individuals of known period of burial and age at death is described. TMA allows multiplexing of subsamples, permitting standardized comparative analysis of adjacent sections in 3-D and of representative cross-sections of a large number of specimens....

  7. Automating dChip: toward reproducible sharing of microarray data analysis

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    Li Cheng

    2008-05-01

    Full Text Available Abstract Background During the past decade, many software packages have been developed for analysis and visualization of various types of microarrays. We have developed and maintained the widely used dChip as a microarray analysis software package accessible to both biologist and data analysts. However, challenges arise when dChip users want to analyze large number of arrays automatically and share data analysis procedures and parameters. Improvement is also needed when the dChip user support team tries to identify the causes of reported analysis errors or bugs from users. Results We report here implementation and application of the dChip automation module. Through this module, dChip automation files can be created to include menu steps, parameters, and data viewpoints to run automatically. A data-packaging function allows convenient transfer from one user to another of the dChip software, microarray data, and analysis procedures, so that the second user can reproduce the entire analysis session of the first user. An analysis report file can also be generated during an automated run, including analysis logs, user comments, and viewpoint screenshots. Conclusion The dChip automation module is a step toward reproducible research, and it can prompt a more convenient and reproducible mechanism for sharing microarray software, data, and analysis procedures and results. Automation data packages can also be used as publication supplements. Similar automation mechanisms could be valuable to the research community if implemented in other genomics and bioinformatics software packages.

  8. MAGMA: analysis of two-channel microarrays made easy.

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    Rehrauer, Hubert; Zoller, Stefan; Schlapbach, Ralph

    2007-07-01

    The web application MAGMA provides a simple and intuitive interface to identify differentially expressed genes from two-channel microarray data. While the underlying algorithms are not superior to those of similar web applications, MAGMA is particularly user friendly and can be used without prior training. The user interface guides the novice user through the most typical microarray analysis workflow consisting of data upload, annotation, normalization and statistical analysis. It automatically generates R-scripts that document MAGMA's entire data processing steps, thereby allowing the user to regenerate all results in his local R installation. The implementation of MAGMA follows the model-view-controller design pattern that strictly separates the R-based statistical data processing, the web-representation and the application logic. This modular design makes the application flexible and easily extendible by experts in one of the fields: statistical microarray analysis, web design or software development. State-of-the-art Java Server Faces technology was used to generate the web interface and to perform user input processing. MAGMA's object-oriented modular framework makes it easily extendible and applicable to other fields and demonstrates that modern Java technology is also suitable for rather small and concise academic projects. MAGMA is freely available at www.magma-fgcz.uzh.ch.

  9. Structured oligonucleotides for target indexing to allow single-vessel PCR amplification and solid support microarray hybridization.

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    Girard, Laurie D; Boissinot, Karel; Peytavi, Régis; Boissinot, Maurice; Bergeron, Michel G

    2015-02-07

    The combination of molecular diagnostic technologies is increasingly used to overcome limitations on sensitivity, specificity or multiplexing capabilities, and provide efficient lab-on-chip devices. Two such techniques, PCR amplification and microarray hybridization are used serially to take advantage of the high sensitivity and specificity of the former combined with high multiplexing capacities of the latter. These methods are usually performed in different buffers and reaction chambers. However, these elaborate methods have high complexity and cost related to reagent requirements, liquid storage and the number of reaction chambers to integrate into automated devices. Furthermore, microarray hybridizations have a sequence dependent efficiency not always predictable. In this work, we have developed the concept of a structured oligonucleotide probe which is activated by cleavage from polymerase exonuclease activity. This technology is called SCISSOHR for Structured Cleavage Induced Single-Stranded Oligonucleotide Hybridization Reaction. The SCISSOHR probes enable indexing the target sequence to a tag sequence. The SCISSOHR technology also allows the combination of nucleic acid amplification and microarray hybridization in a single vessel in presence of the PCR buffer only. The SCISSOHR technology uses an amplification probe that is irreversibly modified in presence of the target, releasing a single-stranded DNA tag for microarray hybridization. Each tag is composed of a 3-nucleotide sequence-dependent segment and a unique "target sequence-independent" 14-nucleotide segment allowing for optimal hybridization with minimal cross-hybridization. We evaluated the performance of five (5) PCR buffers to support microarray hybridization, compared to a conventional hybridization buffer. Finally, as a proof of concept, we developed a multiplexed assay for the amplification, detection, and identification of three (3) DNA targets. This new technology will facilitate the design

  10. MARS: Microarray analysis, retrieval, and storage system

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    Scheideler Marcel

    2005-04-01

    Full Text Available Abstract Background Microarray analysis has become a widely used technique for the study of gene-expression patterns on a genomic scale. As more and more laboratories are adopting microarray technology, there is a need for powerful and easy to use microarray databases facilitating array fabrication, labeling, hybridization, and data analysis. The wealth of data generated by this high throughput approach renders adequate database and analysis tools crucial for the pursuit of insights into the transcriptomic behavior of cells. Results MARS (Microarray Analysis and Retrieval System provides a comprehensive MIAME supportive suite for storing, retrieving, and analyzing multi color microarray data. The system comprises a laboratory information management system (LIMS, a quality control management, as well as a sophisticated user management system. MARS is fully integrated into an analytical pipeline of microarray image analysis, normalization, gene expression clustering, and mapping of gene expression data onto biological pathways. The incorporation of ontologies and the use of MAGE-ML enables an export of studies stored in MARS to public repositories and other databases accepting these documents. Conclusion We have developed an integrated system tailored to serve the specific needs of microarray based research projects using a unique fusion of Web based and standalone applications connected to the latest J2EE application server technology. The presented system is freely available for academic and non-profit institutions. More information can be found at http://genome.tugraz.at.

  11. The EADGENE Microarray Data Analysis Workshop

    DEFF Research Database (Denmark)

    de Koning, Dirk-Jan; Jaffrézic, Florence; Lund, Mogens Sandø

    2007-01-01

    Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from...... 10 countries performed and discussed the statistical analyses of real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays...... statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful...

  12. Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis of Microarray Images

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    Jianping Hua

    2004-01-01

    Full Text Available This paper presents microarray BASICA: an integrated image processing tool for background adjustment, segmentation, image compression, and analysis of cDNA microarray images. BASICA uses a fast Mann-Whitney test-based algorithm to segment cDNA microarray images, and performs postprocessing to eliminate the segmentation irregularities. The segmentation results, along with the foreground and background intensities obtained with the background adjustment, are then used for independent compression of the foreground and background. We introduce a new distortion measurement for cDNA microarray image compression and devise a coding scheme by modifying the embedded block coding with optimized truncation (EBCOT algorithm (Taubman, 2000 to achieve optimal rate-distortion performance in lossy coding while still maintaining outstanding lossless compression performance. Experimental results show that the bit rate required to ensure sufficiently accurate gene expression measurement varies and depends on the quality of cDNA microarray images. For homogeneously hybridized cDNA microarray images, BASICA is able to provide from a bit rate as low as 5 bpp the gene expression data that are 99% in agreement with those of the original 32 bpp images.

  13. Goober: A fully integrated and user-friendly microarray data management and analysis solution for core labs and bench biologists

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    Luo Wen

    2009-03-01

    Full Text Available Despite the large number of software tools developed to address different areas of microarray data analysis, very few offer an all-in-one solution with little learning curve. For microarray core labs, there are even fewer software packages available to help with their routine but critical tasks, such as data quality control (QC and inventory management. We have developed a simple-to-use web portal to allow bench biologists to analyze and query complicated microarray data and related biological pathways without prior training. Both experiment-based and gene-based analysis can be easily performed, even for the first-time user, through the intuitive multi-layer design and interactive graphic links. While being friendly to inexperienced users, most parameters in Goober can be easily adjusted via drop-down menus to allow advanced users to tailor their needs and perform more complicated analysis. Moreover, we have integrated graphic pathway analysis into the website to help users examine microarray data within the relevant biological content. Goober also contains features that cover most of the common tasks in microarray core labs, such as real time array QC, data loading, array usage and inventory tracking. Overall, Goober is a complete microarray solution to help biologists instantly discover valuable information from a microarray experiment and enhance the quality and productivity of microarray core labs. The whole package is freely available at http://sourceforge.net/projects/goober. A demo web server is available at http://www.goober-array.org.

  14. Integrating Biological Perspectives:. a Quantum Leap for Microarray Expression Analysis

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    Wanke, Dierk; Kilian, Joachim; Bloss, Ulrich; Mangelsen, Elke; Supper, Jochen; Harter, Klaus; Berendzen, Kenneth W.

    2009-02-01

    Biologists and bioinformatic scientists cope with the analysis of transcript abundance and the extraction of meaningful information from microarray expression data. By exploiting biological information accessible in public databases, we try to extend our current knowledge over the plant model organism Arabidopsis thaliana. Here, we give two examples of increasing the quality of information gained from large scale expression experiments by the integration of microarray-unrelated biological information: First, we utilize Arabidopsis microarray data to demonstrate that expression profiles are usually conserved between orthologous genes of different organisms. In an initial step of the analysis, orthology has to be inferred unambiguously, which then allows comparison of expression profiles between orthologs. We make use of the publicly available microarray expression data of Arabidopsis and barley, Hordeum vulgare. We found a generally positive correlation in expression trajectories between true orthologs although both organisms are only distantly related in evolutionary time scale. Second, extracting clusters of co-regulated genes implies similarities in transcriptional regulation via similar cis-regulatory elements (CREs). Vice versa approaches, where co-regulated gene clusters are found by investigating on CREs were not successful in general. Nonetheless, in some cases the presence of CREs in a defined position, orientation or CRE-combinations is positively correlated with co-regulated gene clusters. Here, we make use of genes involved in the phenylpropanoid biosynthetic pathway, to give one positive example for this approach.

  15. GEPAS, a web-based tool for microarray data analysis and interpretation

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    Tárraga, Joaquín; Medina, Ignacio; Carbonell, José; Huerta-Cepas, Jaime; Minguez, Pablo; Alloza, Eva; Al-Shahrour, Fátima; Vegas-Azcárate, Susana; Goetz, Stefan; Escobar, Pablo; Garcia-Garcia, Francisco; Conesa, Ana; Montaner, David; Dopazo, Joaquín

    2008-01-01

    Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org. PMID:18508806

  16. Polyadenylation state microarray (PASTA) analysis.

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    Beilharz, Traude H; Preiss, Thomas

    2011-01-01

    Nearly all eukaryotic mRNAs terminate in a poly(A) tail that serves important roles in mRNA utilization. In the cytoplasm, the poly(A) tail promotes both mRNA stability and translation, and these functions are frequently regulated through changes in tail length. To identify the scope of poly(A) tail length control in a transcriptome, we developed the polyadenylation state microarray (PASTA) method. It involves the purification of mRNA based on poly(A) tail length using thermal elution from poly(U) sepharose, followed by microarray analysis of the resulting fractions. In this chapter we detail our PASTA approach and describe some methods for bulk and mRNA-specific poly(A) tail length measurements of use to monitor the procedure and independently verify the microarray data.

  17. Metric learning for DNA microarray data analysis

    International Nuclear Information System (INIS)

    Takeuchi, Ichiro; Nakagawa, Masao; Seto, Masao

    2009-01-01

    In many microarray studies, gene set selection is an important preliminary step for subsequent main task such as tumor classification, cancer subtype identification, etc. In this paper, we investigate the possibility of using metric learning as an alternative to gene set selection. We develop a simple metric learning algorithm aiming to use it for microarray data analysis. Exploiting a property of the algorithm, we introduce a novel approach for extending the metric learning to be adaptive. We apply the algorithm to previously studied microarray data on malignant lymphoma subtype identification.

  18. AMDA: an R package for the automated microarray data analysis

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    Foti Maria

    2006-07-01

    Full Text Available Abstract Background Microarrays are routinely used to assess mRNA transcript levels on a genome-wide scale. Large amount of microarray datasets are now available in several databases, and new experiments are constantly being performed. In spite of this fact, few and limited tools exist for quickly and easily analyzing the results. Microarray analysis can be challenging for researchers without the necessary training and it can be time-consuming for service providers with many users. Results To address these problems we have developed an automated microarray data analysis (AMDA software, which provides scientists with an easy and integrated system for the analysis of Affymetrix microarray experiments. AMDA is free and it is available as an R package. It is based on the Bioconductor project that provides a number of powerful bioinformatics and microarray analysis tools. This automated pipeline integrates different functions available in the R and Bioconductor projects with newly developed functions. AMDA covers all of the steps, performing a full data analysis, including image analysis, quality controls, normalization, selection of differentially expressed genes, clustering, correspondence analysis and functional evaluation. Finally a LaTEX document is dynamically generated depending on the performed analysis steps. The generated report contains comments and analysis results as well as the references to several files for a deeper investigation. Conclusion AMDA is freely available as an R package under the GPL license. The package as well as an example analysis report can be downloaded in the Services/Bioinformatics section of the Genopolis http://www.genopolis.it/

  19. PIIKA 2: an expanded, web-based platform for analysis of kinome microarray data.

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    Brett Trost

    Full Text Available Kinome microarrays are comprised of peptides that act as phosphorylation targets for protein kinases. This platform is growing in popularity due to its ability to measure phosphorylation-mediated cellular signaling in a high-throughput manner. While software for analyzing data from DNA microarrays has also been used for kinome arrays, differences between the two technologies and associated biologies previously led us to develop Platform for Intelligent, Integrated Kinome Analysis (PIIKA, a software tool customized for the analysis of data from kinome arrays. Here, we report the development of PIIKA 2, a significantly improved version with new features and improvements in the areas of clustering, statistical analysis, and data visualization. Among other additions to the original PIIKA, PIIKA 2 now allows the user to: evaluate statistically how well groups of samples cluster together; identify sets of peptides that have consistent phosphorylation patterns among groups of samples; perform hierarchical clustering analysis with bootstrapping; view false negative probabilities and positive and negative predictive values for t-tests between pairs of samples; easily assess experimental reproducibility; and visualize the data using volcano plots, scatterplots, and interactive three-dimensional principal component analyses. Also new in PIIKA 2 is a web-based interface, which allows users unfamiliar with command-line tools to easily provide input and download the results. Collectively, the additions and improvements described here enhance both the breadth and depth of analyses available, simplify the user interface, and make the software an even more valuable tool for the analysis of kinome microarray data. Both the web-based and stand-alone versions of PIIKA 2 can be accessed via http://saphire.usask.ca.

  20. PIIKA 2: an expanded, web-based platform for analysis of kinome microarray data.

    Science.gov (United States)

    Trost, Brett; Kindrachuk, Jason; Määttänen, Pekka; Napper, Scott; Kusalik, Anthony

    2013-01-01

    Kinome microarrays are comprised of peptides that act as phosphorylation targets for protein kinases. This platform is growing in popularity due to its ability to measure phosphorylation-mediated cellular signaling in a high-throughput manner. While software for analyzing data from DNA microarrays has also been used for kinome arrays, differences between the two technologies and associated biologies previously led us to develop Platform for Intelligent, Integrated Kinome Analysis (PIIKA), a software tool customized for the analysis of data from kinome arrays. Here, we report the development of PIIKA 2, a significantly improved version with new features and improvements in the areas of clustering, statistical analysis, and data visualization. Among other additions to the original PIIKA, PIIKA 2 now allows the user to: evaluate statistically how well groups of samples cluster together; identify sets of peptides that have consistent phosphorylation patterns among groups of samples; perform hierarchical clustering analysis with bootstrapping; view false negative probabilities and positive and negative predictive values for t-tests between pairs of samples; easily assess experimental reproducibility; and visualize the data using volcano plots, scatterplots, and interactive three-dimensional principal component analyses. Also new in PIIKA 2 is a web-based interface, which allows users unfamiliar with command-line tools to easily provide input and download the results. Collectively, the additions and improvements described here enhance both the breadth and depth of analyses available, simplify the user interface, and make the software an even more valuable tool for the analysis of kinome microarray data. Both the web-based and stand-alone versions of PIIKA 2 can be accessed via http://saphire.usask.ca.

  1. Radioactive cDNA microarray in neurospsychiatry

    International Nuclear Information System (INIS)

    Choe, Jae Gol; Shin, Kyung Ho; Lee, Min Soo; Kim, Meyoung Kon

    2003-01-01

    Microarray technology allows the simultaneous analysis of gene expression patterns of thousands of genes, in a systematic fashion, under a similar set of experimental conditions, thus making the data highly comparable. In some cases arrays are used simply as a primary screen leading to downstream molecular characterization of individual gene candidates. In other cases, the goal of expression profiling is to begin to identify complex regulatory networks underlying developmental processes and disease states. Microarrays were originally used with cell lines or other simple model systems. More recently, microarrays have been used in the analysis of more complex biological tissues including neural systems and the brain. The application of cDNA arrays in neuropsychiatry has lagged behind other fields for a number of reasons. These include a requirement for a large amount of input probe RNA in fluorescent-glass based array systems and the cellular complexity introduced by multicellular brain and neural tissues. An additional factor that impacts the general use of microarrays in neuropsychiatry is the lack of availability of sequenced clone sets from model systems. While human cDNA clones have been widely available, high quality rat, mouse, and drosophilae, among others are just becoming widely available. A final factor in the application of cDNA microarrays in neuropsychiatry is cost of commercial arrays. As academic microarray facilitates become more commonplace custom made arrays will become more widely available at a lower cost allowing more widespread applications. In summary, microarray technology is rapidly having an impact on many areas of biomedical research. Radioisotope-nylon based microarrays offer alternatives that may in some cases be more sensitive, flexible, inexpensive, and universal as compared to other array formats, such as fluorescent-glass arrays. In some situations of limited RNA or exotic species, radioactive membrane microarrays may be the most

  2. Radioactive cDNA microarray in neurospsychiatry

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    Choe, Jae Gol; Shin, Kyung Ho; Lee, Min Soo; Kim, Meyoung Kon [Korea University Medical School, Seoul (Korea, Republic of)

    2003-02-01

    Microarray technology allows the simultaneous analysis of gene expression patterns of thousands of genes, in a systematic fashion, under a similar set of experimental conditions, thus making the data highly comparable. In some cases arrays are used simply as a primary screen leading to downstream molecular characterization of individual gene candidates. In other cases, the goal of expression profiling is to begin to identify complex regulatory networks underlying developmental processes and disease states. Microarrays were originally used with cell lines or other simple model systems. More recently, microarrays have been used in the analysis of more complex biological tissues including neural systems and the brain. The application of cDNA arrays in neuropsychiatry has lagged behind other fields for a number of reasons. These include a requirement for a large amount of input probe RNA in fluorescent-glass based array systems and the cellular complexity introduced by multicellular brain and neural tissues. An additional factor that impacts the general use of microarrays in neuropsychiatry is the lack of availability of sequenced clone sets from model systems. While human cDNA clones have been widely available, high quality rat, mouse, and drosophilae, among others are just becoming widely available. A final factor in the application of cDNA microarrays in neuropsychiatry is cost of commercial arrays. As academic microarray facilitates become more commonplace custom made arrays will become more widely available at a lower cost allowing more widespread applications. In summary, microarray technology is rapidly having an impact on many areas of biomedical research. Radioisotope-nylon based microarrays offer alternatives that may in some cases be more sensitive, flexible, inexpensive, and universal as compared to other array formats, such as fluorescent-glass arrays. In some situations of limited RNA or exotic species, radioactive membrane microarrays may be the most

  3. Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays

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    Wu Xiaogang

    2012-06-01

    Full Text Available In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease, and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI database, and pathway enrichment from the human pathway database (HPD. We use a recently-published microarray dataset (GSE24215 related to insulin resistance and type 2 diabetes (T2D as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

  4. arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays

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    Moreau Yves

    2005-05-01

    Full Text Available Abstract Background The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH. One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment. Results We have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser. Conclusion ArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at http://medgen.ugent.be/arrayCGHbase/.

  5. The Use of Atomic Force Microscopy for 3D Analysis of Nucleic Acid Hybridization on Microarrays.

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    Dubrovin, E V; Presnova, G V; Rubtsova, M Yu; Egorov, A M; Grigorenko, V G; Yaminsky, I V

    2015-01-01

    Oligonucleotide microarrays are considered today to be one of the most efficient methods of gene diagnostics. The capability of atomic force microscopy (AFM) to characterize the three-dimensional morphology of single molecules on a surface allows one to use it as an effective tool for the 3D analysis of a microarray for the detection of nucleic acids. The high resolution of AFM offers ways to decrease the detection threshold of target DNA and increase the signal-to-noise ratio. In this work, we suggest an approach to the evaluation of the results of hybridization of gold nanoparticle-labeled nucleic acids on silicon microarrays based on an AFM analysis of the surface both in air and in liquid which takes into account of their three-dimensional structure. We suggest a quantitative measure of the hybridization results which is based on the fraction of the surface area occupied by the nanoparticles.

  6. A flexible representation of omic knowledge for thorough analysis of microarray data

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    Demura Taku

    2006-03-01

    Full Text Available Abstract Background In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data. Results In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi. Conclusion GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be

  7. BASE - 2nd generation software for microarray data management and analysis

    Directory of Open Access Journals (Sweden)

    Nordborg Nicklas

    2009-10-01

    Full Text Available Abstract Background Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2. Results The new BASE presented in this report is a comprehensive annotable local microarray data repository and analysis application providing researchers with an efficient information management and analysis tool. The information management system tracks all material from biosource, via sample and through extraction and labelling to raw data and analysis. All items in BASE can be annotated and the annotations can be used as experimental factors in downstream analysis. BASE stores all microarray experiment related data regardless if analysis tools for specific techniques or data formats are readily available. The BASE team is committed to continue improving and extending BASE to make it usable for even more experimental setups and techniques, and we encourage other groups to target their specific needs leveraging on the infrastructure provided by BASE. Conclusion BASE is a comprehensive management application for information, data, and analysis of microarray experiments, available as free open source software at http://base.thep.lu.se under the terms of the GPLv3 license.

  8. BASE--2nd generation software for microarray data management and analysis.

    Science.gov (United States)

    Vallon-Christersson, Johan; Nordborg, Nicklas; Svensson, Martin; Häkkinen, Jari

    2009-10-12

    Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2. The new BASE presented in this report is a comprehensive annotable local microarray data repository and analysis application providing researchers with an efficient information management and analysis tool. The information management system tracks all material from biosource, via sample and through extraction and labelling to raw data and analysis. All items in BASE can be annotated and the annotations can be used as experimental factors in downstream analysis. BASE stores all microarray experiment related data regardless if analysis tools for specific techniques or data formats are readily available. The BASE team is committed to continue improving and extending BASE to make it usable for even more experimental setups and techniques, and we encourage other groups to target their specific needs leveraging on the infrastructure provided by BASE. BASE is a comprehensive management application for information, data, and analysis of microarray experiments, available as free open source software at http://base.thep.lu.se under the terms of the GPLv3 license.

  9. A Versatile Microarray Platform for Capturing Rare Cells

    Science.gov (United States)

    Brinkmann, Falko; Hirtz, Michael; Haller, Anna; Gorges, Tobias M.; Vellekoop, Michael J.; Riethdorf, Sabine; Müller, Volkmar; Pantel, Klaus; Fuchs, Harald

    2015-10-01

    Analyses of rare events occurring at extremely low frequencies in body fluids are still challenging. We established a versatile microarray-based platform able to capture single target cells from large background populations. As use case we chose the challenging application of detecting circulating tumor cells (CTCs) - about one cell in a billion normal blood cells. After incubation with an antibody cocktail, targeted cells are extracted on a microarray in a microfluidic chip. The accessibility of our platform allows for subsequent recovery of targets for further analysis. The microarray facilitates exclusion of false positive capture events by co-localization allowing for detection without fluorescent labelling. Analyzing blood samples from cancer patients with our platform reached and partly outreached gold standard performance, demonstrating feasibility for clinical application. Clinical researchers free choice of antibody cocktail without need for altered chip manufacturing or incubation protocol, allows virtual arbitrary targeting of capture species and therefore wide spread applications in biomedical sciences.

  10. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

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    Eils Roland

    2005-11-01

    Full Text Available Abstract Background The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. Results In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85% were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. Conclusion Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and

  11. Supervised group Lasso with applications to microarray data analysis

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    Huang Jian

    2007-02-01

    Full Text Available Abstract Background A tremendous amount of efforts have been devoted to identifying genes for diagnosis and prognosis of diseases using microarray gene expression data. It has been demonstrated that gene expression data have cluster structure, where the clusters consist of co-regulated genes which tend to have coordinated functions. However, most available statistical methods for gene selection do not take into consideration the cluster structure. Results We propose a supervised group Lasso approach that takes into account the cluster structure in gene expression data for gene selection and predictive model building. For gene expression data without biological cluster information, we first divide genes into clusters using the K-means approach and determine the optimal number of clusters using the Gap method. The supervised group Lasso consists of two steps. In the first step, we identify important genes within each cluster using the Lasso method. In the second step, we select important clusters using the group Lasso. Tuning parameters are determined using V-fold cross validation at both steps to allow for further flexibility. Prediction performance is evaluated using leave-one-out cross validation. We apply the proposed method to disease classification and survival analysis with microarray data. Conclusion We analyze four microarray data sets using the proposed approach: two cancer data sets with binary cancer occurrence as outcomes and two lymphoma data sets with survival outcomes. The results show that the proposed approach is capable of identifying a small number of influential gene clusters and important genes within those clusters, and has better prediction performance than existing methods.

  12. Microarray analysis in the archaeon Halobacterium salinarum strain R1.

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    Jens Twellmeyer

    Full Text Available BACKGROUND: Phototrophy of the extremely halophilic archaeon Halobacterium salinarum was explored for decades. The research was mainly focused on the expression of bacteriorhodopsin and its functional properties. In contrast, less is known about genome wide transcriptional changes and their impact on the physiological adaptation to phototrophy. The tool of choice to record transcriptional profiles is the DNA microarray technique. However, the technique is still rarely used for transcriptome analysis in archaea. METHODOLOGY/PRINCIPAL FINDINGS: We developed a whole-genome DNA microarray based on our sequence data of the Hbt. salinarum strain R1 genome. The potential of our tool is exemplified by the comparison of cells growing under aerobic and phototrophic conditions, respectively. We processed the raw fluorescence data by several stringent filtering steps and a subsequent MAANOVA analysis. The study revealed a lot of transcriptional differences between the two cell states. We found that the transcriptional changes were relatively weak, though significant. Finally, the DNA microarray data were independently verified by a real-time PCR analysis. CONCLUSION/SIGNIFICANCE: This is the first DNA microarray analysis of Hbt. salinarum cells that were actually grown under phototrophic conditions. By comparing the transcriptomics data with current knowledge we could show that our DNA microarray tool is well applicable for transcriptome analysis in the extremely halophilic archaeon Hbt. salinarum. The reliability of our tool is based on both the high-quality array of DNA probes and the stringent data handling including MAANOVA analysis. Among the regulated genes more than 50% had unknown functions. This underlines the fact that haloarchaeal phototrophy is still far away from being completely understood. Hence, the data recorded in this study will be subject to future systems biology analysis.

  13. Prediction of Pectin Yield and Quality by FTIR and Carbohydrate Microarray Analysis

    DEFF Research Database (Denmark)

    Baum, Andreas; Dominiak, Malgorzata Maria; Vidal-Melgosa, Silvia

    2017-01-01

    and carbohydrate microarray analysis were performed directly on the crude lime peel extracts during the time course of the extractions. Multivariate analysis of the data was carried out to predict final pectin yields. Fourier transform infrared spectroscopy (FTIR) was found applicable for determining the optimal...... extraction time for the enzymatic and acidic extraction processes, respectively. The combined results of FTIR and carbohydrate microarray analysis suggested major differences in the crude pectin extracts obtained by enzymatic and acid extraction, respectively. Enzymatically extracted pectin, thus, showed......, and that FTIR and carbohydrate microarray analysis have potential to be developed into online process analysis tools for prediction of pectin extraction yields and pectin features from measurements on crude pectin extracts....

  14. Microarray technology for major chemical contaminants analysis in food: current status and prospects.

    Science.gov (United States)

    Zhang, Zhaowei; Li, Peiwu; Hu, Xiaofeng; Zhang, Qi; Ding, Xiaoxia; Zhang, Wen

    2012-01-01

    Chemical contaminants in food have caused serious health issues in both humans and animals. Microarray technology is an advanced technique suitable for the analysis of chemical contaminates. In particular, immuno-microarray approach is one of the most promising methods for chemical contaminants analysis. The use of microarrays for the analysis of chemical contaminants is the subject of this review. Fabrication strategies and detection methods for chemical contaminants are discussed in detail. Application to the analysis of mycotoxins, biotoxins, pesticide residues, and pharmaceutical residues is also described. Finally, future challenges and opportunities are discussed.

  15. A cell spot microarray method for production of high density siRNA transfection microarrays

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    Mpindi John-Patrick

    2011-03-01

    Full Text Available Abstract Background High-throughput RNAi screening is widely applied in biological research, but remains expensive, infrastructure-intensive and conversion of many assays to HTS applications in microplate format is not feasible. Results Here, we describe the optimization of a miniaturized cell spot microarray (CSMA method, which facilitates utilization of the transfection microarray technique for disparate RNAi analyses. To promote rapid adaptation of the method, the concept has been tested with a panel of 92 adherent cell types, including primary human cells. We demonstrate the method in the systematic screening of 492 GPCR coding genes for impact on growth and survival of cultured human prostate cancer cells. Conclusions The CSMA method facilitates reproducible preparation of highly parallel cell microarrays for large-scale gene knockdown analyses. This will be critical towards expanding the cell based functional genetic screens to include more RNAi constructs, allow combinatorial RNAi analyses, multi-parametric phenotypic readouts or comparative analysis of many different cell types.

  16. Microarray-based screening of heat shock protein inhibitors.

    Science.gov (United States)

    Schax, Emilia; Walter, Johanna-Gabriela; Märzhäuser, Helene; Stahl, Frank; Scheper, Thomas; Agard, David A; Eichner, Simone; Kirschning, Andreas; Zeilinger, Carsten

    2014-06-20

    Based on the importance of heat shock proteins (HSPs) in diseases such as cancer, Alzheimer's disease or malaria, inhibitors of these chaperons are needed. Today's state-of-the-art techniques to identify HSP inhibitors are performed in microplate format, requiring large amounts of proteins and potential inhibitors. In contrast, we have developed a miniaturized protein microarray-based assay to identify novel inhibitors, allowing analysis with 300 pmol of protein. The assay is based on competitive binding of fluorescence-labeled ATP and potential inhibitors to the ATP-binding site of HSP. Therefore, the developed microarray enables the parallel analysis of different ATP-binding proteins on a single microarray. We have demonstrated the possibility of multiplexing by immobilizing full-length human HSP90α and HtpG of Helicobacter pylori on microarrays. Fluorescence-labeled ATP was competed by novel geldanamycin/reblastatin derivatives with IC50 values in the range of 0.5 nM to 4 μM and Z(*)-factors between 0.60 and 0.96. Our results demonstrate the potential of a target-oriented multiplexed protein microarray to identify novel inhibitors for different members of the HSP90 family. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

    Science.gov (United States)

    Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

    2008-06-18

    Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson

  18. Washing scaling of GeneChip microarray expression

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    Krohn Knut

    2010-05-01

    Full Text Available Abstract Background Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities. Results We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM and mismatch (MM probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values. Conclusions Washing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental

  19. Support vector machine and principal component analysis for microarray data classification

    Science.gov (United States)

    Astuti, Widi; Adiwijaya

    2018-03-01

    Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

  20. Significance analysis of lexical bias in microarray data

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    Falkow Stanley

    2003-04-01

    Full Text Available Abstract Background Genes that are determined to be significantly differentially regulated in microarray analyses often appear to have functional commonalities, such as being components of the same biochemical pathway. This results in certain words being under- or overrepresented in the list of genes. Distinguishing between biologically meaningful trends and artifacts of annotation and analysis procedures is of the utmost importance, as only true biological trends are of interest for further experimentation. A number of sophisticated methods for identification of significant lexical trends are currently available, but these methods are generally too cumbersome for practical use by most microarray users. Results We have developed a tool, LACK, for calculating the statistical significance of apparent lexical bias in microarray datasets. The frequency of a user-specified list of search terms in a list of genes which are differentially regulated is assessed for statistical significance by comparison to randomly generated datasets. The simplicity of the input files and user interface targets the average microarray user who wishes to have a statistical measure of apparent lexical trends in analyzed datasets without the need for bioinformatics skills. The software is available as Perl source or a Windows executable. Conclusion We have used LACK in our laboratory to generate biological hypotheses based on our microarray data. We demonstrate the program's utility using an example in which we confirm significant upregulation of SPI-2 pathogenicity island of Salmonella enterica serovar Typhimurium by the cation chelator dipyridyl.

  1. Parallel scan hyperspectral fluorescence imaging system and biomedical application for microarrays

    International Nuclear Information System (INIS)

    Liu Zhiyi; Ma Suihua; Liu Le; Guo Jihua; He Yonghong; Ji Yanhong

    2011-01-01

    Microarray research offers great potential for analysis of gene expression profile and leads to greatly improved experimental throughput. A number of instruments have been reported for microarray detection, such as chemiluminescence, surface plasmon resonance, and fluorescence markers. Fluorescence imaging is popular for the readout of microarrays. In this paper we develop a quasi-confocal, multichannel parallel scan hyperspectral fluorescence imaging system for microarray research. Hyperspectral imaging records the entire emission spectrum for every voxel within the imaged area in contrast to recording only fluorescence intensities of filter-based scanners. Coupled with data analysis, the recorded spectral information allows for quantitative identification of the contributions of multiple, spectrally overlapping fluorescent dyes and elimination of unwanted artifacts. The mechanism of quasi-confocal imaging provides a high signal-to-noise ratio, and parallel scan makes this approach a high throughput technique for microarray analysis. This system is improved with a specifically designed spectrometer which can offer a spectral resolution of 0.2 nm, and operates with spatial resolutions ranging from 2 to 30 μm . Finally, the application of the system is demonstrated by reading out microarrays for identification of bacteria.

  2. Robust gene selection methods using weighting schemes for microarray data analysis.

    Science.gov (United States)

    Kang, Suyeon; Song, Jongwoo

    2017-09-02

    A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

  3. Tissue Microarray Analysis Applied to Bone Diagenesis.

    Science.gov (United States)

    Mello, Rafael Barrios; Silva, Maria Regina Regis; Alves, Maria Teresa Seixas; Evison, Martin Paul; Guimarães, Marco Aurelio; Francisco, Rafaella Arrabaca; Astolphi, Rafael Dias; Iwamura, Edna Sadayo Miazato

    2017-01-04

    Taphonomic processes affecting bone post mortem are important in forensic, archaeological and palaeontological investigations. In this study, the application of tissue microarray (TMA) analysis to a sample of femoral bone specimens from 20 exhumed individuals of known period of burial and age at death is described. TMA allows multiplexing of subsamples, permitting standardized comparative analysis of adjacent sections in 3-D and of representative cross-sections of a large number of specimens. Standard hematoxylin and eosin, periodic acid-Schiff and silver methenamine, and picrosirius red staining, and CD31 and CD34 immunohistochemistry were applied to TMA sections. Osteocyte and osteocyte lacuna counts, percent bone matrix loss, and fungal spheroid element counts could be measured and collagen fibre bundles observed in all specimens. Decalcification with 7% nitric acid proceeded more rapidly than with 0.5 M EDTA and may offer better preservation of histological and cellular structure. No endothelial cells could be detected using CD31 and CD34 immunohistochemistry. Correlation between osteocytes per lacuna and age at death may reflect reported age-related responses to microdamage. Methodological limitations and caveats, and results of the TMA analysis of post mortem diagenesis in bone are discussed, and implications for DNA survival and recovery considered.

  4. Extended analysis of benchmark datasets for Agilent two-color microarrays

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    Kerr Kathleen F

    2007-10-01

    Full Text Available Abstract Background As part of its broad and ambitious mission, the MicroArray Quality Control (MAQC project reported the results of experiments using External RNA Controls (ERCs on five microarray platforms. For most platforms, several different methods of data processing were considered. However, there was no similar consideration of different methods for processing the data from the Agilent two-color platform. While this omission is understandable given the scale of the project, it can create the false impression that there is consensus about the best way to process Agilent two-color data. It is also important to consider whether ERCs are representative of all the probes on a microarray. Results A comparison of different methods of processing Agilent two-color data shows substantial differences among methods for low-intensity genes. The sensitivity and specificity for detecting differentially expressed genes varies substantially for different methods. Analysis also reveals that the ERCs in the MAQC data only span the upper half of the intensity range, and therefore cannot be representative of all genes on the microarray. Conclusion Although ERCs demonstrate good agreement between observed and expected log-ratios on the Agilent two-color platform, such an analysis is incomplete. Simple loess normalization outperformed data processing with Agilent's Feature Extraction software for accurate identification of differentially expressed genes. Results from studies using ERCs should not be over-generalized when ERCs are not representative of all probes on a microarray.

  5. Normalization for triple-target microarray experiments

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    Magniette Frederic

    2008-04-01

    Full Text Available Abstract Background Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes are Cy3 and Cy5. Recent improvements in the technology (dye-labelling, scanner and, image analysis allow hybridization up to four samples simultaneously. The two additional dyes are Alexa488 and Alexa494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data. Results We propose a two-step normalization procedure for triple-target experiments. First the dye bleeding is evaluated and corrected if necessary. Then the signal in each channel is normalized using a generalized lowess procedure to correct a global dye bias. The normalization procedure is validated using triple-self experiments and by comparing the results of triple-target and two-color experiments. Although the focus is on triple-target microarrays, the proposed method can be used to normalize p differently labelled targets co-hybridized on a same array, for any value of p greater than 2. Conclusion The proposed normalization procedure is effective: the technical biases are reduced, the number of false positives is under control in the analysis of differentially expressed genes, and the triple-target experiments are more powerful than the corresponding two-color experiments. There is room for improving the microarray experiments by simultaneously hybridizing more than two samples.

  6. Identification of potential biomarkers from microarray experiments using multiple criteria optimization

    International Nuclear Information System (INIS)

    Sánchez-Peña, Matilde L; Isaza, Clara E; Pérez-Morales, Jaileene; Rodríguez-Padilla, Cristina; Castro, José M; Cabrera-Ríos, Mauricio

    2013-01-01

    Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization

  7. Bayesian meta-analysis models for microarray data: a comparative study

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    Song Joon J

    2007-03-01

    Full Text Available Abstract Background With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods. Results Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets

  8. The IronChip evaluation package: a package of perl modules for robust analysis of custom microarrays

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    Brazma Alvis

    2010-03-01

    Full Text Available Abstract Background Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available. Results The IronChip Evaluation Package (ICEP is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls. Conclusions ICEP is a stand-alone Windows application to obtain optimal data quality from custom-designed microarrays and is freely available here (see "Additional Files" section and at: http://www.alice-dsl.net/evgeniy.vainshtein/ICEP/

  9. Mutation analysis of 272 Spanish families affected by autosomal recessive retinitis pigmentosa using a genotyping microarray.

    Science.gov (United States)

    Ávila-Fernández, Almudena; Cantalapiedra, Diego; Aller, Elena; Vallespín, Elena; Aguirre-Lambán, Jana; Blanco-Kelly, Fiona; Corton, M; Riveiro-Álvarez, Rosa; Allikmets, Rando; Trujillo-Tiebas, María José; Millán, José M; Cremers, Frans P M; Ayuso, Carmen

    2010-12-03

    Retinitis pigmentosa (RP) is a genetically heterogeneous disorder characterized by progressive loss of vision. The aim of this study was to identify the causative mutations in 272 Spanish families using a genotyping microarray. 272 unrelated Spanish families, 107 with autosomal recessive RP (arRP) and 165 with sporadic RP (sRP), were studied using the APEX genotyping microarray. The families were also classified by clinical criteria: 86 juveniles and 186 typical RP families. Haplotype and sequence analysis were performed to identify the second mutated allele. At least one-gene variant was found in 14% and 16% of the juvenile and typical RP groups respectively. Further study identified four new mutations, providing both causative changes in 11% of the families. Retinol Dehydrogenase 12 (RDH12) was the most frequently mutated gene in the juvenile RP group, and Usher Syndrome 2A (USH2A) and Ceramide Kinase-Like (CERKL) were the most frequently mutated genes in the typical RP group. The only variant found in CERKL was p.Arg257Stop, the most frequent mutation. The genotyping microarray combined with segregation and sequence analysis allowed us to identify the causative mutations in 11% of the families. Due to the low number of characterized families, this approach should be used in tandem with other techniques.

  10. Comparative RNA-Seq and microarray analysis of gene expression changes in B-cell lymphomas of Canis familiaris.

    Directory of Open Access Journals (Sweden)

    Marie Mooney

    Full Text Available Comparative oncology is a developing research discipline that is being used to assist our understanding of human neoplastic diseases. Companion canines are a preferred animal oncology model due to spontaneous tumor development and similarity to human disease at the pathophysiological level. We use a paired RNA sequencing (RNA-Seq/microarray analysis of a set of four normal canine lymph nodes and ten canine lymphoma fine needle aspirates to identify technical biases and variation between the technologies and convergence on biological disease pathways. Surrogate Variable Analysis (SVA provides a formal multivariate analysis of the combined RNA-Seq/microarray data set. Applying SVA to the data allows us to decompose variation into contributions associated with transcript abundance, differences between the technology, and latent variation within each technology. A substantial and highly statistically significant component of the variation reflects transcript abundance, and RNA-Seq appeared more sensitive for detection of transcripts expressed at low levels. Latent random variation among RNA-Seq samples is also distinct in character from that impacting microarray samples. In particular, we observed variation between RNA-Seq samples that reflects transcript GC content. Platform-independent variable decomposition without a priori knowledge of the sources of variation using SVA represents a generalizable method for accomplishing cross-platform data analysis. We identified genes differentially expressed between normal lymph nodes of disease free dogs and a subset of the diseased dogs diagnosed with B-cell lymphoma using each technology. There is statistically significant overlap between the RNA-Seq and microarray sets of differentially expressed genes. Analysis of overlapping genes in the context of biological systems suggests elevated expression and activity of PI3K signaling in B-cell lymphoma biopsies compared with normal biopsies, consistent with

  11. Direct calibration of PICKY-designed microarrays

    Directory of Open Access Journals (Sweden)

    Ronald Pamela C

    2009-10-01

    Full Text Available Abstract Background Few microarrays have been quantitatively calibrated to identify optimal hybridization conditions because it is difficult to precisely determine the hybridization characteristics of a microarray using biologically variable cDNA samples. Results Using synthesized samples with known concentrations of specific oligonucleotides, a series of microarray experiments was conducted to evaluate microarrays designed by PICKY, an oligo microarray design software tool, and to test a direct microarray calibration method based on the PICKY-predicted, thermodynamically closest nontarget information. The complete set of microarray experiment results is archived in the GEO database with series accession number GSE14717. Additional data files and Perl programs described in this paper can be obtained from the website http://www.complex.iastate.edu under the PICKY Download area. Conclusion PICKY-designed microarray probes are highly reliable over a wide range of hybridization temperatures and sample concentrations. The microarray calibration method reported here allows researchers to experimentally optimize their hybridization conditions. Because this method is straightforward, uses existing microarrays and relatively inexpensive synthesized samples, it can be used by any lab that uses microarrays designed by PICKY. In addition, other microarrays can be reanalyzed by PICKY to obtain the thermodynamically closest nontarget information for calibration.

  12. Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays

    Directory of Open Access Journals (Sweden)

    Jouventin Pierre

    2010-05-01

    Full Text Available Abstract Background Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. Results We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. Conclusions MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions.

  13. GenePublisher: automated analysis of DNA microarray data

    DEFF Research Database (Denmark)

    Knudsen, Steen; Workman, Christopher; Sicheritz-Ponten, T.

    2003-01-01

    GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with aspecification of the data. The server performs normalization...

  14. Hierarchical information representation and efficient classification of gene expression microarray data

    OpenAIRE

    Bosio, Mattia

    2014-01-01

    In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and eficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample...

  15. Bioinformatics and Microarray Data Analysis on the Cloud.

    Science.gov (United States)

    Calabrese, Barbara; Cannataro, Mario

    2016-01-01

    High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

  16. DNA microarray data and contextual analysis of correlation graphs

    Directory of Open Access Journals (Sweden)

    Hingamp Pascal

    2003-04-01

    Full Text Available Abstract Background DNA microarrays are used to produce large sets of expression measurements from which specific biological information is sought. Their analysis requires efficient and reliable algorithms for dimensional reduction, classification and annotation. Results We study networks of co-expressed genes obtained from DNA microarray experiments. The mathematical concept of curvature on graphs is used to group genes or samples into clusters to which relevant gene or sample annotations are automatically assigned. Application to publicly available yeast and human lymphoma data demonstrates the reliability of the method in spite of its simplicity, especially with respect to the small number of parameters involved. Conclusions We provide a method for automatically determining relevant gene clusters among the many genes monitored with microarrays. The automatic annotations and the graphical interface improve the readability of the data. A C++ implementation, called Trixy, is available from http://tagc.univ-mrs.fr/bioinformatics/trixy.html.

  17. Multivariate analysis of microarray data: differential expression and differential connection.

    Science.gov (United States)

    Kiiveri, Harri T

    2011-02-01

    Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

  18. Cyclin D1 and Ewing's sarcoma/PNET: A microarray analysis.

    Science.gov (United States)

    Fagone, Paolo; Nicoletti, Ferdinando; Salvatorelli, Lucia; Musumeci, Giuseppe; Magro, Gaetano

    2015-10-01

    Recent immunohistochemical analyses have showed that cyclin D1 is expressed in soft tissue Ewing's sarcoma/peripheral neuroectodermal tumor (PNET) of childhood and adolescents, while it is undetectable in both embryonal and alveolar rhabdomyosarcoma. In the present paper, microarray analysis provided evidence of a significant upregulation of cyclin D1 in Ewing's sarcoma as compared to normal tissues. In addition, we confirmed our previous findings of a significant over-expression of cyclin D1 in Ewing sarcoma as compared to rhabdomyosarcoma. Bioinformatic analysis also allowed to identify some other genes, strongly correlated to cyclin D1, which, although not previously studied in pediatric tumors, could represent novel markers for the diagnosis and prognosis of Ewing's sarcoma/PNET. The data herein provided support not only the use of cyclin D1 as a diagnostic marker of Ewing sarcoma/PNET but also the possibility of using drugs targeting cyclin D1 as potential therapeutic strategies. Copyright © 2015 Elsevier GmbH. All rights reserved.

  19. Immunohistochemistry - Microarray Analysis of Patients with Peritoneal Metastases of Appendiceal or Colorectal Origin

    Directory of Open Access Journals (Sweden)

    Danielle E Green

    2015-01-01

    Full Text Available BackgroundThe value of immunohistochemistry (IHC-microarray analysis of pathological specimens in the management of patients is controversial although preliminary data suggests potential benefit. We describe the characteristics of patients undergoing a commercially available IHC-microarray method in patients with peritoneal metastases (PM and the feasibility of this technique in this population.MethodsWe retrospectively analyzed consecutive patients with pathologically confirmed PM from appendiceal or colorectal primary who underwent Caris Molecular IntelligenceTM testing. IHC, microarray, FISH and mutational analysis were included and stratified by PCI score, histology and treatment characteristics. Statistical analysis was performed using non-parametric tests.ResultsOur study included 5 patients with appendiceal and 11 with colorectal PM. The median age of patients was 51 (IQR 39-65 years, with 11(68% female. The median PCI score of the patients was 17(IQR 10-25. Hyperthermic intra-peritoneal chemoperfusion (HIPEC was performed in 4 (80% patients with appendiceal primary tumors and 4 (36% with colorectal primary. KRAS mutations were encountered in 40% of appendiceal vs. 30% colorectal tumors, while BRAF mutations were seen in 40% of colorectal PM and none of the patients with appendiceal PM (p=0.06. IHC biomarker expression was not significantly different between the two primaries. Sufficient tumor for microarray analysis was found in 44% (n=7 patients, which was not associated with previous use of chemotherapy (p>0.20 for 5-FU/LV, Irinotecan and Oxaliplatin.ConclusionsIn a small sample of patients with peritoneal metastases, the feasibility and results of IHC-microarray staining based on a commercially available test is reported. The apparent high incidence of the BRAF mutation in patients with PM may potentially offer opportunities for novel therapeutics and suggest that IHC-microarray is a method that can be used in this population.

  20. Development of a novel multiplex DNA microarray for Fusarium graminearum and analysis of azole fungicide responses

    Directory of Open Access Journals (Sweden)

    Deising Holger B

    2011-01-01

    Full Text Available Abstract Background The toxigenic fungal plant pathogen Fusarium graminearum compromises wheat production worldwide. Azole fungicides play a prominent role in controlling this pathogen. Sequencing of its genome stimulated the development of high-throughput technologies to study mechanisms of coping with fungicide stress and adaptation to fungicides at a previously unprecedented precision. DNA-microarrays have been used to analyze genome-wide gene expression patterns and uncovered complex transcriptional responses. A recently developed one-color multiplex array format allowed flexible, effective, and parallel examinations of eight RNA samples. Results We took advantage of the 8 × 15 k Agilent format to design, evaluate, and apply a novel microarray covering the whole F. graminearum genome to analyze transcriptional responses to azole fungicide treatment. Comparative statistical analysis of expression profiles uncovered 1058 genes that were significantly differentially expressed after azole-treatment. Quantitative RT-PCR analysis for 31 selected genes indicated high conformity to results from the microarray hybridization. Among the 596 genes with significantly increased transcript levels, analyses using GeneOntology and FunCat annotations detected the ergosterol-biosynthesis pathway genes as the category most significantly responding, confirming the mode-of-action of azole fungicides. Cyp51A, which is one of the three F. graminearum paralogs of Cyp51 encoding the target of azoles, was the most consistently differentially expressed gene of the entire study. A molecular phylogeny analyzing the relationships of the three CYP51 proteins in the context of 38 fungal genomes belonging to the Pezizomycotina indicated that CYP51C (FGSG_11024 groups with a new clade of CYP51 proteins. The transcriptional profiles for genes encoding ABC transporters and transcription factors suggested several involved in mechanisms alleviating the impact of the fungicide

  1. Identification of cytokinin-responsive genes using microarray meta-analysis and RNA-Seq in Arabidopsis.

    Science.gov (United States)

    Bhargava, Apurva; Clabaugh, Ivory; To, Jenn P; Maxwell, Bridey B; Chiang, Yi-Hsuan; Schaller, G Eric; Loraine, Ann; Kieber, Joseph J

    2013-05-01

    Cytokinins are N(6)-substituted adenine derivatives that play diverse roles in plant growth and development. We sought to define a robust set of genes regulated by cytokinin as well as to query the response of genes not represented on microarrays. To this end, we performed a meta-analysis of microarray data from a variety of cytokinin-treated samples and used RNA-seq to examine cytokinin-regulated gene expression in Arabidopsis (Arabidopsis thaliana). Microarray meta-analysis using 13 microarray experiments combined with empirically defined filtering criteria identified a set of 226 genes differentially regulated by cytokinin, a subset of which has previously been validated by other methods. RNA-seq validated about 73% of the up-regulated genes identified by this meta-analysis. In silico promoter analysis indicated an overrepresentation of type-B Arabidopsis response regulator binding elements, consistent with the role of type-B Arabidopsis response regulators as primary mediators of cytokinin-responsive gene expression. RNA-seq analysis identified 73 cytokinin-regulated genes that were not represented on the ATH1 microarray. Representative genes were verified using quantitative reverse transcription-polymerase chain reaction and NanoString analysis. Analysis of the genes identified reveals a substantial effect of cytokinin on genes encoding proteins involved in secondary metabolism, particularly those acting in flavonoid and phenylpropanoid biosynthesis, as well as in the regulation of redox state of the cell, particularly a set of glutaredoxin genes. Novel splicing events were found in members of some gene families that are known to play a role in cytokinin signaling or metabolism. The genes identified in this analysis represent a robust set of cytokinin-responsive genes that are useful in the analysis of cytokinin function in plants.

  2. The Arabidopsis co-expression tool (act): a WWW-based tool and database for microarray-based gene expression analysis

    DEFF Research Database (Denmark)

    Jen, C. H.; Manfield, I. W.; Michalopoulos, D. W.

    2006-01-01

    be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots......We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (act) , based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression...

  3. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

    Science.gov (United States)

    Chatziioannou, Aristotelis; Moulos, Panagiotis; Kolisis, Fragiskos N

    2009-10-27

    The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Gene ARMADA provides a

  4. Seeded Bayesian Networks: Constructing genetic networks from microarray data

    Directory of Open Access Journals (Sweden)

    Quackenbush John

    2008-07-01

    Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

  5. Chromosomal microarrays testing in children with developmental disabilities and congenital anomalies

    Directory of Open Access Journals (Sweden)

    Guillermo Lay-Son

    2015-04-01

    Full Text Available OBJECTIVES: Clinical use of microarray-based techniques for the analysis of many developmental disorders has emerged during the last decade. Thus, chromosomal microarray has been positioned as a first-tier test. This study reports the first experience in a Chilean cohort. METHODS: Chilean patients with developmental disabilities and congenital anomalies were studied with a high-density microarray (CytoScan(tm HD Array, Affymetrix, Inc., Santa Clara, CA, USA. Patients had previous cytogenetic studies with either a normal result or a poorly characterized anomaly. RESULTS: This study tested 40 patients selected by two or more criteria, including: major congenital anomalies, facial dysmorphism, developmental delay, and intellectual disability. Copy number variants (CNVs were found in 72.5% of patients, while a pathogenic CNV was found in 25% of patients and a CNV of uncertain clinical significance was found in 2.5% of patients. CONCLUSION: Chromosomal microarray analysis is a useful and powerful tool for diagnosis of developmental diseases, by allowing accurate diagnosis, improving the diagnosis rate, and discovering new etiologies. The higher cost is a limitation for widespread use in this setting.

  6. Microarray analysis of the gene expression profile in triethylene ...

    African Journals Online (AJOL)

    Microarray analysis of the gene expression profile in triethylene glycol dimethacrylate-treated human dental pulp cells. ... Conclusions: Our results suggest that TEGDMA can change the many functions of hDPCs through large changes in gene expression levels and complex interactions with different signaling pathways.

  7. Microarray expression profiling of human dental pulp from single subject.

    Science.gov (United States)

    Tete, Stefano; Mastrangelo, Filiberto; Scioletti, Anna Paola; Tranasi, Michelangelo; Raicu, Florina; Paolantonio, Michele; Stuppia, Liborio; Vinci, Raffaele; Gherlone, Enrico; Ciampoli, Cristian; Sberna, Maria Teresa; Conti, Pio

    2008-01-01

    Microarray is a recently developed simultaneous analysis of expression patterns of thousand of genes. The aim of this research was to evaluate the expression profile of human healthy dental pulp in order to find the presence of genes activated and encoding for proteins involved in the physiological process of human dental pulp. We report data obtained by analyzing expression profiles of human tooth pulp from single subjects, using an approach based on the amplification of the total RNA. Experiments were performed on a high-density array able to analyse about 21,000 oligonucleotide sequences of about 70 bases in duplicate, using an approach based on the amplification of the total RNA from the pulp of a single tooth. Obtained data were analyzed using the S.A.M. system (Significance Analysis of Microarray) and genes were merged according to their molecular functions and biological process by the Onto-Express software. The microarray analysis revealed 362 genes with specific pulp expression. Genes showing significant high expression were classified in genes involved in tooth development, protoncogenes, genes of collagen, DNAse, Metallopeptidases and Growth factors. We report a microarray analysis, carried out by extraction of total RNA from specimens of healthy human dental pulp tissue. This approach represents a powerful tool in the study of human normal and pathological pulp, allowing minimization of the genetic variability due to the pooling of samples from different individuals.

  8. Multivariate analysis of microarray data: differential expression and differential connection

    Directory of Open Access Journals (Sweden)

    Kiiveri Harri T

    2011-02-01

    Full Text Available Abstract Background Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. Results We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. Conclusion The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

  9. NMD Microarray Analysis for Rapid Genome-Wide Screen of Mutated Genes in Cancer

    Directory of Open Access Journals (Sweden)

    Maija Wolf

    2005-01-01

    Full Text Available Gene mutations play a critical role in cancer development and progression, and their identification offers possibilities for accurate diagnostics and therapeutic targeting. Finding genes undergoing mutations is challenging and slow, even in the post-genomic era. A new approach was recently developed by Noensie and Dietz to prioritize and focus the search, making use of nonsense-mediated mRNA decay (NMD inhibition and microarray analysis (NMD microarrays in the identification of transcripts containing nonsense mutations. We combined NMD microarrays with array-based CGH (comparative genomic hybridization in order to identify inactivation of tumor suppressor genes in cancer. Such a “mutatomics” screening of prostate cancer cell lines led to the identification of inactivating mutations in the EPHB2 gene. Up to 8% of metastatic uncultured prostate cancers also showed mutations of this gene whose loss of function may confer loss of tissue architecture. NMD microarray analysis could turn out to be a powerful research method to identify novel mutated genes in cancer cell lines, providing targets that could then be further investigated for their clinical relevance and therapeutic potential.

  10. A Reliable and Distributed LIMS for Efficient Management of the Microarray Experiment Environment

    Directory of Open Access Journals (Sweden)

    Jin Hee-Jeong

    2007-03-01

    Full Text Available A microarray is a principal technology in molecular biology. It generates thousands of expressions of genotypes at once. Typically, a microarray experiment contains many kinds of information, such as gene names, sequences, expression profiles, scanned images, and annotation. So, the organization and analysis of vast amounts of data are required. Microarray LIMS (Laboratory Information Management System provides data management, search, and basic analysis. Recently, microarray joint researches, such as the skeletal system disease and anti-cancer medicine have been widely conducted. This research requires data sharing among laboratories within the joint research group. In this paper, we introduce a web based microarray LIMS, SMILE (Small and solid MIcroarray Lims for Experimenters, especially for shared data management. The data sharing function of SMILE is based on Friend-to-Friend (F2F, which is based on anonymous P2P (Peer-to-Peer, in which people connect directly with their “friends”. It only allows its friends to exchange data directly using IP addresses or digital signatures you trust. In SMILE, there are two types of friends: “service provider”, which provides data, and “client”, which is provided with data. So, the service provider provides shared data only to its clients. SMILE provides useful functions for microarray experiments, such as variant data management, image analysis, normalization, system management, project schedule management, and shared data management. Moreover, it connections with two systems: ArrayMall for analyzing microarray images and GENAW for constructing a genetic network. SMILE is available on http://neobio.cs.pusan.ac.kr:8080/smile.

  11. Integration of microarray analysis into the clinical diagnosis of hematological malignancies: How much can we improve cytogenetic testing?

    Science.gov (United States)

    Peterson, Jess F.; Aggarwal, Nidhi; Smith, Clayton A.; Gollin, Susanne M.; Surti, Urvashi; Rajkovic, Aleksandar; Swerdlow, Steven H.; Yatsenko, Svetlana A.

    2015-01-01

    Purpose To evaluate the clinical utility, diagnostic yield and rationale of integrating microarray analysis in the clinical diagnosis of hematological malignancies in comparison with classical chromosome karyotyping/fluorescence in situ hybridization (FISH). Methods G-banded chromosome analysis, FISH and microarray studies using customized CGH and CGH+SNP designs were performed on 27 samples from patients with hematological malignancies. A comprehensive comparison of the results obtained by three methods was conducted to evaluate benefits and limitations of these techniques for clinical diagnosis. Results Overall, 89.7% of chromosomal abnormalities identified by karyotyping/FISH studies were also detectable by microarray. Among 183 acquired copy number alterations (CNAs) identified by microarray, 94 were additional findings revealed in 14 cases (52%), and at least 30% of CNAs were in genomic regions of diagnostic/prognostic significance. Approximately 30% of novel alterations detected by microarray were >20 Mb in size. Balanced abnormalities were not detected by microarray; however, of the 19 apparently “balanced” rearrangements, 55% (6/11) of recurrent and 13% (1/8) of non-recurrent translocations had alterations at the breakpoints discovered by microarray. Conclusion Microarray technology enables accurate, cost-effective and time-efficient whole-genome analysis at a resolution significantly higher than that of conventional karyotyping and FISH. Array-CGH showed advantage in identification of cryptic imbalances and detection of clonal aberrations in population of non-dividing cancer cells and samples with poor chromosome morphology. The integration of microarray analysis into the cytogenetic diagnosis of hematologic malignancies has the potential to improve patient management by providing clinicians with additional disease specific and potentially clinically actionable genomic alterations. PMID:26299921

  12. Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

    Science.gov (United States)

    Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

    2017-06-06

    Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

  13. Assessment of centrifugation using for accelerated immunological microarray analysis for blood cells investigation

    Directory of Open Access Journals (Sweden)

    A. V. Shishkin

    2011-01-01

    Full Text Available Phase of incubation microarray with cell suspension is prolonged when cells are investigated. It takes from 20 to 60 min if cell sedimentation on the surface of microarray is the result of gravity . Decrease of this stage duration is possible due to centrifugation. In th is article influence of centrifugation on results of analysis is considered. Changes of morphological description of cells are estimated when they a re precipitatedwith different acceleration. Also availability of centrifugation using when it is necessary to obtain the high density of cell binding in test regions of microarray if cells concentration in sample is small is demonstrated.

  14. Microarray analysis of gene expression profiles in ripening pineapple fruits.

    Science.gov (United States)

    Koia, Jonni H; Moyle, Richard L; Botella, Jose R

    2012-12-18

    Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the molecular basis of pineapple fruit

  15. Development and Validation of Protein Microarray Technology for Simultaneous Inflammatory Mediator Detection in Human Sera

    Directory of Open Access Journals (Sweden)

    Senthooran Selvarajah

    2014-01-01

    Full Text Available Biomarkers, including cytokines, can help in the diagnosis, prognosis, and prediction of treatment response across a wide range of disease settings. Consequently, the recent emergence of protein microarray technology, which is able to quantify a range of inflammatory mediators in a large number of samples simultaneously, has become highly desirable. However, the cost of commercial systems remains somewhat prohibitive. Here we show the development, validation, and implementation of an in-house microarray platform which enables the simultaneous quantitative analysis of multiple protein biomarkers. The accuracy and precision of the in-house microarray system were investigated according to the Food and Drug Administration (FDA guidelines for pharmacokinetic assay validation. The assay fell within these limits for all but the very low-abundant cytokines, such as interleukin- (IL- 10. Additionally, there were no significant differences between cytokine detection using our microarray system and the “gold standard” ELISA format. Crucially, future biomarker detection need not be limited to the 16 cytokines shown here but could be expanded as required. In conclusion, we detail a bespoke protein microarray system, utilizing well-validated ELISA reagents, that allows accurate, precise, and reproducible multiplexed biomarker quantification, comparable with commercial ELISA, and allowing customization beyond that of similar commercial microarrays.

  16. DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Tewfik Ahmed H

    2006-01-01

    Full Text Available Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.

  17. Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

    Science.gov (United States)

    Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia

    2012-01-01

    Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of

  18. Development and application of a microarray meter tool to optimize microarray experiments

    Directory of Open Access Journals (Sweden)

    Rouse Richard JD

    2008-07-01

    Full Text Available Abstract Background Successful microarray experimentation requires a complex interplay between the slide chemistry, the printing pins, the nucleic acid probes and targets, and the hybridization milieu. Optimization of these parameters and a careful evaluation of emerging slide chemistries are a prerequisite to any large scale array fabrication effort. We have developed a 'microarray meter' tool which assesses the inherent variations associated with microarray measurement prior to embarking on large scale projects. Findings The microarray meter consists of nucleic acid targets (reference and dynamic range control and probe components. Different plate designs containing identical probe material were formulated to accommodate different robotic and pin designs. We examined the variability in probe quality and quantity (as judged by the amount of DNA printed and remaining post-hybridization using three robots equipped with capillary printing pins. Discussion The generation of microarray data with minimal variation requires consistent quality control of the (DNA microarray manufacturing and experimental processes. Spot reproducibility is a measure primarily of the variations associated with printing. The microarray meter assesses array quality by measuring the DNA content for every feature. It provides a post-hybridization analysis of array quality by scoring probe performance using three metrics, a a measure of variability in the signal intensities, b a measure of the signal dynamic range and c a measure of variability of the spot morphologies.

  19. A comparative analysis of DNA barcode microarray feature size

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    Smith Andrew M

    2009-10-01

    Full Text Available Abstract Background Microarrays are an invaluable tool in many modern genomic studies. It is generally perceived that decreasing the size of microarray features leads to arrays with higher resolution (due to greater feature density, but this increase in resolution can compromise sensitivity. Results We demonstrate that barcode microarrays with smaller features are equally capable of detecting variation in DNA barcode intensity when compared to larger feature sizes within a specific microarray platform. The barcodes used in this study are the well-characterized set derived from the Yeast KnockOut (YKO collection used for screens of pooled yeast (Saccharomyces cerevisiae deletion mutants. We treated these pools with the glycosylation inhibitor tunicamycin as a test compound. Three generations of barcode microarrays at 30, 8 and 5 μm features sizes independently identified the primary target of tunicamycin to be ALG7. Conclusion We show that the data obtained with 5 μm feature size is of comparable quality to the 30 μm size and propose that further shrinking of features could yield barcode microarrays with equal or greater resolving power and, more importantly, higher density.

  20. A probabilistic framework for microarray data analysis: fundamental probability models and statistical inference.

    Science.gov (United States)

    Ogunnaike, Babatunde A; Gelmi, Claudio A; Edwards, Jeremy S

    2010-05-21

    Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

  1. Factorial microarray analysis of zebra mussel (Dreissena polymorpha: Dreissenidae, Bivalvia adhesion

    Directory of Open Access Journals (Sweden)

    Faisal Mohamed

    2010-05-01

    Full Text Available Abstract Background The zebra mussel (Dreissena polymorpha has been well known for its expertise in attaching to substances under the water. Studies in past decades on this underwater adhesion focused on the adhesive protein isolated from the byssogenesis apparatus of the zebra mussel. However, the mechanism of the initiation, maintenance, and determination of the attachment process remains largely unknown. Results In this study, we used a zebra mussel cDNA microarray previously developed in our lab and a factorial analysis to identify the genes that were involved in response to the changes of four factors: temperature (Factor A, current velocity (Factor B, dissolved oxygen (Factor C, and byssogenesis status (Factor D. Twenty probes in the microarray were found to be modified by one of the factors. The transcription products of four selected genes, DPFP-BG20_A01, EGP-BG97/192_B06, EGP-BG13_G05, and NH-BG17_C09 were unique to the zebra mussel foot based on the results of quantitative reverse transcription PCR (qRT-PCR. The expression profiles of these four genes under the attachment and non-attachment were also confirmed by qRT-PCR and the result is accordant to that from microarray assay. The in situ hybridization with the RNA probes of two identified genes DPFP-BG20_A01 and EGP-BG97/192_B06 indicated that both of them were expressed by a type of exocrine gland cell located in the middle part of the zebra mussel foot. Conclusions The results of this study suggested that the changes of D. polymorpha byssogenesis status and the environmental factors can dramatically affect the expression profiles of the genes unique to the foot. It turns out that the factorial design and analysis of the microarray experiment is a reliable method to identify the influence of multiple factors on the expression profiles of the probesets in the microarray; therein it provides a powerful tool to reveal the mechanism of zebra mussel underwater attachment.

  2. Factorial microarray analysis of zebra mussel (Dreissena polymorpha: Dreissenidae, Bivalvia) adhesion.

    Science.gov (United States)

    Xu, Wei; Faisal, Mohamed

    2010-05-28

    The zebra mussel (Dreissena polymorpha) has been well known for its expertise in attaching to substances under the water. Studies in past decades on this underwater adhesion focused on the adhesive protein isolated from the byssogenesis apparatus of the zebra mussel. However, the mechanism of the initiation, maintenance, and determination of the attachment process remains largely unknown. In this study, we used a zebra mussel cDNA microarray previously developed in our lab and a factorial analysis to identify the genes that were involved in response to the changes of four factors: temperature (Factor A), current velocity (Factor B), dissolved oxygen (Factor C), and byssogenesis status (Factor D). Twenty probes in the microarray were found to be modified by one of the factors. The transcription products of four selected genes, DPFP-BG20_A01, EGP-BG97/192_B06, EGP-BG13_G05, and NH-BG17_C09 were unique to the zebra mussel foot based on the results of quantitative reverse transcription PCR (qRT-PCR). The expression profiles of these four genes under the attachment and non-attachment were also confirmed by qRT-PCR and the result is accordant to that from microarray assay. The in situ hybridization with the RNA probes of two identified genes DPFP-BG20_A01 and EGP-BG97/192_B06 indicated that both of them were expressed by a type of exocrine gland cell located in the middle part of the zebra mussel foot. The results of this study suggested that the changes of D. polymorpha byssogenesis status and the environmental factors can dramatically affect the expression profiles of the genes unique to the foot. It turns out that the factorial design and analysis of the microarray experiment is a reliable method to identify the influence of multiple factors on the expression profiles of the probesets in the microarray; therein it provides a powerful tool to reveal the mechanism of zebra mussel underwater attachment.

  3. A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy

    Directory of Open Access Journals (Sweden)

    Archer Kellie J

    2008-02-01

    Full Text Available Abstract Background With the popularity of DNA microarray technology, multiple groups of researchers have studied the gene expression of similar biological conditions. Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods. However, often the sample size for each individual microarray experiment is small. Therefore, in this paper we present a non-parametric meta-analysis approach for combining data from independent microarray studies, and illustrate its application on two independent Affymetrix GeneChip studies that compared the gene expression of biopsies from kidney transplant recipients with chronic allograft nephropathy (CAN to those with normal functioning allograft. Results The simulation study comparing the non-parametric meta-analysis approach to a commonly used t-statistic based approach shows that the non-parametric approach has better sensitivity and specificity. For the application on the two CAN studies, we identified 309 distinct genes that expressed differently in CAN. By applying Fisher's exact test to identify enriched KEGG pathways among those genes called differentially expressed, we found 6 KEGG pathways to be over-represented among the identified genes. We used the expression measurements of the identified genes as predictors to predict the class labels for 6 additional biopsy samples, and the predicted results all conformed to their pathologist diagnosed class labels. Conclusion We present a new approach for combining data from multiple independent microarray studies. This approach is non-parametric and does not rely on any distributional assumptions. The rationale behind the approach is logically intuitive and can be easily understood by researchers not having advanced training in statistics. Some of the identified genes and pathways have been

  4. Microarray Analysis of Late Response to Boron Toxicity in Barley (Hordeum vulgare L.) Leaves

    NARCIS (Netherlands)

    Oz, M.T.; Yilmaz, R.; Eyidogan, F.; Graaff, de L.H.; Yucel, M.; Oktem, H.A.

    2009-01-01

    DNA microarrays, being high-density and high-throughput, allow quantitative analyses of thousands of genes and their expression patterns in parallel. In this study, Barley1 GereChip was used to investigate transcriptome changes associated with boron (B) toxicity in a sensitive barley cultivar

  5. Universal Reference RNA as a standard for microarray experiments

    Directory of Open Access Journals (Sweden)

    Fero Michael

    2004-03-01

    Full Text Available Abstract Background Obtaining reliable and reproducible two-color microarray gene expression data is critically important for understanding the biological significance of perturbations made on a cellular system. Microarray design, RNA preparation and labeling, hybridization conditions and data acquisition and analysis are variables difficult to simultaneously control. A useful tool for monitoring and controlling intra- and inter-experimental variation is Universal Reference RNA (URR, developed with the goal of providing hybridization signal at each microarray probe location (spot. Measuring signal at each spot as the ratio of experimental RNA to reference RNA targets, rather than relying on absolute signal intensity, decreases variability by normalizing signal output in any two-color hybridization experiment. Results Human, mouse and rat URR (UHRR, UMRR and URRR, respectively were prepared from pools of RNA derived from individual cell lines representing different tissues. A variety of microarrays were used to determine percentage of spots hybridizing with URR and producing signal above a user defined threshold (microarray coverage. Microarray coverage was consistently greater than 80% for all arrays tested. We confirmed that individual cell lines contribute their own unique set of genes to URR, arguing for a pool of RNA from several cell lines as a better configuration for URR as opposed to a single cell line source for URR. Microarray coverage comparing two separately prepared batches each of UHRR, UMRR and URRR were highly correlated (Pearson's correlation coefficients of 0.97. Conclusion Results of this study demonstrate that large quantities of pooled RNA from individual cell lines are reproducibly prepared and possess diverse gene representation. This type of reference provides a standard for reducing variation in microarray experiments and allows more reliable comparison of gene expression data within and between experiments and

  6. Geiger mode avalanche photodiodes for microarray systems

    Science.gov (United States)

    Phelan, Don; Jackson, Carl; Redfern, R. Michael; Morrison, Alan P.; Mathewson, Alan

    2002-06-01

    New Geiger Mode Avalanche Photodiodes (GM-APD) have been designed and characterized specifically for use in microarray systems. Critical parameters such as excess reverse bias voltage, hold-off time and optimum operating temperature have been experimentally determined for these photon-counting devices. The photon detection probability, dark count rate and afterpulsing probability have been measured under different operating conditions. An active- quench circuit (AQC) is presented for operating these GM- APDs. This circuit is relatively simple, robust and has such benefits as reducing average power dissipation and afterpulsing. Arrays of these GM-APDs have already been designed and together with AQCs open up the possibility of having a solid-state microarray detector that enables parallel analysis on a single chip. Another advantage of these GM-APDs over current technology is their low voltage CMOS compatibility which could allow for the fabrication of an AQC on the same device. Small are detectors have already been employed in the time-resolved detection of fluorescence from labeled proteins. It is envisaged that operating these new GM-APDs with this active-quench circuit will have numerous applications for the detection of fluorescence in microarray systems.

  7. ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

    Directory of Open Access Journals (Sweden)

    Krasnogor Natalio

    2009-10-01

    Full Text Available Abstract Background Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

  8. [Diagnosis of a case with Williams-Beuren syndrome with nephrocalcinosis using chromosome microarray analysis].

    Science.gov (United States)

    Jin, S J; Liu, M; Long, W J; Luo, X P

    2016-12-02

    Objective: To explore the clinical phenotypes and the genetic cause for a boy with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders. Method: Routine G-banding and chromosome microarray analysis were applied to a child with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders treated in the Department of Pediatrics of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology in September 2015 and his parents to conduct the chromosomal karyotype analysis and the whole genome scanning. Deleted genes were searched in the Decipher and NCBI databases, and their relationships with the clinical phenotypes were analyzed. Result: A six-month-old boy was refered to us because of unexplained growth retardation and feeding intolerance.The affected child presented with abnormal manifestation such as special face, umbilical hernia, growth retardation, hypothyroidism, congenital heart disease, right ear sensorineural deafness, hypercalcemia and nephrocalcinosis. The child's karyotype was 46, XY, 16qh + , and his parents' karyotypes were normal. Chromosome microarray analysis revealed a 1 436 kb deletion on the 7q11.23(72701098_74136633) region of the child. This region included 23 protein-coding genes, which were reported to be corresponding to Williams-Beuren syndrome and its certain clinical phenotypes. His parents' results of chromosome microarray analysis were normal. Conclusion: A boy with characteristic manifestation of Williams-Beuren syndrome and rare nephrocalcinosis was diagnosed using chromosome microarray analysis. The deletion on the 7q11.23 might be related to the clinical phenotypes of Williams-Beuren syndrome, yet further studies are needed.

  9. Carbohydrate microarrays

    DEFF Research Database (Denmark)

    Park, Sungjin; Gildersleeve, Jeffrey C; Blixt, Klas Ola

    2012-01-01

    In the last decade, carbohydrate microarrays have been core technologies for analyzing carbohydrate-mediated recognition events in a high-throughput fashion. A number of methods have been exploited for immobilizing glycans on the solid surface in a microarray format. This microarray...... of substrate specificities of glycosyltransferases. This review covers the construction of carbohydrate microarrays, detection methods of carbohydrate microarrays and their applications in biological and biomedical research....

  10. Evaluation of a gene information summarization system by users during the analysis process of microarray datasets

    Directory of Open Access Journals (Sweden)

    Cohen Aaron

    2009-02-01

    Full Text Available Abstract Background Summarization of gene information in the literature has the potential to help genomics researchers translate basic research into clinical benefits. Gene expression microarrays have been used to study biomarkers for disease and discover novel types of therapeutics and the task of finding information in journal articles on sets of genes is common for translational researchers working with microarray data. However, manually searching and scanning the literature references returned from PubMed is a time-consuming task for scientists. We built and evaluated an automatic summarizer of information on genes studied in microarray experiments. The Gene Information Clustering and Summarization System (GICSS is a system that integrates two related steps of the microarray data analysis process: functional gene clustering and gene information gathering. The system evaluation was conducted during the process of genomic researchers analyzing their own experimental microarray datasets. Results The clusters generated by GICSS were validated by scientists during their microarray analysis process. In addition, presenting sentences in the abstract provided significantly more important information to the users than just showing the title in the default PubMed format. Conclusion The evaluation results suggest that GICSS can be useful for researchers in genomic area. In addition, the hybrid evaluation method, partway between intrinsic and extrinsic system evaluation, may enable researchers to gauge the true usefulness of the tool for the scientists in their natural analysis workflow and also elicit suggestions for future enhancements. Availability GICSS can be accessed online at: http://ir.ohsu.edu/jianji/index.html

  11. Annotating breast cancer microarray samples using ontologies

    Science.gov (United States)

    Liu, Hongfang; Li, Xin; Yoon, Victoria; Clarke, Robert

    2008-01-01

    As the most common cancer among women, breast cancer results from the accumulation of mutations in essential genes. Recent advance in high-throughput gene expression microarray technology has inspired researchers to use the technology to assist breast cancer diagnosis, prognosis, and treatment prediction. However, the high dimensionality of microarray experiments and public access of data from many experiments have caused inconsistencies which initiated the development of controlled terminologies and ontologies for annotating microarray experiments, such as the standard microarray Gene Expression Data (MGED) ontology (MO). In this paper, we developed BCM-CO, an ontology tailored specifically for indexing clinical annotations of breast cancer microarray samples from the NCI Thesaurus. Our research showed that the coverage of NCI Thesaurus is very limited with respect to i) terms used by researchers to describe breast cancer histology (covering 22 out of 48 histology terms); ii) breast cancer cell lines (covering one out of 12 cell lines); and iii) classes corresponding to the breast cancer grading and staging. By incorporating a wider range of those terms into BCM-CO, we were able to indexed breast cancer microarray samples from GEO using BCM-CO and MGED ontology and developed a prototype system with web interface that allows the retrieval of microarray data based on the ontology annotations. PMID:18999108

  12. The Utility of Chromosomal Microarray Analysis in Developmental and Behavioral Pediatrics

    Science.gov (United States)

    Beaudet, Arthur L.

    2013-01-01

    Chromosomal microarray analysis (CMA) has emerged as a powerful new tool to identify genomic abnormalities associated with a wide range of developmental disabilities including congenital malformations, cognitive impairment, and behavioral abnormalities. CMA includes array comparative genomic hybridization (CGH) and single nucleotide polymorphism…

  13. The application of DNA microarrays in gene expression analysis.

    Science.gov (United States)

    van Hal, N L; Vorst, O; van Houwelingen, A M; Kok, E J; Peijnenburg, A; Aharoni, A; van Tunen, A J; Keijer, J

    2000-03-31

    DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.

  14. The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

    Science.gov (United States)

    Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

    2009-01-01

    We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

  15. Array2BIO: from microarray expression data to functional annotation of co-regulated genes

    Directory of Open Access Journals (Sweden)

    Rasley Amy

    2006-06-01

    Full Text Available Abstract Background There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility. Results Array2BIO converts raw intensities into probe expression values, automatically maps those to genes, and subsequently identifies groups of co-expressed genes using two complementary approaches: (1 comparative analysis of signal versus control and (2 clustering analysis of gene expression across different conditions. The identified genes are assigned to functional categories based on Gene Ontology classification and KEGG protein interaction pathways. Array2BIO reliably handles low-expressor genes and provides a set of statistical methods for quantifying expression levels, including Benjamini-Hochberg and Bonferroni multiple testing corrections. An automated interface with the ECR Browser provides evolutionary conservation analysis for the identified gene loci while the interconnection with Crème allows prediction of gene regulatory elements that underlie observed expression patterns. Conclusion We have developed Array2BIO – a web based tool for rapid comprehensive analysis of Affymetrix microarray expression data, which also allows users to link expression data to Dcode.org comparative genomics tools and integrates a system for translating co-expression data into mechanisms of gene co-regulation. Array2BIO is publicly available at http://array2bio.dcode.org.

  16. Nanotechnology: moving from microarrays toward nanoarrays.

    Science.gov (United States)

    Chen, Hua; Li, Jun

    2007-01-01

    Microarrays are important tools for high-throughput analysis of biomolecules. The use of microarrays for parallel screening of nucleic acid and protein profiles has become an industry standard. A few limitations of microarrays are the requirement for relatively large sample volumes and elongated incubation time, as well as the limit of detection. In addition, traditional microarrays make use of bulky instrumentation for the detection, and sample amplification and labeling are quite laborious, which increase analysis cost and delays the time for obtaining results. These problems limit microarray techniques from point-of-care and field applications. One strategy for overcoming these problems is to develop nanoarrays, particularly electronics-based nanoarrays. With further miniaturization, higher sensitivity, and simplified sample preparation, nanoarrays could potentially be employed for biomolecular analysis in personal healthcare and monitoring of trace pathogens. In this chapter, it is intended to introduce the concept and advantage of nanotechnology and then describe current methods and protocols for novel nanoarrays in three aspects: (1) label-free nucleic acids analysis using nanoarrays, (2) nanoarrays for protein detection by conventional optical fluorescence microscopy as well as by novel label-free methods such as atomic force microscopy, and (3) nanoarray for enzymatic-based assay. These nanoarrays will have significant applications in drug discovery, medical diagnosis, genetic testing, environmental monitoring, and food safety inspection.

  17. Transcriptomic identification of candidate genes involved in sunflower responses to chilling and salt stresses based on cDNA microarray analysis

    Directory of Open Access Journals (Sweden)

    Paniego Norma

    2008-01-01

    Full Text Available Abstract Background Considering that sunflower production is expanding to arid regions, tolerance to abiotic stresses as drought, low temperatures and salinity arises as one of the main constrains nowadays. Differential organ-specific sunflower ESTs (expressed sequence tags were previously generated by a subtractive hybridization method that included a considerable number of putative abiotic stress associated sequences. The objective of this work is to analyze concerted gene expression profiles of organ-specific ESTs by fluorescence microarray assay, in response to high sodium chloride concentration and chilling treatments with the aim to identify and follow up candidate genes for early responses to abiotic stress in sunflower. Results Abiotic-related expressed genes were the target of this characterization through a gene expression analysis using an organ-specific cDNA fluorescence microarray approach in response to high salinity and low temperatures. The experiment included three independent replicates from leaf samples. We analyzed 317 unigenes previously isolated from differential organ-specific cDNA libraries from leaf, stem and flower at R1 and R4 developmental stage. A statistical analysis based on mean comparison by ANOVA and ordination by Principal Component Analysis allowed the detection of 80 candidate genes for either salinity and/or chilling stresses. Out of them, 50 genes were up or down regulated under both stresses, supporting common regulatory mechanisms and general responses to chilling and salinity. Interestingly 15 and 12 sequences were up regulated or down regulated specifically in one stress but not in the other, respectively. These genes are potentially involved in different regulatory mechanisms including transcription/translation/protein degradation/protein folding/ROS production or ROS-scavenging. Differential gene expression patterns were confirmed by qRT-PCR for 12.5% of the microarray candidate sequences. Conclusion

  18. Investigating the effect of paralogs on microarray gene-set analysis

    LENUS (Irish Health Repository)

    Faure, Andre J

    2011-01-24

    Abstract Background In order to interpret the results obtained from a microarray experiment, researchers often shift focus from analysis of individual differentially expressed genes to analyses of sets of genes. These gene-set analysis (GSA) methods use previously accumulated biological knowledge to group genes into sets and then aim to rank these gene sets in a way that reflects their relative importance in the experimental situation in question. We suspect that the presence of paralogs affects the ability of GSA methods to accurately identify the most important sets of genes for subsequent research. Results We show that paralogs, which typically have high sequence identity and similar molecular functions, also exhibit high correlation in their expression patterns. We investigate this correlation as a potential confounding factor common to current GSA methods using Indygene http:\\/\\/www.cbio.uct.ac.za\\/indygene, a web tool that reduces a supplied list of genes so that it includes no pairwise paralogy relationships above a specified sequence similarity threshold. We use the tool to reanalyse previously published microarray datasets and determine the potential utility of accounting for the presence of paralogs. Conclusions The Indygene tool efficiently removes paralogy relationships from a given dataset and we found that such a reduction, performed prior to GSA, has the ability to generate significantly different results that often represent novel and plausible biological hypotheses. This was demonstrated for three different GSA approaches when applied to the reanalysis of previously published microarray datasets and suggests that the redundancy and non-independence of paralogs is an important consideration when dealing with GSA methodologies.

  19. Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review.

    Science.gov (United States)

    Raddatz, Barbara B; Spitzbarth, Ingo; Matheis, Katja A; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang; Ulrich, Reiner

    2017-09-01

    High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.

  20. SLIMarray: Lightweight software for microarray facility management

    Directory of Open Access Journals (Sweden)

    Marzolf Bruz

    2006-10-01

    Full Text Available Abstract Background Microarray core facilities are commonplace in biological research organizations, and need systems for accurately tracking various logistical aspects of their operation. Although these different needs could be handled separately, an integrated management system provides benefits in organization, automation and reduction in errors. Results We present SLIMarray (System for Lab Information Management of Microarrays, an open source, modular database web application capable of managing microarray inventories, sample processing and usage charges. The software allows modular configuration and is well suited for further development, providing users the flexibility to adapt it to their needs. SLIMarray Lite, a version of the software that is especially easy to install and run, is also available. Conclusion SLIMarray addresses the previously unmet need for free and open source software for managing the logistics of a microarray core facility.

  1. Simulation of microarray data with realistic characteristics

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    Lehmussola Antti

    2006-07-01

    Full Text Available Abstract Background Microarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed. Results We present a microarray simulation model which can be used to validate different kinds of data analysis algorithms. The proposed model is unique in the sense that it includes all the steps that affect the quality of real microarray data. These steps include the simulation of biological ground truth data, applying biological and measurement technology specific error models, and finally simulating the microarray slide manufacturing and hybridization. After all these steps are taken into account, the simulated data has realistic biological and statistical characteristics. The applicability of the proposed model is demonstrated by several examples. Conclusion The proposed microarray simulation model is modular and can be used in different kinds of applications. It includes several error models that have been proposed earlier and it can be used with different types of input data. The model can be used to simulate both spotted two-channel and oligonucleotide based single-channel microarrays. All this makes the model a valuable tool for example in validation of data analysis algorithms.

  2. Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

    Science.gov (United States)

    Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

    2010-01-18

    The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

  3. A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

    Directory of Open Access Journals (Sweden)

    Reinders Marcel JT

    2009-11-01

    Full Text Available Abstract Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical

  4. Computational biology of genome expression and regulation--a review of microarray bioinformatics.

    Science.gov (United States)

    Wang, Junbai

    2008-01-01

    Microarray technology is being used widely in various biomedical research areas; the corresponding microarray data analysis is an essential step toward the best utilizing of array technologies. Here we review two components of the microarray data analysis: a low level of microarray data analysis that emphasizes the designing, the quality control, and the preprocessing of microarray experiments, then a high level of microarray data analysis that focuses on the domain-specific microarray applications such as tumor classification, biomarker prediction, analyzing array CGH experiments, and reverse engineering of gene expression networks. Additionally, we will review the recent development of building a predictive model in genome expression and regulation studies. This review may help biologists grasp a basic knowledge of microarray bioinformatics as well as its potential impact on the future evolvement of biomedical research fields.

  5. Accurate detection of carcinoma cells by use of a cell microarray chip.

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    Shohei Yamamura

    Full Text Available BACKGROUND: Accurate detection and analysis of circulating tumor cells plays an important role in the diagnosis and treatment of metastatic cancer treatment. METHODS AND FINDINGS: A cell microarray chip was used to detect spiked carcinoma cells among leukocytes. The chip, with 20,944 microchambers (105 µm width and 50 µm depth, was made from polystyrene; and the formation of monolayers of leukocytes in the microchambers was observed. Cultured human T lymphoblastoid leukemia (CCRF-CEM cells were used to examine the potential of the cell microarray chip for the detection of spiked carcinoma cells. A T lymphoblastoid leukemia suspension was dispersed on the chip surface, followed by 15 min standing to allow the leukocytes to settle down into the microchambers. Approximately 29 leukocytes were found in each microchamber when about 600,000 leukocytes in total were dispersed onto a cell microarray chip. Similarly, when leukocytes isolated from human whole blood were used, approximately 89 leukocytes entered each microchamber when about 1,800,000 leukocytes in total were placed onto the cell microarray chip. After washing the chip surface, PE-labeled anti-cytokeratin monoclonal antibody and APC-labeled anti-CD326 (EpCAM monoclonal antibody solution were dispersed onto the chip surface and allowed to react for 15 min; and then a microarray scanner was employed to detect any fluorescence-positive cells within 20 min. In the experiments using spiked carcinoma cells (NCI-H1650, 0.01 to 0.0001%, accurate detection of carcinoma cells was achieved with PE-labeled anti-cytokeratin monoclonal antibody. Furthermore, verification of carcinoma cells in the microchambers was performed by double staining with the above monoclonal antibodies. CONCLUSION: The potential application of the cell microarray chip for the detection of CTCs was shown, thus demonstrating accurate detection by double staining for cytokeratin and EpCAM at the single carcinoma cell level.

  6. The application of DNA microarrays in gene expression analysis

    NARCIS (Netherlands)

    Hal, van N.L.W.; Vorst, O.; Houwelingen, van A.M.M.L.; Kok, E.J.; Peijnenburg, A.A.C.M.; Aharoni, A.; Tunen, van A.J.; Keijer, J.

    2000-01-01

    DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed.

  7. Microarray evaluation of age-related changes in human dental pulp.

    Science.gov (United States)

    Tranasi, Michelangelo; Sberna, Maria Teresa; Zizzari, Vincenzo; D'Apolito, Giuseppe; Mastrangelo, Filiberto; Salini, Luisa; Stuppia, Liborio; Tetè, Stefano

    2009-09-01

    The dental pulp undergoes age-related changes that could be ascribed to physiological, defensive, or pathological irritant-induced changes. These changes are regulated by pulp cell activity and by a variety of extracellular matrix (ECM) macromolecules, playing important roles in growth regulation, tissue differentiation and organization, formation of calcified tissue, and defense mechanisms and reactions to inflammatory stimuli. The aim of this research was to better understand the genetic changes that underlie the histological modification of the dental pulp in aging. The gene expression profile of the human dental pulp in young and older subjects was compared by RNA microarray analysis that allowed to simultaneously analyze the expression levels of thousands of genes. Data were statistically analyzed by Significance Analysis of Microarrays (SAM) Ingenuity Pathway Analysis (IPA) software. Semiquantitative and real-time reverse-transcriptase polymerase chain reaction analyses were performed to confirm the results. Microarray analysis revealed several differentially expressed genes that were categorized in growth factors, transcription regulators, apoptosis regulators, and genes of the ECM. The comparison analysis showed a high expression level of the biological functions of cell and tissue differentiation, development, and proliferation and of the immune, lymphatic, and hematologic system in young dental pulp, whereas the pathway of apoptosis was highly expressed in older dental pulp. Expression profile analyses of human dental pulp represent a sensible and useful tool for the study of mechanisms involved in differentiation, growth and aging of human dental pulp in physiological and pathological conditions.

  8. A Critical Perspective On Microarray Breast Cancer Gene Expression Profiling

    NARCIS (Netherlands)

    Sontrop, H.M.J.

    2015-01-01

    Microarrays offer biologists an exciting tool that allows the simultaneous assessment of gene expression levels for thousands of genes at once. At the time of their inception, microarrays were hailed as the new dawn in cancer biology and oncology practice with the hope that within a decade diseases

  9. The pathogenesis shared between abdominal aortic aneurysms and intracranial aneurysms: a microarray analysis.

    Science.gov (United States)

    Wang, Wen; Li, Hao; Zhao, Zheng; Wang, Haoyuan; Zhang, Dong; Zhang, Yan; Lan, Qing; Wang, Jiangfei; Cao, Yong; Zhao, Jizong

    2018-04-01

    Abdominal aortic aneurysms (AAAs) and intracranial saccular aneurysms (IAs) are the most common types of aneurysms. This study was to investigate the common pathogenesis shared between these two kinds of aneurysms. We collected 12 IAs samples and 12 control arteries from the Beijing Tiantan Hospital and performed microarray analysis. In addition, we utilized the microarray datasets of IAs and AAAs from the Gene Expression Omnibus (GEO), in combination with our microarray results, to generate messenger RNA expression profiles for both AAAs and IAs in our study. Functional exploration and protein-protein interaction (PPI) analysis were performed. A total of 727 common genes were differentially expressed (404 was upregulated; 323 was downregulated) for both AAAs and IAs. The GO and pathway analyses showed that the common dysregulated genes were mainly enriched in vascular smooth muscle contraction, muscle contraction, immune response, defense response, cell activation, IL-6 signaling and chemokine signaling pathways, etc. The further protein-protein analysis identified 35 hub nodes, including TNF, IL6, MAPK13, and CCL5. These hub node genes were enriched in inflammatory response, positive regulation of IL-6 production, chemokine signaling pathway, and T/B cell receptor signaling pathway. Our study will gain new insight into the molecular mechanisms for the pathogenesis of both types of aneurysms and provide new therapeutic targets for the patients harboring AAAs and IAs.

  10. Addressable droplet microarrays for single cell protein analysis.

    Science.gov (United States)

    Salehi-Reyhani, Ali; Burgin, Edward; Ces, Oscar; Willison, Keith R; Klug, David R

    2014-11-07

    Addressable droplet microarrays are potentially attractive as a way to achieve miniaturised, reduced volume, high sensitivity analyses without the need to fabricate microfluidic devices or small volume chambers. We report a practical method for producing oil-encapsulated addressable droplet microarrays which can be used for such analyses. To demonstrate their utility, we undertake a series of single cell analyses, to determine the variation in copy number of p53 proteins in cells of a human cancer cell line.

  11. Emerging use of gene expression microarrays in plant physiology.

    Science.gov (United States)

    Wullschleger, Stan D; Difazio, Stephen P

    2003-01-01

    Microarrays have become an important technology for the global analysis of gene expression in humans, animals, plants, and microbes. Implemented in the context of a well-designed experiment, cDNA and oligonucleotide arrays can provide highthroughput, simultaneous analysis of transcript abundance for hundreds, if not thousands, of genes. However, despite widespread acceptance, the use of microarrays as a tool to better understand processes of interest to the plant physiologist is still being explored. To help illustrate current uses of microarrays in the plant sciences, several case studies that we believe demonstrate the emerging application of gene expression arrays in plant physiology were selected from among the many posters and presentations at the 2003 Plant and Animal Genome XI Conference. Based on this survey, microarrays are being used to assess gene expression in plants exposed to the experimental manipulation of air temperature, soil water content and aluminium concentration in the root zone. Analysis often includes characterizing transcript profiles for multiple post-treatment sampling periods and categorizing genes with common patterns of response using hierarchical clustering techniques. In addition, microarrays are also providing insights into developmental changes in gene expression associated with fibre and root elongation in cotton and maize, respectively. Technical and analytical limitations of microarrays are discussed and projects attempting to advance areas of microarray design and data analysis are highlighted. Finally, although much work remains, we conclude that microarrays are a valuable tool for the plant physiologist interested in the characterization and identification of individual genes and gene families with potential application in the fields of agriculture, horticulture and forestry.

  12. Microarray analysis of gender- and parasite-specific gene transcription in Strongyloides ratti

    NARCIS (Netherlands)

    Evans, Helen; Mello, Luciane V.; Fang, Yongxiang; Wit, Ernst; Thompson, Fiona J.; Viney, Mark E.; Paterson, Steve

    2008-01-01

    The molecular mechanisms by which parasitic nematodes reproduce and have adapted to life within a host are unclear. In the present study, microarray analysis was used to explore differential transcription among the different stages and sexes of Strongyloides ratti, a parasitic nematode of brown

  13. NAIMA: target amplification strategy allowing quantitative on-chip detection of GMOs.

    Science.gov (United States)

    Morisset, Dany; Dobnik, David; Hamels, Sandrine; Zel, Jana; Gruden, Kristina

    2008-10-01

    We have developed a novel multiplex quantitative DNA-based target amplification method suitable for sensitive, specific and quantitative detection on microarray. This new method named NASBA Implemented Microarray Analysis (NAIMA) was applied to GMO detection in food and feed, but its application can be extended to all fields of biology requiring simultaneous detection of low copy number DNA targets. In a first step, the use of tailed primers allows the multiplex synthesis of template DNAs in a primer extension reaction. A second step of the procedure consists of transcription-based amplification using universal primers. The cRNA product is further on directly ligated to fluorescent dyes labelled 3DNA dendrimers allowing signal amplification and hybridized without further purification on an oligonucleotide probe-based microarray for multiplex detection. Two triplex systems have been applied to test maize samples containing several transgenic lines, and NAIMA has shown to be sensitive down to two target copies and to provide quantitative data on the transgenic contents in a range of 0.1-25%. Performances of NAIMA are comparable to singleplex quantitative real-time PCR. In addition, NAIMA amplification is faster since 20 min are sufficient to achieve full amplification.

  14. Micro-Analyzer: automatic preprocessing of Affymetrix microarray data.

    Science.gov (United States)

    Guzzi, Pietro Hiram; Cannataro, Mario

    2013-08-01

    A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power

  15. Comparing microarrays and next-generation sequencing technologies for microbial ecology research.

    Science.gov (United States)

    Roh, Seong Woon; Abell, Guy C J; Kim, Kyoung-Ho; Nam, Young-Do; Bae, Jin-Woo

    2010-06-01

    Recent advances in molecular biology have resulted in the application of DNA microarrays and next-generation sequencing (NGS) technologies to the field of microbial ecology. This review aims to examine the strengths and weaknesses of each of the methodologies, including depth and ease of analysis, throughput and cost-effectiveness. It also intends to highlight the optimal application of each of the individual technologies toward the study of a particular environment and identify potential synergies between the two main technologies, whereby both sample number and coverage can be maximized. We suggest that the efficient use of microarray and NGS technologies will allow researchers to advance the field of microbial ecology, and importantly, improve our understanding of the role of microorganisms in their various environments.

  16. Comparison of gene coverage of mouse oligonucleotide microarray platforms

    Directory of Open Access Journals (Sweden)

    Medrano Juan F

    2006-03-01

    Full Text Available Abstract Background The increasing use of DNA microarrays for genetical genomics studies generates a need for platforms with complete coverage of the genome. We have compared the effective gene coverage in the mouse genome of different commercial and noncommercial oligonucleotide microarray platforms by performing an in-house gene annotation of probes. We only used information about probes that is available from vendors and followed a process that any researcher may take to find the gene targeted by a given probe. In order to make consistent comparisons between platforms, probes in each microarray were annotated with an Entrez Gene id and the chromosomal position for each gene was obtained from the UCSC Genome Browser Database. Gene coverage was estimated as the percentage of Entrez Genes with a unique position in the UCSC Genome database that is tested by a given microarray platform. Results A MySQL relational database was created to store the mapping information for 25,416 mouse genes and for the probes in five microarray platforms (gene coverage level in parenthesis: Affymetrix430 2.0 (75.6%, ABI Genome Survey (81.24%, Agilent (79.33%, Codelink (78.09%, Sentrix (90.47%; and four array-ready oligosets: Sigma (47.95%, Operon v.3 (69.89%, Operon v.4 (84.03%, and MEEBO (84.03%. The differences in coverage between platforms were highly conserved across chromosomes. Differences in the number of redundant and unspecific probes were also found among arrays. The database can be queried to compare specific genomic regions using a web interface. The software used to create, update and query the database is freely available as a toolbox named ArrayGene. Conclusion The software developed here allows researchers to create updated custom databases by using public or proprietary information on genes for any organisms. ArrayGene allows easy comparisons of gene coverage between microarray platforms for any region of the genome. The comparison presented here

  17. Emerging Use of Gene Expression Microarrays in Plant Physiology

    Directory of Open Access Journals (Sweden)

    Stephen P. Difazio

    2006-04-01

    Full Text Available Microarrays have become an important technology for the global analysis of gene expression in humans, animals, plants, and microbes. Implemented in the context of a well-designed experiment, cDNA and oligonucleotide arrays can provide highthroughput, simultaneous analysis of transcript abundance for hundreds, if not thousands, of genes. However, despite widespread acceptance, the use of microarrays as a tool to better understand processes of interest to the plant physiologist is still being explored. To help illustrate current uses of microarrays in the plant sciences, several case studies that we believe demonstrate the emerging application of gene expression arrays in plant physiology were selected from among the many posters and presentations at the 2003 Plant and Animal Genome XI Conference. Based on this survey, microarrays are being used to assess gene expression in plants exposed to the experimental manipulation of air temperature, soil water content and aluminium concentration in the root zone. Analysis often includes characterizing transcript profiles for multiple post-treatment sampling periods and categorizing genes with common patterns of response using hierarchical clustering techniques. In addition, microarrays are also providing insights into developmental changes in gene expression associated with fibre and root elongation in cotton and maize, respectively. Technical and analytical limitations of microarrays are discussed and projects attempting to advance areas of microarray design and data analysis are highlighted. Finally, although much work remains, we conclude that microarrays are a valuable tool for the plant physiologist interested in the characterization and identification of individual genes and gene families with potential application in the fields of agriculture, horticulture and forestry.

  18. Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data

    DEFF Research Database (Denmark)

    Tan, Qihua; Thomassen, Mads; Burton, Mark

    2017-01-01

    the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray...... time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health....

  19. Development, characterization and experimental validation of a cultivated sunflower (Helianthus annuus L. gene expression oligonucleotide microarray.

    Directory of Open Access Journals (Sweden)

    Paula Fernandez

    Full Text Available Oligonucleotide-based microarrays with accurate gene coverage represent a key strategy for transcriptional studies in orphan species such as sunflower, H. annuus L., which lacks full genome sequences. The goal of this study was the development and functional annotation of a comprehensive sunflower unigene collection and the design and validation of a custom sunflower oligonucleotide-based microarray. A large scale EST (>130,000 ESTs curation, assembly and sequence annotation was performed using Blast2GO (www.blast2go.de. The EST assembly comprises 41,013 putative transcripts (12,924 contigs and 28,089 singletons. The resulting Sunflower Unigen Resource (SUR version 1.0 was used to design an oligonucleotide-based Agilent microarray for cultivated sunflower. This microarray includes a total of 42,326 features: 1,417 Agilent controls, 74 control probes for sunflower replicated 10 times (740 controls and 40,169 different non-control probes. Microarray performance was validated using a model experiment examining the induction of senescence by water deficit. Pre-processing and differential expression analysis of Agilent microarrays was performed using the Bioconductor limma package. The analyses based on p-values calculated by eBayes (p<0.01 allowed the detection of 558 differentially expressed genes between water stress and control conditions; from these, ten genes were further validated by qPCR. Over-represented ontologies were identified using FatiScan in the Babelomics suite. This work generated a curated and trustable sunflower unigene collection, and a custom, validated sunflower oligonucleotide-based microarray using Agilent technology. Both the curated unigene collection and the validated oligonucleotide microarray provide key resources for sunflower genome analysis, transcriptional studies, and molecular breeding for crop improvement.

  20. Porous Silicon Antibody Microarrays for Quantitative Analysis: Measurement of Free and Total PSA in Clinical Plasma Samples

    Science.gov (United States)

    Tojo, Axel; Malm, Johan; Marko-Varga, György; Lilja, Hans; Laurell, Thomas

    2014-01-01

    The antibody microarrays have become widespread, but their use for quantitative analyses in clinical samples has not yet been established. We investigated an immunoassay based on nanoporous silicon antibody microarrays for quantification of total prostate-specific-antigen (PSA) in 80 clinical plasma samples, and provide quantitative data from a duplex microarray assay that simultaneously quantifies free and total PSA in plasma. To further develop the assay the porous silicon chips was placed into a standard 96-well microtiter plate for higher throughput analysis. The samples analyzed by this quantitative microarray were 80 plasma samples obtained from men undergoing clinical PSA testing (dynamic range: 0.14-44ng/ml, LOD: 0.14ng/ml). The second dataset, measuring free PSA (dynamic range: 0.40-74.9ng/ml, LOD: 0.47ng/ml) and total PSA (dynamic range: 0.87-295ng/ml, LOD: 0.76ng/ml), was also obtained from the clinical routine. The reference for the quantification was a commercially available assay, the ProStatus PSA Free/Total DELFIA. In an analysis of 80 plasma samples the microarray platform performs well across the range of total PSA levels. This assay might have the potential to substitute for the large-scale microtiter plate format in diagnostic applications. The duplex assay paves the way for a future quantitative multiplex assay, which analyses several prostate cancer biomarkers simultaneously. PMID:22921878

  1. Microintaglio Printing for Soft Lithography-Based in Situ Microarrays

    Directory of Open Access Journals (Sweden)

    Manish Biyani

    2015-07-01

    Full Text Available Advances in lithographic approaches to fabricating bio-microarrays have been extensively explored over the last two decades. However, the need for pattern flexibility, a high density, a high resolution, affordability and on-demand fabrication is promoting the development of unconventional routes for microarray fabrication. This review highlights the development and uses of a new molecular lithography approach, called “microintaglio printing technology”, for large-scale bio-microarray fabrication using a microreactor array (µRA-based chip consisting of uniformly-arranged, femtoliter-size µRA molds. In this method, a single-molecule-amplified DNA microarray pattern is self-assembled onto a µRA mold and subsequently converted into a messenger RNA or protein microarray pattern by simultaneously producing and transferring (immobilizing a messenger RNA or a protein from a µRA mold to a glass surface. Microintaglio printing allows the self-assembly and patterning of in situ-synthesized biomolecules into high-density (kilo-giga-density, ordered arrays on a chip surface with µm-order precision. This holistic aim, which is difficult to achieve using conventional printing and microarray approaches, is expected to revolutionize and reshape proteomics. This review is not written comprehensively, but rather substantively, highlighting the versatility of microintaglio printing for developing a prerequisite platform for microarray technology for the postgenomic era.

  2. Microarrays in ecological research: A case study of a cDNA microarray for plant-herbivore interactions

    Directory of Open Access Journals (Sweden)

    Gase Klaus

    2004-09-01

    Full Text Available Abstract Background Microarray technology allows researchers to simultaneously monitor changes in the expression ratios (ERs of hundreds of genes and has thereby revolutionized most of biology. Although this technique has the potential of elucidating early stages in an organism's phenotypic response to complex ecological interactions, to date, it has not been fully incorporated into ecological research. This is partially due to a lack of simple procedures of handling and analyzing the expression ratio (ER data produced from microarrays. Results We describe an analysis of the sources of variation in ERs from 73 hybridized cDNA microarrays, each with 234 herbivory-elicited genes from the model ecological expression system, Nicotiana attenuata, using procedures that are commonly used in ecologic research. Each gene is represented by two independently labeled PCR products and each product was arrayed in quadruplicate. We present a robust method of normalizing and analyzing ERs based on arbitrary thresholds and statistical criteria, and characterize a "norm of reaction" of ERs for 6 genes (4 of known function, 2 of unknown with different ERs as determined across all analyzed arrays to provide a biologically-informed alternative to the use of arbitrary expression ratios in determining significance of expression. These gene-specific ERs and their variance (gene CV were used to calculate array-based variances (array CV, which, in turn, were used to study the effects of array age, probe cDNA quantity and quality, and quality of spotted PCR products as estimates of technical variation. Cluster analysis and a Principal Component Analysis (PCA were used to reveal associations among the transcriptional "imprints" of arrays hybridized with cDNA probes derived from mRNA from N. attenuata plants variously elicited and attacked by different herbivore species and from three congeners: N. quadrivalis, N. longiflora and N. clevelandii. Additionally, the PCA

  3. DNA microarray analysis of fim mutations in Escherichia coli

    DEFF Research Database (Denmark)

    Schembri, Mark; Ussery, David; Workman, Christopher

    2002-01-01

    Bacterial adhesion is often mediated by complex polymeric surface structures referred to as fimbriae. Type I fimbriae of Escherichia coli represent the archetypical and best characterised fimbrial system. These adhesive organelles mediate binding to D-mannose and are directly associated...... we have used DNA microarray analysis to examine the molecular events involved in response to fimbrial gene expression in E. coli K-12. Observed differential expression levels of the fim genes were in good agreement with our current knowledge of the stoichiometry of type I fimbriae. Changes in fim...

  4. Microarray Meta-Analysis of RNA-Binding Protein Functions in Alternative Polyadenylation

    Science.gov (United States)

    Hu, Wenchao; Liu, Yuting; Yan, Jun

    2014-01-01

    Alternative polyadenylation (APA) is a post-transcriptional mechanism to generate diverse mRNA transcripts with different 3′UTRs from the same gene. In this study, we systematically searched for the APA events with differential expression in public mouse microarray data. Hundreds of genes with over-represented differential APA events and the corresponding experiments were identified. We further revealed that global APA differential expression occurred prevalently in tissues such as brain comparing to peripheral tissues, and biological processes such as development, differentiation and immune responses. Interestingly, we also observed widespread differential APA events in RNA-binding protein (RBP) genes such as Rbm3, Eif4e2 and Elavl1. Given the fact that RBPs are considered as the main regulators of differential APA expression, we constructed a co-expression network between APAs and RBPs using the microarray data. Further incorporation of CLIP-seq data of selected RBPs showed that Nova2 represses and Mbnl1 promotes the polyadenylation of closest poly(A) sites respectively. Altogether, our study is the first microarray meta-analysis in a mammal on the regulation of APA by RBPs that integrated massive mRNA expression data under a wide-range of biological conditions. Finally, we present our results as a comprehensive resource in an online website for the research community. PMID:24622240

  5. Adaptation of a Bioinformatics Microarray Analysis Workflow for a Toxicogenomic Study in Rainbow Trout.

    Directory of Open Access Journals (Sweden)

    Sophie Depiereux

    Full Text Available Sex steroids play a key role in triggering sex differentiation in fish, the use of exogenous hormone treatment leading to partial or complete sex reversal. This phenomenon has attracted attention since the discovery that even low environmental doses of exogenous steroids can adversely affect gonad morphology (ovotestis development and induce reproductive failure. Modern genomic-based technologies have enhanced opportunities to find out mechanisms of actions (MOA and identify biomarkers related to the toxic action of a compound. However, high throughput data interpretation relies on statistical analysis, species genomic resources, and bioinformatics tools. The goals of this study are to improve the knowledge of feminisation in fish, by the analysis of molecular responses in the gonads of rainbow trout fry after chronic exposure to several doses (0.01, 0.1, 1 and 10 μg/L of ethynylestradiol (EE2 and to offer target genes as potential biomarkers of ovotestis development. We successfully adapted a bioinformatics microarray analysis workflow elaborated on human data to a toxicogenomic study using rainbow trout, a fish species lacking accurate functional annotation and genomic resources. The workflow allowed to obtain lists of genes supposed to be enriched in true positive differentially expressed genes (DEGs, which were subjected to over-representation analysis methods (ORA. Several pathways and ontologies, mostly related to cell division and metabolism, sexual reproduction and steroid production, were found significantly enriched in our analyses. Moreover, two sets of potential ovotestis biomarkers were selected using several criteria. The first group displayed specific potential biomarkers belonging to pathways/ontologies highlighted in the experiment. Among them, the early ovarian differentiation gene foxl2a was overexpressed. The second group, which was highly sensitive but not specific, included the DEGs presenting the highest fold change and

  6. Genes involved in immunity and apoptosis are associated with human presbycusis based on microarray analysis.

    Science.gov (United States)

    Dong, Yang; Li, Ming; Liu, Puzhao; Song, Haiyan; Zhao, Yuping; Shi, Jianrong

    2014-06-01

    Genes involved in immunity and apoptosis were associated with human presbycusis. CCR3 and GILZ played an important role in the pathogenesis of presbycusis, probably through regulating chemokine receptor, T-cell apoptosis, or T-cell activation pathways. To identify genes associated with human presbycusis and explore the molecular mechanism of presbycusis. Hearing function was tested by pure-tone audiometry. Microarray analysis was performed to identify presbycusis-correlated genes by Illumina Human-6 BeadChip using the peripheral blood samples of subjects. To identify biological process categories and pathways associated with presbycusis-correlated genes, bioinformatics analysis was carried out by Gene Ontology Tree Machine (GOTM) and database for annotation, visualization, and integrated discovery (DAVID). Quantitative RT-PCR (qRT-PCR) was used to validate the microarray data. Microarray analysis identified 469 up-regulated genes and 323 down-regulated genes. Both the dominant biological processes by Gene Ontology (GO) analysis and the enriched pathways by Kyoto encyclopedia of genes and genomes (KEGG) and BIOCARTA showed that genes involved in immunity and apoptosis were associated with presbycusis. In addition, CCR3, GILZ, CXCL10, and CX3CR1 genes showed consistent difference between groups for both the gene chip and qRT-PCR data. The differences of CCR3 and GILZ between presbycusis patients and controls were statistically significant (p < 0.05).

  7. Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

    Science.gov (United States)

    Chavan, Shweta S; Bauer, Michael A; Peterson, Erich A; Heuck, Christoph J; Johann, Donald J

    2013-01-01

    Transcriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, and novel gene fusions. Given the voluminous amounts of historical microarray data, there is now a need to associate and integrate microarray and RNA-seq data via advanced bioinformatic approaches. Custom software was developed following a model-view-controller (MVC) approach to integrate Affymetrix probe set-IDs, and gene annotation information from a variety of sources. The tool/approach employs an assortment of strategies to integrate, cross reference, and associate microarray and RNA-seq datasets. Output from a variety of transcriptome reconstruction and quantitation tools (e.g., Cufflinks) can be directly integrated, and/or associated with Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a diversity of sources. Strategies are employed to maximize the annotation and cross referencing process. Custom gene sets (e.g., MM 70 risk score (GEP-70)) can be specified, and the tool can be directly assimilated into an RNA-seq pipeline. A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data, in conjunction with richer RNA-seq data, is now assisting with the study of MM cancer biology.

  8. A microarray analysis of the rice transcriptome and its comparison to Arabidopsis

    DEFF Research Database (Denmark)

    Ma, Ligeng; Chen, Chen; Liu, Xigang

    2005-01-01

    Arabidopsis and rice are the only two model plants whose finished phase genome sequence has been completed. Here we report the construction of an oligomer microarray based on the presently known and predicted gene models in the rice genome. This microarray was used to analyze the transcriptional...... with similar genome-wide surveys of the Arabidopsis transcriptome, our results indicate that similar proportions of the two genomes are expressed in their corresponding organ types. A large percentage of the rice gene models that lack significant Arabidopsis homologs are expressed. Furthermore, the expression...... patterns of rice and Arabidopsis best-matched homologous genes in distinct functional groups indicate dramatic differences in their degree of conservation between the two species. Thus, this initial comparative analysis reveals some basic similarities and differences between the Arabidopsis and rice...

  9. Integrated olfactory receptor and microarray gene expression databases

    Directory of Open Access Journals (Sweden)

    Crasto Chiquito J

    2007-06-01

    Full Text Available Abstract Background Gene expression patterns of olfactory receptors (ORs are an important component of the signal encoding mechanism in the olfactory system since they determine the interactions between odorant ligands and sensory neurons. We have developed the Olfactory Receptor Microarray Database (ORMD to house OR gene expression data. ORMD is integrated with the Olfactory Receptor Database (ORDB, which is a key repository of OR gene information. Both databases aim to aid experimental research related to olfaction. Description ORMD is a Web-accessible database that provides a secure data repository for OR microarray experiments. It contains both publicly available and private data; accessing the latter requires authenticated login. The ORMD is designed to allow users to not only deposit gene expression data but also manage their projects/experiments. For example, contributors can choose whether to make their datasets public. For each experiment, users can download the raw data files and view and export the gene expression data. For each OR gene being probed in a microarray experiment, a hyperlink to that gene in ORDB provides access to genomic and proteomic information related to the corresponding olfactory receptor. Individual ORs archived in ORDB are also linked to ORMD, allowing users access to the related microarray gene expression data. Conclusion ORMD serves as a data repository and project management system. It facilitates the study of microarray experiments of gene expression in the olfactory system. In conjunction with ORDB, ORMD integrates gene expression data with the genomic and functional data of ORs, and is thus a useful resource for both olfactory researchers and the public.

  10. The use of microarrays in microbial ecology

    Energy Technology Data Exchange (ETDEWEB)

    Andersen, G.L.; He, Z.; DeSantis, T.Z.; Brodie, E.L.; Zhou, J.

    2009-09-15

    Microarrays have proven to be a useful and high-throughput method to provide targeted DNA sequence information for up to many thousands of specific genetic regions in a single test. A microarray consists of multiple DNA oligonucleotide probes that, under high stringency conditions, hybridize only to specific complementary nucleic acid sequences (targets). A fluorescent signal indicates the presence and, in many cases, the abundance of genetic regions of interest. In this chapter we will look at how microarrays are used in microbial ecology, especially with the recent increase in microbial community DNA sequence data. Of particular interest to microbial ecologists, phylogenetic microarrays are used for the analysis of phylotypes in a community and functional gene arrays are used for the analysis of functional genes, and, by inference, phylotypes in environmental samples. A phylogenetic microarray that has been developed by the Andersen laboratory, the PhyloChip, will be discussed as an example of a microarray that targets the known diversity within the 16S rRNA gene to determine microbial community composition. Using multiple, confirmatory probes to increase the confidence of detection and a mismatch probe for every perfect match probe to minimize the effect of cross-hybridization by non-target regions, the PhyloChip is able to simultaneously identify any of thousands of taxa present in an environmental sample. The PhyloChip is shown to reveal greater diversity within a community than rRNA gene sequencing due to the placement of the entire gene product on the microarray compared with the analysis of up to thousands of individual molecules by traditional sequencing methods. A functional gene array that has been developed by the Zhou laboratory, the GeoChip, will be discussed as an example of a microarray that dynamically identifies functional activities of multiple members within a community. The recent version of GeoChip contains more than 24,000 50mer

  11. Multiplexed Analysis of Serum Breast and Ovarian Cancer Markers by Means of Suspension Bead-quantum Dot Microarrays

    Science.gov (United States)

    Brazhnik, Kristina; Sokolova, Zinaida; Baryshnikova, Maria; Bilan, Regina; Nabiev, Igor; Sukhanova, Alyona

    Multiplexed analysis of cancer markers is crucial for early tumor diagnosis and screening. We have designed lab-on-a-bead microarray for quantitative detection of three breast cancer markers in human serum. Quantum dots were used as bead-bound fluorescent tags for identifying each marker by means of flow cytometry. Antigen-specific beads reliably detected CA 15-3, CEA, and CA 125 in serum samples, providing clear discrimination between the samples with respect to the antigen levels. The novel microarray is advantageous over the routine single-analyte ones due to the simultaneous detection of various markers. Therefore the developed microarray is a promising tool for serum tumor marker profiling.

  12. A Java-based tool for the design of classification microarrays.

    Science.gov (United States)

    Meng, Da; Broschat, Shira L; Call, Douglas R

    2008-08-04

    Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for

  13. PATMA: parser of archival tissue microarray

    Directory of Open Access Journals (Sweden)

    Lukasz Roszkowiak

    2016-12-01

    Full Text Available Tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main aim of this study was to create a procedure capable of automatically finding and extracting cores from archival images of the tissue microarrays. This software supports the work of scientists who want to perform further image processing on single cores. The proposed method is an efficient and fast procedure, working in fully automatic or semi-automatic mode. A total of 89% of punches were correctly extracted with automatic selection. With an addition of manual correction, it is possible to fully prepare the whole slide image for extraction in 2 min per tissue microarray. The proposed technique requires minimum skill and time to parse big array of cores from tissue microarray whole slide image into individual core images.

  14. ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying.

    Science.gov (United States)

    Nguyen, Hoai Nam; Paveau, Vincent; Cauchois, Cyril; Kervrann, Charles

    2018-04-19

    Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. We

  15. BioconductorBuntu: a Linux distribution that implements a web-based DNA microarray analysis server.

    Science.gov (United States)

    Geeleher, Paul; Morris, Dermot; Hinde, John P; Golden, Aaron

    2009-06-01

    BioconductorBuntu is a custom distribution of Ubuntu Linux that automatically installs a server-side microarray processing environment, providing a user-friendly web-based GUI to many of the tools developed by the Bioconductor Project, accessible locally or across a network. System installation is via booting off a CD image or by using a Debian package provided to upgrade an existing Ubuntu installation. In its current version, several microarray analysis pipelines are supported including oligonucleotide, dual-or single-dye experiments, including post-processing with Gene Set Enrichment Analysis. BioconductorBuntu is designed to be extensible, by server-side integration of further relevant Bioconductor modules as required, facilitated by its straightforward underlying Python-based infrastructure. BioconductorBuntu offers an ideal environment for the development of processing procedures to facilitate the analysis of next-generation sequencing datasets. BioconductorBuntu is available for download under a creative commons license along with additional documentation and a tutorial from (http://bioinf.nuigalway.ie).

  16. A benchmark for statistical microarray data analysis that preserves actual biological and technical variance.

    Science.gov (United States)

    De Hertogh, Benoît; De Meulder, Bertrand; Berger, Fabrice; Pierre, Michael; Bareke, Eric; Gaigneaux, Anthoula; Depiereux, Eric

    2010-01-11

    Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods. Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality. Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

  17. Microarray Gene Expression Analysis to Evaluate Cell Type Specific Expression of Targets Relevant for Immunotherapy of Hematological Malignancies.

    Directory of Open Access Journals (Sweden)

    M J Pont

    Full Text Available Cellular immunotherapy has proven to be effective in the treatment of hematological cancers by donor lymphocyte infusion after allogeneic hematopoietic stem cell transplantation and more recently by targeted therapy with chimeric antigen or T-cell receptor-engineered T cells. However, dependent on the tissue distribution of the antigens that are targeted, anti-tumor responses can be accompanied by undesired side effects. Therefore, detailed tissue distribution analysis is essential to estimate potential efficacy and toxicity of candidate targets for immunotherapy of hematological malignancies. We performed microarray gene expression analysis of hematological malignancies of different origins, healthy hematopoietic cells and various non-hematopoietic cell types from organs that are often targeted in detrimental immune responses after allogeneic stem cell transplantation leading to graft-versus-host disease. Non-hematopoietic cells were also cultured in the presence of IFN-γ to analyze gene expression under inflammatory circumstances. Gene expression was investigated by Illumina HT12.0 microarrays and quality control analysis was performed to confirm the cell-type origin and exclude contamination of non-hematopoietic cell samples with peripheral blood cells. Microarray data were validated by quantitative RT-PCR showing strong correlations between both platforms. Detailed gene expression profiles were generated for various minor histocompatibility antigens and B-cell surface antigens to illustrate the value of the microarray dataset to estimate efficacy and toxicity of candidate targets for immunotherapy. In conclusion, our microarray database provides a relevant platform to analyze and select candidate antigens with hematopoietic (lineage-restricted expression as potential targets for immunotherapy of hematological cancers.

  18. Spot detection and image segmentation in DNA microarray data.

    Science.gov (United States)

    Qin, Li; Rueda, Luis; Ali, Adnan; Ngom, Alioune

    2005-01-01

    Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.

  19. Microarrays in brain research: the good, the bad and the ugly.

    Science.gov (United States)

    Mirnics, K

    2001-06-01

    Making sense of microarray data is a complex process, in which the interpretation of findings will depend on the overall experimental design and judgement of the investigator performing the analysis. As a result, differences in tissue harvesting, microarray types, sample labelling and data analysis procedures make post hoc sharing of microarray data a great challenge. To ensure rapid and meaningful data exchange, we need to create some order out of the existing chaos. In these ground-breaking microarray standardization and data sharing efforts, NIH agencies should take a leading role

  20. Importance of the efficiency of double-stranded DNA formation in cDNA synthesis for the imprecision of microarray expression analysis.

    Science.gov (United States)

    Thormar, Hans G; Gudmundsson, Bjarki; Eiriksdottir, Freyja; Kil, Siyoen; Gunnarsson, Gudmundur H; Magnusson, Magnus Karl; Hsu, Jason C; Jonsson, Jon J

    2013-04-01

    The causes of imprecision in microarray expression analysis are poorly understood, limiting the use of this technology in molecular diagnostics. Two-dimensional strandness-dependent electrophoresis (2D-SDE) separates nucleic acid molecules on the basis of length and strandness, i.e., double-stranded DNA (dsDNA), single-stranded DNA (ssDNA), and RNA·DNA hybrids. We used 2D-SDE to measure the efficiency of cDNA synthesis and its importance for the imprecision of an in vitro transcription-based microarray expression analysis. The relative amount of double-stranded cDNA formed in replicate experiments that used the same RNA sample template was highly variable, ranging between 0% and 72% of the total DNA. Microarray experiments showed an inverse relationship between the difference between sample pairs in probe variance and the relative amount of dsDNA. Approximately 15% of probes showed between-sample variation (P cDNA synthesized can be an important component of the imprecision in T7 RNA polymerase-based microarray expression analysis. © 2013 American Association for Clinical Chemistry

  1. A Java-based tool for the design of classification microarrays

    Directory of Open Access Journals (Sweden)

    Broschat Shira L

    2008-08-01

    Full Text Available Abstract Background Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. Results The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. Conclusion In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays–and mixed-plasmid microarrays in particular–it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm, several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text, and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff. Weights

  2. Early Gene Expression in Wounded Human Keratinocytes Revealed by DNA Microarray Analysis

    Directory of Open Access Journals (Sweden)

    Pascal Barbry

    2006-04-01

    Full Text Available Wound healing involves several steps: spreading of the cells, migration and proliferation. We have profiled gene expression during the early events of wound healing in normal human keratinocytes with a home-made DNA microarray containing about 1000 relevant human probes. An original wounding machine was used, that allows the wounding of up to 40% of the surface of a confluent monolayer of cultured cells grown on a Petri dish (compared with 5% with a classical ‘scratch’ method. The two aims of the present study were: (a to validate a limited number of genes by comparing the expression levels obtained with this technique with those found in the literature; (b to combine the use of the wounding machine with DNA microarray analysis for large-scale detection of the molecular events triggered during the early stages of the wound-healing process. The time-courses of RNA expression observed at 0.5, 1.5, 3, 6 and 15 h after wounding for genes such as c-Fos, c-Jun, Egr1, the plasminogen activator PLAU (uPA and the signal transducer and transcription activator STAT3, were consistent with previously published data. This suggests that our methodologies are able to perform quantitative measurement of gene expression. Transcripts encoding two zinc finger proteins, ZFP36 and ZNF161, and the tumour necrosis factor α-induced protein TNFAIP3, were also overexpressed after wounding. The role of the p38 mitogen-activated protein kinase (p38MAPK in wound healing was shown after the inhibition of p38 by SB203580, but our results also suggest the existence of surrogate activating pathways.

  3. Systematic interpretation of microarray data using experiment annotations

    Directory of Open Access Journals (Sweden)

    Frohme Marcus

    2006-12-01

    Full Text Available Abstract Background Up to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format. Results We provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel and two-channel data, stemming from model organisms such as yeast and drosophila up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design. Conclusion Biological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.

  4. Shared probe design and existing microarray reanalysis using PICKY

    Directory of Open Access Journals (Sweden)

    Chou Hui-Hsien

    2010-04-01

    Full Text Available Abstract Background Large genomes contain families of highly similar genes that cannot be individually identified by microarray probes. This limitation is due to thermodynamic restrictions and cannot be resolved by any computational method. Since gene annotations are updated more frequently than microarrays, another common issue facing microarray users is that existing microarrays must be routinely reanalyzed to determine probes that are still useful with respect to the updated annotations. Results PICKY 2.0 can design shared probes for sets of genes that cannot be individually identified using unique probes. PICKY 2.0 uses novel algorithms to track sharable regions among genes and to strictly distinguish them from other highly similar but nontarget regions during thermodynamic comparisons. Therefore, PICKY does not sacrifice the quality of shared probes when choosing them. The latest PICKY 2.1 includes the new capability to reanalyze existing microarray probes against updated gene sets to determine probes that are still valid to use. In addition, more precise nonlinear salt effect estimates and other improvements are added, making PICKY 2.1 more versatile to microarray users. Conclusions Shared probes allow expressed gene family members to be detected; this capability is generally more desirable than not knowing anything about these genes. Shared probes also enable the design of cross-genome microarrays, which facilitate multiple species identification in environmental samples. The new nonlinear salt effect calculation significantly increases the precision of probes at a lower buffer salt concentration, and the probe reanalysis function improves existing microarray result interpretations.

  5. Single-cell multiple gene expression analysis based on single-molecule-detection microarray assay for multi-DNA determination

    Energy Technology Data Exchange (ETDEWEB)

    Li, Lu [School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100 (China); Wang, Xianwei [School of Life Sciences, Shandong University, Jinan 250100 (China); Zhang, Xiaoli [School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100 (China); Wang, Jinxing [School of Life Sciences, Shandong University, Jinan 250100 (China); Jin, Wenrui, E-mail: jwr@sdu.edu.cn [School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100 (China)

    2015-01-07

    Highlights: • A single-molecule-detection (SMD) microarray for 10 samples is fabricated. • The based-SMD microarray assay (SMA) can determine 8 DNAs for each sample. • The limit of detection of SMA is as low as 1.3 × 10{sup −16} mol L{sup −1}. • The SMA can be applied in single-cell multiple gene expression analysis. - Abstract: We report a novel ultra-sensitive and high-selective single-molecule-detection microarray assay (SMA) for multiple DNA determination. In the SMA, a capture DNA (DNAc) microarray consisting of 10 subarrays with 9 spots for each subarray is fabricated on a silanized glass coverslip as the substrate. On the subarrays, the spot-to-spot spacing is 500 μm and each spot has a diameter of ∼300 μm. The sequence of the DNAcs on the 9 spots of a subarray is different, to determine 8 types of target DNAs (DNAts). Thus, 8 types of DNAts are captured to their complementary DNAcs at 8 spots of a subarray, respectively, and then labeled with quantum dots (QDs) attached to 8 types of detection DNAs (DNAds) with different sequences. The ninth spot is used to detect the blank value. In order to determine the same 8 types of DNAts in 10 samples, the 10 DNAc-modified subarrays on the microarray are identical. Fluorescence single-molecule images of the QD-labeled DNAts on each spot of the subarray are acquired using a home-made single-molecule microarray reader. The amounts of the DNAts are quantified by counting the bright dots from the QDs. For a microarray, 8 types of DNAts in 10 samples can be quantified in parallel. The limit of detection of the SMA for DNA determination is as low as 1.3 × 10{sup −16} mol L{sup −1}. The SMA for multi-DNA determination can also be applied in single-cell multiple gene expression analysis through quantification of complementary DNAs (cDNAs) corresponding to multiple messenger RNAs (mRNAs) in single cells. To do so, total RNA in single cells is extracted and reversely transcribed into their cDNAs. Three

  6. Microarrays: Molecular allergology and nanotechnology for personalised medicine (II).

    Science.gov (United States)

    Lucas, J M

    2010-01-01

    Progress in nanotechnology and DNA recombination techniques have produced tools for the diagnosis and investigation of allergy at molecular level. The most advanced examples of such progress are the microarray techniques, which have been expanded not only in research in the field of proteomics but also in application to the clinical setting. Microarrays of allergic components offer results relating to hundreds of allergenic components in a single test, and using a small amount of serum which can be obtained from capillary blood. The availability of new molecules will allow the development of panels including new allergenic components and sources, which will require evaluation for clinical use. Their application opens the door to component-based diagnosis, to the holistic perception of sensitisation as represented by molecular allergy, and to patient-centred medical practice by allowing great diagnostic accuracy and the definition of individualised immunotherapy for each patient. The present article reviews the application of allergenic component microarrays to allergology for diagnosis, management in the form of specific immunotherapy, and epidemiological studies. A review is also made of the use of protein and gene microarray techniques in basic research and in allergological diseases. Lastly, an evaluation is made of the challenges we face in introducing such techniques to clinical practice, and of the future perspectives of this new technology. Copyright 2010 SEICAP. Published by Elsevier Espana. All rights reserved.

  7. eSensor: an electrochemical detection-based DNA microarray technology enabling sample-to-answer molecular diagnostics

    Science.gov (United States)

    Liu, Robin H.; Longiaru, Mathew

    2009-05-01

    DNA microarrays are becoming a widespread tool used in life science and drug screening due to its many benefits of miniaturization and integration. Microarrays permit a highly multiplexed DNA analysis. Recently, the development of new detection methods and simplified methodologies has rapidly expanded the use of microarray technologies from predominantly gene expression analysis into the arena of diagnostics. Osmetech's eSensor® is an electrochemical detection platform based on a low-to- medium density DNA hybridization array on a cost-effective printed circuit board substrate. eSensor® has been cleared by FDA for Warfarin sensitivity test and Cystic Fibrosis Carrier Detection. Other genetic-based diagnostic and infectious disease detection tests are under development. The eSensor® platform eliminates the need for an expensive laser-based optical system and fluorescent reagents. It allows one to perform hybridization and detection in a single and small instrument without any fluidic processing and handling. Furthermore, the eSensor® platform is readily adaptable to on-chip sample-to-answer genetic analyses using microfluidics technology. The eSensor® platform provides a cost-effective solution to direct sample-to-answer genetic analysis, and thus have a potential impact in the fields of point-of-care genetic analysis, environmental testing, and biological warfare agent detection.

  8. Empirical Bayes ranking and selection methods via semiparametric hierarchical mixture models in microarray studies.

    Science.gov (United States)

    Noma, Hisashi; Matsui, Shigeyuki

    2013-05-20

    The main purpose of microarray studies is screening of differentially expressed genes as candidates for further investigation. Because of limited resources in this stage, prioritizing genes are relevant statistical tasks in microarray studies. For effective gene selections, parametric empirical Bayes methods for ranking and selection of genes with largest effect sizes have been proposed (Noma et al., 2010; Biostatistics 11: 281-289). The hierarchical mixture model incorporates the differential and non-differential components and allows information borrowing across differential genes with separation from nuisance, non-differential genes. In this article, we develop empirical Bayes ranking methods via a semiparametric hierarchical mixture model. A nonparametric prior distribution, rather than parametric prior distributions, for effect sizes is specified and estimated using the "smoothing by roughening" approach of Laird and Louis (1991; Computational statistics and data analysis 12: 27-37). We present applications to childhood and infant leukemia clinical studies with microarrays for exploring genes related to prognosis or disease progression. Copyright © 2012 John Wiley & Sons, Ltd.

  9. Microarray-based transcriptomic analysis of differences between long-term gregarious and solitarious desert locusts.

    Directory of Open Access Journals (Sweden)

    Liesbeth Badisco

    Full Text Available Desert locusts (Schistocerca gregaria show an extreme form of phenotypic plasticity and can transform between a cryptic solitarious phase and a swarming gregarious phase. The two phases differ extensively in behavior, morphology and physiology but very little is known about the molecular basis of these differences. We used our recently generated Expressed Sequence Tag (EST database derived from S. gregaria central nervous system (CNS to design oligonucleotide microarrays and compare the expression of thousands of genes in the CNS of long-term gregarious and solitarious adult desert locusts. This identified 214 differentially expressed genes, of which 40% have been annotated to date. These include genes encoding proteins that are associated with CNS development and modeling, sensory perception, stress response and resistance, and fundamental cellular processes. Our microarray analysis has identified genes whose altered expression may enable locusts of either phase to deal with the different challenges they face. Genes for heat shock proteins and proteins which confer protection from infection were upregulated in gregarious locusts, which may allow them to respond to acute physiological challenges. By contrast the longer-lived solitarious locusts appear to be more strongly protected from the slowly accumulating effects of ageing by an upregulation of genes related to anti-oxidant systems, detoxification and anabolic renewal. Gregarious locusts also had a greater abundance of transcripts for proteins involved in sensory processing and in nervous system development and plasticity. Gregarious locusts live in a more complex sensory environment than solitarious locusts and may require a greater turnover of proteins involved in sensory transduction, and possibly greater neuronal plasticity.

  10. IsoGeneGUI : Multiple approaches for dose-response analysis of microarray data using R

    NARCIS (Netherlands)

    Otava, Martin; Sengupta, Rudradev; Shkedy, Ziv; Lin, Dan; Pramana, Setia; Verbeke, Tobias; Haldermans, Philippe; Hothorn, Ludwig A.; Gerhard, Daniel; Kuiper, Rebecca M.; Klinglmueller, Florian; Kasim, Adetayo

    2017-01-01

    The analysis of transcriptomic experiments with ordered covariates, such as dose-response data, has become a central topic in bioinformatics, in particular in omics studies. Consequently, multiple R packages on CRAN and Bioconductor are designed to analyse microarray data from various perspectives

  11. Gene Expression Analysis Using Agilent DNA Microarrays

    DEFF Research Database (Denmark)

    Stangegaard, Michael

    2009-01-01

    Hybridization of labeled cDNA to microarrays is an intuitively simple and a vastly underestimated process. If it is not performed, optimized, and standardized with the same attention to detail as e.g., RNA amplification, information may be overlooked or even lost. Careful balancing of the amount ...

  12. Analysis of gene expression in resynthesized Brassica napus Allopolyploids using arabidopsis 70mer oligo microarrays.

    Directory of Open Access Journals (Sweden)

    Robert T Gaeta

    Full Text Available BACKGROUND: Studies in resynthesized Brassica napus allopolyploids indicate that homoeologous chromosome exchanges in advanced generations (S(5ratio6 alter gene expression through the loss and doubling of homoeologous genes within the rearrangements. Rearrangements may also indirectly affect global gene expression if homoeologous copies of gene regulators within rearrangements have differential affects on the transcription of genes in networks. METHODOLOGY/PRINCIPAL FINDINGS: We utilized Arabidopsis 70mer oligonucleotide microarrays for exploring gene expression in three resynthesized B. napus lineages at the S(0ratio1 and S(5ratio6 generations as well as their diploid progenitors B. rapa and B. oleracea. Differential gene expression between the progenitors and additive (midparent expression in the allopolyploids were tested. The S(5ratio6 lines differed in the number of genetic rearrangements, allowing us to test if the number of genes displaying nonadditive expression was related to the number of rearrangements. Estimates using per-gene and common variance ANOVA models indicated that 6-15% of 26,107 genes were differentially expressed between the progenitors. Individual allopolyploids showed nonadditive expression for 1.6-32% of all genes. Less than 0.3% of genes displayed nonadditive expression in all S(0ratio1 lines and 0.1-0.2% were nonadditive among all S(5ratio6 lines. Differentially expressed genes in the polyploids were over-represented by genes differential between the progenitors. The total number of differentially expressed genes was correlated with the number of genetic changes in S(5ratio6 lines under the common variance model; however, there was no relationship using a per-gene variance model, and many genes showed nonadditive expression in S(0ratio1 lines. CONCLUSIONS/SIGNIFICANCE: Few genes reproducibly demonstrated nonadditive expression among lineages, suggesting few changes resulted from a general response to polyploidization

  13. Complete gene expression profiling of Saccharopolyspora erythraea using GeneChip DNA microarrays

    Directory of Open Access Journals (Sweden)

    Bordoni Roberta

    2007-11-01

    Full Text Available Abstract Background The Saccharopolyspora erythraea genome sequence, recently published, presents considerable divergence from those of streptomycetes in gene organization and function, confirming the remarkable potential of S. erythraea for producing many other secondary metabolites in addition to erythromycin. In order to investigate, at whole transcriptome level, how S. erythraea genes are modulated, a DNA microarray was specifically designed and constructed on the S. erythraea strain NRRL 2338 genome sequence, and the expression profiles of 6494 ORFs were monitored during growth in complex liquid medium. Results The transcriptional analysis identified a set of 404 genes, whose transcriptional signals vary during growth and characterize three distinct phases: a rapid growth until 32 h (Phase A; a growth slowdown until 52 h (Phase B; and another rapid growth phase from 56 h to 72 h (Phase C before the cells enter the stationary phase. A non-parametric statistical method, that identifies chromosomal regions with transcriptional imbalances, determined regional organization of transcription along the chromosome, highlighting differences between core and non-core regions, and strand specific patterns of expression. Microarray data were used to characterize the temporal behaviour of major functional classes and of all the gene clusters for secondary metabolism. The results confirmed that the ery cluster is up-regulated during Phase A and identified six additional clusters (for terpenes and non-ribosomal peptides that are clearly regulated in later phases. Conclusion The use of a S. erythraea DNA microarray improved specificity and sensitivity of gene expression analysis, allowing a global and at the same time detailed picture of how S. erythraea genes are modulated. This work underlines the importance of using DNA microarrays, coupled with an exhaustive statistical and bioinformatic analysis of the results, to understand the transcriptional

  14. Gene targeting associated with the radiation sensitivity in squamous cell carcinoma by using microarray analysis

    International Nuclear Information System (INIS)

    Nimura, Yoshinori; Kumagai, Ken; Kouzu, Yoshinao; Higo, Morihiro; Kato, Yoshikuni; Seki, Naohiko; Yamada, Shigeru

    2005-01-01

    In order to identify a set of genes related to radiation sensitivity of squamous cell carcinoma (SCC) and establish a predictive method, we compared expression profiles of radio-sensitive/radio-resistant SCC cell lines, using the in-house cDNA microarray consisting of 2,201 human genes derived from full-length enriched SCC cDNA libraries and the Human oligo chip 30 K (Hitachi Software Engineering). Surviving fractions (SF) after irradiation of heavy iron were calculated by colony formation assay. Three pairs (TE2-TE13, YES5-YES6, and HSC3-HSC2), sensitive (SF1 0.6), were selected for the microarray analysis. The results of cDNA microarray analysis showed that 20 genes in resistant cell lines and 5 genes in sensitive cell lines were up regulated more than 1.5-fold compared with sensitive and resistant cell lines respectively. Fourteen out of 25 genes were confirmed the gene expression profiles by real-time polymerase chain reaction (PCR). Twenty-seven genes identified by Human oligo chip 30 K are candidate for the markers to distinguish radio-sensitive from radio-resistant. These results suggest that the isolated 27 genes are the candidates that might be used as specific molecular markers to predict radiation sensitivity. (author)

  15. Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease

    Directory of Open Access Journals (Sweden)

    van Ommen Gert-Jan B

    2008-06-01

    Full Text Available Abstract Background Comparative analysis of expression microarray studies is difficult due to the large influence of technical factors on experimental outcome. Still, the identified differentially expressed genes may hint at the same biological processes. However, manually curated assignment of genes to biological processes, such as pursued by the Gene Ontology (GO consortium, is incomplete and limited. We hypothesised that automatic association of genes with biological processes through thesaurus-controlled mining of Medline abstracts would be more effective. Therefore, we developed a novel algorithm (LAMA: Literature-Aided Meta-Analysis to quantify the similarity between transcriptomics studies. We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease, and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO. Results While the overlap in both genes and overrepresented GO-terms was poor, LAMA retrieved many more biologically meaningful links between studies, with substantially lower influence of technical factors. LAMA correctly grouped muscular dystrophy, regeneration and myositis studies, and linked patient and corresponding mouse model studies. LAMA also retrieves the connecting biological concepts. Among other new discoveries, we associated cullin proteins, a class of ubiquitinylation proteins, with genes down-regulated during muscle regeneration, whereas ubiquitinylation was previously reported to be activated during the inverse process: muscle atrophy. Conclusion Our literature-based association analysis is capable of finding hidden common biological denominators in microarray studies, and circumvents the need for raw data analysis or curated gene annotation databases.

  16. A statistical framework for differential network analysis from microarray data

    Directory of Open Access Journals (Sweden)

    Datta Somnath

    2010-02-01

    Full Text Available Abstract Background It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. Results We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i whether the overall modular structures of the two networks are different, (ii whether the connectivity of a particular set of "interesting genes" has changed between the two networks, and (iii whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity. Conclusions Examining changes in network structure can provide valuable information about the

  17. Design and evaluation of Actichip, a thematic microarray for the study of the actin cytoskeleton

    Science.gov (United States)

    Muller, Jean; Mehlen, André; Vetter, Guillaume; Yatskou, Mikalai; Muller, Arnaud; Chalmel, Frédéric; Poch, Olivier; Friederich, Evelyne; Vallar, Laurent

    2007-01-01

    Background The actin cytoskeleton plays a crucial role in supporting and regulating numerous cellular processes. Mutations or alterations in the expression levels affecting the actin cytoskeleton system or related regulatory mechanisms are often associated with complex diseases such as cancer. Understanding how qualitative or quantitative changes in expression of the set of actin cytoskeleton genes are integrated to control actin dynamics and organisation is currently a challenge and should provide insights in identifying potential targets for drug discovery. Here we report the development of a dedicated microarray, the Actichip, containing 60-mer oligonucleotide probes for 327 genes selected for transcriptome analysis of the human actin cytoskeleton. Results Genomic data and sequence analysis features were retrieved from GenBank and stored in an integrative database called Actinome. From these data, probes were designed using a home-made program (CADO4MI) allowing sequence refinement and improved probe specificity by combining the complementary information recovered from the UniGene and RefSeq databases. Actichip performance was analysed by hybridisation with RNAs extracted from epithelial MCF-7 cells and human skeletal muscle. Using thoroughly standardised procedures, we obtained microarray images with excellent quality resulting in high data reproducibility. Actichip displayed a large dynamic range extending over three logs with a limit of sensitivity between one and ten copies of transcript per cell. The array allowed accurate detection of small changes in gene expression and reliable classification of samples based on the expression profiles of tissue-specific genes. When compared to two other oligonucleotide microarray platforms, Actichip showed similar sensitivity and concordant expression ratios. Moreover, Actichip was able to discriminate the highly similar actin isoforms whereas the two other platforms did not. Conclusion Our data demonstrate that

  18. Production of DNA microarray and expression analysis of genes from Xylella fastidiosa in different culture media

    Directory of Open Access Journals (Sweden)

    Regiane de Fátima Travensolo

    2009-06-01

    Full Text Available DNA Microarray was developed to monitor the expression of many genes from Xylella fastidiosa, allowing the side by-side comparison of two situations in a single experiment. The experiments were performed using X. fastidiosa cells grown in two culture media: BCYE and XDM2. The primers were synthesized, spotted onto glass slides and the array was hybridized against fluorescently labeled cDNAs. The emitted signals were quantified, normalized and the data were statistically analyzed to verify the differentially expressed genes. According to the data, 104 genes were differentially expressed in XDM2 and 30 genes in BCYE media. The present study showed that DNA microarray technique efficiently differentiate the expressed genes under different conditions.DNA Microarray foi desenvolvida para monitorar a expressão de muitos genes de Xylella fastidiosa, permitindo a comparação de duas situações distintas em um único experimento. Os experimentos foram feitos utilizando células de X. fastidiosa cultivada em dois meios de cultura: BCYE e XDM2. Pares de oligonucleotídeos iniciadores foram sintetizados, depositados em lâminas de vidro e o arranjo foi hibridizado contra cDNAs marcados fluorescentemente. Os sinais emitidos foram quantificados, normalizados e os dados foram estatisticamente analisados para verificar os genes diferencialmente expressos. De acordo com nossos dados, 104 genes foram diferencialmente expressos para o meio de cultura XDM2 e 30 genes para o BCYE. No presente estudo, nós demonstramos que a técnica de DNA microarrays eficientemente diferencia genes expressos sob diferentes condições de cultivo.

  19. Cross-platform comparison of microarray data using order restricted inference

    Science.gov (United States)

    Klinglmueller, Florian; Tuechler, Thomas; Posch, Martin

    2013-01-01

    Motivation Titration experiments measuring the gene expression from two different tissues, along with total RNA mixtures of the pure samples, are frequently used for quality evaluation of microarray technologies. Such a design implies that the true mRNA expression of each gene, is either constant or follows a monotonic trend between the mixtures, applying itself to the use of order restricted inference procedures. Exploiting only the postulated monotonicity of titration designs, we propose three statistical analysis methods for the validation of high-throughput genetic data and corresponding preprocessing techniques. Results Our methods allow for inference of accuracy, repeatability and cross-platform agreement, with minimal required assumptions regarding the underlying data generating process. Therefore, they are readily applicable to all sorts of genetic high-throughput data independent of the degree of preprocessing. An application to the EMERALD dataset was used to demonstrate how our methods provide a rich spectrum of easily interpretable quality metrics and allow the comparison of different microarray technologies and normalization methods. The results are on par with previous work, but provide additional new insights that cast doubt on the utility of popular preprocessing techniques, specifically concerning the EMERALD projects dataset. Availability All datasets are available on EBI’s ArrayExpress web site (http://www.ebi.ac.uk/microarray-as/ae/) under accession numbers E-TABM-536, E-TABM-554 and E-TABM-555. Source code implemented in C and R is available at: http://statistics.msi.meduniwien.ac.at/float/cross_platform/. Methods for testing and variance decomposition have been made available in the R-package orQA, which can be downloaded and installed from CRAN http://cran.r-project.org. PMID:21317143

  20. Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing

    DEFF Research Database (Denmark)

    Willenbrock, Hanni; Salomon, Jesper; Søkilde, Rolf

    2009-01-01

    Recently, next-generation sequencing has been introduced as a promising, new platform for assessing the copy number of transcripts, while the existing microarray technology is considered less reliable for absolute, quantitative expression measurements. Nonetheless, so far, results from the two...... technologies have only been compared based on biological data, leading to the conclusion that, although they are somewhat correlated, expression values differ significantly. Here, we use synthetic RNA samples, resembling human microRNA samples, to find that microarray expression measures actually correlate...... better with sample RNA content than expression measures obtained from sequencing data. In addition, microarrays appear highly sensitive and perform equivalently to next-generation sequencing in terms of reproducibility and relative ratio quantification....

  1. Versatile High Resolution Oligosaccharide Microarrays for Plant Glycobiology and Cell Wall Research

    DEFF Research Database (Denmark)

    Pedersen, Henriette Lodberg; Fangel, Jonatan Ulrik; McCleary, Barry

    2012-01-01

    Microarrays are powerful tools for high throughput analysis, and hundreds or thousands of molecular interactions can be assessed simultaneously using very small amounts of analytes. Nucleotide microarrays are well established in plant research, but carbohydrate microarrays are much less establish...

  2. Analysis of Temporal-spatial Co-variation within Gene Expression Microarray Data in an Organogenesis Model

    Science.gov (United States)

    Ehler, Martin; Rajapakse, Vinodh; Zeeberg, Barry; Brooks, Brian; Brown, Jacob; Czaja, Wojciech; Bonner, Robert F.

    The gene networks underlying closure of the optic fissure during vertebrate eye development are poorly understood. We used a novel clustering method based on Laplacian Eigenmaps, a nonlinear dimension reduction method, to analyze microarray data from laser capture microdissected (LCM) cells at the site and developmental stages (days 10.5 to 12.5) of optic fissure closure. Our new method provided greater biological specificity than classical clustering algorithms in terms of identifying more biological processes and functions related to eye development as defined by Gene Ontology at lower false discovery rates. This new methodology builds on the advantages of LCM to isolate pure phenotypic populations within complex tissues and allows improved ability to identify critical gene products expressed at lower copy number. The combination of LCM of embryonic organs, gene expression microarrays, and extracting spatial and temporal co-variations appear to be a powerful approach to understanding the gene regulatory networks that specify mammalian organogenesis.

  3. DATE analysis: A general theory of biological change applied to microarray data.

    Science.gov (United States)

    Rasnick, David

    2009-01-01

    In contrast to conventional data mining, which searches for specific subsets of genes (extensive variables) to correlate with specific phenotypes, DATE analysis correlates intensive state variables calculated from the same datasets. At the heart of DATE analysis are two biological equations of state not dependent on genetic pathways. This result distinguishes DATE analysis from other bioinformatics approaches. The dimensionless state variable F quantifies the relative overall cellular activity of test cells compared to well-chosen reference cells. The variable pi(i) is the fold-change in the expression of the ith gene of test cells relative to reference. It is the fraction phi of the genome undergoing differential expression-not the magnitude pi-that controls biological change. The state variable phi is equivalent to the control strength of metabolic control analysis. For tractability, DATE analysis assumes a linear system of enzyme-connected networks and exploits the small average contribution of each cellular component. This approach was validated by reproducible values of the state variables F, RNA index, and phi calculated from random subsets of transcript microarray data. Using published microarray data, F, RNA index, and phi were correlated with: (1) the blood-feeding cycle of the malaria parasite, (2) embryonic development of the fruit fly, (3) temperature adaptation of Killifish, (4) exponential growth of cultured S. pneumoniae, and (5) human cancers. DATE analysis was applied to aCGH data from the great apes. A good example of the power of DATE analysis is its application to genomically unstable cancers, which have been refractory to data mining strategies. 2009 American Institute of Chemical Engineers Biotechnol.

  4. A comprehensive hybridization model allows whole HERV transcriptome profiling using high density microarray.

    Science.gov (United States)

    Becker, Jérémie; Pérot, Philippe; Cheynet, Valérie; Oriol, Guy; Mugnier, Nathalie; Mommert, Marine; Tabone, Olivier; Textoris, Julien; Veyrieras, Jean-Baptiste; Mallet, François

    2017-04-08

    Human endogenous retroviruses (HERVs) have received much attention for their implications in the etiology of many human diseases and their profound effect on evolution. Notably, recent studies have highlighted associations between HERVs expression and cancers (Yu et al., Int J Mol Med 32, 2013), autoimmunity (Balada et al., Int Rev Immunol 29:351-370, 2010) and neurological (Christensen, J Neuroimmune Pharmacol 5:326-335, 2010) conditions. Their repetitive nature makes their study particularly challenging, where expression studies have largely focused on individual loci (De Parseval et al., J Virol 77:10414-10422, 2003) or general trends within families (Forsman et al., J Virol Methods 129:16-30, 2005; Seifarth et al., J Virol 79:341-352, 2005; Pichon et al., Nucleic Acids Res 34:e46, 2006). To refine our understanding of HERVs activity, we introduce here a new microarray, HERV-V3. This work was made possible by the careful detection and annotation of genomic HERV/MaLR sequences as well as the development of a new hybridization model, allowing the optimization of probe performances and the control of cross-reactions. RESULTS: HERV-V3 offers an almost complete coverage of HERVs and their ancestors (mammalian apparent LTR-retrotransposons, MaLRs) at the locus level along with four other repertoires (active LINE-1 elements, lncRNA, a selection of 1559 human genes and common infectious viruses). We demonstrate that HERV-V3 analytical performances are comparable with commercial Affymetrix arrays, and that for a selection of tissue/pathological specific loci, the patterns of expression measured on HERV-V3 is consistent with those reported in the literature. Given its large HERVs/MaLRs coverage and additional repertoires, HERV-V3 opens the door to multiple applications such as enhancers and alternative promoters identification, biomarkers identification as well as the characterization of genes and HERVs/MaLRs modulation caused by viral infection.

  5. Examination of gene expression in mice exposed to low dose radiation using affymetrix cDNA microarrays

    Energy Technology Data Exchange (ETDEWEB)

    Morris, D.; Knox, D.; Lavoie, J.; Lemon, J.; Boreham, D. [McMaster Univ., Hamilton, Ontario (Canada)

    2005-07-01

    'Full text:' Gamma radiation acts via the indirect effect to damage cells by producing reactive oxygen species (ROS). These ROS are capable damaging macromolecules and, altering signal pathways and gene transcription. Cells have evolved enzymes and mechanisms to scavenge ROS and repair oxidative damage. Microarrays allow the survey of the gene transcription activity of thousands of genes simultaneously. Messenger RNA is extracted from cells, hybridized with the complementary DNA (cDNA) of a microarray chip, and examined with a chip reader. Affymetrix microarray chips have been produced by the CSCHAH in Winnipeg containing 26000 murine genes. Groups of female mice have been exposed to low dose whole body chronic gamma radiation exposures of 0,50,100, and 120 mGy, corresponding to 15,30,60, and 75 weeks, respectively. MRNA from mice brain tissue has been extracted, isolated, converted to cDNA and labeled. Gene expression in each irradiated mouse was compared to the pooled expression of the control mice. Analysis of gene expression levels are performed with microarray analytical software, Array Pro by Media Cybernetics, and powerful statistical software, BRB microarray tools. Differences in gene expressions, focusing on genes for cytokines, DNA repair mechanisms, immuno-modulators, apoptosis pathways, and enzymatic anti-oxidant systems, are being examined and will be reported. (author)

  6. Microarray analysis of thioacetamide-treated type 1 diabetic rats

    International Nuclear Information System (INIS)

    Devi, Sachin S.; Mehendale, Harihara M.

    2006-01-01

    It is well known that diabetes imparts high sensitivity to numerous hepatotoxicants. Previously, we have shown that a normally non-lethal dose of thioacetamide (TA, 300 mg/kg) causes 90% mortality in type 1 diabetic (DB) rats due to inhibited tissue repair allowing progression of liver injury. On the other hand, DB rats exposed to 30 mg TA/kg exhibit delayed tissue repair and delayed recovery from injury. The objective of this study was to investigate the mechanism of impaired tissue repair and progression of liver injury in TA-treated DB rats by using cDNA microarray. Gene expression pattern was examined at 0, 6, and 12 h after TA challenge, and selected mechanistic leads from microarray experiments were confirmed by real-time RT-PCR and further investigated at protein level over the time course of 0 to 36 h after TA treatment. Diabetic condition itself increased gene expression of proteases and decreased gene expression of protease inhibitors. Administration of 300 mg TA/kg to DB rats further elevated gene expression of proteases and suppressed gene expression of protease inhibitors, explaining progression of liver injury in DB rats after TA treatment. Inhibited expression of genes involved in cell division cycle (cyclin D1, IGFBP-1, ras, E2F) was observed after exposure of DB rats to 300 mg TA/kg, explaining inhibited tissue repair in these rats. On the other hand, DB rats receiving 30 mg TA/kg exhibit delayed expression of genes involved in cell division cycle, explaining delayed tissue repair in these rats. In conclusion, impaired cyclin D1 signaling along with increased proteases and decreased protease inhibitors may explain impaired tissue repair that leads to progression of liver injury initiated by TA in DB rats

  7. Multiplex RT-PCR and Automated Microarray for Detection of Eight Bovine Viruses.

    Science.gov (United States)

    Lung, O; Furukawa-Stoffer, T; Burton Hughes, K; Pasick, J; King, D P; Hodko, D

    2017-12-01

    Microarrays can be a useful tool for pathogen detection as it allow for simultaneous interrogation of the presence of a large number of genetic sequences in a sample. However, conventional microarrays require extensive manual handling and multiple pieces of equipment for printing probes, hybridization, washing and signal detection. In this study, a reverse transcription (RT)-PCR with an accompanying novel automated microarray for simultaneous detection of eight viruses that affect cattle [vesicular stomatitis virus (VSV), bovine viral diarrhoea virus type 1 and type 2, bovine herpesvirus 1, bluetongue virus, malignant catarrhal fever virus, rinderpest virus (RPV) and parapox viruses] is described. The assay accurately identified a panel of 37 strains of the target viruses and identified a mixed infection. No non-specific reactions were observed with a panel of 23 non-target viruses associated with livestock. Vesicular stomatitis virus was detected as early as 2 days post-inoculation in oral swabs from experimentally infected animals. The limit of detection of the microarray assay was as low as 1 TCID 50 /ml for RPV. The novel microarray platform automates the entire post-PCR steps of the assay and integrates electrophoretic-driven capture probe printing in a single user-friendly instrument that allows array layout and assay configuration to be user-customized on-site. © 2016 Her Majesty the Queen in Right of Canada.

  8. Comparative analysis of gene expression by microarray analysis of male and female flowers of Asparagus officinalis.

    Science.gov (United States)

    Gao, Wu-Jun; Li, Shu-Fen; Zhang, Guo-Jun; Wang, Ning-Na; Deng, Chuan-Liang; Lu, Long-Dou

    2013-01-01

    To identify rapidly a number of genes probably involved in sex determination and differentiation of the dioecious plant Asparagus officinalis, gene expression profiles in early flower development for male and female plants were investigated by microarray assay with 8,665 probes. In total, 638 male-biased and 543 female-biased genes were identified. These genes with biased-expression for male and female were involved in a variety of processes associated with molecular functions, cellular components, and biological processes, suggesting that a complex mechanism underlies the sex development of asparagus. Among the differentially expressed genes involved in the reproductive process, a number of genes associated with floral development were identified. Reverse transcription-PCR was performed for validation, and the results were largely consistent with those obtained by microarray analysis. The findings of this study might contribute to understanding of the molecular mechanisms of sex determination and differentiation in dioecious asparagus and provide a foundation for further studies of this plant.

  9. A regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.

    Directory of Open Access Journals (Sweden)

    Daniel Vasiliu

    Full Text Available Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED. Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.

  10. SegMine workflows for semantic microarray data analysis in Orange4WS

    Directory of Open Access Journals (Sweden)

    Kulovesi Kimmo

    2011-10-01

    Full Text Available Abstract Background In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases. Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets. Results We present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes. Conclusions Compared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.

  11. ZODET: software for the identification, analysis and visualisation of outlier genes in microarray expression data.

    Directory of Open Access Journals (Sweden)

    Daniel L Roden

    Full Text Available Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET that enables identification and visualisation of gross abnormalities in gene expression (outliers in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical method, gene tissue index (GTI, using a simulated expression dataset and a publicly available monocyte-derived macrophage microarray dataset. Taken together, these results support ZODET as a novel approach to identify outlier genes of potential pathogenic relevance in complex human diseases. The algorithm is implemented using R packages and Java.The software is freely available from http://www.ucl.ac.uk/medicine/molecular-medicine/publications/microarray-outlier-analysis.

  12. Variance estimation in the analysis of microarray data

    KAUST Repository

    Wang, Yuedong; Ma, Yanyuan; Carroll, Raymond J.

    2009-01-01

    Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t-statistic and other statistics are unreliable owing

  13. A High Phosphorus Diet Affects Lipid Metabolism in Rat Liver: A DNA Microarray Analysis

    Science.gov (United States)

    Chun, Sunwoo; Bamba, Takeshi; Suyama, Tatsuya; Ishijima, Tomoko; Fukusaki, Eiichiro; Abe, Keiko; Nakai, Yuji

    2016-01-01

    A high phosphorus (HP) diet causes disorders of renal function, bone metabolism, and vascular function. We previously demonstrated that DNA microarray analysis is an appropriate method to comprehensively evaluate the effects of a HP diet on kidney dysfunction such as calcification, fibrillization, and inflammation. We reported that type IIb sodium-dependent phosphate transporter is significantly up-regulated in this context. In the present study, we performed DNA microarray analysis to investigate the effects of a HP diet on the liver, which plays a pivotal role in energy metabolism. DNA microarray analysis was performed with total RNA isolated from the livers of rats fed a control diet (containing 0.3% phosphorus) or a HP diet (containing 1.2% phosphorus). Gene Ontology analysis of differentially expressed genes (DEGs) revealed that the HP diet induced down-regulation of genes involved in hepatic amino acid catabolism and lipogenesis, while genes related to fatty acid β-oxidation process were up-regulated. Although genes related to fatty acid biosynthesis were down-regulated in HP diet-fed rats, genes important for the elongation and desaturation reactions of omega-3 and -6 fatty acids were up-regulated. Concentrations of hepatic arachidonic acid and eicosapentaenoic acid were increased in HP diet-fed rats. These essential fatty acids activate peroxisome proliferator-activated receptor alpha (PPARα), a transcription factor for fatty acid β-oxidation. Evaluation of the upstream regulators of DEGs using Ingenuity Pathway Analysis indicated that PPARα was activated in the livers of HP diet-fed rats. Furthermore, the serum concentration of fibroblast growth factor 21, a hormone secreted from the liver that promotes fatty acid utilization in adipose tissue as a PPARα target gene, was higher (p = 0.054) in HP diet-fed rats than in control diet-fed rats. These data suggest that a HP diet enhances energy expenditure through the utilization of free fatty acids

  14. A High Phosphorus Diet Affects Lipid Metabolism in Rat Liver: A DNA Microarray Analysis.

    Directory of Open Access Journals (Sweden)

    Sunwoo Chun

    Full Text Available A high phosphorus (HP diet causes disorders of renal function, bone metabolism, and vascular function. We previously demonstrated that DNA microarray analysis is an appropriate method to comprehensively evaluate the effects of a HP diet on kidney dysfunction such as calcification, fibrillization, and inflammation. We reported that type IIb sodium-dependent phosphate transporter is significantly up-regulated in this context. In the present study, we performed DNA microarray analysis to investigate the effects of a HP diet on the liver, which plays a pivotal role in energy metabolism. DNA microarray analysis was performed with total RNA isolated from the livers of rats fed a control diet (containing 0.3% phosphorus or a HP diet (containing 1.2% phosphorus. Gene Ontology analysis of differentially expressed genes (DEGs revealed that the HP diet induced down-regulation of genes involved in hepatic amino acid catabolism and lipogenesis, while genes related to fatty acid β-oxidation process were up-regulated. Although genes related to fatty acid biosynthesis were down-regulated in HP diet-fed rats, genes important for the elongation and desaturation reactions of omega-3 and -6 fatty acids were up-regulated. Concentrations of hepatic arachidonic acid and eicosapentaenoic acid were increased in HP diet-fed rats. These essential fatty acids activate peroxisome proliferator-activated receptor alpha (PPARα, a transcription factor for fatty acid β-oxidation. Evaluation of the upstream regulators of DEGs using Ingenuity Pathway Analysis indicated that PPARα was activated in the livers of HP diet-fed rats. Furthermore, the serum concentration of fibroblast growth factor 21, a hormone secreted from the liver that promotes fatty acid utilization in adipose tissue as a PPARα target gene, was higher (p = 0.054 in HP diet-fed rats than in control diet-fed rats. These data suggest that a HP diet enhances energy expenditure through the utilization of free fatty

  15. Incorporation of gene-specific variability improves expression analysis using high-density DNA microarrays

    Directory of Open Access Journals (Sweden)

    Spitznagel Edward

    2003-11-01

    Full Text Available Abstract Background The assessment of data reproducibility is essential for application of microarray technology to exploration of biological pathways and disease states. Technical variability in data analysis largely depends on signal intensity. Within that context, the reproducibility of individual probe sets has not been hitherto addressed. Results We used an extraordinarily large replicate data set derived from human placental trophoblast to analyze probe-specific contribution to variability of gene expression. We found that signal variability, in addition to being signal-intensity dependant, is probe set-specific. Importantly, we developed a novel method to quantify the contribution of this probe set-specific variability. Furthermore, we devised a formula that incorporates a priori-computed, replicate-based information on probe set- and intensity-specific variability in determination of expression changes even without technical replicates. Conclusion The strategy of incorporating probe set-specific variability is superior to analysis based on arbitrary fold-change thresholds. We recommend its incorporation to any computation of gene expression changes using high-density DNA microarrays. A Java application implementing our T-score is available at http://www.sadovsky.wustl.edu/tscore.html.

  16. Workflows for microarray data processing in the Kepler environment

    Science.gov (United States)

    2012-01-01

    Background Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. Results We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or

  17. Workflows for microarray data processing in the Kepler environment

    Directory of Open Access Journals (Sweden)

    Stropp Thomas

    2012-05-01

    Full Text Available Abstract Background Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. Results We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data and therefore are close to

  18. Workflows for microarray data processing in the Kepler environment.

    Science.gov (United States)

    Stropp, Thomas; McPhillips, Timothy; Ludäscher, Bertram; Bieda, Mark

    2012-05-17

    Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or R

  19. From microarray to biology: an integrated experimental, statistical and in silico analysis of how the extracellular matrix modulates the phenotype of cancer cells

    OpenAIRE

    Centola Michael B; Dozmorov Igor; Buethe David D; Saban Ricardo; Hauser Paul J; Kyker Kimberly D; Dozmorov Mikhail G; Culkin Daniel J; Hurst Robert E

    2008-01-01

    Abstract A statistically robust and biologically-based approach for analysis of microarray data is described that integrates independent biological knowledge and data with a global F-test for finding genes of interest that minimizes the need for replicates when used for hypothesis generation. First, each microarray is normalized to its noise level around zero. The microarray dataset is then globally adjusted by robust linear regression. Second, genes of interest that capture significant respo...

  20. Eureka-DMA: an easy-to-operate graphical user interface for fast comprehensive investigation and analysis of DNA microarray data.

    Science.gov (United States)

    Abelson, Sagi

    2014-02-24

    In the past decade, the field of molecular biology has become increasingly quantitative; rapid development of new technologies enables researchers to investigate and address fundamental issues quickly and in an efficient manner which were once impossible. Among these technologies, DNA microarray provides methodology for many applications such as gene discovery, diseases diagnosis, drug development and toxicological research and it has been used increasingly since it first emerged. Multiple tools have been developed to interpret the high-throughput data produced by microarrays. However, many times, less consideration has been given to the fact that an extensive and effective interpretation requires close interplay between the bioinformaticians who analyze the data and the biologists who generate it. To bridge this gap and to simplify the usability of such tools we developed Eureka-DMA - an easy-to-operate graphical user interface that allows bioinformaticians and bench-biologists alike to initiate analyses as well as to investigate the data produced by DNA microarrays. In this paper, we describe Eureka-DMA, a user-friendly software that comprises a set of methods for the interpretation of gene expression arrays. Eureka-DMA includes methods for the identification of genes with differential expression between conditions; it searches for enriched pathways and gene ontology terms and combines them with other relevant features. It thus enables the full understanding of the data for following testing as well as generating new hypotheses. Here we show two analyses, demonstrating examples of how Eureka-DMA can be used and its capability to produce relevant and reliable results. We have integrated several elementary expression analysis tools to provide a unified interface for their implementation. Eureka-DMA's simple graphical user interface provides effective and efficient framework in which the investigator has the full set of tools for the visualization and interpretation

  1. Microarrays for Universal Detection and Identification of Phytoplasmas

    DEFF Research Database (Denmark)

    Nicolaisen, Mogens; Nyskjold, Henriette; Bertaccini, Assunta

    2013-01-01

    Detection and identification of phytoplasmas is a laborious process often involving nested PCR followed by restriction enzyme analysis and fine-resolution gel electrophoresis. To improve throughput, other methods are needed. Microarray technology offers a generic assay that can potentially detect...... and differentiate all types of phytoplasmas in one assay. The present protocol describes a microarray-based method for identification of phytoplasmas to 16Sr group level....

  2. A Fisheye Viewer for microarray-based gene expression data.

    Science.gov (United States)

    Wu, Min; Thao, Cheng; Mu, Xiangming; Munson, Ethan V

    2006-10-13

    Microarray has been widely used to measure the relative amounts of every mRNA transcript from the genome in a single scan. Biologists have been accustomed to reading their experimental data directly from tables. However, microarray data are quite large and are stored in a series of files in a machine-readable format, so direct reading of the full data set is not feasible. The challenge is to design a user interface that allows biologists to usefully view large tables of raw microarray-based gene expression data. This paper presents one such interface--an electronic table (E-table) that uses fisheye distortion technology. The Fisheye Viewer for microarray-based gene expression data has been successfully developed to view MIAME data stored in the MAGE-ML format. The viewer can be downloaded from the project web site http://polaris.imt.uwm.edu:7777/fisheye/. The fisheye viewer was implemented in Java so that it could run on multiple platforms. We implemented the E-table by adapting JTable, a default table implementation in the Java Swing user interface library. Fisheye views use variable magnification to balance magnification for easy viewing and compression for maximizing the amount of data on the screen. This Fisheye Viewer is a lightweight but useful tool for biologists to quickly overview the raw microarray-based gene expression data in an E-table.

  3. Creation of antifouling microarrays by photopolymerization of zwitterionic compounds for protein assay and cell patterning.

    Science.gov (United States)

    Sun, Xiuhua; Wang, Huaixin; Wang, Yuanyuan; Gui, Taijiang; Wang, Ke; Gao, Changlu

    2018-04-15

    Nonspecific binding or adsorption of biomolecules presents as a major obstacle to higher sensitivity, specificity and reproducibility in microarray technology. We report herein a method to fabricate antifouling microarray via photopolymerization of biomimetic betaine compounds. In brief, carboxybetaine methacrylate was polymerized as arrays for protein sensing, while sulfobetaine methacrylate was polymerized as background. With the abundant carboxyl groups on array surfaces and zwitterionic polymers on the entire surfaces, this microarray allows biomolecular immobilization and recognition with low nonspecific interactions due to its antifouling property. Therefore, low concentration of target molecules can be captured and detected by this microarray. It was proved that a concentration of 10ngmL -1 bovine serum albumin in the sample matrix of bovine serum can be detected by the microarray derivatized with anti-bovine serum albumin. Moreover, with proper hydrophilic-hydrophobic designs, this approach can be applied to fabricate surface-tension droplet arrays, which allows surface-directed cell adhesion and growth. These light controllable approaches constitute a clear improvement in the design of antifouling interfaces, which may lead to greater flexibility in the development of interfacial architectures and wider application in blood contact microdevices. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Single-Nucleotide Polymorphism-Microarray Ploidy Analysis of Paraffin-Embedded Products of Conception in Recurrent Pregnancy Loss Evaluations.

    Science.gov (United States)

    Maslow, Bat-Sheva L; Budinetz, Tara; Sueldo, Carolina; Anspach, Erica; Engmann, Lawrence; Benadiva, Claudio; Nulsen, John C

    2015-07-01

    To compare the analysis of chromosome number from paraffin-embedded products of conception using single-nucleotide polymorphism (SNP) microarray with the recommended screening for the evaluation of couples presenting with recurrent pregnancy loss who do not have previous fetal cytogenetic data. We performed a retrospective cohort study including all women who presented for a new evaluation of recurrent pregnancy loss over a 2-year period (January 1, 2012, to December 31, 2013). All participants had at least two documented first-trimester losses and both the recommended screening tests and SNP microarray performed on at least one paraffin-embedded products of conception sample. Single-nucleotide polymorphism microarray identifies all 24 chromosomes (22 autosomes, X, and Y). Forty-two women with a total of 178 losses were included in the study. Paraffin-embedded products of conception from 62 losses were sent for SNP microarray. Single-nucleotide polymorphism microarray successfully diagnosed fetal chromosome number in 71% (44/62) of samples, of which 43% (19/44) were euploid and 57% (25/44) were noneuploid. Seven of 42 (17%) participants had abnormalities on recurrent pregnancy loss screening. The per-person detection rate for a cause of pregnancy loss was significantly higher in the SNP microarray (0.50; 95% confidence interval [CI] 0.36-0.64) compared with recurrent pregnancy loss evaluation (0.17; 95% CI 0.08-0.31) (P=.002). Participants with one or more euploid loss identified on paraffin-embedded products of conception were significantly more likely to have an abnormality on recurrent pregnancy loss screening than those with only noneuploid results (P=.028). The significance remained when controlling for age, number of losses, number of samples, and total pregnancies. These results suggest that SNP microarray testing of paraffin-embedded products of conception is a valuable tool for the evaluation of recurrent pregnancy loss in patients without prior fetal

  5. An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

    Directory of Open Access Journals (Sweden)

    Bajcsy Peter

    2006-01-01

    Full Text Available This paper overviews DNA microarray grid alignment and foreground separation approaches. Microarray grid alignment and foreground separation are the basic processing steps of DNA microarray images that affect the quality of gene expression information, and hence impact our confidence in any data-derived biological conclusions. Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis. In the past, the grid alignment and foreground separation steps have not been covered extensively in the survey literature. We present several classifications of existing algorithms, and describe the fundamental principles of these algorithms. Challenges related to automation and reliability of processed image data are outlined at the end of this overview paper.

  6. A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

    Directory of Open Access Journals (Sweden)

    Jaison Bennet

    2014-01-01

    Full Text Available Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN, naive Bayes, and support vector machine (SVM. Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT and moving window technique (MWT is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

  7. Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

    Science.gov (United States)

    Tra, Yolande V.; Evans, Irene M.

    2010-01-01

    "BIO2010" put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on…

  8. Implementation of mutual information and bayes theorem for classification microarray data

    Science.gov (United States)

    Dwifebri Purbolaksono, Mahendra; Widiastuti, Kurnia C.; Syahrul Mubarok, Mohamad; Adiwijaya; Aminy Ma’ruf, Firda

    2018-03-01

    Microarray Technology is one of technology which able to read the structure of gen. The analysis is important for this technology. It is for deciding which attribute is more important than the others. Microarray technology is able to get cancer information to diagnose a person’s gen. Preparation of microarray data is a huge problem and takes a long time. That is because microarray data contains high number of insignificant and irrelevant attributes. So, it needs a method to reduce the dimension of microarray data without eliminating important information in every attribute. This research uses Mutual Information to reduce dimension. System is built with Machine Learning approach specifically Bayes Theorem. This theorem uses a statistical and probability approach. By combining both methods, it will be powerful for Microarray Data Classification. The experiment results show that system is good to classify Microarray data with highest F1-score using Bayesian Network by 91.06%, and Naïve Bayes by 88.85%.

  9. Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data

    International Nuclear Information System (INIS)

    Lenburg, Marc E; Liou, Louis S; Gerry, Norman P; Frampton, Garrett M; Cohen, Herbert T; Christman, Michael F

    2003-01-01

    Renal cell carcinoma is a common malignancy that often presents as a metastatic-disease for which there are no effective treatments. To gain insights into the mechanism of renal cell carcinogenesis, a number of genome-wide expression profiling studies have been performed. Surprisingly, there is very poor agreement among these studies as to which genes are differentially regulated. To better understand this lack of agreement we profiled renal cell tumor gene expression using genome-wide microarrays (45,000 probe sets) and compare our analysis to previous microarray studies. We hybridized total RNA isolated from renal cell tumors and adjacent normal tissue to Affymetrix U133A and U133B arrays. We removed samples with technical defects and removed probesets that failed to exhibit sequence-specific hybridization in any of the samples. We detected differential gene expression in the resulting dataset with parametric methods and identified keywords that are overrepresented in the differentially expressed genes with the Fisher-exact test. We identify 1,234 genes that are more than three-fold changed in renal tumors by t-test, 800 of which have not been previously reported to be altered in renal cell tumors. Of the only 37 genes that have been identified as being differentially expressed in three or more of five previous microarray studies of renal tumor gene expression, our analysis finds 33 of these genes (89%). A key to the sensitivity and power of our analysis is filtering out defective samples and genes that are not reliably detected. The widespread use of sample-wise voting schemes for detecting differential expression that do not control for false positives likely account for the poor overlap among previous studies. Among the many genes we identified using parametric methods that were not previously reported as being differentially expressed in renal cell tumors are several oncogenes and tumor suppressor genes that likely play important roles in renal cell

  10. A pilot study of transcription unit analysis in rice using oligonucleotide tiling-path microarray

    DEFF Research Database (Denmark)

    Stolc, Viktor; Li, Lei; Wang, Xiangfeng

    2005-01-01

    As the international efforts to sequence the rice genome are completed, an immediate challenge and opportunity is to comprehensively and accurately define all transcription units in the rice genome. Here we describe a strategy of using high-density oligonucleotide tiling-path microarrays to map...... transcription of the japonica rice genome. In a pilot experiment to test this approach, one array representing the reverse strand of the last 11.2 Mb sequence of chromosome 10 was analyzed in detail based on a mathematical model developed in this study. Analysis of the array data detected 77% of the reference...... gene models in a mixture of four RNA populations. Moreover, significant transcriptional activities were found in many of the previously annotated intergenic regions. These preliminary results demonstrate the utility of genome tiling microarrays in evaluating annotated rice gene models...

  11. Facilitating functional annotation of chicken microarray data

    Directory of Open Access Journals (Sweden)

    Gresham Cathy R

    2009-10-01

    Full Text Available Abstract Background Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO. However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information. Results We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (AGOM tool to help researchers to quickly retrieve corresponding functional information for their dataset. Conclusion Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using AGOM tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and

  12. Chromosomal Microarray Analysis of Consecutive Individuals with Autism Spectrum Disorders Using an Ultra-High Resolution Chromosomal Microarray Optimized for Neurodevelopmental Disorders

    Directory of Open Access Journals (Sweden)

    Karen S. Ho

    2016-12-01

    Full Text Available Copy number variants (CNVs detected by chromosomal microarray analysis (CMA significantly contribute to understanding the etiology of autism spectrum disorder (ASD and other related conditions. In recognition of the value of CMA testing and its impact on medical management, CMA is in medical guidelines as a first-tier test in the evaluation of children with these disorders. As CMA becomes adopted into routine care for these patients, it becomes increasingly important to report these clinical findings. This study summarizes the results of over 4 years of CMA testing by a CLIA-certified clinical testing laboratory. Using a 2.8 million probe microarray optimized for the detection of CNVs associated with neurodevelopmental disorders, we report an overall CNV detection rate of 28.1% in 10,351 consecutive patients, which rises to nearly 33% in cases without ASD, with only developmental delay/intellectual disability (DD/ID and/or multiple congenital anomalies (MCA. The overall detection rate for individuals with ASD is also significant at 24.4%. The detection rate and pathogenic yield of CMA vary significantly with the indications for testing, age, and gender, as well as the specialty of the ordering doctor. We note discrete differences in the most common recurrent CNVs found in individuals with or without a diagnosis of ASD.

  13. Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution

    DEFF Research Database (Denmark)

    Novak, Jaroslav P; Kim, Seon-Young; Xu, Jun

    2006-01-01

    BACKGROUND: DNA microarrays are a powerful technology that can provide a wealth of gene expression data for disease studies, drug development, and a wide scope of other investigations. Because of the large volume and inherent variability of DNA microarray data, many new statistical methods have...

  14. Extending the scope of diagnostic chromosome analysis: detection of single gene defects using high-resolution SNP microarrays.

    Science.gov (United States)

    Bruno, Damien L; Stark, Zornitza; Amor, David J; Burgess, Trent; Butler, Kathy; Corrie, Sylvea; Francis, David; Ganesamoorthy, Devika; Hills, Louise; James, Paul A; O'Rielly, Darren; Oertel, Ralph; Savarirayan, Ravi; Prabhakara, Krishnamurthy; Salce, Nicholas; Slater, Howard R

    2011-12-01

    Microarray analysis has provided significant advances in the diagnosis of conditions resulting from submicroscopic chromosome abnormalities. It has been recommended that array testing should be a "first tier" test in the evaluation of individuals with intellectual disability, developmental delay, congenital anomalies, and autism. The availability of arrays with increasingly high probe coverage and resolution has increased the detection of decreasingly small copy number changes (CNCs) down to the intragenic or even exon level. Importantly, arrays that genotype SNPs also detect extended regions of homozygosity. We describe 14 examples of single gene disorders caused by intragenic changes from a consecutive set of 6,500 tests using high-resolution SNP microarrays. These cases illustrate the increased scope of cytogenetic testing beyond dominant chromosome rearrangements that typically contain many genes. Nine of the cases confirmed the clinical diagnosis, that is, followed a "phenotype to genotype" approach. Five were diagnosed by the laboratory analysis in the absence of a specific clinical diagnosis, that is, followed a "genotype to phenotype" approach. Two were clinically significant, incidental findings. The importance of astute clinical assessment and laboratory-clinician consultation is emphasized to optimize the value of microarrays in the diagnosis of disorders caused by single gene copy number and sequence mutations. © 2011 Wiley-Liss, Inc.

  15. Integrative missing value estimation for microarray data.

    Science.gov (United States)

    Hu, Jianjun; Li, Haifeng; Waterman, Michael S; Zhou, Xianghong Jasmine

    2006-10-12

    Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (LLS) imputation algorithm by up to 15% improvement in our benchmark tests. We demonstrated that the order-statistics-based integrative imputation algorithms can achieve significant improvements over the state-of-the-art missing value estimation approaches such as LLS and is especially good for imputing microarray datasets with a limited number of samples, high rates of missing data, or very noisy measurements. With the rapid accumulation of microarray datasets, the performance of our approach can be further improved by incorporating larger and more appropriate reference datasets.

  16. A fisheye viewer for microarray-based gene expression data

    Directory of Open Access Journals (Sweden)

    Munson Ethan V

    2006-10-01

    Full Text Available Abstract Background Microarray has been widely used to measure the relative amounts of every mRNA transcript from the genome in a single scan. Biologists have been accustomed to reading their experimental data directly from tables. However, microarray data are quite large and are stored in a series of files in a machine-readable format, so direct reading of the full data set is not feasible. The challenge is to design a user interface that allows biologists to usefully view large tables of raw microarray-based gene expression data. This paper presents one such interface – an electronic table (E-table that uses fisheye distortion technology. Results The Fisheye Viewer for microarray-based gene expression data has been successfully developed to view MIAME data stored in the MAGE-ML format. The viewer can be downloaded from the project web site http://polaris.imt.uwm.edu:7777/fisheye/. The fisheye viewer was implemented in Java so that it could run on multiple platforms. We implemented the E-table by adapting JTable, a default table implementation in the Java Swing user interface library. Fisheye views use variable magnification to balance magnification for easy viewing and compression for maximizing the amount of data on the screen. Conclusion This Fisheye Viewer is a lightweight but useful tool for biologists to quickly overview the raw microarray-based gene expression data in an E-table.

  17. Transcriptome analysis of zebrafish embryogenesis using microarrays.

    Directory of Open Access Journals (Sweden)

    Sinnakaruppan Mathavan

    2005-08-01

    Full Text Available Zebrafish (Danio rerio is a well-recognized model for the study of vertebrate developmental genetics, yet at the same time little is known about the transcriptional events that underlie zebrafish embryogenesis. Here we have employed microarray analysis to study the temporal activity of developmentally regulated genes during zebrafish embryogenesis. Transcriptome analysis at 12 different embryonic time points covering five different developmental stages (maternal, blastula, gastrula, segmentation, and pharyngula revealed a highly dynamic transcriptional profile. Hierarchical clustering, stage-specific clustering, and algorithms to detect onset and peak of gene expression revealed clearly demarcated transcript clusters with maximum gene activity at distinct developmental stages as well as co-regulated expression of gene groups involved in dedicated functions such as organogenesis. Our study also revealed a previously unidentified cohort of genes that are transcribed prior to the mid-blastula transition, a time point earlier than when the zygotic genome was traditionally thought to become active. Here we provide, for the first time to our knowledge, a comprehensive list of developmentally regulated zebrafish genes and their expression profiles during embryogenesis, including novel information on the temporal expression of several thousand previously uncharacterized genes. The expression data generated from this study are accessible to all interested scientists from our institute resource database (http://giscompute.gis.a-star.edu.sg/~govind/zebrafish/data_download.html.

  18. Serum reactome induced by Bordetella pertussis infection and Pertussis vaccines: qualitative differences in serum antibody recognition patterns revealed by peptide microarray analysis.

    Science.gov (United States)

    Valentini, Davide; Ferrara, Giovanni; Advani, Reza; Hallander, Hans O; Maeurer, Markus J

    2015-07-01

    Pertussis (whooping cough) remains a public health problem despite extensive vaccination strategies. Better understanding of the host-pathogen interaction and the detailed B. pertussis (Bp) target recognition pattern will help in guided vaccine design. We characterized the specific epitope antigen recognition profiles of serum antibodies ('the reactome') induced by whooping cough and B. pertussis (Bp) vaccines from a case-control study conducted in 1996 in infants enrolled in a Bp vaccine trial in Sweden (Gustafsson, NEJM, 1996, 334, 349-355). Sera from children with whooping cough, vaccinated with Diphtheria Tetanus Pertussis (DTP) whole-cell (wc), acellular 5 (DPTa5), or with the 2 component (a2) vaccines and from infants receiving only DT (n=10 for each group) were tested with high-content peptide microarrays containing 17 Bp proteins displayed as linear (n=3175) peptide stretches. Slides were incubated with serum and peptide-IgG complexes detected with Cy5-labeled goat anti-human IgG and analyzed using a GenePix 4000B microarray scanner, followed by statistical analysis, using PAM (Prediction Analysis for Microarrays) and the identification of uniquely recognized peptide epitopes. 367/3,085 (11.9%) peptides were recognized in 10/10 sera from children with whooping cough, 239 (7.7%) in DTPwc, 259 (8.4%) in DTPa5, 105 (3.4%) DTPa2, 179 (5.8%) in the DT groups. Recognition of strongly recognized peptides was similar between whooping cough and DPTwc, but statistically different between whooping cough vs. DTPa5 (p<0.05), DTPa2 and DT (p<0.001 vs. both) vaccines. 6/3,085 and 2/3,085 peptides were exclusively recognized in (10/10) sera from children with whooping cough and DTPa2 vaccination, respectively. DTPwc resembles more closely the whooping cough reactome as compared to acellular vaccines. We could identify a unique recognition signature common for each vaccination group (10/10 children). Peptide microarray technology allows detection of subtle differences in

  19. Fibre optic microarrays.

    Science.gov (United States)

    Walt, David R

    2010-01-01

    This tutorial review describes how fibre optic microarrays can be used to create a variety of sensing and measurement systems. This review covers the basics of optical fibres and arrays, the different microarray architectures, and describes a multitude of applications. Such arrays enable multiplexed sensing for a variety of analytes including nucleic acids, vapours, and biomolecules. Polymer-coated fibre arrays can be used for measuring microscopic chemical phenomena, such as corrosion and localized release of biochemicals from cells. In addition, these microarrays can serve as a substrate for fundamental studies of single molecules and single cells. The review covers topics of interest to chemists, biologists, materials scientists, and engineers.

  20. Calibration and assessment of channel-specific biases in microarray data with extended dynamical range.

    Science.gov (United States)

    Bengtsson, Henrik; Jönsson, Göran; Vallon-Christersson, Johan

    2004-11-12

    Non-linearities in observed log-ratios of gene expressions, also known as intensity dependent log-ratios, can often be accounted for by global biases in the two channels being compared. Any step in a microarray process may introduce such offsets and in this article we study the biases introduced by the microarray scanner and the image analysis software. By scanning the same spotted oligonucleotide microarray at different photomultiplier tube (PMT) gains, we have identified a channel-specific bias present in two-channel microarray data. For the scanners analyzed it was in the range of 15-25 (out of 65,535). The observed bias was very stable between subsequent scans of the same array although the PMT gain was greatly adjusted. This indicates that the bias does not originate from a step preceding the scanner detector parts. The bias varies slightly between arrays. When comparing estimates based on data from the same array, but from different scanners, we have found that different scanners introduce different amounts of bias. So do various image analysis methods. We propose a scanning protocol and a constrained affine model that allows us to identify and estimate the bias in each channel. Backward transformation removes the bias and brings the channels to the same scale. The result is that systematic effects such as intensity dependent log-ratios are removed, but also that signal densities become much more similar. The average scan, which has a larger dynamical range and greater signal-to-noise ratio than individual scans, can then be obtained. The study shows that microarray scanners may introduce a significant bias in each channel. Such biases have to be calibrated for, otherwise systematic effects such as intensity dependent log-ratios will be observed. The proposed scanning protocol and calibration method is simple to use and is useful for evaluating scanner biases or for obtaining calibrated measurements with extended dynamical range and better precision. The

  1. Diagnostic Yield of Chromosomal Microarray Analysis in an Autism Primary Care Practice: Which Guidelines to Implement?

    Science.gov (United States)

    McGrew, Susan G.; Peters, Brittany R.; Crittendon, Julie A.; Veenstra-VanderWeele, Jeremy

    2012-01-01

    Genetic testing is recommended for patients with ASD; however specific recommendations vary by specialty. American Academy of Pediatrics and American Academy of Neurology guidelines recommend G-banded karyotype and Fragile X DNA. The American College of Medical Genetics recommends Chromosomal Microarray Analysis (CMA). We determined the yield of…

  2. Prenatal chromosomal microarray analysis in fetuses with congenital heart disease: a prospective cohort study.

    Science.gov (United States)

    Wang, Yan; Cao, Li; Liang, Dong; Meng, Lulu; Wu, Yun; Qiao, Fengchang; Ji, Xiuqing; Luo, Chunyu; Zhang, Jingjing; Xu, Tianhui; Yu, Bin; Wang, Leilei; Wang, Ting; Pan, Qiong; Ma, Dingyuan; Hu, Ping; Xu, Zhengfeng

    2018-02-01

    Currently, chromosomal microarray analysis is considered the first-tier test in pediatric care and prenatal diagnosis. However, the diagnostic yield of chromosomal microarray analysis for prenatal diagnosis of congenital heart disease has not been evaluated based on a large cohort. Our aim was to evaluate the clinical utility of chromosomal microarray as the first-tier test for chromosomal abnormalities in fetuses with congenital heart disease. In this prospective study, 602 prenatal cases of congenital heart disease were investigated using single nucleotide polymorphism array over a 5-year period. Overall, pathogenic chromosomal abnormalities were identified in 125 (20.8%) of 602 prenatal cases of congenital heart disease, with 52.0% of them being numerical chromosomal abnormalities. The detection rates of likely pathogenic copy number variations and variants of uncertain significance were 1.3% and 6.0%, respectively. The detection rate of pathogenic chromosomal abnormalities in congenital heart disease plus additional structural anomalies (48.9% vs 14.3%, P congenital heart disease group. Additionally, the detection rate in congenital heart disease with additional structural anomalies group was significantly higher than that in congenital heart disease with soft markers group (48.9% vs 19.8%, P congenital heart disease with additional structural anomalies and congenital heart disease with intrauterine growth retardation groups (48.9% vs 50.0%), congenital heart disease with soft markers and congenital heart disease with intrauterine growth retardation groups (19.8% vs 50.0%), or congenital heart disease with soft markers and isolated congenital heart disease groups (19.8% vs 14.3%). The detection rate in fetuses with congenital heart disease plus mild ventriculomegaly was significantly higher than in those with other types of soft markers (50.0% vs 15.6%, P congenital heart disease in clinical practice. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Gametogenesis in the Pacific oyster Crassostrea gigas: a microarrays-based analysis identifies sex and stage specific genes.

    Directory of Open Access Journals (Sweden)

    Nolwenn M Dheilly

    Full Text Available BACKGROUND: The Pacific oyster Crassostrea gigas (Mollusca, Lophotrochozoa is an alternative and irregular protandrous hermaphrodite: most individuals mature first as males and then change sex several times. Little is known about genetic and phenotypic basis of sex differentiation in oysters, and little more about the molecular pathways regulating reproduction. We have recently developed and validated a microarray containing 31,918 oligomers (Dheilly et al., 2011 representing the oyster transcriptome. The application of this microarray to the study of mollusk gametogenesis should provide a better understanding of the key factors involved in sex differentiation and the regulation of oyster reproduction. METHODOLOGY/PRINCIPAL FINDINGS: Gene expression was studied in gonads of oysters cultured over a yearly reproductive cycle. Principal component analysis and hierarchical clustering showed a significant divergence in gene expression patterns of males and females coinciding with the start of gonial mitosis. ANOVA analysis of the data revealed 2,482 genes differentially expressed during the course of males and/or females gametogenesis. The expression of 434 genes could be localized in either germ cells or somatic cells of the gonad by comparing the transcriptome of female gonads to the transcriptome of stripped oocytes and somatic tissues. Analysis of the annotated genes revealed conserved molecular mechanisms between mollusks and mammals: genes involved in chromatin condensation, DNA replication and repair, mitosis and meiosis regulation, transcription, translation and apoptosis were expressed in both male and female gonads. Most interestingly, early expressed male-specific genes included bindin and a dpy-30 homolog and female-specific genes included foxL2, nanos homolog 3, a pancreatic lipase related protein, cd63 and vitellogenin. Further functional analyses are now required in order to investigate their role in sex differentiation in oysters

  4. Integrative missing value estimation for microarray data

    Directory of Open Access Journals (Sweden)

    Zhou Xianghong

    2006-10-01

    Full Text Available Abstract Background Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. Results We present the integrative Missing Value Estimation method (iMISS by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (LLS imputation algorithm by up to 15% improvement in our benchmark tests. Conclusion We demonstrated that the order-statistics-based integrative imputation algorithms can achieve significant improvements over the state-of-the-art missing value estimation approaches such as LLS and is especially good for imputing microarray datasets with a limited number of samples, high rates of missing data, or very noisy measurements. With the rapid accumulation of microarray datasets, the performance of our approach can be further improved by incorporating larger and more appropriate reference datasets.

  5. CDNA Microarray Based Comparative Gene Expression Analysis of Primary Breast Tumors Versus In Vitro Transformed Neoplastic Breast Epithelium

    National Research Council Canada - National Science Library

    Szallasi, Zoltan

    2001-01-01

    .... The first group of clones is being sorted by their ability to form tumors. We are currently performing cDNA microarray analysis quantifying the expression level of about 15,000 genes in these cell lines...

  6. Clonal diversity analysis using SNP microarray: a new prognostic tool for chronic lymphocytic leukemia.

    Science.gov (United States)

    Zhang, Linsheng; Znoyko, Iya; Costa, Luciano J; Conlin, Laura K; Daber, Robert D; Self, Sally E; Wolff, Daynna J

    2011-12-01

    Chronic lymphocytic leukemia (CLL) is a clinically heterogeneous disease. The methods currently used for monitoring CLL and determining conditions for treatment are limited in their ability to predict disease progression, patient survival, and response to therapy. Although clonal diversity and the acquisition of new chromosomal abnormalities during the disease course (clonal evolution) have been associated with disease progression, their prognostic potential has been underappreciated because cytogenetic and fluorescence in situ hybridization (FISH) studies have a restricted ability to detect genomic abnormalities and clonal evolution. We hypothesized that whole genome analysis using high resolution single nucleotide polymorphism (SNP) microarrays would be useful to detect diversity and infer clonal evolution to offer prognostic information. In this study, we used the Infinium Omni1 BeadChip (Illumina, San Diego, CA) array for the analysis of genetic variation and percent mosaicism in 25 non-selected CLL patients to explore the prognostic value of the assessment of clonal diversity in patients with CLL. We calculated the percentage of mosaicism for each abnormality by applying a mathematical algorithm to the genotype frequency data and by manual determination using the Simulated DNA Copy Number (SiDCoN) tool, which was developed from a computer model of mosaicism. At least one genetic abnormality was identified in each case, and the SNP data was 98% concordant with FISH results. Clonal diversity, defined as the presence of two or more genetic abnormalities with differing percentages of mosaicism, was observed in 12 patients (48%), and the diversity correlated with the disease stage. Clonal diversity was present in most cases of advanced disease (Rai stages III and IV) or those with previous treatment, whereas 9 of 13 patients without detected clonal diversity were asymptomatic or clinically stable. In conclusion, SNP microarray studies with simultaneous evaluation

  7. Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments

    Directory of Open Access Journals (Sweden)

    Pistoia Vito

    2008-10-01

    Full Text Available Abstract Background Most microarray experiments are carried out with the purpose of identifying genes whose expression varies in relation with specific conditions or in response to environmental stimuli. In such studies, genes showing similar mean expression values between two or more groups are considered as not differentially expressed, even if hidden subclasses with different expression values may exist. In this paper we propose a new method for identifying differentially expressed genes, based on the area between the ROC curve and the rising diagonal (ABCR. ABCR represents a more general approach than the standard area under the ROC curve (AUC, because it can identify both proper (i.e., concave and not proper ROC curves (NPRC. In particular, NPRC may correspond to those genes that tend to escape standard selection methods. Results We assessed the performance of our method using data from a publicly available database of 4026 genes, including 14 normal B cell samples (NBC and 20 heterogeneous lymphomas (namely: 9 follicular lymphomas and 11 chronic lymphocytic leukemias. Moreover, NBC also included two sub-classes, i.e., 6 heavily stimulated and 8 slightly or not stimulated samples. We identified 1607 differentially expressed genes with an estimated False Discovery Rate of 15%. Among them, 16 corresponded to NPRC and all escaped standard selection procedures based on AUC and t statistics. Moreover, a simple inspection to the shape of such plots allowed to identify the two subclasses in either one class in 13 cases (81%. Conclusion NPRC represent a new useful tool for the analysis of microarray data.

  8. Construction of a cDNA microarray derived from the ascidian Ciona intestinalis.

    Science.gov (United States)

    Azumi, Kaoru; Takahashi, Hiroki; Miki, Yasufumi; Fujie, Manabu; Usami, Takeshi; Ishikawa, Hisayoshi; Kitayama, Atsusi; Satou, Yutaka; Ueno, Naoto; Satoh, Nori

    2003-10-01

    A cDNA microarray was constructed from a basal chordate, the ascidian Ciona intestinalis. The draft genome of Ciona has been read and inferred to contain approximately 16,000 protein-coding genes, and cDNAs for transcripts of 13,464 genes have been characterized and compiled as the "Ciona intestinalis Gene Collection Release I". In the present study, we constructed a cDNA microarray of these 13,464 Ciona genes. A preliminary experiment with Cy3- and Cy5-labeled probes showed extensive differential gene expression between fertilized eggs and larvae. In addition, there was a good correlation between results obtained by the present microarray analysis and those from previous EST analyses. This first microarray of a large collection of Ciona intestinalis cDNA clones should facilitate the analysis of global gene expression and gene networks during the embryogenesis of basal chordates.

  9. ValWorkBench: an open source Java library for cluster validation, with applications to microarray data analysis.

    Science.gov (United States)

    Giancarlo, R; Scaturro, D; Utro, F

    2015-02-01

    The prediction of the number of clusters in a dataset, in particular microarrays, is a fundamental task in biological data analysis, usually performed via validation measures. Unfortunately, it has received very little attention and in fact there is a growing need for software tools/libraries dedicated to it. Here we present ValWorkBench, a software library consisting of eleven well known validation measures, together with novel heuristic approximations for some of them. The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re-used. Consequently, the focus of the presentation is on the architecture of the library, since it provides an essential map that can be used to access the full software documentation, which is available at the supplementary material website [1]. The mentioned main features of ValWorkBench are also discussed and exemplified, with emphasis on software abstraction design and re-usability. A comparison with existing cluster validation software libraries, mainly in terms of the mentioned features, is also offered. It suggests that ValWorkBench is a much needed contribution to the microarray software development/algorithm engineering community. For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Large scale aggregate microarray analysis reveals three distinct molecular subclasses of human preeclampsia.

    Science.gov (United States)

    Leavey, Katherine; Bainbridge, Shannon A; Cox, Brian J

    2015-01-01

    Preeclampsia (PE) is a life-threatening hypertensive pathology of pregnancy affecting 3-5% of all pregnancies. To date, PE has no cure, early detection markers, or effective treatments short of the removal of what is thought to be the causative organ, the placenta, which may necessitate a preterm delivery. Additionally, numerous small placental microarray studies attempting to identify "PE-specific" genes have yielded inconsistent results. We therefore hypothesize that preeclampsia is a multifactorial disease encompassing several pathology subclasses, and that large cohort placental gene expression analysis will reveal these groups. To address our hypothesis, we utilized known bioinformatic methods to aggregate 7 microarray data sets across multiple platforms in order to generate a large data set of 173 patient samples, including 77 with preeclampsia. Unsupervised clustering of these patient samples revealed three distinct molecular subclasses of PE. This included a "canonical" PE subclass demonstrating elevated expression of known PE markers and genes associated with poor oxygenation and increased secretion, as well as two other subclasses potentially representing a poor maternal response to pregnancy and an immunological presentation of preeclampsia. Our analysis sheds new light on the heterogeneity of PE patients, and offers up additional avenues for future investigation. Hopefully, our subclassification of preeclampsia based on molecular diversity will finally lead to the development of robust diagnostics and patient-based treatments for this disorder.

  11. Classification across gene expression microarray studies

    Directory of Open Access Journals (Sweden)

    Kuner Ruprecht

    2009-12-01

    Full Text Available Abstract Background The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive and histological grade (low/high of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM, predictive analysis of microarrays (PAM, random forest (RF and k-top scoring pairs (kTSP. Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. Results For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In

  12. Automatic Identification and Quantification of Extra-Well Fluorescence in Microarray Images.

    Science.gov (United States)

    Rivera, Robert; Wang, Jie; Yu, Xiaobo; Demirkan, Gokhan; Hopper, Marika; Bian, Xiaofang; Tahsin, Tasnia; Magee, D Mitchell; Qiu, Ji; LaBaer, Joshua; Wallstrom, Garrick

    2017-11-03

    In recent studies involving NAPPA microarrays, extra-well fluorescence is used as a key measure for identifying disease biomarkers because there is evidence to support that it is better correlated with strong antibody responses than statistical analysis involving intraspot intensity. Because this feature is not well quantified by traditional image analysis software, identification and quantification of extra-well fluorescence is performed manually, which is both time-consuming and highly susceptible to variation between raters. A system that could automate this task efficiently and effectively would greatly improve the process of data acquisition in microarray studies, thereby accelerating the discovery of disease biomarkers. In this study, we experimented with different machine learning methods, as well as novel heuristics, for identifying spots exhibiting extra-well fluorescence (rings) in microarray images and assigning each ring a grade of 1-5 based on its intensity and morphology. The sensitivity of our final system for identifying rings was found to be 72% at 99% specificity and 98% at 92% specificity. Our system performs this task significantly faster than a human, while maintaining high performance, and therefore represents a valuable tool for microarray image analysis.

  13. DNA Microarray Technologies: A Novel Approach to Geonomic Research

    Energy Technology Data Exchange (ETDEWEB)

    Hinman, R.; Thrall, B.; Wong, K,

    2002-01-01

    A cDNA microarray allows biologists to examine the expression of thousands of genes simultaneously. Researchers may analyze the complete transcriptional program of an organism in response to specific physiological or developmental conditions. By design, a cDNA microarray is an experiment with many variables and few controls. One question that inevitably arises when working with a cDNA microarray is data reproducibility. How easy is it to confirm mRNA expression patterns? In this paper, a case study involving the treatment of a murine macrophage RAW 264.7 cell line with tumor necrosis factor alpha (TNF) was used to obtain a rough estimate of data reproducibility. Two trials were examined and a list of genes displaying either a > 2-fold or > 4-fold increase in gene expression was compiled. Variations in signal mean ratios between the two slides were observed. We can assume that erring in reproducibility may be compensated by greater inductive levels of similar genes. Steps taken to obtain results included serum starvation of cells before treatment, tests of mRNA for quality/consistency, and data normalization.

  14. Exon microarray analysis of human dorsolateral prefrontal cortex in alcoholism.

    Science.gov (United States)

    Manzardo, Ann M; Gunewardena, Sumedha; Wang, Kun; Butler, Merlin G

    2014-06-01

    Alcohol abuse is associated with cellular and biochemical disturbances that impact upon protein and nucleic acid synthesis, brain development, function, and behavioral responses. To further characterize the genetic influences in alcoholism and the effects of alcohol consumption on gene expression, we used a highly sensitive exon microarray to examine mRNA expression in human frontal cortex of alcoholics and control males. Messenger RNA was isolated from the dorsolateral prefrontal cortex (dlPFC; Brodmann area 9) of 7 adult alcoholic (6 males, 1 female, mean age 49 years) and 7 matched controls. Affymetrix Human Exon 1.0 ST array was performed according to standard procedures and the results analyzed at the gene level. Microarray findings were validated using quantitative reverse transcription polymerase chain reaction, and the ontology of disturbed genes characterized using Ingenuity Pathway Analysis (IPA). Decreased mRNA expression was observed for genes involved in cellular adhesion (e.g., CTNNA3, ITGA2), transport (e.g., TF, ABCA8), nervous system development (e.g., LRP2, UGT8, GLDN), and signaling (e.g., RASGRP3, LGR5) with influence over lipid and myelin synthesis (e.g., ASPA, ENPP2, KLK6). IPA identified disturbances in network functions associated with neurological disease and development including cellular assembly and organization impacting on psychological disorders. Our data in alcoholism support a reduction in expression of dlPFC mRNA for genes involved with neuronal growth, differentiation, and signaling that targets white matter of the brain. Copyright © 2014 by the Research Society on Alcoholism.

  15. Performance analysis of clustering techniques over microarray data: A case study

    Science.gov (United States)

    Dash, Rasmita; Misra, Bijan Bihari

    2018-03-01

    Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

  16. Screening for copy-number alterations and loss of heterozygosity in chronic lymphocytic leukemia--a comparative study of four differently designed, high resolution microarray platforms

    DEFF Research Database (Denmark)

    Gunnarsson, R.; Staaf, J.; Jansson, M.

    2008-01-01

    Screening for gene copy-number alterations (CNAs) has improved by applying genome-wide microarrays, where SNP arrays also allow analysis of loss of heterozygozity (LOH). We here analyzed 10 chronic lymphocytic leukemia (CLL) samples using four different high-resolution platforms: BAC arrays (32K)...

  17. Fast gene ontology based clustering for microarray experiments.

    Science.gov (United States)

    Ovaska, Kristian; Laakso, Marko; Hautaniemi, Sampsa

    2008-11-21

    Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

  18. Interim report on updated microarray probes for the LLNL Burkholderia pseudomallei SNP array

    Energy Technology Data Exchange (ETDEWEB)

    Gardner, S; Jaing, C

    2012-03-27

    The overall goal of this project is to forensically characterize 100 unknown Burkholderia isolates in the US-Australia collaboration. We will identify genome-wide single nucleotide polymorphisms (SNPs) from B. pseudomallei and near neighbor species including B. mallei, B. thailandensis and B. oklahomensis. We will design microarray probes to detect these SNP markers and analyze 100 Burkholderia genomic DNAs extracted from environmental, clinical and near neighbor isolates from Australian collaborators on the Burkholderia SNP microarray. We will analyze the microarray genotyping results to characterize the genetic diversity of these new isolates and triage the samples for whole genome sequencing. In this interim report, we described the SNP analysis and the microarray probe design for the Burkholderia SNP microarray.

  19. A tiling microarray for global analysis of chloroplast genome expression in cucumber and other plants

    Directory of Open Access Journals (Sweden)

    Pląder Wojciech

    2011-09-01

    Full Text Available Abstract Plastids are small organelles equipped with their own genomes (plastomes. Although these organelles are involved in numerous plant metabolic pathways, current knowledge about the transcriptional activity of plastomes is limited. To solve this problem, we constructed a plastid tiling microarray (PlasTi-microarray consisting of 1629 oligonucleotide probes. The oligonucleotides were designed based on the cucumber chloroplast genomic sequence and targeted both strands of the plastome in a non-contiguous arrangement. Up to 4 specific probes were designed for each gene/exon, and the intergenic regions were covered regularly, with 70-nt intervals. We also developed a protocol for direct chemical labeling and hybridization of as little as 2 micrograms of chloroplast RNA. We used this protocol for profiling the expression of the cucumber chloroplast plastome on the PlasTi-microarray. Owing to the high sequence similarity of plant plastomes, the newly constructed microarray can be used to study plants other than cucumber. Comparative hybridization of chloroplast transcriptomes from cucumber, Arabidopsis, tomato and spinach showed that the PlasTi-microarray is highly versatile.

  20. Identification of late O{sub 3}-responsive genes in Arabidopsis thaliana by cDNA microarray analysis

    Energy Technology Data Exchange (ETDEWEB)

    D' Haese, D. [Univ. of Antwerp, Dept. of Biology, Antwerp (BE) and Univ. of Newcastle, School of Biology and Psychology, Div. of Biology, Newcastle-Upon-Tyne (United Kingdom); Horemans, N.; Coen, W. De; Guisez, Y. [Univ. of Antwerp, Dept. of Biology, Antwerp (Belgium)

    2006-09-15

    To better understand the response of a plant to 0{sub 3} stress, an integrated microarray analysis was performed on Arabidopsis plants exposed during 2 days to purified air or 150 nl l{sup -1} O{sub 3}, 8 h day-l. Agilent Arabidopsis 2 Oligo Microarrays were used of which the reliability was confirmed by quantitative real-time PCR of nine randomly selected genes. We confirmed the O{sub 3} responsiveness of heat shock proteins (HSPs), glutathione-S-tranferases and genes involved in cell wall stiffening and microbial defence. Whereas, a previous study revealed that during an early stage of the O{sub 3} stress response, gene expression was strongly dependent on jasmonic acid and ethylene, we report that at a later stage (48 h) synthesis of jasrnonic acid and ethylene was downregulated. In addition, we observed the simultaneous induction of salicylic acid synthesis and genes involved in programmed cell death and senescence. Also typically, the later stage of the response to O{sub 3} appeared to be the induction of the complete pathway leading to the biosynthesis of anthocyanin diglucosides and the induction of thioredoxin-based redox control. Surprisingly absent in the list of induced genes were genes involved in ASC-dependent antioxidation, few of which were found to be induced after 12 h of 0{sub 3} exposure in another study. We discuss these and other particular results of the microarray analysis and provide a map depicting significantly affected genes and their pathways highlighting their interrelationships and subcellular localization. (au)

  1. A molecular beacon microarray based on a quantum dot label for detecting single nucleotide polymorphisms.

    Science.gov (United States)

    Guo, Qingsheng; Bai, Zhixiong; Liu, Yuqian; Sun, Qingjiang

    2016-03-15

    In this work, we report the application of streptavidin-coated quantum dot (strAV-QD) in molecular beacon (MB) microarray assays by using the strAV-QD to label the immobilized MB, avoiding target labeling and meanwhile obviating the use of amplification. The MBs are stem-loop structured oligodeoxynucleotides, modified with a thiol and a biotin at two terminals of the stem. With the strAV-QD labeling an "opened" MB rather than a "closed" MB via streptavidin-biotin reaction, a sensitive and specific detection of label-free target DNA sequence is demonstrated by the MB microarray, with a signal-to-background ratio of 8. The immobilized MBs can be perfectly regenerated, allowing the reuse of the microarray. The MB microarray also is able to detect single nucleotide polymorphisms, exhibiting genotype-dependent fluorescence signals. It is demonstrated that the MB microarray can perform as a 4-to-2 encoder, compressing the genotype information into two outputs. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Microfluidic extraction and microarray detection of biomarkers from cancer tissue slides

    Science.gov (United States)

    Nguyen, H. T.; Dupont, L. N.; Jean, A. M.; Géhin, T.; Chevolot, Y.; Laurenceau, E.; Gijs, M. A. M.

    2018-03-01

    We report here a new microfluidic method allowing for the quantification of human epidermal growth factor receptor 2 (HER2) expression levels from formalin-fixed breast cancer tissues. After partial extraction of proteins from the tissue slide, the extract is routed to an antibody (Ab) microarray for HER2 titration by fluorescence. Then the HER2-expressing cell area is evaluated by immunofluorescence (IF) staining of the tissue slide and used to normalize the fluorescent HER2 signal measured from the Ab microarray. The number of HER2 gene copies measured by fluorescence in situ hybridization (FISH) on an adjacent tissue slide is concordant with the normalized HER2 expression signal. This work is the first study implementing biomarker extraction and detection from cancer tissue slides using microfluidics in combination with a microarray system, paving the way for further developments towards multiplex and precise quantification of cancer biomarkers.

  3. Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation

    Directory of Open Access Journals (Sweden)

    Lundberg Cathryn

    2008-01-01

    Full Text Available Abstract Background Gene expression measurements from breast cancer (BrCa tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely to have disease recurrence. However, diverse breast cancer datasets in conjunction with diagnostic clinical arrays show little overlap in the sets of genes identified. One approach to identify a set of consistently dysregulated candidate genes in these tumors is to employ meta-analysis of multiple independent microarray datasets. This allows one to compare expression data from a diverse collection of breast tumor array datasets generated on either cDNA or oligonucleotide arrays. Results We gathered expression data from 9 published microarray studies examining estrogen receptor positive (ER+ and estrogen receptor negative (ER- BrCa tumor cases from the Oncomine database. We performed a meta-analysis and identified genes that were universally up or down regulated with respect to ER+ versus ER- tumor status. We surveyed both the proximal promoter and 3' untranslated regions (3'UTR of our top-ranking genes in each expression group to test whether common sequence elements may contribute to the observed expression patterns. Utilizing a combination of known transcription factor binding sites (TFBS, evolutionarily conserved mammalian promoter and 3'UTR motifs, and microRNA (miRNA seed sequences, we identified numerous motifs that were disproportionately represented between the two gene classes suggesting a common regulatory network for the observed gene expression patterns. Conclusion Some of the genes we identified distinguish key transcripts previously seen in array studies, while others are newly defined. Many of the genes identified as overexpressed in ER- tumors were previously identified as expression markers for neoplastic transformation in multiple human cancers. Moreover, our motif analysis identified a collection of

  4. Detecting variants with Metabolic Design, a new software tool to design probes for explorative functional DNA microarray development

    Directory of Open Access Journals (Sweden)

    Gravelat Fabrice

    2010-09-01

    Full Text Available Abstract Background Microorganisms display vast diversity, and each one has its own set of genes, cell components and metabolic reactions. To assess their huge unexploited metabolic potential in different ecosystems, we need high throughput tools, such as functional microarrays, that allow the simultaneous analysis of thousands of genes. However, most classical functional microarrays use specific probes that monitor only known sequences, and so fail to cover the full microbial gene diversity present in complex environments. We have thus developed an algorithm, implemented in the user-friendly program Metabolic Design, to design efficient explorative probes. Results First we have validated our approach by studying eight enzymes involved in the degradation of polycyclic aromatic hydrocarbons from the model strain Sphingomonas paucimobilis sp. EPA505 using a designed microarray of 8,048 probes. As expected, microarray assays identified the targeted set of genes induced during biodegradation kinetics experiments with various pollutants. We have then confirmed the identity of these new genes by sequencing, and corroborated the quantitative discrimination of our microarray by quantitative real-time PCR. Finally, we have assessed metabolic capacities of microbial communities in soil contaminated with aromatic hydrocarbons. Results show that our probe design (sensitivity and explorative quality can be used to study a complex environment efficiently. Conclusions We successfully use our microarray to detect gene expression encoding enzymes involved in polycyclic aromatic hydrocarbon degradation for the model strain. In addition, DNA microarray experiments performed on soil polluted by organic pollutants without prior sequence assumptions demonstrate high specificity and sensitivity for gene detection. Metabolic Design is thus a powerful, efficient tool that can be used to design explorative probes and monitor metabolic pathways in complex environments

  5. Microarray analysis of DNA damage repair gene expression profiles in cervical cancer cells radioresistant to 252Cf neutron and X-rays

    International Nuclear Information System (INIS)

    Qing, Yi; Wang, Ge; Wang, Dong; Yang, Xue-Qin; Zhong, Zhao-Yang; Lei, Xin; Xie, Jia-Yin; Li, Meng-Xia; Xiang, De-Bing; Li, Zeng-Peng; Yang, Zhen-Zhou

    2010-01-01

    The aim of the study was to obtain stable radioresistant sub-lines from the human cervical cancer cell line HeLa by prolonged exposure to 252 Cf neutron and X-rays. Radioresistance mechanisms were investigated in the resulting cells using microarray analysis of DNA damage repair genes. HeLa cells were treated with fractionated 252 Cf neutron and X-rays, with a cumulative dose of 75 Gy each, over 8 months, yielding the sub-lines HeLaNR and HeLaXR. Radioresistant characteristics were detected by clone formation assay, ultrastructural observations, cell doubling time, cell cycle distribution, and apoptosis assay. Gene expression patterns of the radioresistant sub-lines were studied through microarray analysis and verified by Western blotting and real-time PCR. The radioresistant sub-lines HeLaNR and HeLaXR were more radioresisitant to 252 Cf neutron and X-rays than parental HeLa cells by detecting their radioresistant characteristics, respectively. Compared to HeLa cells, the expression of 24 genes was significantly altered by at least 2-fold in HeLaNR cells. Of these, 19 genes were up-regulated and 5 down-regulated. In HeLaXR cells, 41 genes were significantly altered by at least 2-fold; 38 genes were up-regulated and 3 down-regulated. Chronic exposure of cells to ionizing radiation induces adaptive responses that enhance tolerance of ionizing radiation and allow investigations of cellular radioresistance mechanisms. The insights gained into the molecular mechanisms activated by these 'radioresistance' genes will lead to new therapeutic targets for cervical cancer

  6. Microarray analysis of serum mRNA in patients with head and neck squamous cell carcinoma at whole-genome scale

    Czech Academy of Sciences Publication Activity Database

    Čapková, M.; Šáchová, Jana; Strnad, Hynek; Kolář, Michal; Hroudová, Miluše; Chovanec, M.; Čada, Z.; Štefl, M.; Valach, J.; Kastner, J.; Smetana, K. Jr.; Plzák, J.

    -, April 23 (2014) ISSN 2314-6141 R&D Projects: GA MZd(CZ) NT13488 Institutional support: RVO:68378050 Keywords : Microarray Analysis * Head and Neck Squamous Cell Carcinoma * whole-genome scale Subject RIV: EB - Genetics ; Molecular Biology

  7. Evaluation of chronic lymphocytic leukemia by BAC-based microarray analysis

    Directory of Open Access Journals (Sweden)

    McDaniel Lisa D

    2011-02-01

    Full Text Available Abstract Background Chronic lymphocytic leukemia (CLL is a highly variable disease with life expectancies ranging from months to decades. Cytogenetic findings play an integral role in defining the prognostic significance and treatment for individual patients. Results We have evaluated 25 clinical cases from a tertiary cancer center that have an established diagnosis of CLL and for which there was prior cytogenetic and/or fluorescence in situ hybridization (FISH data. We performed microarray-based comparative genomic hybridization (aCGH using a bacterial artificial chromosome (BAC-based microarray designed for the detection of known constitutional genetic syndromes. In 15 of the 25 cases, aCGH detected all copy number imbalances identified by prior cytogenetic and/or FISH studies. For the majority of those not detected, the aberrations were present at low levels of mosaicism. Furthermore, for 15 of the 25 cases, additional abnormalities were detected. Four of those cases had deletions that mapped to intervals implicated in inherited predisposition to CLL. For most cases, aCGH was able to detect abnormalities present in as few as 10% of cells. Although changes in ploidy are not easily discernable by aCGH, results for two cases illustrate the detection of additional copy gains and losses present within a mosaic tetraploid cell population. Conclusions Our results illustrate the successful evaluation of CLL using a microarray optimized for the interrogation of inherited disorders and the identification of alterations with possible relevance to CLL susceptibility.

  8. Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

    Directory of Open Access Journals (Sweden)

    E. V. Thomas

    2009-01-01

    Full Text Available Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in part to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition.

  9. A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis

    Directory of Open Access Journals (Sweden)

    Giuseppe Agapito

    2018-06-01

    Full Text Available Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS, Genome-Wide Association Studies (GWAS, Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g., high execution time and required memory to bioinformatic software. Thus a main requirement of modern bioinformatic softwares, is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based Single Nucleotide Polymorphism (SNP genotyping data. A use case in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data. microPipe is available under Creative Commons license, and is freely downloadable for academic and not-for-profit institutions.

  10. A novel method to quantify local CpG methylation density by regional methylation elongation assay on microarray

    Directory of Open Access Journals (Sweden)

    Qiao Yingjuan

    2008-01-01

    Full Text Available Abstract Background DNA methylation based techniques are important tools in both clinical diagnostics and therapeutics. But most of these methods only analyze a few CpG sites in a target region. Indeed, difference of site-specific methylation may also lead to a change of methylation density in many cases, and it has been found that the density of methylation is more important than methylation of single CpG site for gene silencing. Results We have developed a novel approach for quantitative analysis of CpG methylation density on the basis of microarray-based hybridization and incorporation of Cy5-dCTP into the Cy3 labeled target DNA by using Taq DNA Polymerase on microarray. The quantification is achieved by measuring Cy5/Cy3 signal ratio which is proportional to methylation density. This methylation-sensitive technique, termed RMEAM (regional methylation elongation assay on microarray, provides several advantages over existing methods used for methylation analysis. It can determine an exact methylation density of the given region, and has potential of high throughput. We demonstrate a use of this method in determining the methylation density of the promoter region of the tumor-related gene MLH1, TERT and MGMT in colorectal carcinoma patients. Conclusion This technique allows for quantitative analysis of regional methylation density, which is the representative of all allelic methylation patterns in the sample. The results show that this technique has the characteristics of simplicity, rapidness, specificity and high-throughput.

  11. Nonlinear matching measure for the analysis of on-off type DNA microarray images

    Science.gov (United States)

    Kim, Jong D.; Park, Misun; Kim, Jongwon

    2003-07-01

    In this paper, we propose a new nonlinear matching measure for automatic analysis of the on-off type DNA microarray images in which the hybridized spots are detected by the template matching method. The targeting spots of HPV DNA chips are designed for genotyping the human papilloma virus(HPV). The proposed measure is obtained by binarythresholding over the whole template region and taking the number of white pixels inside the spotted area. This measure is evaluated in terms of the accuracy of the estimated marker location to show better performance than the normalized covariance.

  12. MICROARRAY IMAGE GRIDDING USING GRID LINE REFINEMENT TECHNIQUE

    Directory of Open Access Journals (Sweden)

    V.G. Biju

    2015-05-01

    Full Text Available An important stage in microarray image analysis is gridding. Microarray image gridding is done to locate sub arrays in a microarray image and find co-ordinates of spots within each sub array. For accurate identification of spots, most of the proposed gridding methods require human intervention. In this paper a fully automatic gridding method which enhances spot intensity in the preprocessing step as per a histogram based threshold method is used. The gridding step finds co-ordinates of spots from horizontal and vertical profile of the image. To correct errors due to the grid line placement, a grid line refinement technique is proposed. The algorithm is applied on different image databases and results are compared based on spot detection accuracy and time. An average spot detection accuracy of 95.06% depicts the proposed method’s flexibility and accuracy in finding the spot co-ordinates for different database images.

  13. Xylella fastidiosa gene expression analysis by DNA microarrays

    OpenAIRE

    Travensolo,Regiane F.; Carareto-Alves,Lucia M.; Costa,Maria V.C.G.; Lopes,Tiago J.S.; Carrilho,Emanuel; Lemos,Eliana G.M.

    2009-01-01

    Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcrip...

  14. The efficacy of microarray screening for autosomal recessive retinitis pigmentosa in routine clinical practice

    Science.gov (United States)

    van Huet, Ramon A. C.; Pierrache, Laurence H.M.; Meester-Smoor, Magda A.; Klaver, Caroline C.W.; van den Born, L. Ingeborgh; Hoyng, Carel B.; de Wijs, Ilse J.; Collin, Rob W. J.; Hoefsloot, Lies H.

    2015-01-01

    Purpose To determine the efficacy of multiple versions of a commercially available arrayed primer extension (APEX) microarray chip for autosomal recessive retinitis pigmentosa (arRP). Methods We included 250 probands suspected of arRP who were genetically analyzed with the APEX microarray between January 2008 and November 2013. The mode of inheritance had to be autosomal recessive according to the pedigree (including isolated cases). If the microarray identified a heterozygous mutation, we performed Sanger sequencing of exons and exon–intron boundaries of that specific gene. The efficacy of this microarray chip with the additional Sanger sequencing approach was determined by the percentage of patients that received a molecular diagnosis. We also collected data from genetic tests other than the APEX analysis for arRP to provide a detailed description of the molecular diagnoses in our study cohort. Results The APEX microarray chip for arRP identified the molecular diagnosis in 21 (8.5%) of the patients in our cohort. Additional Sanger sequencing yielded a second mutation in 17 patients (6.8%), thereby establishing the molecular diagnosis. In total, 38 patients (15.2%) received a molecular diagnosis after analysis using the microarray and additional Sanger sequencing approach. Further genetic analyses after a negative result of the arRP microarray (n = 107) resulted in a molecular diagnosis of arRP (n = 23), autosomal dominant RP (n = 5), X-linked RP (n = 2), and choroideremia (n = 1). Conclusions The efficacy of the commercially available APEX microarray chips for arRP appears to be low, most likely caused by the limitations of this technique and the genetic and allelic heterogeneity of RP. Diagnostic yields up to 40% have been reported for next-generation sequencing (NGS) techniques that, as expected, thereby outperform targeted APEX analysis. PMID:25999674

  15. Automated detection of regions of interest for tissue microarray experiments: an image texture analysis

    International Nuclear Information System (INIS)

    Karaçali, Bilge; Tözeren, Aydin

    2007-01-01

    Recent research with tissue microarrays led to a rapid progress toward quantifying the expressions of large sets of biomarkers in normal and diseased tissue. However, standard procedures for sampling tissue for molecular profiling have not yet been established. This study presents a high throughput analysis of texture heterogeneity on breast tissue images for the purpose of identifying regions of interest in the tissue for molecular profiling via tissue microarray technology. Image texture of breast histology slides was described in terms of three parameters: the percentage of area occupied in an image block by chromatin (B), percentage occupied by stroma-like regions (P), and a statistical heterogeneity index H commonly used in image analysis. Texture parameters were defined and computed for each of the thousands of image blocks in our dataset using both the gray scale and color segmentation. The image blocks were then classified into three categories using the texture feature parameters in a novel statistical learning algorithm. These categories are as follows: image blocks specific to normal breast tissue, blocks specific to cancerous tissue, and those image blocks that are non-specific to normal and disease states. Gray scale and color segmentation techniques led to identification of same regions in histology slides as cancer-specific. Moreover the image blocks identified as cancer-specific belonged to those cell crowded regions in whole section image slides that were marked by two pathologists as regions of interest for further histological studies. These results indicate the high efficiency of our automated method for identifying pathologic regions of interest on histology slides. Automation of critical region identification will help minimize the inter-rater variability among different raters (pathologists) as hundreds of tumors that are used to develop an array have typically been evaluated (graded) by different pathologists. The region of interest

  16. CoPub: a literature-based keyword enrichment tool for microarray data analysis.

    Science.gov (United States)

    Frijters, Raoul; Heupers, Bart; van Beek, Pieter; Bouwhuis, Maurice; van Schaik, René; de Vlieg, Jacob; Polman, Jan; Alkema, Wynand

    2008-07-01

    Medline is a rich information source, from which links between genes and keywords describing biological processes, pathways, drugs, pathologies and diseases can be extracted. We developed a publicly available tool called CoPub that uses the information in the Medline database for the biological interpretation of microarray data. CoPub allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs. CoPub is freely accessible at http://services.nbic.nl/cgi-bin/copub/CoPub.pl.

  17. Microarray analysis of the gene expression profile in triethylene glycol dimethacrylate-treated human dental pulp cells.

    Science.gov (United States)

    Torun, D; Torun, Z Ö; Demirkaya, K; Sarper, M; Elçi, M P; Avcu, F

    2017-11-01

    Triethylene glycol dimethacrylate (TEGDMA) is an important resin monomer commonly used in the structure of dental restorative materials. Recent studies have shown that unpolymerized resin monomers may be released into the oral environment and cause harmful biological effects. We investigated changes in the gene expression profiles of TEGDMA-treated human dental pulp cells (hDPCs) following short- (1-day) and long-term (7-days) exposure. HDPCs were exposed to a noncytotoxic concentration of TEGDMA, and gene expression profiles were evaluated by microarray analysis. The results were confirmed by quantitative reverse-transcriptase PCR (qRT PCR). In total, 1282 and 1319 genes (up- or down-regulated) were differentially expressed compared with control group after the 1- and 7-day incubation periods, respectively. Biological ontology-based analyses revealed that metabolic, cellular, and developmental processes constituted the largest groups of biological functional processes. qRT-PCR analysis on bone morphogenetic protein-2 (BMP-2), BMP-4, secreted protein, acidic, cysteine-rich, collagen type I alpha 1, oxidative stress-induced growth inhibitor 1, MMP3, interleukin-6, and heme oxygenase-1 genes confirmed the changes in expression observed in the microarray analysis. Our results suggest that TEGDMA can change the many functions of hDPCs through large changes in gene expression levels and complex interactions with different signaling pathways.

  18. Bacterial identification and subtyping using DNA microarray and DNA sequencing.

    Science.gov (United States)

    Al-Khaldi, Sufian F; Mossoba, Magdi M; Allard, Marc M; Lienau, E Kurt; Brown, Eric D

    2012-01-01

    The era of fast and accurate discovery of biological sequence motifs in prokaryotic and eukaryotic cells is here. The co-evolution of direct genome sequencing and DNA microarray strategies not only will identify, isotype, and serotype pathogenic bacteria, but also it will aid in the discovery of new gene functions by detecting gene expressions in different diseases and environmental conditions. Microarray bacterial identification has made great advances in working with pure and mixed bacterial samples. The technological advances have moved beyond bacterial gene expression to include bacterial identification and isotyping. Application of new tools such as mid-infrared chemical imaging improves detection of hybridization in DNA microarrays. The research in this field is promising and future work will reveal the potential of infrared technology in bacterial identification. On the other hand, DNA sequencing by using 454 pyrosequencing is so cost effective that the promise of $1,000 per bacterial genome sequence is becoming a reality. Pyrosequencing technology is a simple to use technique that can produce accurate and quantitative analysis of DNA sequences with a great speed. The deposition of massive amounts of bacterial genomic information in databanks is creating fingerprint phylogenetic analysis that will ultimately replace several technologies such as Pulsed Field Gel Electrophoresis. In this chapter, we will review (1) the use of DNA microarray using fluorescence and infrared imaging detection for identification of pathogenic bacteria, and (2) use of pyrosequencing in DNA cluster analysis to fingerprint bacterial phylogenetic trees.

  19. Ontology-based, Tissue MicroArray oriented, image centered tissue bank

    Directory of Open Access Journals (Sweden)

    Viti Federica

    2008-04-01

    Full Text Available Abstract Background Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information. Results In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data. Conclusions Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes.

  20. Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

    Science.gov (United States)

    Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

    2013-01-01

    DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

  1. Microarray-based method for the parallel analysis of genotypes and expression profiles of wood-forming tissues in Eucalyptus grandis

    CSIR Research Space (South Africa)

    Barros, E

    2009-05-01

    Full Text Available of Eucalyptus grandis planting stock that exhibit preferred wood qualities is thus a priority of the South African forestry industry. The researchers used microarray-based DNA-amplified fragment length polymorphism (AFLP) analysis in combination with expression...

  2. Transcription analysis of apple fruit development using cDNA microarrays

    NARCIS (Netherlands)

    Soglio, V.; Costa, F.; Molthoff, J.W.; Weemen-Hendriks, M.; Schouten, H.J.; Gianfranceschi, L.

    2009-01-01

    The knowledge of the molecular mechanisms underlying fruit quality traits is fundamental to devise efficient marker-assisted selection strategies and to improve apple breeding. In this study, cDNA microarray technology was used to identify genes whose expression changes during fruit development and

  3. Gene Expression Browser: Large-Scale and Cross-Experiment Microarray Data Management, Search & Visualization

    Science.gov (United States)

    The amount of microarray gene expression data in public repositories has been increasing exponentially for the last couple of decades. High-throughput microarray data integration and analysis has become a critical step in exploring the large amount of expression data for biological discovery. Howeve...

  4. Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data

    Directory of Open Access Journals (Sweden)

    Ghosh Debashis

    2004-12-01

    Full Text Available Abstract Background An increasing number of studies have profiled tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most intriguing yet challenging tasks is to develop robust statistical models to integrate the findings. Results By applying a two-stage Bayesian mixture modeling strategy, we were able to assimilate and analyze four independent microarray studies to derive an inter-study validated "meta-signature" associated with breast cancer prognosis. Combining multiple studies (n = 305 samples on a common probability scale, we developed a 90-gene meta-signature, which strongly associated with survival in breast cancer patients. Given the set of independent studies using different microarray platforms which included spotted cDNAs, Affymetrix GeneChip, and inkjet oligonucleotides, the individually identified classifiers yielded gene sets predictive of survival in each study cohort. The study-specific gene signatures, however, had minimal overlap with each other, and performed poorly in pairwise cross-validation. The meta-signature, on the other hand, accommodated such heterogeneity and achieved comparable or better prognostic performance when compared with the individual signatures. Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage. Functional annotation revealed that genes involved in cell cycle and signal transduction activities were over-represented in the meta-signature. Conclusion The mixture modeling approach unifies disparate gene expression data on a common probability scale allowing for robust, inter-study validated prognostic signatures to be obtained. With the emerging utility of microarrays for cancer prognosis, it will be important to establish paradigms to meta

  5. Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.

    Science.gov (United States)

    Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E; Allen, Peter J; Sempere, Lorenzo F; Haab, Brian B

    2015-10-06

    Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu's method for selected images. SFT promises to advance the goal of full automation in image analysis.

  6. Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

    Science.gov (United States)

    Johnstone, Daniel M.; Riveros, Carlos; Heidari, Moones; Graham, Ross M.; Trinder, Debbie; Berretta, Regina; Olynyk, John K.; Scott, Rodney J.; Moscato, Pablo; Milward, Elizabeth A.

    2013-01-01

    While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation—if not adequately minimized by effective normalization—may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes. PMID:27605185

  7. Translating microarray data for diagnostic testing in childhood leukaemia

    International Nuclear Information System (INIS)

    Hoffmann, Katrin; Firth, Martin J; Beesley, Alex H; Klerk, Nicholas H de; Kees, Ursula R

    2006-01-01

    Recent findings from microarray studies have raised the prospect of a standardized diagnostic gene expression platform to enhance accurate diagnosis and risk stratification in paediatric acute lymphoblastic leukaemia (ALL). However, the robustness as well as the format for such a diagnostic test remains to be determined. As a step towards clinical application of these findings, we have systematically analyzed a published ALL microarray data set using Robust Multi-array Analysis (RMA) and Random Forest (RF). We examined published microarray data from 104 ALL patients specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent patient cohort. We achieved very high overall ALL subgroup prediction accuracies of about 98%, and were able to verify the robustness of these genes in an independent panel of 68 specimens obtained from a different institution and processed in a different laboratory. Our study established that the selection of discriminating genes is strongly dependent on the analysis method. This may have profound implications for clinical use, particularly when the classifier is reduced to a small set of genes. We have demonstrated that as few as 26 genes yield accurate class prediction and importantly, almost 70% of these genes have not been previously identified as essential for class distinction of the six ALL subgroups. Our finding supports the feasibility of qRT-PCR technology for standardized diagnostic testing in paediatric ALL and should, in conjunction with conventional cytogenetics lead to a more accurate classification of the disease. In addition, we have demonstrated that microarray findings from one study can be confirmed in an independent study, using an entirely independent patient cohort

  8. Surface-enhanced Raman scattering detection of bacteria on microarrays at single cell levels using silver nanoparticles

    International Nuclear Information System (INIS)

    Zhou, Haibo; Yang, Danting; Mircescu, Nicoleta E.; Ivleva, Natalia P.; Schwarzmeier, Kathrin; Niessner, Reinhard; Haisch, Christoph; Wieser, Andreas; Schubert, Sören

    2015-01-01

    We describe a method for the synthesis of SERS-active silver nanoparticles (AgNPs) directly on the surface of bacteria (bacteria-AgNPs), specifically of E. coli cells. This straightforward strategy allows for the sensitive determination of bacteria on a microarray platform. Antibodies were used as selective receptors on the microarray surface. The Raman signal of bacteria-AgNPs is about 10 times higher than that obtained previously with microarrays based on mixing bacteria and AgNPs (bacteria+AgNPs). The optimum SERS enhancement of bacteria-AgNPs is obtained under 633-nm laser excitation, and this most likely is due to the plasmonic interaction of aggregated AgNPs. The method allows for an identification and quantification even of single E. coli bacteria. In our perception, this straightforward approach represents a most valuable tool for the detection of E. coli and, conceivably, of other bacteria, and thus has a large potential in environmental monitoring, medical diagnosis, and in food safety and quality control. (author)

  9. Age-Specific Gene Expression Profiles of Rhesus Monkey Ovaries Detected by Microarray Analysis

    Directory of Open Access Journals (Sweden)

    Hengxi Wei

    2015-01-01

    Full Text Available The biological function of human ovaries declines with age. To identify the potential molecular changes in ovarian aging, we performed genome-wide gene expression analysis by microarray of ovaries from young, middle-aged, and old rhesus monkeys. Microarray data was validated by quantitative real-time PCR. Results showed that a total of 503 (60 upregulated, 443 downregulated and 84 (downregulated genes were differentially expressed in old ovaries compared to young and middle-aged groups, respectively. No difference in gene expression was found between middle-aged and young groups. Differentially expressed genes were mainly enriched in cell and organelle, cellular and physiological process, binding, and catalytic activity. These genes were primarily associated with KEGG pathways of cell cycle, DNA replication and repair, oocyte meiosis and maturation, MAPK, TGF-beta, and p53 signaling pathway. Genes upregulated were involved in aging, defense response, oxidation reduction, and negative regulation of cellular process; genes downregulated have functions in reproduction, cell cycle, DNA and RNA process, macromolecular complex assembly, and positive regulation of macromolecule metabolic process. These findings show that monkey ovary undergoes substantial change in global transcription with age. Gene expression profiles are useful in understanding the mechanisms underlying ovarian aging and age-associated infertility in primates.

  10. Xylella fastidiosa gene expression analysis by DNA microarrays

    Directory of Open Access Journals (Sweden)

    Regiane F. Travensolo

    2009-01-01

    Full Text Available Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcription reactions and which were obtained from bacteria grown under two different conditions (liquid XDM2 and liquid BCYE. All data were statistically analyzed to verify which genes were differentially expressed. In addition to exploring conditions for X. fastidiosa genome-wide transcriptome analysis, the present work observed the differential expression of several classes of genes (energy, protein, amino acid and nucleotide metabolism, transport, degradation of substances, toxins and hypothetical proteins, among others. The understanding of expressed genes in these two different media will be useful in comprehending the metabolic characteristics of X. fastidiosa, and in evaluating how important certain genes are for the functioning and survival of these bacteria in plants.

  11. 16S rRNA based microarray analysis of ten periodontal bacteria in patients with different forms of periodontitis.

    Science.gov (United States)

    Topcuoglu, Nursen; Kulekci, Guven

    2015-10-01

    DNA microarray analysis is a computer based technology, that a reverse capture, which targets 10 periodontal bacteria (ParoCheck) is available for rapid semi-quantitative determination. The aim of this three-year retrospective study was to display the microarray analysis results for the subgingival biofilm samples taken from patient cases diagnosed with different forms of periodontitis. A total of 84 patients with generalized aggressive periodontitis (GAP,n:29), generalized chronic periodontitis (GCP, n:25), peri-implantitis (PI,n:14), localized aggressive periodontitis (LAP,n:8) and refractory chronic periodontitis (RP,n:8) were consecutively selected from the archives of the Oral Microbiological Diagnostic Laboratory. The subgingival biofilm samples were analyzed by the microarray-based identification of 10 selected species. All the tested species were detected in the samples. The red complex bacteria were the most prevalent with very high levels in all groups. Fusobacterium nucleatum was detected in all samples at high levels. The green and blue complex bacteria were less prevalent compared with red and orange complex, except Aggregatibacter actinomycetemcomitas was detected in all LAP group. Positive correlations were found within all the red complex bacteria and between red and orange complex bacteria especially in GCP and GAP groups. Parocheck enables to monitoring of periodontal pathogens in all forms of periodontal disease and can be alternative to other guiding and reliable microbiologic tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Microarray and cDNA sequence analysis of transcription during nerve-dependent limb regeneration

    Directory of Open Access Journals (Sweden)

    Bryant Susan V

    2009-01-01

    Full Text Available Abstract Background Microarray analysis and 454 cDNA sequencing were used to investigate a centuries-old problem in regenerative biology: the basis of nerve-dependent limb regeneration in salamanders. Innervated (NR and denervated (DL forelimbs of Mexican axolotls were amputated and transcripts were sampled after 0, 5, and 14 days of regeneration. Results Considerable similarity was observed between NR and DL transcriptional programs at 5 and 14 days post amputation (dpa. Genes with extracellular functions that are critical to wound healing were upregulated while muscle-specific genes were downregulated. Thus, many processes that are regulated during early limb regeneration do not depend upon nerve-derived factors. The majority of the transcriptional differences between NR and DL limbs were correlated with blastema formation; cell numbers increased in NR limbs after 5 dpa and this yielded distinct transcriptional signatures of cell proliferation in NR limbs at 14 dpa. These transcriptional signatures were not observed in DL limbs. Instead, gene expression changes within DL limbs suggest more diverse and protracted wound-healing responses. 454 cDNA sequencing complemented the microarray analysis by providing deeper sampling of transcriptional programs and associated biological processes. Assembly of new 454 cDNA sequences with existing expressed sequence tag (EST contigs from the Ambystoma EST database more than doubled (3935 to 9411 the number of non-redundant human-A. mexicanum orthologous sequences. Conclusion Many new candidate gene sequences were discovered for the first time and these will greatly enable future studies of wound healing, epigenetics, genome stability, and nerve-dependent blastema formation and outgrowth using the axolotl model.

  13. Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer

    Directory of Open Access Journals (Sweden)

    Dermody James J

    2004-11-01

    Full Text Available Abstract Background A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups. Results We present here a simple non parametric approach coupled with noise filtering to identify sets of genes differentially expressed between the normal and cancer states in oral, breast, lung, prostate and ovarian tumors. An important feature of this study is the ability to integrate data from different laboratories, improving the analytical power of the individual results. One of the most interesting findings is the down regulation of genes involved in tissue differentiation. Conclusions This study presents the development and application of a noise model that suppresses noise, limits false positives in the results, and allows integration of results from individual studies derived from different research groups.

  14. Filtering for increased power for microarray data analysis

    Directory of Open Access Journals (Sweden)

    Hess Ann M

    2009-01-01

    Full Text Available Abstract Background Due to the large number of hypothesis tests performed during the process of routine analysis of microarray data, a multiple testing adjustment is certainly warranted. However, when the number of tests is very large and the proportion of differentially expressed genes is relatively low, the use of a multiple testing adjustment can result in very low power to detect those genes which are truly differentially expressed. Filtering allows for a reduction in the number of tests and a corresponding increase in power. Common filtering methods include filtering by variance, average signal or MAS detection call (for Affymetrix arrays. We study the effects of filtering in combination with the Benjamini-Hochberg method for false discovery rate control and q-value for false discovery rate estimation. Results Three case studies are used to compare three different filtering methods in combination with the two false discovery rate methods and three different preprocessing methods. For the case studies considered, filtering by detection call and variance (on the original scale consistently led to an increase in the number of differentially expressed genes identified. On the other hand, filtering by variance on the log2 scale had a detrimental effect when paired with MAS5 or PLIER preprocessing methods, even when the testing was done on the log2 scale. A simulation study was done to further examine the effect of filtering by variance. We find that filtering by variance leads to higher power, often with a decrease in false discovery rate, when paired with either of the false discovery rate methods considered. This holds regardless of the proportion of genes which are differentially expressed or whether we assume dependence or independence among genes. Conclusion The case studies show that both detection call and variance filtering are viable methods of filtering which can increase the number of differentially expressed genes identified. The

  15. Fast Gene Ontology based clustering for microarray experiments

    Directory of Open Access Journals (Sweden)

    Ovaska Kristian

    2008-11-01

    Full Text Available Abstract Background Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. Results We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Conclusion Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

  16. BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network.

    Science.gov (United States)

    Ishiwata, Ryosuke R; Morioka, Masaki S; Ogishima, Soichi; Tanaka, Hiroshi

    2009-02-15

    BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions. BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.

  17. Deciphering cellular morphology and biocompatibility using polymer microarrays

    International Nuclear Information System (INIS)

    Pernagallo, Salvatore; Unciti-Broceta, Asier; DIaz-Mochon, Juan Jose; Bradley, Mark

    2008-01-01

    A quantitative and qualitative analysis of cellular adhesion, morphology and viability is essential in understanding and designing biomaterials such as those involved in implant surfaces or as tissue-engineering scaffolds. As a means to simultaneously perform these studies in a high-throughput (HT) manner, we report a normalized protocol which allows the rapid analysis of a large number of potential cell binding substrates using polymer microarrays and high-content fluorescence microscopy. The method was successfully applied to the discovery of optimal polymer substrates from a 214-member polyurethane library with mouse fibroblast cells (L929), as well as simultaneous evaluation of cell viability and cellular morphology. Analysis demonstrated high biocompatibility of the binding polymers and permitted the identification of several different cellular morphologies, showing that specific polymer interactions may provoke changes in cell shape. In addition, SAR studies showed a clear correspondence between cellular adhesion and polymer structure. The approach can be utilized to perform multiple experiments (up to 1024 single experiments per slide) in a highly reproducible manner, leading to the generation of vast amounts of data in a short time period (48-72 h) while reducing dramatically the quantities of polymers, reagents and cells used

  18. Development, characterization and experimental validation of a cultivated sunflower (Helianthus annuus L.) gene expression oligonucleotide microarray.

    Science.gov (United States)

    Fernandez, Paula; Soria, Marcelo; Blesa, David; DiRienzo, Julio; Moschen, Sebastian; Rivarola, Maximo; Clavijo, Bernardo Jose; Gonzalez, Sergio; Peluffo, Lucila; Príncipi, Dario; Dosio, Guillermo; Aguirrezabal, Luis; García-García, Francisco; Conesa, Ana; Hopp, Esteban; Dopazo, Joaquín; Heinz, Ruth Amelia; Paniego, Norma

    2012-01-01

    Oligonucleotide-based microarrays with accurate gene coverage represent a key strategy for transcriptional studies in orphan species such as sunflower, H. annuus L., which lacks full genome sequences. The goal of this study was the development and functional annotation of a comprehensive sunflower unigene collection and the design and validation of a custom sunflower oligonucleotide-based microarray. A large scale EST (>130,000 ESTs) curation, assembly and sequence annotation was performed using Blast2GO (www.blast2go.de). The EST assembly comprises 41,013 putative transcripts (12,924 contigs and 28,089 singletons). The resulting Sunflower Unigen Resource (SUR version 1.0) was used to design an oligonucleotide-based Agilent microarray for cultivated sunflower. This microarray includes a total of 42,326 features: 1,417 Agilent controls, 74 control probes for sunflower replicated 10 times (740 controls) and 40,169 different non-control probes. Microarray performance was validated using a model experiment examining the induction of senescence by water deficit. Pre-processing and differential expression analysis of Agilent microarrays was performed using the Bioconductor limma package. The analyses based on p-values calculated by eBayes (psunflower unigene collection, and a custom, validated sunflower oligonucleotide-based microarray using Agilent technology. Both the curated unigene collection and the validated oligonucleotide microarray provide key resources for sunflower genome analysis, transcriptional studies, and molecular breeding for crop improvement.

  19. Microarray Expression Profile and Functional Analysis of Circular RNAs in Osteosarcoma

    Directory of Open Access Journals (Sweden)

    Weihai Liu

    2017-09-01

    Full Text Available Background/Aims: Osteosarcoma (OS is the most common primary malignant bone tumor in children and adolescents. However, the molecular mechanisms regulating osteosarcoma tumorigenesis and progression are still poorly understood. Circular RNAs (circRNAs have been identified as microRNA sponges and are involved in many important biological processes. This study aims to investigate the global changes in the expression pattern of circRNAs in osteosarcoma and provide a comprehensive understanding of differentially expressed circRNAs. Methods: Microarray based circRNA expression was determined in osteosarcoma cell lines and compared with hFOB1.19, which was used as the normal control. We confirmed the microarray data by real time-qPCR in both osteosarcoma cell lines and tissues. The circRNA/microRNA/mRNA interaction network was predicted using bioinformatics. Gene Ontology analysis and 4 annotation tools for pathway analysis (KEGG, Biocarta, PANTHER and Reactome were used to predict the functions of differentially expressed circRNAs. Results: We revealed a number of differentially expressed circRNAs and 12 of them were confirmed, which suggests a potential role of circRNAs in OS. Among these differentially expressed circRNAs, hsa_circRNA_103801 was up-regulated in both osteosarcoma cell lines and tissues, while hsa_circRNA_104980 was down-regulated. The most likely potential target miRNAs for hsa_circRNA_103801 include hsa-miR-370-3p, hsa-miR-338-3p and hsa-miR-877-3p, while the most potential target miRNAs of hsa_circRNA_104980 consist of hsa-miR-1298-3p and hsa-miR-660-3p. Functional analysis found that hsa_circRNA_103801 was involved in pathways in cancer, such as the HIF-1, VEGF and angiogenesis pathway, the Rap1 signaling pathway and the PI3K-Akt signaling pathway, while hsa_circRNA_104980 was related to some pathways such as the tight junction pathway. Conclusions: This study has identified the comprehensive expression profile of circRNAs in

  20. Using microarray analysis as a prognostic and predictive tool in oncology: focus on breast cancer and normal tissue toxicity

    NARCIS (Netherlands)

    Nuyten, Dimitry S. A.; van de Vijver, Marc J.

    2008-01-01

    Microarray analysis makes it possible to study the expression levels of tens of thousands of genes in one single experiment and is widely available for research purposes. Gene expression profiling is currently being used in many research projects aimed at identifying gene expression signatures in

  1. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

    Science.gov (United States)

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-09-19

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

  2. DNA microarrays : a molecular cloning manual

    National Research Council Canada - National Science Library

    Sambrook, Joseph; Bowtell, David

    2002-01-01

    .... DNA Microarrays provides authoritative, detailed instruction on the design, construction, and applications of microarrays, as well as comprehensive descriptions of the software tools and strategies...

  3. Different responsiveness to a high-fat/cholesterol diet in two inbred mice and underlying genetic factors: a whole genome microarray analysis

    Directory of Open Access Journals (Sweden)

    Jin Gang

    2009-10-01

    Full Text Available Abstract Background To investigate different responses to a high-fat/cholesterol diet and uncover their underlying genetic factors between C57BL/6J (B6 and DBA/2J (D2 inbred mice. Methods B6 and D2 mice were fed a high-fat/cholesterol diet for a series of time-points. Serum and bile lipid profiles, bile acid yields, hepatic apoptosis, gallstones and atherosclerosis formation were measured. Furthermore, a whole genome microarray was performed to screen hepatic genes expression profile. Quantitative real-time PCR, western blot and TUNEL assay were conducted to validate microarray data. Results After fed the high-fat/cholesterol diet, serum and bile total cholesterol, serum cholesterol esters, HDL cholesterol and Non-HDL cholesterol levels were altered in B6 but not significantly changed in D2; meanwhile, biliary bile acid was decreased in B6 but increased in D2. At the same time, hepatic apoptosis, gallstones and atherosclerotic lesions occurred in B6 but not in D2. The hepatic microarray analysis revealed distinctly different genes expression patterns between B6 and D2 mice. Their functional pathway groups included lipid metabolism, oxidative stress, immune/inflammation response and apoptosis. Quantitative real time PCR, TUNEL assay and western-blot results were consistent with microarray analysis. Conclusion Different genes expression patterns between B6 and D2 mice might provide a genetic basis for their distinctive responses to a high-fat/cholesterol diet, and give us an opportunity to identify novel pharmaceutical targets in related diseases in the future.

  4. Current Knowledge on Microarray Technology - An Overview

    African Journals Online (AJOL)

    Erah

    This paper reviews basics and updates of each microarray technology and serves to .... through protein microarrays. Protein microarrays also known as protein chips are nothing but grids that ... conditioned media, patient sera, plasma and urine. Clontech ... based antibody arrays) is similar to membrane-based antibody ...

  5. Diagnostic and analytical applications of protein microarrays

    DEFF Research Database (Denmark)

    Dufva, Hans Martin; Christensen, C.B.V.

    2005-01-01

    DNA microarrays have changed the field of biomedical sciences over the past 10 years. For several reasons, antibody and other protein microarrays have not developed at the same rate. However, protein and antibody arrays have emerged as a powerful tool to complement DNA microarrays during the post...

  6. Elimination of heparin interference during microarray processing of fresh and biobank-archived blood samples.

    Science.gov (United States)

    Hebels, Dennie G A J; van Herwijnen, Marcel H M; Brauers, Karen J J; de Kok, Theo M C M; Chalkiadaki, Georgia; Kyrtopoulos, Soterios A; Kleinjans, Jos C S

    2014-07-01

    In the context of environmental health research, biobank blood samples have recently been identified as suitable for high-throughput omics analyses enabling the identification of new biomarkers of exposure and disease. However, blood samples containing the anti-coagulant heparin could complicate transcriptomic analysis because heparin may inhibit RNA polymerase causing inefficient cRNA synthesis and fluorophore labelling. We investigated the inhibitory effect of heparin and the influence of storage conditions (0 or 3 hr bench times, storage at room temperature or -80°C) on fluorophore labelling in heparinized fresh human buffy coat and whole blood biobank samples during the mRNA work-up protocol for microarray analysis. Subsequently, we removed heparin by lithium chloride (LiCl) treatment and performed a quality control analysis of LiCl-treated biobank sample microarrays to prove their suitability for downstream data analysis. Both fresh and biobank samples experienced varying degrees of heparin-induced inhibition of fluorophore labelling, making most samples unusable for microarray analysis. RNA derived from EDTA and citrate blood was not inhibited. No effect of bench time was observed but room temperature storage gave slightly better results. Strong correlations were observed between original blood sample RNA yield and the amount of synthesized cRNA. LiCl treatment restored sample quality to normal standards in both fresh and biobank samples and the previously identified correlations disappeared. Microarrays hybridized with LiCl-treated biobank samples were of excellent quality with no identifiable influence of heparin. We conclude that, to obtain high quality results, in most cases heparin removal is essential in blood-derived RNA samples intended for microarray analysis. Copyright © 2014 Wiley Periodicals, Inc.

  7. Antimicrobial resistance determinant microarray for analysis of multi-drug resistant isolates

    Science.gov (United States)

    Taitt, Chris Rowe; Leski, Tomasz; Stenger, David; Vora, Gary J.; House, Brent; Nicklasson, Matilda; Pimentel, Guillermo; Zurawski, Daniel V.; Kirkup, Benjamin C.; Craft, David; Waterman, Paige E.; Lesho, Emil P.; Bangurae, Umaru; Ansumana, Rashid

    2012-06-01

    The prevalence of multidrug-resistant infections in personnel wounded in Iraq and Afghanistan has made it challenging for physicians to choose effective therapeutics in a timely fashion. To address the challenge of identifying the potential for drug resistance, we have developed the Antimicrobial Resistance Determinant Microarray (ARDM) to provide DNAbased analysis for over 250 resistance genes covering 12 classes of antibiotics. Over 70 drug-resistant bacteria from different geographic regions have been analyzed on ARDM, with significant differences in patterns of resistance identified: genes for resistance to sulfonamides, trimethoprim, chloramphenicol, rifampin, and macrolide-lincosamidesulfonamide drugs were more frequently identified in isolates from sources in Iraq/Afghanistan. Of particular concern was the presence of genes responsible for resistance to many of the last-resort antibiotics used to treat war traumaassociated infections.

  8. OpWise: Operons aid the identification of differentially expressed genes in bacterial microarray experiments

    Directory of Open Access Journals (Sweden)

    Arkin Adam P

    2006-01-01

    Full Text Available Abstract Background Differentially expressed genes are typically identified by analyzing the variation between replicate measurements. These procedures implicitly assume that there are no systematic errors in the data even though several sources of systematic error are known. Results OpWise estimates the amount of systematic error in bacterial microarray data by assuming that genes in the same operon have matching expression patterns. OpWise then performs a Bayesian analysis of a linear model to estimate significance. In simulations, OpWise corrects for systematic error and is robust to deviations from its assumptions. In several bacterial data sets, significant amounts of systematic error are present, and replicate-based approaches overstate the confidence of the changers dramatically, while OpWise does not. Finally, OpWise can identify additional changers by assigning genes higher confidence if they are consistent with other genes in the same operon. Conclusion Although microarray data can contain large amounts of systematic error, operons provide an external standard and allow for reasonable estimates of significance. OpWise is available at http://microbesonline.org/OpWise.

  9. Microarray analysis identifies a common set of cellular genes modulated by different HCV replicon clones

    Directory of Open Access Journals (Sweden)

    Gerosolimo Germano

    2008-06-01

    Full Text Available Abstract Background Hepatitis C virus (HCV RNA synthesis and protein expression affect cell homeostasis by modulation of gene expression. The impact of HCV replication on global cell transcription has not been fully evaluated. Thus, we analysed the expression profiles of different clones of human hepatoma-derived Huh-7 cells carrying a self-replicating HCV RNA which express all viral proteins (HCV replicon system. Results First, we compared the expression profile of HCV replicon clone 21-5 with both the Huh-7 parental cells and the 21-5 cured (21-5c cells. In these latter, the HCV RNA has been eliminated by IFN-α treatment. To confirm data, we also analyzed microarray results from both the 21-5 and two other HCV replicon clones, 22-6 and 21-7, compared to the Huh-7 cells. The study was carried out by using the Applied Biosystems (AB Human Genome Survey Microarray v1.0 which provides 31,700 probes that correspond to 27,868 human genes. Microarray analysis revealed a specific transcriptional program induced by HCV in replicon cells respect to both IFN-α-cured and Huh-7 cells. From the original datasets of differentially expressed genes, we selected by Venn diagrams a final list of 38 genes modulated by HCV in all clones. Most of the 38 genes have never been described before and showed high fold-change associated with significant p-value, strongly supporting data reliability. Classification of the 38 genes by Panther System identified functional categories that were significantly enriched in this gene set, such as histones and ribosomal proteins as well as extracellular matrix and intracellular protein traffic. The dataset also included new genes involved in lipid metabolism, extracellular matrix and cytoskeletal network, which may be critical for HCV replication and pathogenesis. Conclusion Our data provide a comprehensive analysis of alterations in gene expression induced by HCV replication and reveal modulation of new genes potentially useful

  10. Microarray-based analysis of differential gene expression between infective and noninfective larvae of Strongyloides stercoralis.

    Directory of Open Access Journals (Sweden)

    Roshan Ramanathan

    2011-05-01

    Full Text Available Differences between noninfective first-stage (L1 and infective third-stage (L3i larvae of parasitic nematode Strongyloides stercoralis at the molecular level are relatively uncharacterized. DNA microarrays were developed and utilized for this purpose.Oligonucleotide hybridization probes for the array were designed to bind 3,571 putative mRNA transcripts predicted by analysis of 11,335 expressed sequence tags (ESTs obtained as part of the Nematode EST project. RNA obtained from S. stercoralis L3i and L1 was co-hybridized to each array after labeling the individual samples with different fluorescent tags. Bioinformatic predictions of gene function were developed using a novel cDNA Annotation System software. We identified 935 differentially expressed genes (469 L3i-biased; 466 L1-biased having two-fold expression differences or greater and microarray signals with a p value<0.01. Based on a functional analysis, L1 larvae have a larger number of genes putatively involved in transcription (p = 0.004, and L3i larvae have biased expression of putative heat shock proteins (such as hsp-90. Genes with products known to be immunoreactive in S. stercoralis-infected humans (such as SsIR and NIE had L3i biased expression. Abundantly expressed L3i contigs of interest included S. stercoralis orthologs of cytochrome oxidase ucr 2.1 and hsp-90, which may be potential chemotherapeutic targets. The S. stercoralis ortholog of fatty acid and retinol binding protein-1, successfully used in a vaccine against Ancylostoma ceylanicum, was identified among the 25 most highly expressed L3i genes. The sperm-containing glycoprotein domain, utilized in a vaccine against the nematode Cooperia punctata, was exclusively found in L3i biased genes and may be a valuable S. stercoralis target of interest.A new DNA microarray tool for the examination of S. stercoralis biology has been developed and provides new and valuable insights regarding differences between infective and

  11. Variance estimation in the analysis of microarray data

    KAUST Repository

    Wang, Yuedong

    2009-04-01

    Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t-statistic and other statistics are unreliable owing to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of freedom problem. In this context, it is commonly observed that the variance increases proportionally with the intensity level, which has led many researchers to assume that the variance is a function of the mean. Here we concentrate on estimation of the variance as a function of an unknown mean in two models: the constant coefficient of variation model and the quadratic variance-mean model. Because the means are unknown and estimated with few degrees of freedom, naive methods that use the sample mean in place of the true mean are generally biased because of the errors-in-variables phenomenon. We propose three methods for overcoming this bias. The first two are variations on the theme of the so-called heteroscedastic simulation-extrapolation estimator, modified to estimate the variance function consistently. The third class of estimators is entirely different, being based on semiparametric information calculations. Simulations show the power of our methods and their lack of bias compared with the naive method that ignores the measurement error. The methodology is illustrated by using microarray data from leukaemia patients.

  12. A Customized DNA Microarray for Microbial Source Tracking ...

    Science.gov (United States)

    It is estimated that more than 160, 000 miles of rivers and streams in the United States are impaired due to the presence of waterborne pathogens. These pathogens typically originate from human and other animal fecal pollution sources; therefore, a rapid microbial source tracking (MST) method is needed to facilitate water quality assessment and impaired water remediation. We report a novel qualitative DNA microarray technology consisting of 453 probes for the detection of general fecal and host-associated bacteria, viruses, antibiotic resistance, and other environmentally relevant genetic indicators. A novel data normalization and reduction approach is also presented to help alleviate false positives often associated with high-density microarray applications. To evaluate the performance of the approach, DNA and cDNA was isolated from swine, cattle, duck, goose and gull fecal reference samples, as well as soiled poultry liter and raw municipal sewage. Based on nonmetric multidimensional scaling analysis of results, findings suggest that the novel microarray approach may be useful for pathogen detection and identification of fecal contamination in recreational waters. The ability to simultaneously detect a large collection of environmentally important genetic indicators in a single test has the potential to provide water quality managers with a wide range of information in a short period of time. Future research is warranted to measure microarray performance i

  13. DNA microarray-based PCR ribotyping of Clostridium difficile.

    Science.gov (United States)

    Schneeberg, Alexander; Ehricht, Ralf; Slickers, Peter; Baier, Vico; Neubauer, Heinrich; Zimmermann, Stefan; Rabold, Denise; Lübke-Becker, Antina; Seyboldt, Christian

    2015-02-01

    This study presents a DNA microarray-based assay for fast and simple PCR ribotyping of Clostridium difficile strains. Hybridization probes were designed to query the modularly structured intergenic spacer region (ISR), which is also the template for conventional and PCR ribotyping with subsequent capillary gel electrophoresis (seq-PCR) ribotyping. The probes were derived from sequences available in GenBank as well as from theoretical ISR module combinations. A database of reference hybridization patterns was set up from a collection of 142 well-characterized C. difficile isolates representing 48 seq-PCR ribotypes. The reference hybridization patterns calculated by the arithmetic mean were compared using a similarity matrix analysis. The 48 investigated seq-PCR ribotypes revealed 27 array profiles that were clearly distinguishable. The most frequent human-pathogenic ribotypes 001, 014/020, 027, and 078/126 were discriminated by the microarray. C. difficile strains related to 078/126 (033, 045/FLI01, 078, 126, 126/FLI01, 413, 413/FLI01, 598, 620, 652, and 660) and 014/020 (014, 020, and 449) showed similar hybridization patterns, confirming their genetic relatedness, which was previously reported. A panel of 50 C. difficile field isolates was tested by seq-PCR ribotyping and the DNA microarray-based assay in parallel. Taking into account that the current version of the microarray does not discriminate some closely related seq-PCR ribotypes, all isolates were typed correctly. Moreover, seq-PCR ribotypes without reference profiles available in the database (ribotype 009 and 5 new types) were correctly recognized as new ribotypes, confirming the performance and expansion potential of the microarray. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  14. Evaluation of gene expression data generated from expired Affymetrix GeneChip® microarrays using MAQC reference RNA samples

    Directory of Open Access Journals (Sweden)

    Tong Weida

    2010-10-01

    Full Text Available Abstract Background The Affymetrix GeneChip® system is a commonly used platform for microarray analysis but the technology is inherently expensive. Unfortunately, changes in experimental planning and execution, such as the unavailability of previously anticipated samples or a shift in research focus, may render significant numbers of pre-purchased GeneChip® microarrays unprocessed before their manufacturer’s expiration dates. Researchers and microarray core facilities wonder whether expired microarrays are still useful for gene expression analysis. In addition, it was not clear whether the two human reference RNA samples established by the MAQC project in 2005 still maintained their transcriptome integrity over a period of four years. Experiments were conducted to answer these questions. Results Microarray data were generated in 2009 in three replicates for each of the two MAQC samples with either expired Affymetrix U133A or unexpired U133Plus2 microarrays. These results were compared with data obtained in 2005 on the U133Plus2 microarray. The percentage of overlap between the lists of differentially expressed genes (DEGs from U133Plus2 microarray data generated in 2009 and in 2005 was 97.44%. While there was some degree of fold change compression in the expired U133A microarrays, the percentage of overlap between the lists of DEGs from the expired and unexpired microarrays was as high as 96.99%. Moreover, the microarray data generated using the expired U133A microarrays in 2009 were highly concordant with microarray and TaqMan® data generated by the MAQC project in 2005. Conclusions Our results demonstrated that microarray data generated using U133A microarrays, which were more than four years past the manufacturer’s expiration date, were highly specific and consistent with those from unexpired microarrays in identifying DEGs despite some appreciable fold change compression and decrease in sensitivity. Our data also suggested that the

  15. Microarray analysis in the zebrafish (Danio rerio) liver and telencephalon after exposure to low concentration of 17alpha-ethinylestradiol

    Energy Technology Data Exchange (ETDEWEB)

    Martyniuk, Christopher J. [Centre for Advanced Research in Environmental Genomics, 30 Marie Curie, Department of Biology, University of Ottawa, Ottawa, Ontario, K1N 6N5 (Canada); Gerrie, Emily R. [Centre for Advanced Research in Environmental Genomics, 30 Marie Curie, Department of Biology, University of Ottawa, Ottawa, Ontario, K1N 6N5 (Canada); Popesku, Jason T. [Centre for Advanced Research in Environmental Genomics, 30 Marie Curie, Department of Biology, University of Ottawa, Ottawa, Ontario, K1N 6N5 (Canada); Ekker, Marc [Centre for Advanced Research in Environmental Genomics, 30 Marie Curie, Department of Biology, University of Ottawa, Ottawa, Ontario, K1N 6N5 (Canada); Trudeau, Vance L. [Centre for Advanced Research in Environmental Genomics, 30 Marie Curie, Department of Biology, University of Ottawa, Ottawa, Ontario, K1N 6N5 (Canada)]. E-mail: trudeauv@uottawa.ca

    2007-08-15

    17alpha-ethinylestradiol (EE2) is detected in sewage effluent at concentrations that can disrupt normal reproductive function in fish. The objectives of this study were to identify novel genomic responses to EE2 exposure using microarray and real-time RT-PCR analysis in the liver and telencephalon of male zebrafish. Zebrafish were exposed to an environmentally relevant nominal concentration of 10 ng/L EE2 for a 21-day period. In the liver, common biomarkers for estrogenic exposure such as vitellogenin 1 and 3 (vtg1; vtg3), estrogen receptor alpha (esr1), and apolipoprotein A1 (apoA1) mRNA were identified by microarray analysis as being differentially regulated. Real-time RT-PCR confirmed that vtg1 was induced {approx}700-fold, vtg3 was induced {approx}100-fold and esr1 was induced {approx}20-fold. As determined by microarray analysis, ATPase Na+/K+ alpha 1a.4 (atp1a1a.4) and ATPase Na+/K+ beta 1a (atp1b1a) mRNA were down-regulated in the liver. Gene ontology (GO) analysis revealed that there were common biological processes and molecular functions regulated by EE2 in both tissues (e.g. electron transport and cell communication) but there were tissue specific changes in gene categories. For example, genes involved in protein metabolism, carbohydrate metabolism were down-regulated in the liver but were induced in the telencephalon. This study demonstrates that (1) tissues exhibit different gene responses to low EE2 exposure; (2) there are pronounced genomic effects in the liver and (3) multi-tissue gene profiling is needed to improve understanding of the effects of human pharmaceuticals on aquatic organisms.

  16. Versatile High Throughput Microarray Analysis for Marine Glycobiology

    DEFF Research Database (Denmark)

    Asunción Salmeán, Armando

    to concept proof that is possible to use the Comprehensive Microarray Polymer Profiling (CoMPP) as a tool for other extracellular matrixes such as marine animals and not only for algal or plant cell walls. Thus, we discovered fucoidan and cellulose epitopes in several tissues of various marine animals from...... in cell development. Another part of this work focused in the development of a novel methodology for the discovery of unknown algal polysaccharides and characterization of carbohydrate binding proteins. Based on the coevolution between alga and marine saprophytic microorganisms, which use the algal...

  17. Elucidation of the antibacterial mechanism of the Curvularia haloperoxidase system by DNA microarray profiling

    DEFF Research Database (Denmark)

    Hansen, E.H.; Schembri, Mark; Klemm, Per

    2004-01-01

    was the wild type. Our results demonstrate that DNA microarray technology cannot be used as the only technique to investigate the mechanisms of action of new antimicrobial compounds. However, by combining DNA microarray analysis with the subsequent creation of knockout mutants, we were able to pinpoint one...

  18. Short-term arginine deprivation results in large-scale modulation of hepatic gene expression in both normal and tumor cells: microarray bioinformatic analysis

    Directory of Open Access Journals (Sweden)

    Sabo Edmond

    2006-09-01

    Full Text Available Abstract Background We have reported arginine-sensitive regulation of LAT1 amino acid transporter (SLC 7A5 in normal rodent hepatic cells with loss of arginine sensitivity and high level constitutive expression in tumor cells. We hypothesized that liver cell gene expression is highly sensitive to alterations in the amino acid microenvironment and that tumor cells may differ substantially in gene sets sensitive to amino acid availability. To assess the potential number and classes of hepatic genes sensitive to arginine availability at the RNA level and compare these between normal and tumor cells, we used an Affymetrix microarray approach, a paired in vitro model of normal rat hepatic cells and a tumorigenic derivative with triplicate independent replicates. Cells were exposed to arginine-deficient or control conditions for 18 hours in medium formulated to maintain differentiated function. Results Initial two-way analysis with a p-value of 0.05 identified 1419 genes in normal cells versus 2175 in tumor cells whose expression was altered in arginine-deficient conditions relative to controls, representing 9–14% of the rat genome. More stringent bioinformatic analysis with 9-way comparisons and a minimum of 2-fold variation narrowed this set to 56 arginine-responsive genes in normal liver cells and 162 in tumor cells. Approximately half the arginine-responsive genes in normal cells overlap with those in tumor cells. Of these, the majority was increased in expression and included multiple growth, survival, and stress-related genes. GADD45, TA1/LAT1, and caspases 11 and 12 were among this group. Previously known amino acid regulated genes were among the pool in both cell types. Available cDNA probes allowed independent validation of microarray data for multiple genes. Among genes downregulated under arginine-deficient conditions were multiple genes involved in cholesterol and fatty acid metabolism. Expression of low-density lipoprotein receptor was

  19. Microarray-Based Identification of Transcription Factor Target Genes

    NARCIS (Netherlands)

    Gorte, M.; Horstman, A.; Page, R.B.; Heidstra, R.; Stromberg, A.; Boutilier, K.A.

    2011-01-01

    Microarray analysis is widely used to identify transcriptional changes associated with genetic perturbation or signaling events. Here we describe its application in the identification of plant transcription factor target genes with emphasis on the design of suitable DNA constructs for controlling TF

  20. Tumour auto-antibody screening: performance of protein microarrays using SEREX derived antigens

    International Nuclear Information System (INIS)

    Stempfer, René; Weinhäusel, Andreas; Syed, Parvez; Vierlinger, Klemens; Pichler, Rudolf; Meese, Eckart; Leidinger, Petra; Ludwig, Nicole; Kriegner, Albert; Nöhammer, Christa

    2010-01-01

    The simplicity and potential of minimal invasive testing using serum from patients make auto-antibody based biomarkers a very promising tool for use in diagnostics of cancer and auto-immune disease. Although several methods exist for elucidating candidate-protein markers, immobilizing these onto membranes and generating so called macroarrays is of limited use for marker validation. Especially when several hundred samples have to be analysed, microarrays could serve as a good alternative since processing macro membranes is cumbersome and reproducibility of results is moderate. Candidate markers identified by SEREX (serological identification of antigens by recombinant expression cloning) screenings of brain and lung tumour were used for macroarray and microarray production. For microarray production recombinant proteins were expressed in E. coli by autoinduction and purified His-tag (histidine-tagged) proteins were then used for the production of protein microarrays. Protein arrays were hybridized with the serum samples from brain and lung tumour patients. Methods for the generation of microarrays were successfully established when using antigens derived from membrane-based selection. Signal patterns obtained by microarrays analysis of brain and lung tumour patients' sera were highly reproducible (R = 0.92-0.96). This provides the technical foundation for diagnostic applications on the basis of auto-antibody patterns. In this limited test set, the assay provided high reproducibility and a broad dynamic range to classify all brain and lung samples correctly. Protein microarray is an efficient means for auto-antibody-based detection when using SEREX-derived clones expressing antigenic proteins. Protein microarrays are preferred to macroarrays due to the easier handling and the high reproducibility of auto-antibody testing. Especially when using only a few microliters of patient samples protein microarrays are ideally suited for validation of auto

  1. Global pathway analysis using DNA microarrays in skeletal muscle of women with polycystic ovary syndrome

    DEFF Research Database (Denmark)

    Skov, Vibe

    2007-01-01

    (study 1), to investigate whether pioglitazone therapy could reverse abnormalities in the transcriptional profile of muscle associated with insulin resistance in skeletal muscle of obese PCOS patients (study 2), and to develop a microarray platform for global gene expression profiling (study 3). In study...... comparable to other commercial and custom made microarrays and is a cost-effective alternative especially in larger epidemiological studies....

  2. Development of a Novel Protein Microarray Method for Serotyping Salmonella enterica Strains

    OpenAIRE

    Cai, H. Y.; Lu, L.; Muckle, C. A.; Prescott, J. F.; Chen, S.

    2005-01-01

    An antibody microarray assay was developed for Salmonella serotyping based on the Kauffmann-White scheme. A model (8 by 15) array was constructed using 35 antibodies for identification of 20 common Salmonella serovars and evaluated using 117 target and 73 nontarget Salmonella strains. The assay allowed complete serovar identification of 86 target strains and partial identification of 30 target strains and allowed exclusion of the 73 nontarget strains from the target serovars.

  3. Increasing the specificity and function of DNA microarrays by processing arrays at different stringencies

    DEFF Research Database (Denmark)

    Dufva, Martin; Petersen, Jesper; Poulsen, Lena

    2009-01-01

    DNA microarrays have for a decade been the only platform for genome-wide analysis and have provided a wealth of information about living organisms. DNA microarrays are processed today under one condition only, which puts large demands on assay development because all probes on the array need to f...

  4. Investigation of cellular signalling responses to non-ionising radiation in melanocytes by microarray analysis

    International Nuclear Information System (INIS)

    Boyle, G.M.; Pedley, J.; Martyn, A.C.; Fraser, L.M.; Banducci, K.J.; Parsons, P.G.; Breit, S.N.

    2003-01-01

    Melanoma is a highly aggressive cancer resulting from the abnormal proliferation and spread of specialised pigment cells in the skin, known as melanocytes. Extensive epidemiological and molecular evidence suggests that a major risk factor for melanoma formation is exposure to non-ionising radiation in the form of solar ultra-violet (UV) light. However, the exact role of solar UV in the development of melanoma is unclear. To elucidate the molecular events that occur in melanocytes following solar UV exposure and determine how they lead to melanoma development, cDNA microarray analysis was used to analyse the gene expression profile of normal melanocytes, melanocytes exposed to simulated solar UV and melanoma cells. The development of cDNA microarray technology has allowed gene expression profiling at the mRNA level to be conducted for many thousands of genes simultaneously by hybridising an array of known sequences with labelled cDNA reverse transcribed form the sample RNA. Gene expression analysis was performed for over 13,000 genes. More than 500 genes were identified as differentially expressed in melanocytes following a single UV exposure, although overall there was a general suppression of transcription. Genes that were up-regulated included oncogenes and cytoskeletal genes; in contrast, genes encoding protein tyrosine kinases and apoptosis effectors were down-regulated. Many of the genes identified as being differentially expressed represent novel UV-regulated targets. Repeated exposure to solar UV resulted in the elevation in expression of a novel member of the transforming growth factor-b (TGF-b) superfamily, the Macrophage Inhibitory Cytokine-1 (MIC-1). Our results have shown that MIC-1 is up-regulated by solar UV in melanocytes, and is highly expressed (>3 fold) in a number of metastatic melanoma cell lines (31/61) in comparison to primary melanocytes. Furthermore functional, dimerised MIC-1 was found to be secreted by melanocytes, and secreted levels were

  5. Microarray analysis of gene expression profiles of Schistosoma japonicum derived from less-susceptible host water buffalo and susceptible host goat.

    Directory of Open Access Journals (Sweden)

    Jianmei Yang

    Full Text Available BACKGROUND: Water buffalo and goats are natural hosts for S. japonicum in endemic areas of China. The susceptibility of these two hosts to schistosome infection is different, as water buffalo are less conducive to S. japonicum growth and development. To identify genes that may affect schistosome development and survival, we compared gene expression profiles of schistosomes derived from these two natural hosts using high-throughput microarray technology. RESULTS: The worm recovery rate was lower and the length and width of worms from water buffalo were smaller compared to those from goats following S. japonicum infection for 7 weeks. Besides obvious morphological difference between the schistosomes derived from the two hosts, differences were also observed by scanning and transmission electron microscopy. Microarray analysis showed differentially expressed gene patterns for parasites from the two hosts, which revealed that genes related to lipid and nucleotide metabolism, as well as protein folding, sorting, and degradation were upregulated, while others associated with signal transduction, endocrine function, development, immune function, endocytosis, and amino acid/carbohydrate/glycan metabolism were downregulated in schistosomes from water buffalo. KEGG pathway analysis deduced that the differentially expressed genes mainly involved lipid metabolism, the MAPK and ErbB signaling pathways, progesterone-mediated oocyte maturation, dorso-ventral axis formation, reproduction, and endocytosis, etc. CONCLUSION: The microarray gene analysis in schistosomes derived from water buffalo and goats provide a useful platform to disclose differences determining S. japonicum host compatibility to better understand the interplay between natural hosts and parasites, and identify schistosome target genes associated with susceptibility to screen vaccine candidates.

  6. Application of microarray analysis on computer cluster and cloud platforms.

    Science.gov (United States)

    Bernau, C; Boulesteix, A-L; Knaus, J

    2013-01-01

    Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

  7. Meta-Analysis of Microarray Data of Rainbow Trout Fry Gonad Differentiation Modulated by Ethynylestradiol.

    Directory of Open Access Journals (Sweden)

    Sophie Depiereux

    Full Text Available Sex differentiation in fish is a highly labile process easily reversed by the use of exogenous hormonal treatment and has led to environmental concerns since low doses of estrogenic molecules can adversely impact fish reproduction. The goal of this study was to identify pathways altered by treatment with ethynylestradiol (EE2 in developing fish and to find new target genes to be tested further for their possible role in male-to-female sex transdifferentiation. To this end, we have successfully adapted a previously developed bioinformatics workflow to a meta-analysis of two datasets studying sex reversal following exposure to EE2 in juvenile rainbow trout. The meta-analysis consisted of retrieving the intersection of the top gene lists generated for both datasets, performed at different levels of stringency. The intersecting gene lists, enriched in true positive differentially expressed genes (DEGs, were subjected to over-representation analysis (ORA which allowed identifying several statistically significant enriched pathways altered by EE2 treatment and several new candidate pathways, such as progesterone-mediated oocyte maturation and PPAR signalling. Moreover, several relevant key genes potentially implicated in the early transdifferentiation process were selected. Altogether, the results show that EE2 has a great effect on gene expression in juvenile rainbow trout. The feminization process seems to result from the altered transcription of genes implicated in normal female gonad differentiation, resulting in expression similar to that observed in normal females (i.e. the repression of key testicular markers cyp17a1, cyp11b, tbx1, as well as from other genes (including transcription factors that respond specifically to the EE2 treatment. The results also showed that the bioinformatics workflow can be applied to different types of microarray platforms and could be generalized to (ecotoxicogenomics studies for environmental risk assessment

  8. DNA Microarray Technology; TOPICAL

    International Nuclear Information System (INIS)

    WERNER-WASHBURNE, MARGARET; DAVIDSON, GEORGE S.

    2002-01-01

    Collaboration between Sandia National Laboratories and the University of New Mexico Biology Department resulted in the capability to train students in microarray techniques and the interpretation of data from microarray experiments. These studies provide for a better understanding of the role of stationary phase and the gene regulation involved in exit from stationary phase, which may eventually have important clinical implications. Importantly, this research trained numerous students and is the basis for three new Ph.D. projects

  9. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

    Science.gov (United States)

    Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

    2015-06-25

    Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

  10. Scaling of gene expression data allowing the comparison of different gene expression platforms

    NARCIS (Netherlands)

    van Ruissen, Fred; Schaaf, Gerben J.; Kool, Marcel; Baas, Frank; Ruijter, Jan M.

    2008-01-01

    Serial analysis of gene expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the amalgamation of the data resulting from these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce

  11. RDFBuilder: a tool to automatically build RDF-based interfaces for MAGE-OM microarray data sources.

    Science.gov (United States)

    Anguita, Alberto; Martin, Luis; Garcia-Remesal, Miguel; Maojo, Victor

    2013-07-01

    This paper presents RDFBuilder, a tool that enables RDF-based access to MAGE-ML-compliant microarray databases. We have developed a system that automatically transforms the MAGE-OM model and microarray data stored in the ArrayExpress database into RDF format. Additionally, the system automatically enables a SPARQL endpoint. This allows users to execute SPARQL queries for retrieving microarray data, either from specific experiments or from more than one experiment at a time. Our system optimizes response times by caching and reusing information from previous queries. In this paper, we describe our methods for achieving this transformation. We show that our approach is complementary to other existing initiatives, such as Bio2RDF, for accessing and retrieving data from the ArrayExpress database. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  12. Improved elucidation of biological processes linked to diabetic nephropathy by single probe-based microarray data analysis.

    Directory of Open Access Journals (Sweden)

    Clemens D Cohen

    Full Text Available BACKGROUND: Diabetic nephropathy (DN is a complex and chronic metabolic disease that evolves into a progressive fibrosing renal disorder. Effective transcriptomic profiling of slowly evolving disease processes such as DN can be problematic. The changes that occur are often subtle and can escape detection by conventional oligonucleotide DNA array analyses. METHODOLOGY/PRINCIPAL FINDINGS: We examined microdissected human renal tissue with or without DN using Affymetrix oligonucleotide microarrays (HG-U133A by standard Robust Multi-array Analysis (RMA. Subsequent gene ontology analysis by Database for Annotation, Visualization and Integrated Discovery (DAVID showed limited detection of biological processes previously identified as central mechanisms in the development of DN (e.g. inflammation and angiogenesis. This apparent lack of sensitivity may be associated with the gene-oriented averaging of oligonucleotide probe signals, as this includes signals from cross-hybridizing probes and gene annotation that is based on out of date genomic data. We then examined the same CEL file data using a different methodology to determine how well it could correlate transcriptomic data with observed biology. ChipInspector (CI is based on single probe analysis and de novo gene annotation that bypasses probe set definitions. Both methods, RMA and CI, used at default settings yielded comparable numbers of differentially regulated genes. However, when verified by RT-PCR, the single probe based analysis demonstrated reduced background noise with enhanced sensitivity and fewer false positives. CONCLUSIONS/SIGNIFICANCE: Using a single probe based analysis approach with de novo gene annotation allowed an improved representation of the biological processes linked to the development and progression of DN. The improved analysis was exemplified by the detection of Wnt signaling pathway activation in DN, a process not previously reported to be involved in this disease.

  13. ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping

    DEFF Research Database (Denmark)

    Hansen, Christian Skjødt; Østerbye, Thomas; Marcatili, Paolo

    2017-01-01

    and characterization of linear B cell epitopes. Using exhaustive amino acid substitution analysis of peptides originating from target antigens, these microarrays can be used to address the specificity of polyclonal antibodies raised against such antigens containing hundreds of epitopes. However, the interpretation....... The application takes as input quantitative peptide data of fully or partially substituted overlapping peptides from a given antigen sequence and identifies epitope residues (residues that are significantly affected by substitutions) and visualize the selectivity towards each residue by sequence logo plots...

  14. Employing image processing techniques for cancer detection using microarray images.

    Science.gov (United States)

    Dehghan Khalilabad, Nastaran; Hassanpour, Hamid

    2017-02-01

    Microarray technology is a powerful genomic tool for simultaneously studying and analyzing the behavior of thousands of genes. The analysis of images obtained from this technology plays a critical role in the detection and treatment of diseases. The aim of the current study is to develop an automated system for analyzing data from microarray images in order to detect cancerous cases. The proposed system consists of three main phases, namely image processing, data mining, and the detection of the disease. The image processing phase performs operations such as refining image rotation, gridding (locating genes) and extracting raw data from images the data mining includes normalizing the extracted data and selecting the more effective genes. Finally, via the extracted data, cancerous cell is recognized. To evaluate the performance of the proposed system, microarray database is employed which includes Breast cancer, Myeloid Leukemia and Lymphomas from the Stanford Microarray Database. The results indicate that the proposed system is able to identify the type of cancer from the data set with an accuracy of 95.45%, 94.11%, and 100%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

    Directory of Open Access Journals (Sweden)

    Raftery Adrian E

    2009-02-01

    Full Text Available Abstract Background Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes. Results We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation dataset into high- and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test. Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p

  16. Application of Microarray technology in research and diagnostics

    DEFF Research Database (Denmark)

    Helweg-Larsen, Rehannah Borup

    The overall purpose of this thesis is to evaluate the use of microarray analysis to investigate the transcriptome of human cancers and human follicular cells and define the correlation between expression of human genes and specific cancer types as well as the developmental competence of the oocyte...

  17. SIGMA: A System for Integrative Genomic Microarray Analysis of Cancer Genomes

    Directory of Open Access Journals (Sweden)

    Davies Jonathan J

    2006-12-01

    Full Text Available Abstract Background The prevalence of high resolution profiling of genomes has created a need for the integrative analysis of information generated from multiple methodologies and platforms. Although the majority of data in the public domain are gene expression profiles, and expression analysis software are available, the increase of array CGH studies has enabled integration of high throughput genomic and gene expression datasets. However, tools for direct mining and analysis of array CGH data are limited. Hence, there is a great need for analytical and display software tailored to cross platform integrative analysis of cancer genomes. Results We have created a user-friendly java application to facilitate sophisticated visualization and analysis such as cross-tumor and cross-platform comparisons. To demonstrate the utility of this software, we assembled array CGH data representing Affymetrix SNP chip, Stanford cDNA arrays and whole genome tiling path array platforms for cross comparison. This cancer genome database contains 267 profiles from commonly used cancer cell lines representing 14 different tissue types. Conclusion In this study we have developed an application for the visualization and analysis of data from high resolution array CGH platforms that can be adapted for analysis of multiple types of high throughput genomic datasets. Furthermore, we invite researchers using array CGH technology to deposit both their raw and processed data, as this will be a continually expanding database of cancer genomes. This publicly available resource, the System for Integrative Genomic Microarray Analysis (SIGMA of cancer genomes, can be accessed at http://sigma.bccrc.ca.

  18. Development and assessment of microarray-based DNA fingerprinting in Eucalyptus grandis.

    Science.gov (United States)

    Lezar, Sabine; Myburg, A A; Berger, D K; Wingfield, M J; Wingfield, B D

    2004-11-01

    Development of improved Eucalyptus genotypes involves the routine identification of breeding stock and superior clones. Currently, microsatellites and random amplified polymorphic DNA markers are the most widely used DNA-based techniques for fingerprinting of these trees. While these techniques have provided rapid and powerful fingerprinting assays, they are constrained by their reliance on gel or capillary electrophoresis, and therefore, relatively low throughput of fragment analysis. In contrast, recently developed microarray technology holds the promise of parallel analysis of thousands of markers in plant genomes. The aim of this study was to develop a DNA fingerprinting chip for Eucalyptus grandis and to investigate its usefulness for fingerprinting of eucalypt trees. A prototype chip was prepared using a partial genomic library from total genomic DNA of 23 E. grandis trees, of which 22 were full siblings. A total of 384 cloned genomic fragments were individually amplified and arrayed onto glass slides. DNA fingerprints were obtained for 17 individuals by hybridizing labeled genome representations of the individual trees to the 384-element chip. Polymorphic DNA fragments were identified by evaluating the binary distribution of their background-corrected signal intensities across full-sib individuals. Among 384 DNA fragments on the chip, 104 (27%) were found to be polymorphic. Hybridization of these polymorphic fragments was highly repeatable (R2>0.91) within the E. grandis individuals, and they allowed us to identify all 17 full-sib individuals. Our results suggest that DNA microarrays can be used to effectively fingerprint large numbers of closely related Eucalyptus trees.

  19. Clustering approaches to identifying gene expression patterns from DNA microarray data.

    Science.gov (United States)

    Do, Jin Hwan; Choi, Dong-Kug

    2008-04-30

    The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

  20. A multiplex reverse transcription PCR and automated electronic microarray assay for detection and differentiation of seven viruses affecting swine.

    Science.gov (United States)

    Erickson, A; Fisher, M; Furukawa-Stoffer, T; Ambagala, A; Hodko, D; Pasick, J; King, D P; Nfon, C; Ortega Polo, R; Lung, O

    2018-04-01

    Microarray technology can be useful for pathogen detection as it allows simultaneous interrogation of the presence or absence of a large number of genetic signatures. However, most microarray assays are labour-intensive and time-consuming to perform. This study describes the development and initial evaluation of a multiplex reverse transcription (RT)-PCR and novel accompanying automated electronic microarray assay for simultaneous detection and differentiation of seven important viruses that affect swine (foot-and-mouth disease virus [FMDV], swine vesicular disease virus [SVDV], vesicular exanthema of swine virus [VESV], African swine fever virus [ASFV], classical swine fever virus [CSFV], porcine respiratory and reproductive syndrome virus [PRRSV] and porcine circovirus type 2 [PCV2]). The novel electronic microarray assay utilizes a single, user-friendly instrument that integrates and automates capture probe printing, hybridization, washing and reporting on a disposable electronic microarray cartridge with 400 features. This assay accurately detected and identified a total of 68 isolates of the seven targeted virus species including 23 samples of FMDV, representing all seven serotypes, and 10 CSFV strains, representing all three genotypes. The assay successfully detected viruses in clinical samples from the field, experimentally infected animals (as early as 1 day post-infection (dpi) for FMDV and SVDV, 4 dpi for ASFV, 5 dpi for CSFV), as well as in biological material that were spiked with target viruses. The limit of detection was 10 copies/μl for ASFV, PCV2 and PRRSV, 100 copies/μl for SVDV, CSFV, VESV and 1,000 copies/μl for FMDV. The electronic microarray component had reduced analytical sensitivity for several of the target viruses when compared with the multiplex RT-PCR. The integration of capture probe printing allows custom onsite array printing as needed, while electrophoretically driven hybridization generates results faster than conventional

  1. 16S rRNA gene-based phylogenetic microarray for simultaneous identification of members of the genus Burkholderia.

    Science.gov (United States)

    Schönmann, Susan; Loy, Alexander; Wimmersberger, Céline; Sobek, Jens; Aquino, Catharine; Vandamme, Peter; Frey, Beat; Rehrauer, Hubert; Eberl, Leo

    2009-04-01

    For cultivation-independent and highly parallel analysis of members of the genus Burkholderia, an oligonucleotide microarray (phylochip) consisting of 131 hierarchically nested 16S rRNA gene-targeted oligonucleotide probes was developed. A novel primer pair was designed for selective amplification of a 1.3 kb 16S rRNA gene fragment of Burkholderia species prior to microarray analysis. The diagnostic performance of the microarray for identification and differentiation of Burkholderia species was tested with 44 reference strains of the genera Burkholderia, Pandoraea, Ralstonia and Limnobacter. Hybridization patterns based on presence/absence of probe signals were interpreted semi-automatically using the novel likelihood-based strategy of the web-tool Phylo- Detect. Eighty-eight per cent of the reference strains were correctly identified at the species level. The evaluated microarray was applied to investigate shifts in the Burkholderia community structure in acidic forest soil upon addition of cadmium, a condition that selected for Burkholderia species. The microarray results were in agreement with those obtained from phylogenetic analysis of Burkholderia 16S rRNA gene sequences recovered from the same cadmiumcontaminated soil, demonstrating the value of the Burkholderia phylochip for determinative and environmental studies.

  2. Analysis of human HPRT- deletion mutants by the microarray-CGH (comparative genomic hybridization)

    International Nuclear Information System (INIS)

    Kodaira, M.; Sasaki, K.; Tagawa, H.; Omine, H.; Kushiro, J.; Takahashi, N.; Katayama, H.

    2003-01-01

    We are trying to evaluate genetic effects of radiation on human using mutation frequency as an indicator. For the efficient detection of mutations, it is important to understand the mechanism and the characteristics of radiation-induced mutations. We have started the analysis of hypoxanthine-guanine phosphoribosyl transferase (HPRT) mutants induced by X-ray in order to clarify the deletion size and the mutation-distribution. We analyzed 39 human X-ray induced HPRT-deletion mutants by using the microarray-CGH. The array for this analysis contains 57 BAC clones covering as much as possible of the 4Mb of the 5' side and 10Mb of the 3' side of the HPRT gene based on the NCBI genome database. DNA from parent strain and each HPRT-mutant strain are labeled with Cy5 and Cy3 respectively, and were mixed and hybridized on the array. Fluorescent intensity ratio of the obtained spots was analyzed using software we developed to identify clones corresponding to the deletion region. The deletion in these strains ranged up to 3.5 Mb on the 5' side and 6 Mb on the 3' side of the HPRT gene. Deletions in 13 strains ended around BAC clones located at about 3 Mb on the 5' side. On the 3' side, deletions extended up to the specific clones located at 1.5 Mb in 11 strains. The mutations seem to be complex on the 3' end of deletion; some accompanied duplications with deletions and others could not be explained by one mutation event. We need to confirm these results, taking into account the experimental reproducibility and the accuracy of the published genetic map. The results of the research using the microarray-CGH help us to search the regions where deletions are easily induced and to identify the factors affecting the range of deletions

  3. Microarray meta-analysis to explore abiotic stress-specific gene expression patterns in Arabidopsis.

    Science.gov (United States)

    Shen, Po-Chih; Hour, Ai-Ling; Liu, Li-Yu Daisy

    2017-12-01

    Abiotic stresses are the major limiting factors that affect plant growth, development, yield and final quality. Deciphering the underlying mechanisms of plants' adaptations to stresses using few datasets might overlook the different aspects of stress tolerance in plants, which might be simultaneously and consequently operated in the system. Fortunately, the accumulated microarray expression data offer an opportunity to infer abiotic stress-specific gene expression patterns through meta-analysis. In this study, we propose to combine microarray gene expression data under control, cold, drought, heat, and salt conditions and determined modules (gene sets) of genes highly associated with each other according to the observed expression data. By analyzing the expression variations of the Eigen genes from different conditions, we had identified two, three, and five gene modules as cold-, heat-, and salt-specific modules, respectively. Most of the cold- or heat-specific modules were differentially expressed to a particular degree in shoot samples, while most of the salt-specific modules were differentially expressed to a particular degree in root samples. A gene ontology (GO) analysis on the stress-specific modules suggested that the gene modules exclusively enriched stress-related GO terms and that different genes under the same GO terms may be alternatively disturbed in different conditions. The gene regulatory events for two genes, DREB1A and DEAR1, in the cold-specific gene module had also been validated, as evidenced through the literature search. Our protocols study the specificity of the gene modules that were specifically activated under a particular type of abiotic stress. The biplot can also assist to visualize the stress-specific gene modules. In conclusion, our approach has the potential to further elucidate mechanisms in plants and beneficial for future experiments design under different abiotic stresses.

  4. How the RNA isolation method can affect microRNA microarray results

    DEFF Research Database (Denmark)

    Podolska, Agnieszka; Kaczkowski, Bogumil; Litman, Thomas

    2011-01-01

    RNA microarray analysis on porcine brain tissue. One method is a phenol-guanidine isothiocyanate-based procedure that permits isolation of total RNA. The second method, miRVana™ microRNA isolation, is column based and recovers the small RNA fraction alone. We found that microarray analyses give different results...... that depend on the RNA fraction used, in particular because some microRNAs appear very sensitive to the RNA isolation method. We conclude that precautions need to be taken when comparing microarray studies based on RNA isolated with different methods.......The quality of RNA is crucial in gene expression experiments. RNA degradation interferes in the measurement of gene expression, and in this context, microRNA quantification can lead to an incorrect estimation. In the present study, two different RNA isolation methods were used to perform micro...

  5. DNA Microarrays in Comparative Genomics and Transcriptomics

    DEFF Research Database (Denmark)

    Willenbrock, Hanni

    2007-01-01

    at identifying the exact breakpoints where DNA has been gained or lost. In this thesis, three popular methods are compared and a realistic simulation model is presented for generating artificial data with known breakpoints and known DNA copy number. By using simulated data, we obtain a realistic evaluation......During the past few years, innovations in the DNA sequencing technology has led to an explosion in available DNA sequence information. This has revolutionized biological research and promoted the development of high throughput analysis methods that can take advantage of the vast amount of sequence...... data. For this, the DNA microarray technology has gained enormous popularity due to its ability to measure the presence or the activity of thousands of genes simultaneously. Microarrays for high throughput data analyses are not limited to a few organisms but may be applied to everything from bacteria...

  6. EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

    Directory of Open Access Journals (Sweden)

    Zhu Yuelin

    2008-01-01

    Full Text Available Abstract Background Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand. Results EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO and allows instantaneous re-analysis of published array data. Conclusion EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from http://www.ezarray.com/.

  7. A novel multifunctional oligonucleotide microarray for Toxoplasma gondii

    Directory of Open Access Journals (Sweden)

    Chen Feng

    2010-10-01

    Full Text Available Abstract Background Microarrays are invaluable tools for genome interrogation, SNP detection, and expression analysis, among other applications. Such broad capabilities would be of value to many pathogen research communities, although the development and use of genome-scale microarrays is often a costly undertaking. Therefore, effective methods for reducing unnecessary probes while maintaining or expanding functionality would be relevant to many investigators. Results Taking advantage of available genome sequences and annotation for Toxoplasma gondii (a pathogenic parasite responsible for illness in immunocompromised individuals and Plasmodium falciparum (a related parasite responsible for severe human malaria, we designed a single oligonucleotide microarray capable of supporting a wide range of applications at relatively low cost, including genome-wide expression profiling for Toxoplasma, and single-nucleotide polymorphism (SNP-based genotyping of both T. gondii and P. falciparum. Expression profiling of the three clonotypic lineages dominating T. gondii populations in North America and Europe provides a first comprehensive view of the parasite transcriptome, revealing that ~49% of all annotated genes are expressed in parasite tachyzoites (the acutely lytic stage responsible for pathogenesis and 26% of genes are differentially expressed among strains. A novel design utilizing few probes provided high confidence genotyping, used here to resolve recombination points in the clonal progeny of sexual crosses. Recent sequencing of additional T. gondii isolates identifies >620 K new SNPs, including ~11 K that intersect with expression profiling probes, yielding additional markers for genotyping studies, and further validating the utility of a combined expression profiling/genotyping array design. Additional applications facilitating SNP and transcript discovery, alternative statistical methods for quantifying gene expression, etc. are also pursued at

  8. Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset

    Directory of Open Access Journals (Sweden)

    Yamada Yoichi

    2012-12-01

    Full Text Available Abstract Background We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these “differentially expressed GO terms” and have named the algorithm “matrix-assisted identification method of differentially expressed GO terms” (MIMGO. MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms. Findings We combined Gene Set Enrichment Analysis (GSEA with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO correctly identified (p Conclusions MIMGO is a reliable method to identify differentially expressed GO terms comprehensively.

  9. Novel statistical framework to identify differentially expressed genes allowing transcriptomic background differences.

    Science.gov (United States)

    Ling, Zhi-Qiang; Wang, Yi; Mukaisho, Kenichi; Hattori, Takanori; Tatsuta, Takeshi; Ge, Ming-Hua; Jin, Li; Mao, Wei-Min; Sugihara, Hiroyuki

    2010-06-01

    Tests of differentially expressed genes (DEGs) from microarray experiments are based on the null hypothesis that genes that are irrelevant to the phenotype/stimulus are expressed equally in the target and control samples. However, this strict hypothesis is not always true, as there can be several transcriptomic background differences between target and control samples, including different cell/tissue types, different cell cycle stages and different biological donors. These differences lead to increased false positives, which have little biological/medical significance. In this article, we propose a statistical framework to identify DEGs between target and control samples from expression microarray data allowing transcriptomic background differences between these samples by introducing a modified null hypothesis that the gene expression background difference is normally distributed. We use an iterative procedure to perform robust estimation of the null hypothesis and identify DEGs as outliers. We evaluated our method using our own triplicate microarray experiment, followed by validations with reverse transcription-polymerase chain reaction (RT-PCR) and on the MicroArray Quality Control dataset. The evaluations suggest that our technique (i) results in less false positive and false negative results, as measured by the degree of agreement with RT-PCR of the same samples, (ii) can be applied to different microarray platforms and results in better reproducibility as measured by the degree of DEG identification concordance both intra- and inter-platforms and (iii) can be applied efficiently with only a few microarray replicates. Based on these evaluations, we propose that this method not only identifies more reliable and biologically/medically significant DEG, but also reduces the power-cost tradeoff problem in the microarray field. Source code and binaries freely available for download at http://comonca.org.cn/fdca/resources/softwares/deg.zip.

  10. [Application of chromosome microarray analysis for fetuses with multicystic dysplastic kidney].

    Science.gov (United States)

    Chen, Feifei; Lei, Tingying; Fu, Fang; Li, Ru; Zhang, Yongling; Jing, Xiangyi; Yang, Xin; Han, Jin; Zhen, Li; Pan, Min; Liao, Can

    2016-12-10

    To explore the genetic etiology of fetuses with multicystic dysplastic kidney (MCDK) by chromosome microarray analysis (CMA). Seventy-two fetuses with MCDK were analyzed with conventional cytogenetic technique, among which 30 fetuses with a normal karyotype were subjected to CMA analysis with Affymetrix CytoScan HD arrays by following the manufacturer's protocol. The data was analyzed with ChAS software. Conventional cytogenetic technique has revealed three fetuses (4.2%) with identifiable chromosomal aberrations. CMA analysis has detected pathogenic CNVs in 5 fetuses (16.7%), which included two well-known microdeletion or microduplication syndromes, i.e., 17q12 microdeletion syndrome and Williams-Beuren syndrome (WBS) and three submicroscopic imbalances at 4q35.2, 22q13.33, and 1p33. PEX26, FKBP6, TUBGCP6, ALG12, and CYP4A11 are likely the causative genes. CMA can identify the submicroscopic imbalances unidentifiable by conventional cytogenetic technique, and therefore has a significant role in prenatal diagnosis and genetic counseling. The detection rate of pathogenic CNVs in fetuses with MCDK was 16.7% by CMA. 17q12 microdeletion syndrome and WBS are associated with MCDK. Mutations of PEX26, FKBP6, TUBGCP6, ALG12, and CYP4A11 genes may be the causes for MCDK.

  11. cDNA microarray screening in food safety

    International Nuclear Information System (INIS)

    Roy, Sashwati; Sen, Chandan K.

    2006-01-01

    The cDNA microarray technology and related bioinformatics tools presents a wide range of novel application opportunities. The technology may be productively applied to address food safety. In this mini-review article, we present an update highlighting the late breaking discoveries that demonstrate the vitality of cDNA microarray technology as a tool to analyze food safety with reference to microbial pathogens and genetically modified foods. In order to bring the microarray technology to mainstream food safety, it is important to develop robust user-friendly tools that may be applied in a field setting. In addition, there needs to be a standardized process for regulatory agencies to interpret and act upon microarray-based data. The cDNA microarray approach is an emergent technology in diagnostics. Its values lie in being able to provide complimentary molecular insight when employed in addition to traditional tests for food safety, as part of a more comprehensive battery of tests

  12. Prognostic value of matrix metalloproteinase 9 expression in patients with juvenile nasopharyngeal angiofibroma: tissue microarray analysis.

    Science.gov (United States)

    Sun, Xicai; Guo, Limin; Wang, Jingjing; Wang, Huan; Liu, Zhuofu; Liu, Juan; Yu, Huapeng; Hu, Li; Li, Han; Wang, Dehui

    2014-08-01

    Although JNA is a benign neoplasm histopathologically, it has a propensity for locally destructive growth and remains a higher postoperative recurrence rate. The aim of this study was to analyze the expression and localization of MMP-9 in JNA using tissue microarray to elucidate its correlation with clinicopathological features and recurrence. The expression of MMP-9 was assessed by immunohistochemistry in a tissue microarray from 70 patients with JNA and 10 control subjects. Correlation between the levels of MMP-9 expression and clinicopathologic variables, as well as tumor recurrence, were analyzed. MMP-9 was detected in perivascular and extravascular less differentiated cells and stromal cells of patients with JNA but not in the matured vascular endothelial cells of these patients. The presence of MMP-9 expression in JNA was correlated with patient's age (p=0.001). Spearman correlation analysis suggested that high expression of MMP-9 in JNA had negative correlation with patient's age (r=-0.412, p<0.001). The recurrence rate in JNA patients with high MMP-9 expression was significantly higher than those with low MMP-9 expression (p=0.002). In multivariate and ROC curve analysis, MMP-9 was a good prognostic factor for tumor recurrence of JNA. Higher MMP-9 expression is a poor prognostic factor for patients with JNA who have been surgically treated. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  13. Bystander effect: Biological endpoints and microarray analysis

    Energy Technology Data Exchange (ETDEWEB)

    Chaudhry, M. Ahmad [Department of Medical Laboratory and Radiation Sciences, College of Nursing and Health Sciences, University of Vermont, 302 Rowell Building, Burlington, VT 05405 (United States) and DNA Microarray Facility, University of Vermont, Burlington, VT 05405 (United States)]. E-mail: mchaudhr@uvm.edu

    2006-05-11

    In cell populations exposed to ionizing radiation, the biological effects occur in a much larger proportion of cells than are estimated to be traversed by radiation. It has been suggested that irradiated cells are capable of providing signals to the neighboring unirradiated cells resulting in damage to these cells. This phenomenon is termed the bystander effect. The bystander effect induces persistent, long-term, transmissible changes that result in delayed death and neoplastic transformation. Because the bystander effect is relevant to carcinogenesis, it could have significant implications for risk estimation for radiation exposure. The nature of the bystander effect signal and how it impacts the unirradiated cells remains to be elucidated. Examination of the changes in gene expression could provide clues to understanding the bystander effect and could define the signaling pathways involved in sustaining damage to these cells. The microarray technology serves as a tool to gain insight into the molecular pathways leading to bystander effect. Using medium from irradiated normal human diploid lung fibroblasts as a model system we examined gene expression alterations in bystander cells. The microarray data revealed that the radiation-induced gene expression profile in irradiated cells is different from unirradiated bystander cells suggesting that the pathways leading to biological effects in the bystander cells are different from the directly irradiated cells. The genes known to be responsive to ionizing radiation were observed in irradiated cells. Several genes were upregulated in cells receiving media from irradiated cells. Surprisingly no genes were found to be downregulated in these cells. A number of genes belonging to extracellular signaling, growth factors and several receptors were identified in bystander cells. Interestingly 15 genes involved in the cell communication processes were found to be upregulated. The induction of receptors and the cell

  14. Bystander effect: Biological endpoints and microarray analysis

    International Nuclear Information System (INIS)

    Chaudhry, M. Ahmad

    2006-01-01

    In cell populations exposed to ionizing radiation, the biological effects occur in a much larger proportion of cells than are estimated to be traversed by radiation. It has been suggested that irradiated cells are capable of providing signals to the neighboring unirradiated cells resulting in damage to these cells. This phenomenon is termed the bystander effect. The bystander effect induces persistent, long-term, transmissible changes that result in delayed death and neoplastic transformation. Because the bystander effect is relevant to carcinogenesis, it could have significant implications for risk estimation for radiation exposure. The nature of the bystander effect signal and how it impacts the unirradiated cells remains to be elucidated. Examination of the changes in gene expression could provide clues to understanding the bystander effect and could define the signaling pathways involved in sustaining damage to these cells. The microarray technology serves as a tool to gain insight into the molecular pathways leading to bystander effect. Using medium from irradiated normal human diploid lung fibroblasts as a model system we examined gene expression alterations in bystander cells. The microarray data revealed that the radiation-induced gene expression profile in irradiated cells is different from unirradiated bystander cells suggesting that the pathways leading to biological effects in the bystander cells are different from the directly irradiated cells. The genes known to be responsive to ionizing radiation were observed in irradiated cells. Several genes were upregulated in cells receiving media from irradiated cells. Surprisingly no genes were found to be downregulated in these cells. A number of genes belonging to extracellular signaling, growth factors and several receptors were identified in bystander cells. Interestingly 15 genes involved in the cell communication processes were found to be upregulated. The induction of receptors and the cell

  15. Microarray-based analysis of IncA/C plasmid-associated genes from multidrug-resistant Salmonella enterica.

    Science.gov (United States)

    Lindsey, Rebecca L; Frye, Jonathan G; Fedorka-Cray, Paula J; Meinersmann, Richard J

    2011-10-01

    In the family Enterobacteriaceae, plasmids have been classified according to 27 incompatibility (Inc) or replicon types that are based on the inability of different plasmids with the same replication mechanism to coexist in the same cell. Certain replicon types such as IncA/C are associated with multidrug resistance (MDR). We developed a microarray that contains 286 unique 70-mer oligonucleotide probes based on sequences from five IncA/C plasmids: pYR1 (Yersinia ruckeri), pPIP1202 (Yersinia pestis), pP99-018 (Photobacterium damselae), pSN254 (Salmonella enterica serovar Newport), and pP91278 (Photobacterium damselae). DNA from 59 Salmonella enterica isolates was hybridized to the microarray and analyzed for the presence or absence of genes. These isolates represented 17 serovars from 14 different animal hosts and from different geographical regions in the United States. Qualitative cluster analysis was performed using CLUSTER 3.0 to group microarray hybridization results. We found that IncA/C plasmids occurred in two lineages distinguished by a major insertion-deletion (indel) region that contains genes encoding mostly hypothetical proteins. The most variable genes were represented by transposon-associated genes as well as four antimicrobial resistance genes (aphA, merP, merA, and aadA). Sixteen mercury resistance genes were identified and highly conserved, suggesting that mercury ion-related exposure is a stronger pressure than anticipated. We used these data to construct a core IncA/C genome and an accessory genome. The results of our studies suggest that the transfer of antimicrobial resistance determinants by transfer of IncA/C plasmids is somewhat less common than exchange within the plasmids orchestrated by transposable elements, such as transposons, integrating and conjugative elements (ICEs), and insertion sequence common regions (ISCRs), and thus pose less opportunity for exchange of antimicrobial resistance.

  16. Intestinal microbiota in healthy U.S. young children and adults--a high throughput microarray analysis.

    Directory of Open Access Journals (Sweden)

    Tamar Ringel-Kulka

    Full Text Available It is generally believed that the infant's microbiota is established during the first 1-2 years of life. However, there is scarce data on its characterization and its comparison to the adult-like microbiota in consecutive years.To characterize and compare the intestinal microbiota in healthy young children (1-4 years and healthy adults from the North Carolina region in the U.S. using high-throughput bacterial phylogenetic microarray analysis.Detailed characterization and comparison of the intestinal microbiota of healthy children aged 1-4 years old (n = 28 and healthy adults of 21-60 years (n = 23 was carried out using the Human Intestinal Tract Chip (HITChip phylogenetic microarray targeting the V1 and V6 regions of 16S rRNA and quantitative PCR.The HITChip microarray data indicate that Actinobacteria, Bacilli, Clostridium cluster IV and Bacteroidetes are the predominant phylum-like groups that exhibit differences between young children and adults. The phylum-like group Clostridium cluster XIVa was equally predominant in young children and adults and is thus considered to be established at an early age. The genus-like level show significant 3.6 fold (higher or lower differences in the abundance of 26 genera between young children and adults. Young U.S. children have a significantly 3.5-fold higher abundance of Bifidobacterium species than the adults from the same location. However, the microbiota of young children is less diverse than that of adults.We show that the establishment of an adult-like intestinal microbiota occurs at a later age than previously reported. Characterizing the microbiota and its development in the early years of life may help identify 'windows of opportunity' for interventional strategies that may promote health and prevent or mitigate disease processes.

  17. Dual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data

    Directory of Open Access Journals (Sweden)

    Fernandez Pedro L

    2005-08-01

    Full Text Available Abstract Background We use an approach based on Factor Analysis to analyze datasets generated for transcriptional profiling. The method groups samples into biologically relevant categories, and enables the identification of genes and pathways most significantly associated to each phenotypic group, while allowing for the participation of a given gene in more than one cluster. Genes assigned to each cluster are used for the detection of pathways predominantly activated in that cluster by finding statistically significant associated GO terms. We tested the approach with a published dataset of microarray experiments in yeast. Upon validation with the yeast dataset, we applied the technique to a prostate cancer dataset. Results Two major pathways are shown to be activated in organ-confined, non-metastatic prostate cancer: those regulated by the androgen receptor and by receptor tyrosine kinases. A number of gene markers (HER3, IQGAP2 and POR1 highlighted by the software and related to the later pathway have been validated experimentally a posteriori on independent samples. Conclusion Using a new microarray analysis tool followed by a posteriori experimental validation of the results, we have confirmed several putative markers of malignancy associated with peptide growth factor signalling in prostate cancer and revealed others, most notably ERRB3 (HER3. Our study suggest that, in primary prostate cancer, HER3, together or not with HER4, rather than in receptor complexes involving HER2, could play an important role in the biology of these tumors. These results provide new evidence for the role of receptor tyrosine kinases in the establishment and progression of prostate cancer.

  18. FiGS: a filter-based gene selection workbench for microarray data

    Directory of Open Access Journals (Sweden)

    Yun Taegyun

    2010-01-01

    Full Text Available Abstract Background The selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers. Although various gene selection methods are currently available and some of them have shown excellent performance, no single method can retain the best performance for all types of microarray datasets. It is desirable to use a comparative approach to find the best gene selection result after rigorous test of different methodological strategies for a given microarray dataset. Results FiGS is a web-based workbench that automatically compares various gene selection procedures and provides the optimal gene selection result for an input microarray dataset. FiGS builds up diverse gene selection procedures by aligning different feature selection techniques and classifiers. In addition to the highly reputed techniques, FiGS diversifies the gene selection procedures by incorporating gene clustering options in the feature selection step and different data pre-processing options in classifier training step. All candidate gene selection procedures are evaluated by the .632+ bootstrap errors and listed with their classification accuracies and selected gene sets. FiGS runs on parallelized computing nodes that capacitate heavy computations. FiGS is freely accessible at http://gexp.kaist.ac.kr/figs. Conclusion FiGS is an web-based application that automates an extensive search for the optimized gene selection analysis for a microarray dataset in a parallel computing environment. FiGS will provide both an efficient and comprehensive means of acquiring optimal gene sets that discriminate disease states from microarray datasets.

  19. Broad spectrum microarray for fingerprint-based bacterial species identification

    Directory of Open Access Journals (Sweden)

    Frey Jürg E

    2010-02-01

    Full Text Available Abstract Background Microarrays are powerful tools for DNA-based molecular diagnostics and identification of pathogens. Most target a limited range of organisms and are based on only one or a very few genes for specific identification. Such microarrays are limited to organisms for which specific probes are available, and often have difficulty discriminating closely related taxa. We have developed an alternative broad-spectrum microarray that employs hybridisation fingerprints generated by high-density anonymous markers distributed over the entire genome for identification based on comparison to a reference database. Results A high-density microarray carrying 95,000 unique 13-mer probes was designed. Optimized methods were developed to deliver reproducible hybridisation patterns that enabled confident discrimination of bacteria at the species, subspecies, and strain levels. High correlation coefficients were achieved between replicates. A sub-selection of 12,071 probes, determined by ANOVA and class prediction analysis, enabled the discrimination of all samples in our panel. Mismatch probe hybridisation was observed but was found to have no effect on the discriminatory capacity of our system. Conclusions These results indicate the potential of our genome chip for reliable identification of a wide range of bacterial taxa at the subspecies level without laborious prior sequencing and probe design. With its high resolution capacity, our proof-of-principle chip demonstrates great potential as a tool for molecular diagnostics of broad taxonomic groups.

  20. A new locally weighted K-means for cancer-aided microarray data analysis.

    Science.gov (United States)

    Iam-On, Natthakan; Boongoen, Tossapon

    2012-11-01

    Cancer has been identified as the leading cause of death. It is predicted that around 20-26 million people will be diagnosed with cancer by 2020. With this alarming rate, there is an urgent need for a more effective methodology to understand, prevent and cure cancer. Microarray technology provides a useful basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes and individualized treatment. Amongst clustering techniques, k-means is normally chosen for its simplicity and efficiency. However, it does not account for the different importance of data attributes. This paper presents a new locally weighted extension of k-means, which has proven more accurate across many published datasets than the original and other extensions found in the literature.

  1. Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles.

    Science.gov (United States)

    Fan, Shengjun; Pan, Zhenyu; Geng, Qiang; Li, Xin; Wang, Yefan; An, Yu; Xu, Yan; Tie, Lu; Pan, Yan; Li, Xuejun

    2013-01-01

    Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups. In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks. Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one. The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis

  2. Design of an Enterobacteriaceae Pan-genome Microarray Chip

    DEFF Research Database (Denmark)

    Lukjancenko, Oksana; Ussery, David

    2010-01-01

    -density microarray chip has been designed, using 116 Enterobacteriaceae genome sequences, taking into account the enteric pan-genome. Probes for the microarray were checked in silico and performance of the chip, based on experimental strains from four different genera, demonstrate a relatively high ability...... to distinguish those strains on genus, species, and pathotype/serovar levels. Additionally, the microarray performed well when investigating which genes were found in a given strain of interest. The Enterobacteriaceae pan-genome microarray, based on 116 genomes, provides a valuable tool for determination...

  3. Assessing Bacterial Interactions Using Carbohydrate-Based Microarrays

    Directory of Open Access Journals (Sweden)

    Andrea Flannery

    2015-12-01

    Full Text Available Carbohydrates play a crucial role in host-microorganism interactions and many host glycoconjugates are receptors or co-receptors for microbial binding. Host glycosylation varies with species and location in the body, and this contributes to species specificity and tropism of commensal and pathogenic bacteria. Additionally, bacterial glycosylation is often the first bacterial molecular species encountered and responded to by the host system. Accordingly, characterising and identifying the exact structures involved in these critical interactions is an important priority in deciphering microbial pathogenesis. Carbohydrate-based microarray platforms have been an underused tool for screening bacterial interactions with specific carbohydrate structures, but they are growing in popularity in recent years. In this review, we discuss carbohydrate-based microarrays that have been profiled with whole bacteria, recombinantly expressed adhesins or serum antibodies. Three main types of carbohydrate-based microarray platform are considered; (i conventional carbohydrate or glycan microarrays; (ii whole mucin microarrays; and (iii microarrays constructed from bacterial polysaccharides or their components. Determining the nature of the interactions between bacteria and host can help clarify the molecular mechanisms of carbohydrate-mediated interactions in microbial pathogenesis, infectious disease and host immune response and may lead to new strategies to boost therapeutic treatments.

  4. A microarray-based genotyping and genetic mapping approach for highly heterozygous outcrossing species enables localization of a large fraction of the unassembled Populus trichocarpa genome sequence.

    Science.gov (United States)

    Drost, Derek R; Novaes, Evandro; Boaventura-Novaes, Carolina; Benedict, Catherine I; Brown, Ryan S; Yin, Tongming; Tuskan, Gerald A; Kirst, Matias

    2009-06-01

    Microarrays have demonstrated significant power for genome-wide analyses of gene expression, and recently have also revolutionized the genetic analysis of segregating populations by genotyping thousands of loci in a single assay. Although microarray-based genotyping approaches have been successfully applied in yeast and several inbred plant species, their power has not been proven in an outcrossing species with extensive genetic diversity. Here we have developed methods for high-throughput microarray-based genotyping in such species using a pseudo-backcross progeny of 154 individuals of Populus trichocarpa and P. deltoides analyzed with long-oligonucleotide in situ-synthesized microarray probes. Our analysis resulted in high-confidence genotypes for 719 single-feature polymorphism (SFP) and 1014 gene expression marker (GEM) candidates. Using these genotypes and an established microsatellite (SSR) framework map, we produced a high-density genetic map comprising over 600 SFPs, GEMs and SSRs. The abundance of gene-based markers allowed us to localize over 35 million base pairs of previously unplaced whole-genome shotgun (WGS) scaffold sequence to putative locations in the genome of P. trichocarpa. A high proportion of sampled scaffolds could be verified for their placement with independently mapped SSRs, demonstrating the previously un-utilized power that high-density genotyping can provide in the context of map-based WGS sequence reassembly. Our results provide a substantial contribution to the continued improvement of the Populus genome assembly, while demonstrating the feasibility of microarray-based genotyping in a highly heterozygous population. The strategies presented are applicable to genetic mapping efforts in all plant species with similarly high levels of genetic diversity.

  5. A new modified histogram matching normalization for time series microarray analysis

    NARCIS (Netherlands)

    Astola, L.J.; Molenaar, J.

    2014-01-01

    Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on

  6. Visual Analysis of DNA Microarray Data for Accurate Molecular Identification of Non-albicans Candida Isolates from Patients with Candidemia Episodes

    OpenAIRE

    De Luca Ferrari, Michela; Ribeiro Resende, Mariângela; Sakai, Kanae; Muraosa, Yasunori; Lyra, Luzia; Gonoi, Tohru; Mikami, Yuzuru; Tominaga, Kenichiro; Kamei, Katsuhiko; Zaninelli Schreiber, Angelica; Trabasso, Plinio; Moretti, Maria Luiza

    2013-01-01

    The performance of a visual slide-based DNA microarray for the identification of non-albicans Candida spp. was evaluated. Among 167 isolates that had previously been identified by Vitek 2, the agreement between DNA microarray and sequencing results was 97.6%. This DNA microarray platform showed excellent performance.

  7. "Harshlighting" small blemishes on microarrays

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    Wittkowski Knut M

    2005-03-01

    Full Text Available Abstract Background Microscopists are familiar with many blemishes that fluorescence images can have due to dust and debris, glass flaws, uneven distribution of fluids or surface coatings, etc. Microarray scans show similar artefacts, which affect the analysis, particularly when one tries to detect subtle changes. However, most blemishes are hard to find by the unaided eye, particularly in high-density oligonucleotide arrays (HDONAs. Results We present a method that harnesses the statistical power provided by having several HDONAs available, which are obtained under similar conditions except for the experimental factor. This method "harshlights" blemishes and renders them evident. We find empirically that about 25% of our chips are blemished, and we analyze the impact of masking them on screening for differentially expressed genes. Conclusion Experiments attempting to assess subtle expression changes should be carefully screened for blemishes on the chips. The proposed method provides investigators with a novel robust approach to improve the sensitivity of microarray analyses. By utilizing topological information to identify and mask blemishes prior to model based analyses, the method prevents artefacts from confounding the process of background correction, normalization, and summarization.

  8. Profiling Humoral Immune Responses to Clostridium difficile-Specific Antigens by Protein Microarray Analysis.

    Science.gov (United States)

    Negm, Ola H; Hamed, Mohamed R; Dilnot, Elizabeth M; Shone, Clifford C; Marszalowska, Izabela; Lynch, Mark; Loscher, Christine E; Edwards, Laura J; Tighe, Patrick J; Wilcox, Mark H; Monaghan, Tanya M

    2015-09-01

    Clostridium difficile is an anaerobic, Gram-positive, and spore-forming bacterium that is the leading worldwide infective cause of hospital-acquired and antibiotic-associated diarrhea. Several studies have reported associations between humoral immunity and the clinical course of C. difficile infection (CDI). Host humoral immune responses are determined using conventional enzyme-linked immunosorbent assay (ELISA) techniques. Herein, we report the first use of a novel protein microarray assay to determine systemic IgG antibody responses against a panel of highly purified C. difficile-specific antigens, including native toxins A and B (TcdA and TcdB, respectively), recombinant fragments of toxins A and B (TxA4 and TxB4, respectively), ribotype-specific surface layer proteins (SLPs; 001, 002, 027), and control proteins (tetanus toxoid and Candida albicans). Microarrays were probed with sera from a total of 327 individuals with CDI, cystic fibrosis without diarrhea, and healthy controls. For all antigens, precision profiles demonstrated ELISA in the quantification of antitoxin A and antitoxin B IgG. These results indicate that microarray is a suitable assay for defining humoral immune responses to C. difficile protein antigens and may have potential advantages in throughput, convenience, and cost. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  9. Evaluation of toxicity of the mycotoxin citrinin using yeast ORF DNA microarray and Oligo DNA microarray

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    Nobumasa Hitoshi

    2007-04-01

    Full Text Available Abstract Background Mycotoxins are fungal secondary metabolites commonly present in feed and food, and are widely regarded as hazardous contaminants. Citrinin, one of the very well known mycotoxins that was first isolated from Penicillium citrinum, is produced by more than 10 kinds of fungi, and is possibly spread all over the world. However, the information on the action mechanism of the toxin is limited. Thus, we investigated the citrinin-induced genomic response for evaluating its toxicity. Results Citrinin inhibited growth of yeast cells at a concentration higher than 100 ppm. We monitored the citrinin-induced mRNA expression profiles in yeast using the ORF DNA microarray and Oligo DNA microarray, and the expression profiles were compared with those of the other stress-inducing agents. Results obtained from both microarray experiments clustered together, but were different from those of the mycotoxin patulin. The oxidative stress response genes – AADs, FLR1, OYE3, GRE2, and MET17 – were significantly induced. In the functional category, expression of genes involved in "metabolism", "cell rescue, defense and virulence", and "energy" were significantly activated. In the category of "metabolism", genes involved in the glutathione synthesis pathway were activated, and in the category of "cell rescue, defense and virulence", the ABC transporter genes were induced. To alleviate the induced stress, these cells might pump out the citrinin after modification with glutathione. While, the citrinin treatment did not induce the genes involved in the DNA repair. Conclusion Results from both microarray studies suggest that citrinin treatment induced oxidative stress in yeast cells. The genotoxicity was less severe than the patulin, suggesting that citrinin is less toxic than patulin. The reproducibility of the expression profiles was much better with the Oligo DNA microarray. However, the Oligo DNA microarray did not completely overcome cross

  10. Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips

    Science.gov (United States)

    Lee, Yun-Shien; Chen, Chun-Houh; Tsai, Chi-Neu; Tsai, Chia-Lung; Chao, Angel; Wang, Tzu-Hao

    2009-01-01

    Interlaboratory comparison of microarray data, even when using the same platform, imposes several challenges to scientists. RNA quality, RNA labeling efficiency, hybridization procedures and data-mining tools can all contribute variations in each laboratory. In Affymetrix GeneChips, about 11–20 different 25-mer oligonucleotides are used to measure the level of each transcript. Here, we report that ‘labeling extension values (LEVs)’, which are correlation coefficients between probe intensities and probe positions, are highly correlated with the gene expression levels (GEVs) on eukayotic Affymetrix microarray data. By analyzing LEVs and GEVs in the publicly available 2414 cel files of 20 Affymetrix microarray types covering 13 species, we found that correlations between LEVs and GEVs only exist in eukaryotic RNAs, but not in prokaryotic ones. Surprisingly, Affymetrix results of the same specimens that were analyzed in different laboratories could be clearly differentiated only by LEVs, leading to the identification of ‘laboratory signatures’. In the examined dataset, GSE10797, filtering out high-LEV genes did not compromise the discovery of biological processes that are constructed by differentially expressed genes. In conclusion, LEVs provide a new filtering parameter for microarray analysis of gene expression and it may improve the inter- and intralaboratory comparability of Affymetrix GeneChips data. PMID:19295132

  11. DNA microarrays of baculovirus genomes: differential expression of viral genes in two susceptible insect cell lines.

    Science.gov (United States)

    Yamagishi, J; Isobe, R; Takebuchi, T; Bando, H

    2003-03-01

    We describe, for the first time, the generation of a viral DNA chip for simultaneous expression measurements of nearly all known open reading frames (ORFs) in the best-studied members of the family Baculoviridae, Autographa californica multiple nucleopolyhedrovirus (AcMNPV) and Bombyx mori nucleopolyhedrovirus (BmNPV). In this study, a viral DNA chip (Ac-BmNPV chip) was fabricated and used to characterize the viral gene expression profile for AcMNPV in different cell types. The viral chip is composed of microarrays of viral DNA prepared by robotic deposition of PCR-amplified viral DNA fragments on glass for ORFs in the NPV genome. Viral gene expression was monitored by hybridization to the DNA fragment microarrays with fluorescently labeled cDNAs prepared from infected Spodoptera frugiperda, Sf9 cells and Trichoplusia ni, TnHigh-Five cells, the latter a major producer of baculovirus and recombinant proteins. A comparison of expression profiles of known ORFs in AcMNPV elucidated six genes (ORF150, p10, pk2, and three late gene expression factor genes lef-3, p35 and lef- 6) the expression of each of which was regulated differently in the two cell lines. Most of these genes are known to be closely involved in the viral life cycle such as in DNA replication, late gene expression and the release of polyhedra from infected cells. These results imply that the differential expression of these viral genes accounts for the differences in viral replication between these two cell lines. Thus, these fabricated microarrays of NPV DNA which allow a rapid analysis of gene expression at the viral genome level should greatly speed the functional analysis of large genomes of NPV.

  12. PAX3 gene deletion detected by microarray analysis in a girl with hearing loss.

    Science.gov (United States)

    Drozniewska, Malgorzata; Haus, Olga

    2014-01-01

    Deletions of the PAX3 gene have been rarely reported in the literature. Mutations of this gene are a common cause of Waardenburg syndrome type 1 and 3. We report a 16 year old female presenting hearing loss and normal intellectual development, without major features of Waardenburg syndrome type 1, and without family history of the syndrome. Her phenotype, however, overlaps with features of craniofacial-deafness-hand syndrome. Microarray analysis showed ~862 kb de novo deletion at 2q36.1 including PAX3. The above findings suggest that the rearrangement found in our patient appeared de novo and with high probability is a cause of her phenotype.

  13. Advanced microarray technologies for clinical diagnostics

    NARCIS (Netherlands)

    Pierik, Anke

    2011-01-01

    DNA microarrays become increasingly important in the field of clinical diagnostics. These microarrays, also called DNA chips, are small solid substrates, typically having a maximum surface area of a few cm2, onto which many spots are arrayed in a pre-determined pattern. Each of these spots contains

  14. Amygdala-enriched genes identified by microarray technology are restricted to specific amygdaloid subnuclei

    OpenAIRE

    Zirlinger, M.; Kreiman, Gabriel; Anderson, D. J.

    2001-01-01

    Microarray technology represents a potentially powerful method for identifying cell type- and regionally restricted genes expressed in the brain. Here we have combined a microarray analysis of differential gene expression among five selected brain regions, including the amygdala, cerebellum, hippocampus, olfactory bulb, and periaqueductal gray, with in situ hybridization. On average, 0.3% of the 34,000 genes interrogated were highly enriched in each of the five regions...

  15. Characterization of adjacent breast tumors using oligonucleotide microarrays

    International Nuclear Information System (INIS)

    Unger, Meredith A; Rishi, Mazhar; Clemmer, Virginia B; Hartman, Jennifer L; Keiper, Elizabeth A; Greshock, Joel D; Chodosh, Lewis A; Liebman, Michael N; Weber, Barbara L

    2001-01-01

    Current methodology often cannot distinguish second primary breast cancers from multifocal disease, a potentially important distinction for clinical management. In the present study we evaluated the use of oligonucleotide-based microarray analysis in determining the clonality of tumors by comparing gene expression profiles. Total RNA was extracted from two tumors with no apparent physical connection that were located in the right breast of an 87-year-old woman diagnosed with invasive ductal carcinoma (IDC). The RNA was hybridized to the Affymetrix Human Genome U95A Gene Chip ® (12,500 known human genes) and analyzed using the Gene Chip Analysis Suite ® 3.3 (Affymetrix, Inc, Santa Clara, CA, USA) and JMPIN ® 3.2.6 (SAS Institute, Inc, Cary, NC, USA). Gene expression profiles of tumors from five additional patients were compared in order to evaluate the heterogeneity in gene expression between tumors with similar clinical characteristics. The adjacent breast tumors had a pairwise correlation coefficient of 0.987, and were essentially indistinguishable by microarray analysis. Analysis of gene expression profiles from different individuals, however, generated a pairwise correlation coefficient of 0.710. Transcriptional profiling may be a useful diagnostic tool for determining tumor clonality and heterogeneity, and may ultimately impact on therapeutic decision making

  16. A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

    NARCIS (Netherlands)

    Sontrop, H.M.J.; Moerland, P.D.; Van den Ham, R.; Reinders, M.J.T.; Verhaegh, W.F.J.

    2009-01-01

    Background: Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for

  17. A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

    NARCIS (Netherlands)

    Sontrop, Herman M. J.; Moerland, Perry D.; van den Ham, René; Reinders, Marcel J. T.; Verhaegh, Wim F. J.

    2009-01-01

    Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the

  18. Development of a porcine skeletal muscle cDNA microarray: analysis of differential transcript expression in phenotypically distinct muscles

    Directory of Open Access Journals (Sweden)

    Stear Michael

    2003-03-01

    Full Text Available Abstract Background Microarray profiling has the potential to illuminate the molecular processes that govern the phenotypic characteristics of porcine skeletal muscles, such as hypertrophy or atrophy, and the expression of specific fibre types. This information is not only important for understanding basic muscle biology but also provides underpinning knowledge for enhancing the efficiency of livestock production. Results We report on the de novo development of a composite skeletal muscle cDNA microarray, comprising 5500 clones from two developmentally distinct cDNA libraries (longissimus dorsi of a 50-day porcine foetus and the gastrocnemius of a 3-day-old pig. Clones selected for the microarray assembly were of low to moderate abundance, as indicated by colony hybridisation. We profiled the differential expression of genes between the psoas (red muscle and the longissimus dorsi (white muscle, by co-hybridisation of Cy3 and Cy5 labelled cDNA derived from these two muscles. Results from seven microarray slides (replicates correctly identified genes that were expected to be differentially expressed, as well as a number of novel candidate regulatory genes. Quantitative real-time RT-PCR on selected genes was used to confirm the results from the microarray. Conclusion We have developed a porcine skeletal muscle cDNA microarray and have identified a number of candidate genes that could be involved in muscle phenotype determination, including several members of the casein kinase 2 signalling pathway.

  19. Evaluation of gene importance in microarray data based upon probability of selection

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    Fu Li M

    2005-03-01

    Full Text Available Abstract Background Microarray devices permit a genome-scale evaluation of gene function. This technology has catalyzed biomedical research and development in recent years. As many important diseases can be traced down to the gene level, a long-standing research problem is to identify specific gene expression patterns linking to metabolic characteristics that contribute to disease development and progression. The microarray approach offers an expedited solution to this problem. However, it has posed a challenging issue to recognize disease-related genes expression patterns embedded in the microarray data. In selecting a small set of biologically significant genes for classifier design, the nature of high data dimensionality inherent in this problem creates substantial amount of uncertainty. Results Here we present a model for probability analysis of selected genes in order to determine their importance. Our contribution is that we show how to derive the P value of each selected gene in multiple gene selection trials based on different combinations of data samples and how to conduct a reliability analysis accordingly. The importance of a gene is indicated by its associated P value in that a smaller value implies higher information content from information theory. On the microarray data concerning the subtype classification of small round blue cell tumors, we demonstrate that the method is capable of finding the smallest set of genes (19 genes with optimal classification performance, compared with results reported in the literature. Conclusion In classifier design based on microarray data, the probability value derived from gene selection based on multiple combinations of data samples enables an effective mechanism for reducing the tendency of fitting local data particularities.

  20. Plant-pathogen interactions: what microarray tells about it?

    Science.gov (United States)

    Lodha, T D; Basak, J

    2012-01-01

    Plant defense responses are mediated by elementary regulatory proteins that affect expression of thousands of genes. Over the last decade, microarray technology has played a key role in deciphering the underlying networks of gene regulation in plants that lead to a wide variety of defence responses. Microarray is an important tool to quantify and profile the expression of thousands of genes simultaneously, with two main aims: (1) gene discovery and (2) global expression profiling. Several microarray technologies are currently in use; most include a glass slide platform with spotted cDNA or oligonucleotides. Till date, microarray technology has been used in the identification of regulatory genes, end-point defence genes, to understand the signal transduction processes underlying disease resistance and its intimate links to other physiological pathways. Microarray technology can be used for in-depth, simultaneous profiling of host/pathogen genes as the disease progresses from infection to resistance/susceptibility at different developmental stages of the host, which can be done in different environments, for clearer understanding of the processes involved. A thorough knowledge of plant disease resistance using successful combination of microarray and other high throughput techniques, as well as biochemical, genetic, and cell biological experiments is needed for practical application to secure and stabilize yield of many crop plants. This review starts with a brief introduction to microarray technology, followed by the basics of plant-pathogen interaction, the use of DNA microarrays over the last decade to unravel the mysteries of plant-pathogen interaction, and ends with the future prospects of this technology.

  1. Microarray analysis of peripheral blood lymphocytes from ALS patients and the SAFE detection of the KEGG ALS pathway

    Science.gov (United States)

    2011-01-01

    Background Sporadic amyotrophic lateral sclerosis (sALS) is a motor neuron disease with poorly understood etiology. Results of gene expression profiling studies of whole blood from ALS patients have not been validated and are difficult to relate to ALS pathogenesis because gene expression profiles depend on the relative abundance of the different cell types present in whole blood. We conducted microarray analyses using Agilent Human Whole Genome 4 × 44k Arrays on a more homogeneous cell population, namely purified peripheral blood lymphocytes (PBLs), from ALS patients and healthy controls to identify molecular signatures possibly relevant to ALS pathogenesis. Methods Differentially expressed genes were determined by LIMMA (Linear Models for MicroArray) and SAM (Significance Analysis of Microarrays) analyses. The SAFE (Significance Analysis of Function and Expression) procedure was used to identify molecular pathway perturbations. Proteasome inhibition assays were conducted on cultured peripheral blood mononuclear cells (PBMCs) from ALS patients to confirm alteration of the Ubiquitin/Proteasome System (UPS). Results For the first time, using SAFE in a global gene ontology analysis (gene set size 5-100), we show significant perturbation of the KEGG (Kyoto Encyclopedia of Genes and Genomes) ALS pathway of motor neuron degeneration in PBLs from ALS patients. This was the only KEGG disease pathway significantly upregulated among 25, and contributing genes, including SOD1, represented 54% of the encoded proteins or protein complexes of the KEGG ALS pathway. Further SAFE analysis, including gene set sizes >100, showed that only neurodegenerative diseases (4 out of 34 disease pathways) including ALS were significantly upregulated. Changes in UBR2 expression correlated inversely with time since onset of disease and directly with ALSFRS-R, implying that UBR2 was increased early in the course of ALS. Cultured PBMCs from ALS patients accumulated more ubiquitinated proteins

  2. Massively multiplexed microbial identification using resequencing DNA microarrays for outbreak investigation

    Science.gov (United States)

    Leski, T. A.; Ansumana, R.; Jimmy, D. H.; Bangura, U.; Malanoski, A. P.; Lin, B.; Stenger, D. A.

    2011-06-01

    Multiplexed microbial diagnostic assays are a promising method for detection and identification of pathogens causing syndromes characterized by nonspecific symptoms in which traditional differential diagnosis is difficult. Also such assays can play an important role in outbreak investigations and environmental screening for intentional or accidental release of biothreat agents, which requires simultaneous testing for hundreds of potential pathogens. The resequencing pathogen microarray (RPM) is an emerging technological platform, relying on a combination of massively multiplex PCR and high-density DNA microarrays for rapid detection and high-resolution identification of hundreds of infectious agents simultaneously. The RPM diagnostic system was deployed in Sierra Leone, West Africa in collaboration with Njala University and Mercy Hospital Research Laboratory located in Bo. We used the RPM-Flu microarray designed for broad-range detection of human respiratory pathogens, to investigate a suspected outbreak of avian influenza in a number of poultry farms in which significant mortality of chickens was observed. The microarray results were additionally confirmed by influenza specific real-time PCR. The results of the study excluded the possibility that the outbreak was caused by influenza, but implicated Klebsiella pneumoniae as a possible pathogen. The outcome of this feasibility study confirms that application of broad-spectrum detection platforms for outbreak investigation in low-resource locations is possible and allows for rapid discovery of the responsible agents, even in cases when different agents are suspected. This strategy enables quick and cost effective detection of low probability events such as outbreak of a rare disease or intentional release of a biothreat agent.

  3. DNA Microarray Technology

    Science.gov (United States)

    Skip to main content DNA Microarray Technology Enter Search Term(s): Español Research Funding An Overview Bioinformatics Current Grants Education and Training Funding Extramural Research News Features Funding Divisions Funding ...

  4. Design issues in toxicogenomics using DNA microarray experiment

    International Nuclear Information System (INIS)

    Lee, Kyoung-Mu; Kim, Ju-Han; Kang, Daehee

    2005-01-01

    The methods of toxicogenomics might be classified into omics study (e.g., genomics, proteomics, and metabolomics) and population study focusing on risk assessment and gene-environment interaction. In omics study, microarray is the most popular approach. Genes falling into several categories (e.g., xenobiotics metabolism, cell cycle control, DNA repair etc.) can be selected up to 20,000 according to a priori hypothesis. The appropriate type of samples and species should be selected in advance. Multiple doses and varied exposure durations are suggested to identify those genes clearly linked to toxic response. Microarray experiments can be affected by numerous nuisance variables including experimental designs, sample extraction, type of scanners, etc. The number of slides might be determined from the magnitude and variance of expression change, false-positive rate, and desired power. Instead, pooling samples is an alternative. Online databases on chemicals with known exposure-disease outcomes and genetic information can aid the interpretation of the normalized results. Gene function can be inferred from microarray data analyzed by bioinformatics methods such as cluster analysis. The population study often adopts hospital-based or nested case-control design. Biases in subject selection and exposure assessment should be minimized, and confounding bias should also be controlled for in stratified or multiple regression analysis. Optimal sample sizes are dependent on the statistical test for gene-to-environment or gene-to-gene interaction. The design issues addressed in this mini-review are crucial in conducting toxicogenomics study. In addition, integrative approach of exposure assessment, epidemiology, and clinical trial is required

  5. AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data

    Directory of Open Access Journals (Sweden)

    Xia Yuannan

    2006-12-01

    Full Text Available Abstract Background DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challenge issue becomes how to analyze a large amount of microarray data and make biological sense of them. Affymetrix GeneChips are widely used microarrays, where a variety of statistical algorithms have been explored and used for detecting significant genes in the experiment. These methods rely solely on the quantitative data, i.e., signal intensity; however, qualitative data are also important parameters in detecting differentially expressed genes. Results AffyMiner is a tool developed for detecting differentially expressed genes in Affymetrix GeneChip microarray data and for associating gene annotation and gene ontology information with the genes detected. AffyMiner consists of the functional modules, GeneFinder for detecting significant genes in a treatment versus control experiment and GOTree for mapping genes of interest onto the Gene Ontology (GO space; and interfaces to run Cluster, a program for clustering analysis, and GenMAPP, a program for pathway analysis. AffyMiner has been used for analyzing the GeneChip data and the results were presented in several publications. Conclusion AffyMiner fills an important gap in finding differentially expressed genes in Affymetrix GeneChip microarray data. AffyMiner effectively deals with multiple replicates in the experiment and takes into account both quantitative and qualitative data in identifying significant genes. AffyMiner reduces the time and effort needed to compare data from multiple arrays and to interpret the possible biological implications associated with significant changes in a gene's expression.

  6. Autoregressive-model-based missing value estimation for DNA microarray time series data.

    Science.gov (United States)

    Choong, Miew Keen; Charbit, Maurice; Yan, Hong

    2009-01-01

    Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

  7. Discovering biological progression underlying microarray samples.

    Directory of Open Access Journals (Sweden)

    Peng Qiu

    2011-04-01

    Full Text Available In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD, to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression, and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the

  8. Gene Expression Signature in Endemic Osteoarthritis by Microarray Analysis

    Directory of Open Access Journals (Sweden)

    Xi Wang

    2015-05-01

    Full Text Available Kashin-Beck Disease (KBD is an endemic osteochondropathy with an unknown pathogenesis. Diagnosis of KBD is effective only in advanced cases, which eliminates the possibility of early treatment and leads to an inevitable exacerbation of symptoms. Therefore, we aim to identify an accurate blood-based gene signature for the detection of KBD. Previously published gene expression profile data on cartilage and peripheral blood mononuclear cells (PBMCs from adults with KBD were compared to select potential target genes. Microarray analysis was conducted to evaluate the expression of the target genes in a cohort of 100 KBD patients and 100 healthy controls. A gene expression signature was identified using a training set, which was subsequently validated using an independent test set with a minimum redundancy maximum relevance (mRMR algorithm and support vector machine (SVM algorithm. Fifty unique genes were differentially expressed between KBD patients and healthy controls. A 20-gene signature was identified that distinguished between KBD patients and controls with 90% accuracy, 85% sensitivity, and 95% specificity. This study identified a 20-gene signature that accurately distinguishes between patients with KBD and controls using peripheral blood samples. These results promote the further development of blood-based genetic biomarkers for detection of KBD.

  9. BIOPHYSICAL PROPERTIES OF NUCLEIC ACIDS AT SURFACES RELEVANT TO MICROARRAY PERFORMANCE.

    Science.gov (United States)

    Rao, Archana N; Grainger, David W

    2014-04-01

    Both clinical and analytical metrics produced by microarray-based assay technology have recognized problems in reproducibility, reliability and analytical sensitivity. These issues are often attributed to poor understanding and control of nucleic acid behaviors and properties at solid-liquid interfaces. Nucleic acid hybridization, central to DNA and RNA microarray formats, depends on the properties and behaviors of single strand (ss) nucleic acids (e.g., probe oligomeric DNA) bound to surfaces. ssDNA's persistence length, radius of gyration, electrostatics, conformations on different surfaces and under various assay conditions, its chain flexibility and curvature, charging effects in ionic solutions, and fluorescent labeling all influence its physical chemistry and hybridization under assay conditions. Nucleic acid (e.g., both RNA and DNA) target interactions with immobilized ssDNA strands are highly impacted by these biophysical states. Furthermore, the kinetics, thermodynamics, and enthalpic and entropic contributions to DNA hybridization reflect global probe/target structures and interaction dynamics. Here we review several biophysical issues relevant to oligomeric nucleic acid molecular behaviors at surfaces and their influences on duplex formation that influence microarray assay performance. Correlation of biophysical aspects of single and double-stranded nucleic acids with their complexes in bulk solution is common. Such analysis at surfaces is not commonly reported, despite its importance to microarray assays. We seek to provide further insight into nucleic acid-surface challenges facing microarray diagnostic formats that have hindered their clinical adoption and compromise their research quality and value as genomics tools.

  10. BIOPHYSICAL PROPERTIES OF NUCLEIC ACIDS AT SURFACES RELEVANT TO MICROARRAY PERFORMANCE

    Science.gov (United States)

    Rao, Archana N.; Grainger, David W.

    2014-01-01

    Both clinical and analytical metrics produced by microarray-based assay technology have recognized problems in reproducibility, reliability and analytical sensitivity. These issues are often attributed to poor understanding and control of nucleic acid behaviors and properties at solid-liquid interfaces. Nucleic acid hybridization, central to DNA and RNA microarray formats, depends on the properties and behaviors of single strand (ss) nucleic acids (e.g., probe oligomeric DNA) bound to surfaces. ssDNA’s persistence length, radius of gyration, electrostatics, conformations on different surfaces and under various assay conditions, its chain flexibility and curvature, charging effects in ionic solutions, and fluorescent labeling all influence its physical chemistry and hybridization under assay conditions. Nucleic acid (e.g., both RNA and DNA) target interactions with immobilized ssDNA strands are highly impacted by these biophysical states. Furthermore, the kinetics, thermodynamics, and enthalpic and entropic contributions to DNA hybridization reflect global probe/target structures and interaction dynamics. Here we review several biophysical issues relevant to oligomeric nucleic acid molecular behaviors at surfaces and their influences on duplex formation that influence microarray assay performance. Correlation of biophysical aspects of single and double-stranded nucleic acids with their complexes in bulk solution is common. Such analysis at surfaces is not commonly reported, despite its importance to microarray assays. We seek to provide further insight into nucleic acid-surface challenges facing microarray diagnostic formats that have hindered their clinical adoption and compromise their research quality and value as genomics tools. PMID:24765522

  11. Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes

    Directory of Open Access Journals (Sweden)

    Bihoreau Marie-Thérèse

    2009-02-01

    Full Text Available Abstract Background Microarray technologies are widely used to quantify the abundance of transcripts corresponding to thousands of genes. To maximise the robustness of transcriptome results, we have tested the performance and reproducibility of rat and mouse gene expression data obtained with Affymetrix, Illumina and Operon platforms. Results We present a thorough analysis of the degree of reproducibility provided by analysing the transcriptomic profile of the same animals of several experimental groups under different popular microarray technologies in different tissues. Concordant results from inter- and intra-platform comparisons were maximised by testing many popular computational methods for generating fold changes and significances and by only considering oligonucleotides giving high expression levels. The choice of Affymetrix signal extraction technique was shown to have the greatest effect on the concordance across platforms. In both species, when choosing optimal methods, the agreement between data generated on the Affymetrix and Illumina was excellent; this was verified using qRT-PCR on a selection of genes present on all platforms. Conclusion This study provides an extensive assessment of analytical methods best suited for processing data from different microarray technologies and can assist integration of technologically different gene expression datasets in biological systems.

  12. Microarray Analysis of Gene Expression Alteration in Human Middle Ear Epithelial Cells Induced by Asian Sand Dust.

    Science.gov (United States)

    Go, Yoon Young; Park, Moo Kyun; Kwon, Jee Young; Seo, Young Rok; Chae, Sung-Won; Song, Jae-Jun

    2015-12-01

    The primary aim of this study is to evaluate the gene expression profile of Asian sand dust (ASD)-treated human middle ear epithelial cell (HMEEC) using microarray analysis. The HMEEC was treated with ASD (400 µg/mL) and total RNA was extracted for microarray analysis. Molecular pathways among differentially expressed genes were further analyzed. For selected genes, the changes in gene expression were confirmed by real-time polymerase chain reaction. A total of 1,274 genes were differentially expressed by ASD. Among them, 1,138 genes were 2 folds up-regulated, whereas 136 genes were 2 folds down-regulated. Up-regulated genes were mainly involved in cellular processes, including apoptosis, cell differentiation, and cell proliferation. Down-regulated genes affected cellular processes, including apoptosis, cell cycle, cell differentiation, and cell proliferation. The 10 genes including ADM, CCL5, EDN1, EGR1, FOS, GHRL, JUN, SOCS3, TNF, and TNFSF10 were identified as main modulators in up-regulated genes. A total of 11 genes including CSF3, DKK1, FOSL1, FST, TERT, MMP13, PTHLH, SPRY2, TGFBR2, THBS1, and TIMP1 acted as main components of pathway associated with 2-fold down regulated genes. We identified the differentially expressed genes in ASD-treated HMEEC. Our work indicates that air pollutant like ASD, may play an important role in the pathogenesis of otitis media.

  13. Profiling cellular protein complexes by proximity ligation with dual tag microarray readout.

    Science.gov (United States)

    Hammond, Maria; Nong, Rachel Yuan; Ericsson, Olle; Pardali, Katerina; Landegren, Ulf

    2012-01-01

    Patterns of protein interactions provide important insights in basic biology, and their analysis plays an increasing role in drug development and diagnostics of disease. We have established a scalable technique to compare two biological samples for the levels of all pairwise interactions among a set of targeted protein molecules. The technique is a combination of the proximity ligation assay with readout via dual tag microarrays. In the proximity ligation assay protein identities are encoded as DNA sequences by attaching DNA oligonucleotides to antibodies directed against the proteins of interest. Upon binding by pairs of antibodies to proteins present in the same molecular complexes, ligation reactions give rise to reporter DNA molecules that contain the combined sequence information from the two DNA strands. The ligation reactions also serve to incorporate a sample barcode in the reporter molecules to allow for direct comparison between pairs of samples. The samples are evaluated using a dual tag microarray where information is decoded, revealing which pairs of tags that have become joined. As a proof-of-concept we demonstrate that this approach can be used to detect a set of five proteins and their pairwise interactions both in cellular lysates and in fixed tissue culture cells. This paper provides a general strategy to analyze the extent of any pairwise interactions in large sets of molecules by decoding reporter DNA strands that identify the interacting molecules.

  14. Cell-Based Microarrays for In Vitro Toxicology

    Science.gov (United States)

    Wegener, Joachim

    2015-07-01

    DNA/RNA and protein microarrays have proven their outstanding bioanalytical performance throughout the past decades, given the unprecedented level of parallelization by which molecular recognition assays can be performed and analyzed. Cell microarrays (CMAs) make use of similar construction principles. They are applied to profile a given cell population with respect to the expression of specific molecular markers and also to measure functional cell responses to drugs and chemicals. This review focuses on the use of cell-based microarrays for assessing the cytotoxicity of drugs, toxins, or chemicals in general. It also summarizes CMA construction principles with respect to the cell types that are used for such microarrays, the readout parameters to assess toxicity, and the various formats that have been established and applied. The review ends with a critical comparison of CMAs and well-established microtiter plate (MTP) approaches.

  15. Development of DNA Microarrays for Metabolic Pathway and Bioprocess Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Gregory Stephanopoulos

    2004-07-31

    Transcriptional profiling experiments utilizing DNA microarrays to study the intracellular accumulation of PHB in Synechocystis has proved difficult in large part because strains that show significant differences in PHB which would justify global analysis of gene expression have not been isolated.

  16. Analyses of Aloe polysaccharides using carbohydrate microarray profiling

    DEFF Research Database (Denmark)

    Isager Ahl, Louise; Grace, Olwen M; Pedersen, Henriette Lodberg

    2018-01-01

    As the popularity of Aloe vera extracts continues to rise, a desire to fully understand the individual polymer components of the leaf mesophyll, their relation to one another and the effects they have on the human body are increasing. Polysaccharides present in the leaf mesophyll have been...... identified as the components responsible for the biological activities of Aloe vera, and they have been widely studied in the past decades. However, the commonly used methods do not provide the desired platform to conduct large comparative studies of polysaccharide compositions as most of them require...... a complete or near-complete fractionation of the polymers. The objective for this study was to assess whether carbohydrate microarrays could be used for the high-throughput analysis of cell wall polysaccharides in Aloe leaf mesophyll. The method we chose is known as Comprehensive Microarray Polymer Profiling...

  17. MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis

    Science.gov (United States)

    Chu, Vu T; Gottardo, Raphael; Raftery, Adrian E; Bumgarner, Roger E; Yeung, Ka Yee

    2008-01-01

    We present MeV+R, an integration of the JAVA MultiExperiment Viewer program with Bioconductor packages. This integration of MultiExperiment Viewer and R is easily extensible to other R packages and provides users with point and click access to traditionally command line driven tools written in R. We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA. PMID:18652698

  18. Direct, Specific and Rapid Detection of Staphylococcal Proteins and Exotoxins Using a Multiplex Antibody Microarray.

    Directory of Open Access Journals (Sweden)

    Bettina Stieber

    Full Text Available S. aureus is a pathogen in humans and animals that harbors a wide variety of virulence factors and resistance genes. This bacterium can cause a wide range of mild to life-threatening diseases. In the latter case, fast diagnostic procedures are important. In routine diagnostic laboratories, several genotypic and phenotypic methods are available to identify S. aureus strains and determine their resistances. However, there is a demand for multiplex routine diagnostic tests to directly detect staphylococcal toxins and proteins.In this study, an antibody microarray based assay was established and validated for the rapid detection of staphylococcal markers and exotoxins. The following targets were included: staphylococcal protein A, penicillin binding protein 2a, alpha- and beta-hemolysins, Panton Valentine leukocidin, toxic shock syndrome toxin, enterotoxins A and B as well as staphylokinase. All were detected simultaneously within a single experiment, starting from a clonal culture on standard media. The detection of bound proteins was performed using a new fluorescence reading device for microarrays.110 reference strains and clinical isolates were analyzed using this assay, with a DNA microarray for genotypic characterization performed in parallel. The results showed a general high concordance of genotypic and phenotypic data. However, genotypic analysis found the hla gene present in all S. aureus isolates but its expression under given conditions depended on the clonal complex affiliation of the actual isolate.The multiplex antibody assay described herein allowed a rapid and reliable detection of clinically relevant staphylococcal toxins as well as resistance- and species-specific markers.

  19. Probe Selection for DNA Microarrays using OligoWiz

    DEFF Research Database (Denmark)

    Wernersson, Rasmus; Juncker, Agnieszka; Nielsen, Henrik Bjørn

    2007-01-01

    Nucleotide abundance measurements using DNA microarray technology are possible only if appropriate probes complementary to the target nucleotides can be identified. Here we present a protocol for selecting DNA probes for microarrays using the OligoWiz application. OligoWiz is a client-server appl......Nucleotide abundance measurements using DNA microarray technology are possible only if appropriate probes complementary to the target nucleotides can be identified. Here we present a protocol for selecting DNA probes for microarrays using the OligoWiz application. OligoWiz is a client......-server application that offers a detailed graphical interface and real-time user interaction on the client side, and massive computer power and a large collection of species databases (400, summer 2007) on the server side. Probes are selected according to five weighted scores: cross-hybridization, deltaT(m), folding...... computer skills and can be executed from any Internet-connected computer. The probe selection procedure for a standard microarray design targeting all yeast transcripts can be completed in 1 h....

  20. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

    DEFF Research Database (Denmark)

    Nookaew, Intawat; Papini, Marta; Pornputtapong, Natapol

    2012-01-01

    RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the I......RNA-seq, has recently become an attractive method of choice in the studies of transcriptomes, promising several advantages compared with microarrays. In this study, we sought to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated...... gene expression identification derived from the different statistical methods, as well as their integrated analysis results based on gene ontology annotation are in good agreement. Overall, our study provides a useful and comprehensive comparison between the two platforms (RNA-seq and microrrays...

  1. Assessing probe-specific dye and slide biases in two-color microarray data

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    Goldberg Zelanna

    2008-07-01

    Full Text Available Abstract Background A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide, that bind to probes on the same spot, is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences between the dyes can be adjusted out by standard methods of normalization, so that measures such as log ratios on the same slide are reliable measures of comparative expression. However, even after the normalization, there are still probe specific dye and slide variation among the data. We define a method to quantify the amount of the dye-by-probe and slide-by-probe interaction. This serves as a diagnostic, both visual and numeric, of the existence of probe-specific dye bias. We show how this improved the performance of two-color array analysis for arrays for genomic analysis of biological samples ranging from rice to human tissue. Results We develop a procedure for quantifying the extent of probe-specific dye and slide bias in two-color microarrays. The primary output is a graphical diagnostic of the extent of the bias which called ECDF (Empirical Cumulative Distribution Function, though numerical results are also obtained. Conclusion We show that the dye and slide biases were high for human and rice genomic arrays in two gene expression facilities, even after the standard intensity-based normalization, and describe how this diagnostic allowed the problems causing the probe-specific bias to be addressed, and resulted in important improvements in performance. The R package LMGene which contains the method described in this paper has been available to download from Bioconductor.

  2. The PowerAtlas: a power and sample size atlas for microarray experimental design and research

    Directory of Open Access Journals (Sweden)

    Wang Jelai

    2006-02-01

    Full Text Available Abstract Background Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies. Results To address this challenge, we have developed a Microrarray PowerAtlas 1. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO. The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC. Conclusion This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.

  3. Genotyping microarray (gene chip) for the ABCR (ABCA4) gene.

    Science.gov (United States)

    Jaakson, K; Zernant, J; Külm, M; Hutchinson, A; Tonisson, N; Glavac, D; Ravnik-Glavac, M; Hawlina, M; Meltzer, M R; Caruso, R C; Testa, F; Maugeri, A; Hoyng, C B; Gouras, P; Simonelli, F; Lewis, R A; Lupski, J R; Cremers, F P M; Allikmets, R

    2003-11-01

    Genetic variation in the ABCR (ABCA4) gene has been associated with five distinct retinal phenotypes, including Stargardt disease/fundus flavimaculatus (STGD/FFM), cone-rod dystrophy (CRD), and age-related macular degeneration (AMD). Comparative genetic analyses of ABCR variation and diagnostics have been complicated by substantial allelic heterogeneity and by differences in screening methods. To overcome these limitations, we designed a genotyping microarray (gene chip) for ABCR that includes all approximately 400 disease-associated and other variants currently described, enabling simultaneous detection of all known ABCR variants. The ABCR genotyping microarray (the ABCR400 chip) was constructed by the arrayed primer extension (APEX) technology. Each sequence change in ABCR was included on the chip by synthesis and application of sequence-specific oligonucleotides. We validated the chip by screening 136 confirmed STGD patients and 96 healthy controls, each of whom we had analyzed previously by single strand conformation polymorphism (SSCP) technology and/or heteroduplex analysis. The microarray was >98% effective in determining the existing genetic variation and was comparable to direct sequencing in that it yielded many sequence changes undetected by SSCP. In STGD patient cohorts, the efficiency of the array to detect disease-associated alleles was between 54% and 78%, depending on the ethnic composition and degree of clinical and molecular characterization of a cohort. In addition, chip analysis suggested a high carrier frequency (up to 1:10) of ABCR variants in the general population. The ABCR genotyping microarray is a robust, cost-effective, and comprehensive screening tool for variation in one gene in which mutations are responsible for a substantial fraction of retinal disease. The ABCR chip is a prototype for the next generation of screening and diagnostic tools in ophthalmic genetics, bridging clinical and scientific research. Copyright 2003 Wiley

  4. Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots

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    Song Yang

    2006-01-01

    Full Text Available We developed a quality assurance (QA tool, namely microarray outlier filter (MOF, and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.

  5. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

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    Yeh Cheng-Yu

    2009-12-01

    Full Text Available Abstract Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2 regulated by RUNX1 and STAT3 is correlated to the pathological stage

  6. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

    Science.gov (United States)

    Yeh, Hsiang-Yuan; Cheng, Shih-Wu; Lin, Yu-Chun; Yeh, Cheng-Yu; Lin, Shih-Fang; Soo, Von-Wun

    2009-12-21

    Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage. We provide a computational framework to reconstruct

  7. Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platforms

    Energy Technology Data Exchange (ETDEWEB)

    McHale, Cliona M.; Zhang, Luoping; Lan, Qing; Li, Guilan; Hubbard, Alan E.; Forrest, Matthew S.; Vermeulen, Roel; Chen, Jinsong; Shen, Min; Rappaport, Stephen M.; Yin, Songnian; Smith, Martyn T.; Rothman, Nathaniel

    2009-03-01

    Benzene is an established cause of leukemia and a possible cause of lymphoma in humans but the molecular pathways underlying this remain largely undetermined. This study sought to determine if the use of two different microarray platforms could identify robust global gene expression and pathway changes associated with occupational benzene exposure in the peripheral blood mononuclear cell (PBMC) gene expression of a population of shoe-factory workers with well-characterized occupational exposures to benzene. Microarray data was analyzed by a robust t-test using a Quantile Transformation (QT) approach. Differential expression of 2692 genes using the Affymetrix platform and 1828 genes using the Illumina platform was found. While the overall concordance in genes identified as significantly associated with benzene exposure between the two platforms was 26% (475 genes), the most significant genes identified by either array were more likely to be ranked as significant by the other platform (Illumina = 64%, Affymetrix = 58%). Expression ratios were similar among the concordant genes (mean difference in expression ratio = 0.04, standard deviation = 0.17). Four genes (CXCL16, ZNF331, JUN and PF4), which we previously identified by microarray and confirmed by real-time PCR, were identified by both platforms in the current study and were among the top 100 genes. Gene Ontology analysis showed over representation of genes involved in apoptosis among the concordant genes while Ingenuity{reg_sign} Pathway Analysis (IPA) identified pathways related to lipid metabolism. Using a two-platform approach allows for robust changes in the PBMC transcriptome of benzene-exposed individuals to be identified.

  8. cluML: A markup language for clustering and cluster validity assessment of microarray data.

    Science.gov (United States)

    Bolshakova, Nadia; Cunningham, Pádraig

    2005-01-01

    cluML is a new markup language for microarray data clustering and cluster validity assessment. The XML-based format has been designed to address some of the limitations observed in traditional formats, such as inability to store multiple clustering (including biclustering) and validation results within a dataset. cluML is an effective tool to support biomedical knowledge representation in gene expression data analysis. Although cluML was developed for DNA microarray analysis applications, it can be effectively used for the representation of clustering and for the validation of other biomedical and physical data that has no limitations.

  9. Assessing the Clinical Utility of SNP Microarray for Prader-Willi Syndrome due to Uniparental Disomy.

    Science.gov (United States)

    Santoro, Stephanie L; Hashimoto, Sayaka; McKinney, Aimee; Mihalic Mosher, Theresa; Pyatt, Robert; Reshmi, Shalini C; Astbury, Caroline; Hickey, Scott E

    2017-01-01

    Maternal uniparental disomy (UPD) 15 is one of the molecular causes of Prader-Willi syndrome (PWS), a multisystem disorder which presents with neonatal hypotonia and feeding difficulty. Current diagnostic algorithms differ regarding the use of SNP microarray to detect PWS. We retrospectively examined the frequency with which SNP microarray could identify regions of homozygosity (ROH) in patients with PWS. We determined that 7/12 (58%) patients with previously confirmed PWS by methylation analysis and microsatellite-positive UPD studies had ROH (>10 Mb) by SNP microarray. Additional assessment of 5,000 clinical microarrays, performed from 2013 to present, determined that only a single case of ROH for chromosome 15 was not caused by an imprinting disorder or identity by descent. We observed that ROH for chromosome 15 is rarely incidental and strongly associated with hypotonic infants having features of PWS. Although UPD microsatellite studies remain essential to definitively establish the presence of UPD, SNP microarray has important utility in the timely diagnostic algorithm for PWS. © 2017 S. Karger AG, Basel.

  10. Fabrication of Biomolecule Microarrays for Cell Immobilization Using Automated Microcontact Printing.

    Science.gov (United States)

    Foncy, Julie; Estève, Aurore; Degache, Amélie; Colin, Camille; Cau, Jean Christophe; Malaquin, Laurent; Vieu, Christophe; Trévisiol, Emmanuelle

    2018-01-01

    Biomolecule microarrays are generally produced by conventional microarrayer, i.e., by contact or inkjet printing. Microcontact printing represents an alternative way of deposition of biomolecules on solid supports but even if various biomolecules have been successfully microcontact printed, the production of biomolecule microarrays in routine by microcontact printing remains a challenging task and needs an effective, fast, robust, and low-cost automation process. Here, we describe the production of biomolecule microarrays composed of extracellular matrix protein for the fabrication of cell microarrays by using an automated microcontact printing device. Large scale cell microarrays can be reproducibly obtained by this method.

  11. Analysis of Chromothripsis by Combined FISH and Microarray Analysis.

    Science.gov (United States)

    MacKinnon, Ruth N

    2018-01-01

    Fluorescence in situ hybridization (FISH) to metaphase chromosomes, in conjunction with SNP array, array CGH, or whole genome sequencing, can help determine the organization of abnormal genomes after chromothripsis and other types of complex genome rearrangement. DNA microarrays can identify the changes in copy number, but they do not give information on the organization of the abnormal chromosomes, balanced rearrangements, or abnormalities of the centromeres and other regions comprised of highly repetitive DNA. Many of these details can be determined by the strategic use of metaphase FISH. FISH is a single-cell technique, so it can identify low-frequency chromosome abnormalities, and it can determine which chromosome abnormalities occur in the same or different clonal populations. These are important considerations in cancer. Metaphase chromosomes are intact, so information about abnormalities of the chromosome homologues is preserved. Here we describe strategies for working out the organization of highly rearranged genomes by combining SNP array data with various metaphase FISH methods. This approach can also be used to address some of the uncertainties arising from whole genome or mate-pair sequencing data.

  12. DNA microarray technique for detecting food-borne pathogens

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    Xing GAO

    2012-08-01

    Full Text Available Objective To study the application of DNA microarray technique for screening and identifying multiple food-borne pathogens. Methods The oligonucleotide probes were designed by Clustal X and Oligo 6.0 at the conserved regions of specific genes of multiple food-borne pathogens, and then were validated by bioinformatic analyses. The 5' end of each probe was modified by amino-group and 10 Poly-T, and the optimized probes were synthesized and spotted on aldehyde-coated slides. The bacteria DNA template incubated with Klenow enzyme was amplified by arbitrarily primed PCR, and PCR products incorporated into Aminoallyl-dUTP were coupled with fluorescent dye. After hybridization of the purified PCR products with DNA microarray, the hybridization image and fluorescence intensity analysis was acquired by ScanArray and GenePix Pro 5.1 software. A series of detection conditions such as arbitrarily primed PCR and microarray hybridization were optimized. The specificity of this approach was evaluated by 16 different bacteria DNA, and the sensitivity and reproducibility were verified by 4 food-borne pathogens DNA. The samples of multiple bacteria DNA and simulated water samples of Shigella dysenteriae were detected. Results Nine different food-borne bacteria were successfully discriminated under the same condition. The sensitivity of genomic DNA was 102 -103pg/ μl, and the coefficient of variation (CV of the reproducibility of assay was less than 15%. The corresponding specific hybridization maps of the multiple bacteria DNA samples were obtained, and the detection limit of simulated water sample of Shigella dysenteriae was 3.54×105cfu/ml. Conclusions The DNA microarray detection system based on arbitrarily primed PCR can be employed for effective detection of multiple food-borne pathogens, and this assay may offer a new method for high-throughput platform for detecting bacteria.

  13. Image microarrays derived from tissue microarrays (IMA-TMA: New resource for computer-aided diagnostic algorithm development

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    Jennifer A Hipp

    2012-01-01

    Full Text Available Background: Conventional tissue microarrays (TMAs consist of cores of tissue inserted into a recipient paraffin block such that a tissue section on a single glass slide can contain numerous patient samples in a spatially structured pattern. Scanning TMAs into digital slides for subsequent analysis by computer-aided diagnostic (CAD algorithms all offers the possibility of evaluating candidate algorithms against a near-complete repertoire of variable disease morphologies. This parallel interrogation approach simplifies the evaluation, validation, and comparison of such candidate algorithms. A recently developed digital tool, digital core (dCORE, and image microarray maker (iMAM enables the capture of uniformly sized and resolution-matched images, with these representing key morphologic features and fields of view, aggregated into a single monolithic digital image file in an array format, which we define as an image microarray (IMA. We further define the TMA-IMA construct as IMA-based images derived from whole slide images of TMAs themselves. Methods: Here we describe the first combined use of the previously described dCORE and iMAM tools, toward the goal of generating a higher-order image construct, with multiple TMA cores from multiple distinct conventional TMAs assembled as a single digital image montage. This image construct served as the basis of the carrying out of a massively parallel image analysis exercise, based on the use of the previously described spatially invariant vector quantization (SIVQ algorithm. Results: Multicase, multifield TMA-IMAs of follicular lymphoma and follicular hyperplasia were separately rendered, using the aforementioned tools. Each of these two IMAs contained a distinct spectrum of morphologic heterogeneity with respect to both tingible body macrophage (TBM appearance and apoptotic body morphology. SIVQ-based pattern matching, with ring vectors selected to screen for either tingible body macrophages or apoptotic

  14. A study of metaheuristic algorithms for high dimensional feature selection on microarray data

    Science.gov (United States)

    Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

    2017-11-01

    Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

  15. Identification of listeria species isolated in Tunisia by Microarray based assay : results of a preliminary study

    International Nuclear Information System (INIS)

    Hmaied, Fatma; Helel, Salma; Barkallah, Insaf; Leberre, V.; Francois, J.M.; Kechrid, A.

    2008-01-01

    Microarray-based assay is a new molecular approach for genetic screening and identification of microorganisms. We have developed a rapid microarray-based assay for the reliable detection and discrimination of Listeria spp. in food and clinical isolates from Tunisia. The method used in the present study is based on the PCR amplification of a virulence factor gene (iap gene). the PCR mixture contained cyanine Cy5labeled dCTP. Therefore, The PCR products were fluorescently labeled. The presence of multiple species-specific sequences within the iap gene enabled us to design different oligoprobes per species. The species-specific sequences of the iap gene used in this study were obtained from genBank and then aligned for phylogenetic analysis in order to identify and retrieve the sequences of homologues of the amplified iap gene analysed. 20 probes were used for detection and identification of 22 food isolates and clinical isolates of Listeria spp (L. monocytogenes, L. ivanovi), L. welshimeri, L. seeligeri, and L. grayi). Each bacterial gene was identified by hybridization to oligoprobes specific for each Listeria species and immobilized on a glass surface. The microarray analysis showed that 5 clinical isolates and 2 food isolates were identified listeria monocytogenes. Concerning the remaining 15 food isolates; 13 were identified listeria innocua and 2 isolates could not be identified by microarray based assay. Further phylogenetic and molecular analysis are required to design more species-specific probes for the identification of Listeria spp. Microarray-based assay is a simple and rapid method used for Listeria species discrimination

  16. Development of a cell microarray chip for detection of circulating tumor cells

    Science.gov (United States)

    Yamamura, S.; Yatsushiro, S.; Abe, K.; Baba, Y.; Kataoka, M.

    2012-03-01

    Detection of circulating tumor cells (CTCs) in the peripheral blood of metastatic cancer patients has clinical significance in earlier diagnosis of metastases. In this study, a novel cell microarray chip for accurate and rapid detection of tumor cells from human leukocytes was developed. The chip with 20,944 microchambers (105 μm diameter and 50 μm depth) was made from polystyrene, and the surface was rendered to hydrophilic by means of reactive-ion etching, which led to the formation of mono-layers of leukocytes on the microchambers. As the model of CTCs detection, we spiked human bronchioalveolar carcinoma (H1650) cells into human T lymphoblastoid leukemia (CEM) cells suspension and detected H1650 cells using the chip. A CEM suspension contained with H1650 cells was dispersed on the chip surface, followed by 10 min standing to allow the cells to settle down into the microchambers. About 30 CEM cells were accommodated in each microchamber, over 600,000 CEM cells in total being on a chip. We could detect 1 H1650 cell per 106 CEM cells on the microarray by staining with fluorescence-conjugated antibody (Anti-Cytokeratin) and cell membrane marker (DiD). Thus, this cell microarray chip has highly potential to be a novel tool of accurate and rapid detection of CTCs.

  17. The Plasmodium falciparum Sexual Development Transcriptome: A Microarray Analysis using Ontology-Based Pattern Identification

    National Research Council Canada - National Science Library

    Young, Jason A; Fivelman, Quinton L; Blair, Peter L; de la Vega, Patricia; Le Roch, Karine G; Zhou, Yingyao; Carucci, Daniel J; Baker, David A; Winzeler, Elizabeth A

    2005-01-01

    ... a full-genome high-density oligonucleotide microarray. The interpretation of this transcriptional data was aided by applying a novel knowledge-based data-mining algorithm termed ontology-based pattern identification (OPI...

  18. An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

    Science.gov (United States)

    2012-01-01

    contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially. PMID:22276688

  19. A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

    Science.gov (United States)

    Astola, Laura; Molenaar, Jaap

    2014-07-01

    Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

  20. Screening for C3 deficiency in newborns using microarrays.

    Directory of Open Access Journals (Sweden)

    Magdalena Janzi

    Full Text Available BACKGROUND: Dried blood spot samples (DBSS from newborns are widely used in neonatal screening for selected metabolic diseases and diagnostic possibilities for additional disorders are continuously being evaluated. Primary immunodeficiency disorders comprise a group of more than one hundred diseases, several of which are fatal early in life. Yet, a majority of the patients are not diagnosed due to lack of high-throughput screening methods. METHODOLOGY/PRINCIPAL FINDINGS: We have previously developed a system using reverse phase protein microarrays for analysis of IgA levels in serum samples. In this study, we extended the applicability of the method to include determination of complement component C3 levels in eluates from DBSS collected at birth. Normal levels of C3 were readily detected in 269 DBSS from healthy newborns, while no C3 was detected in sera and DBSS from C3 deficient patients. CONCLUSIONS/SIGNIFICANCE: The findings suggest that patients with deficiencies of specific serum proteins can be identified by analysis of DBSS using reverse phase protein microarrays.

  1. Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

    Directory of Open Access Journals (Sweden)

    Joachim Goschnick

    2004-05-01

    Full Text Available Abstract: The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 oC/mm and 6.7 oC/mm, applied across the sensor elements (segments of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.

  2. ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses

    Science.gov (United States)

    Stokes, Todd H; Torrance, JT; Li, Henry; Wang, May D

    2008-01-01

    Background A survey of microarray databases reveals that most of the repository contents and data models are heterogeneous (i.e., data obtained from different chip manufacturers), and that the repositories provide only basic biological keywords linking to PubMed. As a result, it is difficult to find datasets using research context or analysis parameters information beyond a few keywords. For example, to reduce the "curse-of-dimension" problem in microarray analysis, the number of samples is often increased by merging array data from different datasets. Knowing chip data parameters such as pre-processing steps (e.g., normalization, artefact removal, etc), and knowing any previous biological validation of the dataset is essential due to the heterogeneity of the data. However, most of the microarray repositories do not have meta-data information in the first place, and do not have a a mechanism to add or insert this information. Thus, there is a critical need to create "intelligent" microarray repositories that (1) enable update of meta-data with the raw array data, and (2) provide standardized archiving protocols to minimize bias from the raw data sources. Results To address the problems discussed, we have developed a community maintained system called ArrayWiki that unites disparate meta-data of microarray meta-experiments from multiple primary sources with four key features. First, ArrayWiki provides a user-friendly knowledge management interface in addition to a programmable interface using standards developed by Wikipedia. Second, ArrayWiki includes automated quality control processes (caCORRECT) and novel visualization methods (BioPNG, Gel Plots), which provide extra information about data quality unavailable in other microarray repositories. Third, it provides a user-curation capability through the familiar Wiki interface. Fourth, ArrayWiki provides users with simple text-based searches across all experiment meta-data, and exposes data to search engine crawlers

  3. Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior.

    Science.gov (United States)

    Tsoi, Lam C; Qin, Tingting; Slate, Elizabeth H; Zheng, W Jim

    2011-11-11

    To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets. We combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as AMIGO2, Gem, and CXCL11 that have not been shown to associate with, but may play roles in, metastasis. CDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray

  4. Integrating Multiple Microarray Data for Cancer Pathway Analysis Using Bootstrapping K-S Test

    Directory of Open Access Journals (Sweden)

    Bing Han

    2009-01-01

    Full Text Available Previous applications of microarray technology for cancer research have mostly focused on identifying genes that are differentially expressed between a particular cancer and normal cells. In a biological system, genes perform different molecular functions and regulate various biological processes via interactions with other genes thus forming a variety of complex networks. Therefore, it is critical to understand the relationship (e.g., interactions between genes across different types of cancer in order to gain insights into the molecular mechanisms of cancer. Here we propose an integrative method based on the bootstrapping Kolmogorov-Smirnov test and a large set of microarray data produced with various types of cancer to discover common molecular changes in cells from normal state to cancerous state. We evaluate our method using three key pathways related to cancer and demonstrate that it is capable of finding meaningful alterations in gene relations.

  5. Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells

    Directory of Open Access Journals (Sweden)

    Kim Han

    2012-07-01

    Full Text Available Abstract Background In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy. Results Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2 Gy (SF2, range from 0 to 1 was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2. Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway. Conclusions Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that

  6. The tissue microarray data exchange specification: Extending TMA DES to provide flexible scoring and incorporate virtual slides

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    Alexander Wright

    2011-01-01

    Full Text Available Background: Tissue MicroArrays (TMAs are a high throughput technology for rapid analysis of protein expression across hundreds of patient samples. Often, data relating to TMAs is specific to the clinical trial or experiment it is being used for, and not interoperable. The Tissue Microarray Data Exchange Specification (TMA DES is a set of eXtensible Markup Language (XML-based protocols for storing and sharing digitized Tissue Microarray data. XML data are enclosed by named tags which serve as identifiers. These tag names can be Common Data Elements (CDEs, which have a predefined meaning or semantics. By using this specification in a laboratory setting with increasing demands for digital pathology integration, we found that the data structure lacked the ability to cope with digital slide imaging in respect to web-enabled digital pathology systems and advanced scoring techniques. Materials and Methods: By employing user centric design, and observing behavior in relation to TMA scoring and associated data, the TMA DES format was extended to accommodate the current limitations. This was done with specific focus on developing a generic tool for handling any given scoring system, and utilizing data for multiple observations and observers. Results: DTDs were created to validate the extensions of the TMA DES protocol, and a test set of data containing scores for 6,708 TMA core images was generated. The XML was then read into an image processing algorithm to utilize the digital pathology data extensions, and scoring results were easily stored alongside the existing multiple pathologist scores. Conclusions: By extending the TMA DES format to include digital pathology data and customizable scoring systems for TMAs, the new system facilitates the collaboration between pathologists and organizations, and can be used in automatic or manual data analysis. This allows complying systems to effectively communicate complex and varied scoring data.

  7. Expression microarray meta-analysis identifies genes associated with Ras/MAPK and related pathways in progression of muscle-invasive bladder transition cell carcinoma.

    Directory of Open Access Journals (Sweden)

    Jonathan A Ewald

    Full Text Available The effective detection and management of muscle-invasive bladder Transition Cell Carcinoma (TCC continues to be an urgent clinical challenge. While some differences of gene expression and function in papillary (Ta, superficial (T1 and muscle-invasive (≥T2 bladder cancers have been investigated, the understanding of mechanisms involved in the progression of bladder tumors remains incomplete. Statistical methods of pathway-enrichment, cluster analysis and text-mining can extract and help interpret functional information about gene expression patterns in large sets of genomic data. The public availability of patient-derived expression microarray data allows open access and analysis of large amounts of clinical data. Using these resources, we investigated gene expression differences associated with tumor progression and muscle-invasive TCC. Gene expression was calculated relative to Ta tumors to assess progression-associated differences, revealing a network of genes related to Ras/MAPK and PI3K signaling pathways with increased expression. Further, we identified genes within this network that are similarly expressed in superficial Ta and T1 stages but altered in muscle-invasive T2 tumors, finding 7 genes (COL3A1, COL5A1, COL11A1, FN1, ErbB3, MAPK10 and CDC25C whose expression patterns in muscle-invasive tumors are consistent in 5 to 7 independent outside microarray studies. Further, we found increased expression of the fibrillar collagen proteins COL3A1 and COL5A1 in muscle-invasive tumor samples and metastatic T24 cells. Our results suggest that increased expression of genes involved in mitogenic signaling may support the progression of muscle-invasive bladder tumors that generally lack activating mutations in these pathways, while expression changes of fibrillar collagens, fibronectin and specific signaling proteins are associated with muscle-invasive disease. These results identify potential biomarkers and targets for TCC treatments, and

  8. DNA microarray technology in nutraceutical and food safety.

    Science.gov (United States)

    Liu-Stratton, Yiwen; Roy, Sashwati; Sen, Chandan K

    2004-04-15

    The quality and quantity of diet is a key determinant of health and disease. Molecular diagnostics may play a key role in food safety related to genetically modified foods, food-borne pathogens and novel nutraceuticals. Functional outcomes in biology are determined, for the most part, by net balance between sets of genes related to the specific outcome in question. The DNA microarray technology offers a new dimension of strength in molecular diagnostics by permitting the simultaneous analysis of large sets of genes. Automation of assay and novel bioinformatics tools make DNA microarrays a robust technology for diagnostics. Since its development a few years ago, this technology has been used for the applications of toxicogenomics, pharmacogenomics, cell biology, and clinical investigations addressing the prevention and intervention of diseases. Optimization of this technology to specifically address food safety is a vast resource that remains to be mined. Efforts to develop diagnostic custom arrays and simplified bioinformatics tools for field use are warranted.

  9. Microfluidic DNA microarrays in PMMA chips: streamlined fabrication via simultaneous DNA immobilization and bonding activation by brief UV exposure

    DEFF Research Database (Denmark)

    Sabourin, David; Petersen, J; Snakenborg, Detlef

    2010-01-01

    This report presents and describes a simple and scalable method for producing functional DNA microarrays within enclosed polymeric, PMMA, microfluidic devices. Brief (30 s) exposure to UV simultaneously immobilized poly(T)poly(C)-tagged DNA probes to the surface of unmodified PMMA and activated...... the surface for bonding below the glass transition temperature of the bulk PMMA. Functionality and validation of the enclosed PMMA microarrays was demonstrated as 18 patients were correctly genotyped for all eight mutation sites in the HBB gene interrogated. The fabrication process therefore produced probes...... with desired hybridization properties and sufficient bonding between PMMA layers to allow construction of microfluidic devices. The streamlined fabrication method is suited to the production of low-cost microfluidic microarray-based diagnostic devices and, as such, is equally applicable to the development...

  10. Novel R pipeline for analyzing Biolog Phenotypic MicroArray data.

    Directory of Open Access Journals (Sweden)

    Minna Vehkala

    Full Text Available Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells' respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.

  11. A New Modified Histogram Matching Normalization for Time Series Microarray Analysis

    Directory of Open Access Journals (Sweden)

    Laura Astola

    2014-07-01

    Full Text Available Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

  12. Application of a New Genetic Deafness Microarray for Detecting Mutations in the Deaf in China.

    Directory of Open Access Journals (Sweden)

    Hong Wu

    Full Text Available The aim of this study was to evaluate the GoldenGate microarray as a diagnostic tool and to elucidate the contribution of the genes on this array to the development of both nonsyndromic and syndromic sensorineural hearing loss in China.We developed a microarray to detect 240 mutations underlying syndromic and nonsyndromic sensorineural hearing loss. The microarray was then used for analysis of 382 patients with nonsyndromic sensorineural hearing loss (including 15 patients with enlarged vestibular aqueduct syndrome, 21 patients with Waardenburg syndrome, and 60 unrelated controls. Subsequently, we analyzed the sensitivity, specificity, and reproducibility of this new approach after Sanger sequencing-based verification, and also determined the contribution of the genes on this array to the development of distinct hearing disorders.The sensitivity and specificity of the microarray chip were 98.73% and 98.34%, respectively. Genetic defects were identified in 61.26% of the patients with nonsyndromic sensorineural hearing loss, and 9 causative genes were identified. The molecular etiology was confirmed in 19.05% and 46.67% of the patients with Waardenburg syndrome and enlarged vestibular aqueduct syndrome, respectively.Our new mutation-based microarray comprises an accurate and comprehensive genetic tool for the detection of sensorineural hearing loss. This microarray-based detection method could serve as a first-pass screening (before next-generation-sequencing screening for deafness-causing mutations in China.

  13. Microarray Study of Pathway Analysis Expression Profile Associated with MicroRNA-29a with Regard to Murine Cholestatic Liver Injuries

    Directory of Open Access Journals (Sweden)

    Sung-Chou Li

    2016-03-01

    Full Text Available Accumulating evidence demonstrates that microRNA-29 (miR-29 expression is prominently decreased in patients with hepatic fibrosis, which consequently stimulates hepatic stellate cells’ (HSCs activation. We used a cDNA microarray study to gain a more comprehensive understanding of genome-wide gene expressions by adjusting miR-29a expression in a bile duct-ligation (BDL animal model. Methods: Using miR-29a transgenic mice and wild-type littermates and applying the BDL mouse model, we characterized the function of miR-29a with regard to cholestatic liver fibrosis. Pathway enrichment analysis and/or specific validation were performed for differentially expressed genes found within the comparisons. Results: Analysis of the microarray data identified a number of differentially expressed genes due to the miR-29a transgene, BDL, or both. Additional pathway enrichment analysis revealed that TGF-β signaling had a significantly differential activated pathway depending on the occurrence of miR-29a overexpression or the lack thereof. Furthermore, overexpression was found to elicit changes in Wnt/β-catenin after BDL. Conclusion: This study verified that an elevated miR-29a level could alleviate liver fibrosis caused by cholestasis. Furthermore, the protective effects of miR-29a correlate with the downregulation of TGF-β and associated with Wnt/β-catenin signal pathway following BDL.

  14. cDNA Microarray Analysis of Serially Sampled Cervical Cancer Specimens From Patients Treated With Thermochemoradiotherapy

    International Nuclear Information System (INIS)

    Borkamo, Erling Dahl; Schem, Baard-Christian; Fluge, Oystein; Bruland, Ove; Dahl, Olav; Mella, Olav

    2009-01-01

    Purpose: To elucidate changes in gene expression after treatment with regional thermochemoradiotherapy in locally advanced squamous cell cervical cancer. Methods and Materials: Tru-Cut biopsy specimens were serially collected from 16 patients. Microarray gene expression levels before and 24 h after the first and second trimodality treatment sessions were compared. Pathway and network analyses were conducted by use of Ingenuity Pathways Analysis (IPA; Ingenuity Systems, Redwood City, CA). Single gene expressions were analyzed by quantitative real-time reverse transcription-polymerase chain reaction. Results: We detected 53 annotated genes that were differentially expressed after trimodality treatment. Central in the three top networks detected by IPA were interferon alfa, interferon beta, and interferon gamma receptor; nuclear factor κB; and tumor necrosis factor, respectively. These genes encode proteins that are important in regulation cell signaling, proliferation, gene expression, and immune stimulation. Biological processes over-represented among the 53 genes were fibrosis, tumorigenesis, and immune response. Conclusions: Microarrays showed minor changes in gene expression after thermochemoradiotherapy in locally advanced cervical cancer. We detected 53 differentially expressed genes, mainly involved in fibrosis, tumorigenesis, and immune response. A limitation with the use of serial biopsy specimens was low quality of ribonucleic acid from tumors that respond to highly effective therapy. Another 'key limitation' is timing of the post-treatment biopsy, because 24 h may be too late to adequately assess the impact of hyperthermia on gene expression.

  15. Improved precision and accuracy for microarrays using updated probe set definitions

    Directory of Open Access Journals (Sweden)

    Larsson Ola

    2007-02-01

    Full Text Available Abstract Background Microarrays enable high throughput detection of transcript expression levels. Different investigators have recently introduced updated probe set definitions to more accurately map probes to our current knowledge of genes and transcripts. Results We demonstrate that updated probe set definitions provide both better precision and accuracy in probe set estimates compared to the original Affymetrix definitions. We show that the improved precision mainly depends on the increased number of probes that are integrated into each probe set, but we also demonstrate an improvement when the same number of probes is used. Conclusion Updated probe set definitions does not only offer expression levels that are more accurately associated to genes and transcripts but also improvements in the estimated transcript expression levels. These results give support for the use of updated probe set definitions for analysis and meta-analysis of microarray data.

  16. EST and microarray analysis of horn development in Onthophagus beetles

    Directory of Open Access Journals (Sweden)

    Tang Zuojian

    2009-10-01

    Full Text Available Abstract Background The origin of novel traits and their subsequent diversification represent central themes in evo-devo and evolutionary ecology. Here we explore the genetic and genomic basis of a class of traits that is both novel and highly diverse, in a group of organisms that is ecologically complex and experimentally tractable: horned beetles. Results We developed two high quality, normalized cDNA libraries for larval and pupal Onthophagus taurus and sequenced 3,488 ESTs that assembled into 451 contigs and 2,330 singletons. We present the annotation and a comparative analysis of the conservation of the sequences. Microarrays developed from the combined libraries were then used to contrast the transcriptome of developing primordia of head horns, prothoracic horns, and legs. Our experiments identify a first comprehensive list of candidate genes for the evolution and diversification of beetle horns. We find that developing horns and legs show many similarities as well as important differences in their transcription profiles, suggesting that the origin of horns was mediated partly, but not entirely, by the recruitment of genes involved in the formation of more traditional appendages such as legs. Furthermore, we find that horns developing from the head and prothorax differ in their transcription profiles to a degree that suggests that head and prothoracic horns are not serial homologs, but instead may have evolved independently from each other. Conclusion We have laid the foundation for a systematic analysis of the genetic basis of horned beetle development and diversification with the potential to contribute significantly to several major frontiers in evolutionary developmental biology.

  17. A Combinatory Approach for Selecting Prognostic Genes in Microarray Studies of Tumour Survivals

    Directory of Open Access Journals (Sweden)

    Qihua Tan

    2009-01-01

    Full Text Available Different from significant gene expression analysis which looks for genes that are differentially regulated, feature selection in the microarray-based prognostic gene expression analysis aims at finding a subset of marker genes that are not only differentially expressed but also informative for prediction. Unfortunately feature selection in literature of microarray study is predominated by the simple heuristic univariate gene filter paradigm that selects differentially expressed genes according to their statistical significances. We introduce a combinatory feature selection strategy that integrates differential gene expression analysis with the Gram-Schmidt process to identify prognostic genes that are both statistically significant and highly informative for predicting tumour survival outcomes. Empirical application to leukemia and ovarian cancer survival data through-within- and cross-study validations shows that the feature space can be largely reduced while achieving improved testing performances.

  18. Phytoremediation potential of Arabidopsis with reference to acrylamide and microarray analysis of acrylamide-response genes.

    Science.gov (United States)

    Gao, Jian-Jie; Peng, Ri-He; Zhu, Bo; Wang, Bo; Wang, Li-Juan; Xu, Jing; Sun, Miao; Yao, Quan-Hong

    2015-10-01

    Acrylamide (ACR) is a widely used industrial chemical. However, it is a dangerous compound because it showed neurotoxic effects in humans and act as reproductive toxicant and carcinogen in many animal species. In the environment, acrylamide has high soil mobility and may travel via groundwater. Phytoremediation is an effective method to remove the environmental pollutants, but the mechanism of plant response to acrylamide remains unknown. With the purpose of assessing remediation potentials of plants for acrylamide, we have examined acrylamide uptake by the model plant Arabidopsis grown on contaminated substrates with high performance liquid chromatography (HPLC) analysis. The result revealed that acrylamide could be absorbed and degraded by Arabidopsis. Further microarray analysis showed that 527 transcripts were up-regulated within 2-days under acrylamide exposure condition. We have found many potential acrylamide-induced genes playing a major role in plant metabolism and phytoremediation. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Protein microarray: sensitive and effective immunodetection for drug residues

    Directory of Open Access Journals (Sweden)

    Zer Cindy

    2010-02-01

    Full Text Available Abstract Background Veterinary drugs such as clenbuterol (CL and sulfamethazine (SM2 are low molecular weight ( Results The artificial antigens were spotted on microarray slides. Standard concentrations of the compounds were added to compete with the spotted antigens for binding to the antisera to determine the IC50. Our microarray assay showed the IC50 were 39.6 ng/ml for CL and 48.8 ng/ml for SM2, while the traditional competitive indirect-ELISA (ci-ELISA showed the IC50 were 190.7 ng/ml for CL and 156.7 ng/ml for SM2. We further validated the two methods with CL fortified chicken muscle tissues, and the protein microarray assay showed 90% recovery while the ci-ELISA had 76% recovery rate. When tested with CL-fed chicken muscle tissues, the protein microarray assay had higher sensitivity (0.9 ng/g than the ci-ELISA (0.1 ng/g for detection of CL residues. Conclusions The protein microarrays showed 4.5 and 3.5 times lower IC50 than the ci-ELISA detection for CL and SM2, respectively, suggesting that immunodetection of small molecules with protein microarray is a better approach than the traditional ELISA technique.

  20. RankProdIt: A web-interactive Rank Products analysis tool

    Directory of Open Access Journals (Sweden)

    Laing Emma

    2010-08-01

    Full Text Available Abstract Background The first objective of a DNA microarray experiment is typically to generate a list of genes or probes that are found to be differentially expressed or represented (in the case of comparative genomic hybridizations and/or copy number variation between two conditions or strains. Rank Products analysis comprises a robust algorithm for deriving such lists from microarray experiments that comprise small numbers of replicates, for example, less than the number required for the commonly used t-test. Currently, users wishing to apply Rank Products analysis to their own microarray data sets have been restricted to the use of command line-based software which can limit its usage within the biological community. Findings Here we have developed a web interface to existing Rank Products analysis tools allowing users to quickly process their data in an intuitive and step-wise manner to obtain the respective Rank Product or Rank Sum, probability of false prediction and p-values in a downloadable file. Conclusions The online interactive Rank Products analysis tool RankProdIt, for analysis of any data set containing measurements for multiple replicated conditions, is available at: http://strep-microarray.sbs.surrey.ac.uk/RankProducts

  1. MiMiR: a comprehensive solution for storage, annotation and exchange of microarray data

    Directory of Open Access Journals (Sweden)

    Rahman Fatimah

    2005-11-01

    Full Text Available Abstract Background The generation of large amounts of microarray data presents challenges for data collection, annotation, exchange and analysis. Although there are now widely accepted formats, minimum standards for data content and ontologies for microarray data, only a few groups are using them together to build and populate large-scale databases. Structured environments for data management are crucial for making full use of these data. Description The MiMiR database provides a comprehensive infrastructure for microarray data annotation, storage and exchange and is based on the MAGE format. MiMiR is MIAME-supportive, customised for use with data generated on the Affymetrix platform and includes a tool for data annotation using ontologies. Detailed information on the experiment, methods, reagents and signal intensity data can be captured in a systematic format. Reports screens permit the user to query the database, to view annotation on individual experiments and provide summary statistics. MiMiR has tools for automatic upload of the data from the microarray scanner and export to databases using MAGE-ML. Conclusion MiMiR facilitates microarray data management, annotation and exchange, in line with international guidelines. The database is valuable for underpinning research activities and promotes a systematic approach to data handling. Copies of MiMiR are freely available to academic groups under licence.

  2. ArraySolver: An Algorithm for Colour-Coded Graphical Display and Wilcoxon Signed-Rank Statistics for Comparing Microarray Gene Expression Data

    OpenAIRE

    Khan, Haseeb Ahmad

    2004-01-01

    The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for tra...

  3. Analysis of gene expression profile microarray data in complex regional pain syndrome.

    Science.gov (United States)

    Tan, Wulin; Song, Yiyan; Mo, Chengqiang; Jiang, Shuangjian; Wang, Zhongxing

    2017-09-01

    The aim of the present study was to predict key genes and proteins associated with complex regional pain syndrome (CRPS) using bioinformatics analysis. The gene expression profiling microarray data, GSE47603, which included peripheral blood samples from 4 patients with CRPS and 5 healthy controls, was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in CRPS patients compared with healthy controls were identified using the GEO2R online tool. Functional enrichment analysis was then performed using The Database for Annotation Visualization and Integrated Discovery online tool. Protein‑protein interaction (PPI) network analysis was subsequently performed using Search Tool for the Retrieval of Interaction Genes database and analyzed with Cytoscape software. A total of 257 DEGs were identified, including 243 upregulated genes and 14 downregulated ones. Genes in the human leukocyte antigen (HLA) family were most significantly differentially expressed. Enrichment analysis demonstrated that signaling pathways, including immune response, cell motion, adhesion and angiogenesis were associated with CRPS. PPI network analysis revealed that key genes, including early region 1A binding protein p300 (EP300), CREB‑binding protein (CREBBP), signal transducer and activator of transcription (STAT)3, STAT5A and integrin α M were associated with CRPS. The results suggest that the immune response may therefore serve an important role in CRPS development. In addition, genes in the HLA family, such as HLA‑DQB1 and HLA‑DRB1, may present potential biomarkers for the diagnosis of CRPS. Furthermore, EP300, its paralog CREBBP, and the STAT family genes, STAT3 and STAT5 may be important in the development of CRPS.

  4. The construction and use of bacterial DNA microarrays based on an optimized two-stage PCR strategy

    Directory of Open Access Journals (Sweden)

    Pesta David

    2003-06-01

    Full Text Available Abstract Background DNA microarrays are a powerful tool with important applications such as global gene expression profiling. Construction of bacterial DNA microarrays from genomic sequence data using a two-stage PCR amplification approach for the production of arrayed DNA is attractive because it allows, in principal, the continued re-amplification of DNA fragments and facilitates further utilization of the DNA fragments for additional uses (e.g. over-expression of protein. We describe the successful construction and use of DNA microarrays by the two-stage amplification approach and discuss the technical challenges that were met and resolved during the project. Results Chimeric primers that contained both gene-specific and shared, universal sequence allowed the two-stage amplification of the 3,168 genes identified on the genome of Synechocystis sp. PCC6803, an important prokaryotic model organism for the study of oxygenic photosynthesis. The gene-specific component of the primer was of variable length to maintain uniform annealing temperatures during the 1st round of PCR synthesis, and situated to preserve full-length ORFs. Genes were truncated at 2 kb for efficient amplification, so that about 92% of the PCR fragments were full-length genes. The two-stage amplification had the additional advantage of normalizing the yield of PCR products and this improved the uniformity of DNA features robotically deposited onto the microarray surface. We also describe the techniques utilized to optimize hybridization conditions and signal-to-noise ratio of the transcription profile. The inter-lab transportability was demonstrated by the virtual error-free amplification of the entire genome complement of 3,168 genes using the universal primers in partner labs. The printed slides have been successfully used to identify differentially expressed genes in response to a number of environmental conditions, including salt stress. Conclusions The technique detailed

  5. Validation of tissue microarray technology in squamous cell carcinoma of the esophagus

    NARCIS (Netherlands)

    Boone, Judith; van Hillegersberg, Richard; van Diest, Paul J.; Offerhaus, G. Johan A.; Borel Rinkes, Inne H. M.; ten Kate, Fiebo J. W.

    2008-01-01

    Tissue microarray (TMA) technology has been developed to facilitate high-throughput immunohistochemical and in situ hybridization analysis of tissues by inserting small tissue biopsy cores into a single paraffin block. Several studies have revealed novel prognostic biomarkers in esophageal squamous

  6. Teolenn: an efficient and customizable workflow to design high-quality probes for microarray experiments

    Science.gov (United States)

    Jourdren, Laurent; Duclos, Aurélie; Brion, Christian; Portnoy, Thomas; Mathis, Hugues; Margeot, Antoine; Le Crom, Stéphane

    2010-01-01

    Despite the development of new high-throughput sequencing techniques, microarrays are still attractive tools to study small genome organisms, thanks to sample multiplexing and high-feature densities. However, the oligonucleotide design remains a delicate step for most users. A vast array of software is available to deal with this problem, but each program is developed with its own strategy, which makes the choice of the best solution difficult. Here we describe Teolenn, a universal probe design workflow developed with a flexible and customizable module organization allowing fixed or variable length oligonucleotide generation. In addition, our software is able to supply quality scores for each of the designed probes. In order to assess the relevance of these scores, we performed a real hybridization using a tiling array designed against the Trichoderma reesei fungus genome. We show that our scoring pipeline correlates with signal quality for 97.2% of all the designed probes, allowing for a posteriori comparisons between quality scores and signal intensities. This result is useful in discarding any bad scoring probes during the design step in order to get high-quality microarrays. Teolenn is available at http://transcriptome.ens.fr/teolenn/. PMID:20176570

  7. Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer

    Directory of Open Access Journals (Sweden)

    Utro Filippo

    2008-10-01

    Full Text Available Abstract Background Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. Results We consider five such measures: Clest, Consensus (Consensus Clustering, FOM (Figure of Merit, Gap (Gap Statistics and ME (Model Explorer, in addition to the classic WCSS (Within Cluster Sum-of-Squares and KL (Krzanowski and Lai index. We perform extensive experiments on six benchmark microarray datasets, using both Hierarchical and K-means clustering algorithms, and we provide an analysis assessing both the intrinsic ability of a measure to predict the correct number of clusters in a dataset and its merit relative to the other measures. We pay particular attention both to precision and speed. Moreover, we also provide various fast approximation algorithms for the computation of Gap, FOM and WCSS. The main result is a hierarchy of those measures in terms of precision and speed, highlighting some of their merits and limitations not reported before in the literature. Conclusion Based on our analysis, we draw several conclusions for the use of those internal measures on microarray data. We report the main ones. Consensus is by far the best performer in terms of predictive power and remarkably algorithm-independent. Unfortunately, on large datasets, it may be of no use because of its non-trivial computer time demand (weeks on a state of the art PC. FOM is the second best performer although, quite surprisingly, it may not be competitive in this scenario: it has essentially the same predictive power of WCSS but it is from 6 to 100 times slower in time

  8. Chromosomal Localization of DNA Amplifications in Neuroblastoma Tumors Using cDNA Microarray Comparative Genomic Hybridization

    Directory of Open Access Journals (Sweden)

    Ben Beheshti

    2003-01-01

    Full Text Available Conventional comparative genomic hybridization (CGH profiling of neuroblastomas has identified many genomic aberrations, although the limited resolution has precluded a precise localization of sequences of interest within amplicons. To map high copy number genomic gains in clinically matched stage IV neuroblastomas, CGH analysis using a 19,200-feature cDNA microarray was used. A dedicated (freely available algorithm was developed for rapid in silico determination of chromosomal localizations of microarray cDNA targets, and for generation of an ideogram-type profile of copy number changes. Using these methodologies, novel gene amplifications undetectable by chromosome CGH were identified, and larger MYCN amplicon sizes (in one tumor up to 6 Mb than those previously reported in neuroblastoma were identified. The genes HPCAL1, LPIN1/KIAA0188, NAG, and NSE1/LOC151354 were found to be coamplified with MYCN. To determine whether stage IV primary tumors could be further subclassified based on their genomic copy number profiles, hierarchical clustering was performed. Cluster analysis of microarray CGH data identified three groups: 1 no amplifications evident, 2 a small MYCN amplicon as the only detectable imbalance, and 3 a large MYCN amplicon with additional gene amplifications. Application of CGH to cDNA microarray targets will help to determine both the variation of amplicon size and help better define amplification-dependent and independent pathways of progression in neuroblastoma.

  9. Acidic preparations of lysed platelets upregulate proliferative pathways in osteoblast-like cells as demonstrated by genome-wide microarray analysis.

    Science.gov (United States)

    Wahlström, Ola; Linder, Cecilia Halling; Ansell, Anna; Kalén, Anders; Söderström, Mats; Magnusson, Per

    2011-01-01

    Platelets contain numerous growth factors essential for wound and fracture healing. We investigated the gene expression in human osteoblast-like cells stimulated with lysed platelets prepared in acidic, neutral, or alkaline buffers. Lysed platelets prepared in buffers at pH 5.4, 7.4, and 7.9, were added after neutralization to hFOB 1.19 cells. Genome-wide microarray analysis was performed using the Affymetrix GeneChip 7G Scanner. Biometric, cluster, and pathway analyses were performed with GeneSpring GX. Biometric analyses demonstrated that 53 genes were differentially regulated (p ≤ 0.005, ≥2-fold increase). Pathway analysis revealed 10 significant pathways of which eight are common ones regulating bone formation and cancer growth. Eleven genes were selected for quantitative real-time polymerase chain reaction (PCR) based on the microarray analysis of the lysed platelets prepared in the pH 5.4 experiments. In conclusion, acidic preparations of lysed platelet concentrates release factors essential for cell proliferation and particularly cell metabolism under hypoxic conditions. The genetic response from these factors was dominated by genes associated with the same pathways observed in bone formation and cancer growth. Activation of TGF-β in the acidic preparation could be a stimulatory key factor of cell proliferation. These results support the hypothesis that acidification of platelets modifies the stimulatory response of mesenchymal cells in vitro, which is analogous with the observed milieu of a low pH present in wound and fracture sites, as well as in growing tumors.

  10. Global microarray analysis of carbohydrate use in alkaliphilic hemicellulolytic bacterium Bacillus sp. N16-5.

    Directory of Open Access Journals (Sweden)

    Yajian Song

    Full Text Available The alkaliphilic hemicellulolytic bacterium Bacillus sp. N16-5 has a broad substrate spectrum and exhibits the capacity to utilize complex carbohydrates such as galactomannan, xylan, and pectin. In the monosaccharide mixture, sequential utilization by Bacillus sp. N16-5 was observed. Glucose appeared to be its preferential monosaccharide, followed by fructose, mannose, arabinose, xylose, and galactose. Global transcription profiles of the strain were determined separately for growth on six monosaccharides (glucose, fructose, mannose, galactose, arabinose, and xylose and four polysaccharides (galactomannan, xylan, pectin, and sodium carboxymethylcellulose using one-color microarrays. Numerous genes potentially related to polysaccharide degradation, sugar transport, and monosaccharide metabolism were found to respond to a specific substrate. Putative gene clusters for different carbohydrates were identified according to transcriptional patterns and genome annotation. Identification and analysis of these gene clusters contributed to pathway reconstruction for carbohydrate utilization in Bacillus sp. N16-5. Several genes encoding putative sugar transporters were highly expressed during growth on specific sugars, suggesting their functional roles. Two phosphoenolpyruvate-dependent phosphotransferase systems were identified as candidate transporters for mannose and fructose, and a major facilitator superfamily transporter was identified as a candidate transporter for arabinose and xylose. Five carbohydrate uptake transporter 1 family ATP-binding cassette transporters were predicted to participate in the uptake of hemicellulose and pectin degradation products. Collectively, microarray data improved the pathway reconstruction involved in carbohydrate utilization of Bacillus sp. N16-5 and revealed that the organism precisely regulates gene transcription in response to fluctuations in energy resources.

  11. Global Microarray Analysis of Carbohydrate Use in Alkaliphilic Hemicellulolytic Bacterium Bacillus sp. N16-5

    Science.gov (United States)

    Song, Yajian; Xue, Yanfen; Ma, Yanhe

    2013-01-01

    The alkaliphilic hemicellulolytic bacterium Bacillus sp. N16-5 has a broad substrate spectrum and exhibits the capacity to utilize complex carbohydrates such as galactomannan, xylan, and pectin. In the monosaccharide mixture, sequential utilization by Bacillus sp. N16-5 was observed. Glucose appeared to be its preferential monosaccharide, followed by fructose, mannose, arabinose, xylose, and galactose. Global transcription profiles of the strain were determined separately for growth on six monosaccharides (glucose, fructose, mannose, galactose, arabinose, and xylose) and four polysaccharides (galactomannan, xylan, pectin, and sodium carboxymethylcellulose) using one-color microarrays. Numerous genes potentially related to polysaccharide degradation, sugar transport, and monosaccharide metabolism were found to respond to a specific substrate. Putative gene clusters for different carbohydrates were identified according to transcriptional patterns and genome annotation. Identification and analysis of these gene clusters contributed to pathway reconstruction for carbohydrate utilization in Bacillus sp. N16-5. Several genes encoding putative sugar transporters were highly expressed during growth on specific sugars, suggesting their functional roles. Two phosphoenolpyruvate-dependent phosphotransferase systems were identified as candidate transporters for mannose and fructose, and a major facilitator superfamily transporter was identified as a candidate transporter for arabinose and xylose. Five carbohydrate uptake transporter 1 family ATP-binding cassette transporters were predicted to participate in the uptake of hemicellulose and pectin degradation products. Collectively, microarray data improved the pathway reconstruction involved in carbohydrate utilization of Bacillus sp. N16-5 and revealed that the organism precisely regulates gene transcription in response to fluctuations in energy resources. PMID:23326578

  12. Functional microarray analysis of nitrogen and carbon cycling genes across an Antarctic latitudinal transect.

    NARCIS (Netherlands)

    Yergeau, E.; Kang, S.; He, Z.; Zhou, J.; Kowalchuk, G.A.

    2007-01-01

    Soil-borne microbial communities were examined via a functional gene microarray approach across a southern polar latitudinal gradient to gain insight into the environmental factors steering soil N- and C-cycling in terrestrial Antarctic ecosystems. The abundance and diversity of functional gene

  13. Tissue microarray immunohistochemical detection of brachyury is not a prognostic indicator in chordoma.

    Science.gov (United States)

    Zhang, Linlin; Guo, Shang; Schwab, Joseph H; Nielsen, G Petur; Choy, Edwin; Ye, Shunan; Zhang, Zhan; Mankin, Henry; Hornicek, Francis J; Duan, Zhenfeng

    2013-01-01

    Brachyury is a marker for notochord-derived tissues and neoplasms, such as chordoma. However, the prognostic relevance of brachyury expression in chordoma is still unknown. The improvement of tissue microarray technology has provided the opportunity to perform analyses of tumor tissues on a large scale in a uniform and consistent manner. This study was designed with the use of tissue microarray to determine the expression of brachyury. Brachyury expression in chordoma tissues from 78 chordoma patients was analyzed by immunohistochemical staining of tissue microarray. The clinicopathologic parameters, including gender, age, location of tumor and metastatic status were evaluated. Fifty-nine of 78 (75.64%) tumors showed nuclear staining for brachyury, and among them, 29 tumors (49.15%) showed 1+ (mobile spine. However, there was no significant relationship between brachyury expression and other clinical variables. By Kaplan-Meier analysis, brachyury expression failed to produce any significant relationship with the overall survival rate. In conclusion, brachyury expression is not a prognostic indicator in chordoma.

  14. permGPU: Using graphics processing units in RNA microarray association studies

    Directory of Open Access Journals (Sweden)

    George Stephen L

    2010-06-01

    Full Text Available Abstract Background Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. Results We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. Conclusions permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.

  15. Multiplexed fluorescent microarray for human salivary protein analysis using polymer microspheres and fiber-optic bundles.

    Science.gov (United States)

    Nie, Shuai; Benito-Peña, Elena; Zhang, Huaibin; Wu, Yue; Walt, David R

    2013-10-10

    Herein, we describe a protocol for simultaneously measuring six proteins in saliva using a fiber-optic microsphere-based antibody array. The immuno-array technology employed combines the advantages of microsphere-based suspension array fabrication with the use of fluorescence microscopy. As described in the video protocol, commercially available 4.5 μm polymer microspheres were encoded into seven different types, differentiated by the concentration of two fluorescent dyes physically trapped inside the microspheres. The encoded microspheres containing surface carboxyl groups were modified with monoclonal capture antibodies through EDC/NHS coupling chemistry. To assemble the protein microarray, the different types of encoded and functionalized microspheres were mixed and randomly deposited in 4.5 μm microwells, which were chemically etched at the proximal end of a fiber-optic bundle. The fiber-optic bundle was used as both a carrier and for imaging the microspheres. Once assembled, the microarray was used to capture proteins in the saliva supernatant collected from the clinic. The detection was based on a sandwich immunoassay using a mixture of biotinylated detection antibodies for different analytes with a streptavidin-conjugated fluorescent probe, R-phycoerythrin. The microarray was imaged by fluorescence microscopy in three different channels, two for microsphere registration and one for the assay signal. The fluorescence micrographs were then decoded and analyzed using a homemade algorithm in MATLAB.

  16. High-Throughput Quantification of SH2 Domain-Phosphopeptide Interactions with Cellulose-Peptide Conjugate Microarrays.

    Science.gov (United States)

    Engelmann, Brett W

    2017-01-01

    The Src Homology 2 (SH2) domain family primarily recognizes phosphorylated tyrosine (pY) containing peptide motifs. The relative affinity preferences among competing SH2 domains for phosphopeptide ligands define "specificity space," and underpins many functional pY mediated interactions within signaling networks. The degree of promiscuity exhibited and the dynamic range of affinities supported by individual domains or phosphopeptides is best resolved by a carefully executed and controlled quantitative high-throughput experiment. Here, I describe the fabrication and application of a cellulose-peptide conjugate microarray (CPCMA) platform to the quantitative analysis of SH2 domain specificity space. Included herein are instructions for optimal experimental design with special attention paid to common sources of systematic error, phosphopeptide SPOT synthesis, microarray fabrication, analyte titrations, data capture, and analysis.

  17. A microarray analysis of two distinct lymphatic endothelial cell populations

    Directory of Open Access Journals (Sweden)

    Bernhard Schweighofer

    2015-06-01

    Full Text Available We have recently identified lymphatic endothelial cells (LECs to form two morphologically different populations, exhibiting significantly different surface protein expression levels of podoplanin, a major surface marker for this cell type. In vitro shockwave treatment (IVSWT of LECs resulted in enrichment of the podoplaninhigh cell population and was accompanied by markedly increased cell proliferation, as well as 2D and 3D migration. Gene expression profiles of these distinct populations were established using Affymetrix microarray analyses. Here we provide additional details about our dataset (NCBI GEO accession number GSE62510 and describe how we analyzed the data to identify differently expressed genes in these two LEC populations.

  18. High-throughput DNA microarray detection of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley, Nepal.

    Science.gov (United States)

    Inoue, Daisuke; Hinoura, Takuji; Suzuki, Noriko; Pang, Junqin; Malla, Rabin; Shrestha, Sadhana; Chapagain, Saroj Kumar; Matsuzawa, Hiroaki; Nakamura, Takashi; Tanaka, Yasuhiro; Ike, Michihiko; Nishida, Kei; Sei, Kazunari

    2015-01-01

    Because of heavy dependence on groundwater for drinking water and other domestic use, microbial contamination of groundwater is a serious problem in the Kathmandu Valley, Nepal. This study investigated comprehensively the occurrence of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley by applying DNA microarray analysis targeting 941 pathogenic bacterial species/groups. Water quality measurements found significant coliform (fecal) contamination in 10 of the 11 investigated groundwater samples and significant nitrogen contamination in some samples. The results of DNA microarray analysis revealed the presence of 1-37 pathogen species/groups, including 1-27 biosafety level 2 ones, in 9 of the 11 groundwater samples. While the detected pathogens included several feces- and animal-related ones, those belonging to Legionella and Arthrobacter, which were considered not to be directly associated with feces, were detected prevalently. This study could provide a rough picture of overall pathogenic bacterial contamination in the Kathmandu Valley, and demonstrated the usefulness of DNA microarray analysis as a comprehensive screening tool of a wide variety of pathogenic bacteria.

  19. GeneRank: Using search engine technology for the analysis of microarray experiments

    Directory of Open Access Journals (Sweden)

    Breitling Rainer

    2005-09-01

    Full Text Available Abstract Background Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method – based on the PageRank algorithm employed by the popular search engine Google – that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. Results GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. Conclusion Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.

  20. GeneRank: using search engine technology for the analysis of microarray experiments.

    Science.gov (United States)

    Morrison, Julie L; Breitling, Rainer; Higham, Desmond J; Gilbert, David R

    2005-09-21

    Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method--based on the PageRank algorithm employed by the popular search engine Google--that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.

  1. Molecular biological identification of Babesia, Theileria, and Anaplasma species in cattle in Egypt using PCR assays, gene sequence analysis and a novel DNA microarray.

    Science.gov (United States)

    El-Ashker, Maged; Hotzel, Helmut; Gwida, Mayada; El-Beskawy, Mohamed; Silaghi, Cornelia; Tomaso, Herbert

    2015-01-30

    In this preliminary study, a novel DNA microarray system was tested for the diagnosis of bovine piroplasmosis and anaplasmosis in comparison with microscopy and PCR assay results. In the Dakahlia Governorate, Egypt, 164 cattle were investigated for the presence of piroplasms and Anaplasma species. All investigated cattle were clinically examined. Blood samples were screened for the presence of blood parasites using microscopy and PCR assays. Seventy-one animals were acutely ill, whereas 93 were apparently healthy. In acutely ill cattle, Babesia/Theileria species (n=11) and Anaplasma marginale (n=10) were detected. Mixed infections with Babesia/Theileria spp. and A. marginale were present in two further cases. A. marginale infections were also detected in apparently healthy subjects (n=23). The results of PCR assays were confirmed by DNA sequencing. All samples that were positive by PCR for Babesia/Theileria spp. gave also positive results in the microarray analysis. The microarray chips identified Babesia bovis (n=12) and Babesia bigemina (n=2). Cattle with babesiosis were likely to have hemoglobinuria and nervous signs when compared to those with anaplasmosis that frequently had bloody feces. We conclude that clinical examination in combination with microscopy are still very useful in diagnosing acute cases of babesiosis and anaplasmosis, but a combination of molecular biological diagnostic assays will detect even asymptomatic carriers. In perspective, parallel detection of Babesia/Theileria spp. and A. marginale infections using a single microarray system will be a valuable improvement. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

  2. Preliminary report for analysis of genome wide mutations from four ciprofloxacin resistant B. anthracis Sterne isolates generated by Illumina, 454 sequencing and microarrays for DHS

    Energy Technology Data Exchange (ETDEWEB)

    Jaing, Crystal [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vergez, Lisa [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Hinckley, Aubree [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Thissen, James [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Gardner, Shea [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); McLoughlin, Kevin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Jackson, Paul [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Ellingson, Sally [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hauser, Loren [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Brettin, Tom [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Fofanov, Viacheslav [Eureka Genomics, Hercules, CA (United States); Koshinsky, Heather [Eureka Genomics, Hercules, CA (United States); Fofanov, Yuriy [Univ. of Houston, TX (United States)

    2011-06-21

    The objective of this project is to provide DHS a comprehensive evaluation of the current genomic technologies including genotyping, Taqman PCR, multiple locus variable tandem repeat analysis (MLVA), microarray and high-throughput DNA sequencing in the analysis of biothreat agents from complex environmental samples. As the result of a different DHS project, we have selected for and isolated a large number of ciprofloxacin resistant B. anthracis Sterne isolates. These isolates vary in the concentrations of ciprofloxacin that they can tolerate, suggesting multiple mutations in the samples. In collaboration with University of Houston, Eureka Genomics and Oak Ridge National Laboratory, we analyzed the ciprofloxacin resistant B. anthracis Sterne isolates by microarray hybridization, Illumina and Roche 454 sequencing to understand the error rates and sensitivity of the different methods. The report provides an assessment of the results and a complete set of all protocols used and all data generated along with information to interpret the protocols and data sets.

  3. Methods for interpreting lists of affected genes obtained in a DNA microarray experiment

    Directory of Open Access Journals (Sweden)

    Hedegaard Jakob

    2009-07-01

    Full Text Available Abstract Background The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. Results Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. Conclusion It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.

  4. Microarray analysis identified Puccinia striiformis f. sp. tritici genes involved in infection and sporulation.

    Science.gov (United States)

    Puccinia striiformis f. sp. tritici (Pst) causes stripe rust, one of the most important diseases of wheat worldwide. To identify Pst genes involved in infection and sporulation, a custom oligonucleotide Genechip was made using sequences of 442 genes selected from Pst cDNA libraries. Microarray analy...

  5. Herpesvirus of turkeys: microarray analysis of host gene responses to infection

    International Nuclear Information System (INIS)

    Karaca, Gamze; Anobile, Jonathan; Downs, Danielle; Burnside, Joan; Schmidt, Carl J.

    2004-01-01

    Herpesvirus of turkeys (HVT) provides an economically important live vaccine for prevention of Marek's disease (MD) of chickens. MD, characterized by both immunosuppression and T-cell lymphoma, is caused by another herpesvirus termed Marek's disease virus (MDV). Microarrays were used to investigate the response of chicken embryonic fibroblasts (CEF) to infection with HVT. Genes responding to HVT infection include several induced by interferon along with others modulating signal transduction, transcription, scaffolding proteins, and the cytoskeleton. Results are compared with earlier studies examining the responses of CEF cells to infection with MDV

  6. Application of broad-spectrum resequencing microarray for genotyping rhabdoviruses.

    Science.gov (United States)

    Dacheux, Laurent; Berthet, Nicolas; Dissard, Gabriel; Holmes, Edward C; Delmas, Olivier; Larrous, Florence; Guigon, Ghislaine; Dickinson, Philip; Faye, Ousmane; Sall, Amadou A; Old, Iain G; Kong, Katherine; Kennedy, Giulia C; Manuguerra, Jean-Claude; Cole, Stewart T; Caro, Valérie; Gessain, Antoine; Bourhy, Hervé

    2010-09-01

    The rapid and accurate identification of pathogens is critical in the control of infectious disease. To this end, we analyzed the capacity for viral detection and identification of a newly described high-density resequencing microarray (RMA), termed PathogenID, which was designed for multiple pathogen detection using database similarity searching. We focused on one of the largest and most diverse viral families described to date, the family Rhabdoviridae. We demonstrate that this approach has the potential to identify both known and related viruses for which precise sequence information is unavailable. In particular, we demonstrate that a strategy based on consensus sequence determination for analysis of RMA output data enabled successful detection of viruses exhibiting up to 26% nucleotide divergence with the closest sequence tiled on the array. Using clinical specimens obtained from rabid patients and animals, this method also shows a high species level concordance with standard reference assays, indicating that it is amenable for the development of diagnostic assays. Finally, 12 animal rhabdoviruses which were currently unclassified, unassigned, or assigned as tentative species within the family Rhabdoviridae were successfully detected. These new data allowed an unprecedented phylogenetic analysis of 106 rhabdoviruses and further suggest that the principles and methodology developed here may be used for the broad-spectrum surveillance and the broader-scale investigation of biodiversity in the viral world.

  7. Application of Broad-Spectrum Resequencing Microarray for Genotyping Rhabdoviruses▿

    Science.gov (United States)

    Dacheux, Laurent; Berthet, Nicolas; Dissard, Gabriel; Holmes, Edward C.; Delmas, Olivier; Larrous, Florence; Guigon, Ghislaine; Dickinson, Philip; Faye, Ousmane; Sall, Amadou A.; Old, Iain G.; Kong, Katherine; Kennedy, Giulia C.; Manuguerra, Jean-Claude; Cole, Stewart T.; Caro, Valérie; Gessain, Antoine; Bourhy, Hervé

    2010-01-01

    The rapid and accurate identification of pathogens is critical in the control of infectious disease. To this end, we analyzed the capacity for viral detection and identification of a newly described high-density resequencing microarray (RMA), termed PathogenID, which was designed for multiple pathogen detection using database similarity searching. We focused on one of the largest and most diverse viral families described to date, the family Rhabdoviridae. We demonstrate that this approach has the potential to identify both known and related viruses for which precise sequence information is unavailable. In particular, we demonstrate that a strategy based on consensus sequence determination for analysis of RMA output data enabled successful detection of viruses exhibiting up to 26% nucleotide divergence with the closest sequence tiled on the array. Using clinical specimens obtained from rabid patients and animals, this method also shows a high species level concordance with standard reference assays, indicating that it is amenable for the development of diagnostic assays. Finally, 12 animal rhabdoviruses which were currently unclassified, unassigned, or assigned as tentative species within the family Rhabdoviridae were successfully detected. These new data allowed an unprecedented phylogenetic analysis of 106 rhabdoviruses and further suggest that the principles and methodology developed here may be used for the broad-spectrum surveillance and the broader-scale investigation of biodiversity in the viral world. PMID:20610710

  8. The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis

    Directory of Open Access Journals (Sweden)

    Chen Yidong

    2004-01-01

    Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.

  9. An automated multiplex specific IgE assay system using a photoimmobilized microarray.

    Science.gov (United States)

    Ito, Yoshihiro; Moritsugu, Nozomi; Matsue, Takahisa; Mitsukoshi, Kiyomi; Ayame, Hirohito; Okochi, Norihiko; Hattori, Hideshi; Tashiro, Hideo; Sato, Sakura; Ebisawa, Motohiro

    2012-11-15

    An automated microarray diagnostic system for specific IgE using photoimmobilized allergen has been developed. Photoimmobilization is useful for preparing microarrays, where various types of biological components are covalently immobilized on a plate. Because the immobilization is based on a photo-induced radical cross-linking reaction, it does not require specific functional groups on the immobilized components. Here, an aqueous solution of a photoreactive poly(ethylene glycol)-based polymer was spin-coated on a plate, and an aqueous solution of each allergen was microspotted on the coated plate and allowed to dry in air. Finally, the plate was irradiated with an ultraviolet lamp for covalent immobilization. An automated machine using these plates was developed for the assay of antigen-specific IgE. Initially, the patient serum was added to the microarray plate, and after reaction of the microspotted allergen with IgE, the adsorbed IgE was detected by a peroxidase-conjugated anti-IgE-antibody. The chemical luminescence intensity of the substrate decomposed by the peroxidase was automatically detected using a sensitive charge-coupled device camera. All the allergens were immobilized stably using this method, which was used to screen for allergen-specific IgE. The results were comparable with those using conventional specific IgE. Using this system, six different allergen-specific IgE were assayed using 10 μL of serum within a period of 20 min. Copyright © 2012 Elsevier B.V. All rights reserved.

  10. Advanced Data Mining of Leukemia Cells Micro-Arrays

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    Richard S. Segall

    2009-12-01

    Full Text Available This paper provides continuation and extensions of previous research by Segall and Pierce (2009a that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM. As Segall and Pierce (2009a and Segall and Pierce (2009b the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is provided on the work of others pertaining to the applications of data mining to micro-array databases of Leukemia cells and micro-array databases in general. As noted in predecessor article by Segall and Pierce (2009a, micro-array databases are one of the most popular functional genomics tools in use today. This research in this paper is intended to use advanced data mining technologies for better interpretations and knowledge discovery as generated by the patterns of gene expressions of HL60, Jurkat, NB4 and U937 Leukemia cells. The advanced data mining performed entailed using other data mining tools such as cubic clustering criterion, variable importance rankings, decision trees, and more detailed examinations of data mining statistics and study of other self-organized maps (SOM clustering regions of workspace as generated by SAS Enterprise Miner version 4. Conclusions and future directions of the research are also presented.

  11. Monitoring expression profiles of rice (Oryza sativa L.) genes under abiotic stresses using cDNA Microarray Analysis (abstract)

    International Nuclear Information System (INIS)

    Rabbani, M.A.

    2005-01-01

    Transcript regulation in response to cold, drought, high salinity and ABA application was investigated in rice (Oryza sativa L., Nipponbare) with microarray analysis including approx. 1700 independent DNA elements derived from three cDNA libraries constructed from 15-day old rice seedlings stressed with drought, cold and high salinity. A total of 141 non-redundant genes were identified, whose expression ratios were more than three-fold compared with the control genes for at least one of stress treatments in microarray analysis. However, after RNA gel blot analysis, a total of 73 genes were identified, among them the transcripts of 36, 62, 57 and 43 genes were found increased after cold, drought, high salinity and ABA application, respectively. Sixteen of these identified genes have been reported previously to be stress inducible in rice, while 57 of which are novel that have not been reported earlier as stress responsive in rice. We observed a strong association in the expression patterns of stress responsive genes and found 15 stress inducible genes that responded to all four treatments. Based on Venn diagram analysis, 56 genes were induced by both drought and high salinity, whereas 22 genes were upregulated by both cold and high salinity stress. Similarly 43 genes were induced by both drought stress and ABA application, while only 17 genes were identified as cold and ABA inducible genes. These results indicated the existence of greater cross talk between drought, ABA and high salinity stress signaling processes than those between cold and ABA, and cold and high salinity stress signaling pathways. The cold, drought, high salinity and ABA inducible genes were classified into four gene groups from their expression profiles. Analysis of data enabled us to identify a number of promoters and possible cis-acting DNA elements of several genes induced by a variety of abiotic stresses by combining expression data with genomic sequence data of rice. Comparative analysis of

  12. Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment.

    Science.gov (United States)

    Severgnini, Marco; Bicciato, Silvio; Mangano, Eleonora; Scarlatti, Francesca; Mezzelani, Alessandra; Mattioli, Michela; Ghidoni, Riccardo; Peano, Clelia; Bonnal, Raoul; Viti, Federica; Milanesi, Luciano; De Bellis, Gianluca; Battaglia, Cristina

    2006-06-01

    Meta-analysis of microarray data is increasingly important, considering both the availability of multiple platforms using disparate technologies and the accumulation in public repositories of data sets from different laboratories. We addressed the issue of comparing gene expression profiles from two microarray platforms by devising a standardized investigative strategy. We tested this procedure by studying MDA-MB-231 cells, which undergo apoptosis on treatment with resveratrol. Gene expression profiles were obtained using high-density, short-oligonucleotide, single-color microarray platforms: GeneChip (Affymetrix) and CodeLink (Amersham). Interplatform analyses were carried out on 8414 common transcripts represented on both platforms, as identified by LocusLink ID, representing 70.8% and 88.6% of annotated GeneChip and CodeLink features, respectively. We identified 105 differentially expressed genes (DEGs) on CodeLink and 42 DEGs on GeneChip. Among them, only 9 DEGs were commonly identified by both platforms. Multiple analyses (BLAST alignment of probes with target sequences, gene ontology, literature mining, and quantitative real-time PCR) permitted us to investigate the factors contributing to the generation of platform-dependent results in single-color microarray experiments. An effective approach to cross-platform comparison involves microarrays of similar technologies, samples prepared by identical methods, and a standardized battery of bioinformatic and statistical analyses.

  13. [Differential gene expression in incompatible interaction between Lilium regale Wilson and Fusarium oxysporum f. sp. lilii revealed by combined SSH and microarray analysis].

    Science.gov (United States)

    Rao, J; Liu, D; Zhang, N; He, H; Ge, F; Chen, C

    2014-01-01

    Fusarium wilt, caused by a soilborne pathogen Fusarium oxysporum f. sp. lilii, is the major disease of lily (Lilium L.). In order to isolate the genes differentially expressed in a resistant reaction to F. oxysporum in L. regale Wilson, a cDNA library was constructed with L. regale root during F. oxysporum infection using the suppression subtractive hybridization (SSH), and a total of 585 unique expressed sequence tags (ESTs) were obtained. Furthermore, the gene expression profiles in the incompatible interaction between L. regale and F. oxysporum were revealed by oligonucleotide microarray analysis of 585 unique ESTs comparison to the compatible interaction between a susceptible Lilium Oriental Hybrid 'Siberia' and F. oxysporum. The result of expression profile analysis indicated that the genes encoding pathogenesis-related proteins (PRs), antioxidative stress enzymes, secondary metabolism enzymes, transcription factors, signal transduction proteins as well as a large number of unknown genes were involved in early defense response of L. regale to F. oxysporum infection. Moreover, the following quantitative reverse transcription PCR (QRT-PCR) analysis confirmed reliability of the oligonucleotide microarray data. In the present study, isolation of differentially expressed genes in L. regale during response to F. oxysporum helped to uncover the molecular mechanism associated with the resistance of L. regale against F. oxysporum.

  14. Identifying Fishes through DNA Barcodes and Microarrays.

    Directory of Open Access Journals (Sweden)

    Marc Kochzius

    2010-09-01

    Full Text Available International fish trade reached an import value of 62.8 billion Euro in 2006, of which 44.6% are covered by the European Union. Species identification is a key problem throughout the life cycle of fishes: from eggs and larvae to adults in fisheries research and control, as well as processed fish products in consumer protection.This study aims to evaluate the applicability of the three mitochondrial genes 16S rRNA (16S, cytochrome b (cyt b, and cytochrome oxidase subunit I (COI for the identification of 50 European marine fish species by combining techniques of "DNA barcoding" and microarrays. In a DNA barcoding approach, neighbour Joining (NJ phylogenetic trees of 369 16S, 212 cyt b, and 447 COI sequences indicated that cyt b and COI are suitable for unambiguous identification, whereas 16S failed to discriminate closely related flatfish and gurnard species. In course of probe design for DNA microarray development, each of the markers yielded a high number of potentially species-specific probes in silico, although many of them were rejected based on microarray hybridisation experiments. None of the markers provided probes to discriminate the sibling flatfish and gurnard species. However, since 16S-probes were less negatively influenced by the "position of label" effect and showed the lowest rejection rate and the highest mean signal intensity, 16S is more suitable for DNA microarray probe design than cty b and COI. The large portion of rejected COI-probes after hybridisation experiments (>90% renders the DNA barcoding marker as rather unsuitable for this high-throughput technology.Based on these data, a DNA microarray containing 64 functional oligonucleotide probes for the identification of 30 out of the 50 fish species investigated was developed. It represents the next step towards an automated and easy-to-handle method to identify fish, ichthyoplankton, and fish products.

  15. Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays

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    Lu Chao

    2004-07-01

    Full Text Available Abstract Background Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor (SF, for every gene on an array. The SF is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values. Results Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd. In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation. Conclusions Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.

  16. An algorithm for finding biologically significant features in microarray data based on a priori manifold learning.

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    Zena M Hira

    Full Text Available Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA which assumes a multivariate Gaussian model of the data. More recently, non-linear methods have been investigated. Among these, manifold learning algorithms, for example Isomap, aim to project the data from a higher dimensional space onto a lower dimension one. We have proposed a priori manifold learning for finding a manifold in which a representative set of microarray data is fused with relevant data taken from the KEGG pathway database. Once the manifold has been constructed the raw microarray data is projected onto it and clustering and classification can take place. In contrast to earlier fusion based methods, the prior knowledge from the KEGG databases is not used in, and does not bias the classification process--it merely acts as an aid to find the best space in which to search the data. In our experiments we have found that using our new manifold method gives better classification results than using either PCA or conventional Isomap.

  17. Multi-gene detection and identification of mosquito-borne RNA viruses using an oligonucleotide microarray.

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    Nathan D Grubaugh

    Full Text Available BACKGROUND: Arthropod-borne viruses are important emerging pathogens world-wide. Viruses transmitted by mosquitoes, such as dengue, yellow fever, and Japanese encephalitis viruses, infect hundreds of millions of people and animals each year. Global surveillance of these viruses in mosquito vectors using molecular based assays is critical for prevention and control of the associated diseases. Here, we report an oligonucleotide DNA microarray design, termed ArboChip5.1, for multi-gene detection and identification of mosquito-borne RNA viruses from the genera Flavivirus (family Flaviviridae, Alphavirus (Togaviridae, Orthobunyavirus (Bunyaviridae, and Phlebovirus (Bunyaviridae. METHODOLOGY/PRINCIPAL FINDINGS: The assay utilizes targeted PCR amplification of three genes from each virus genus for electrochemical detection on a portable, field-tested microarray platform. Fifty-two viruses propagated in cell-culture were used to evaluate the specificity of the PCR primer sets and the ArboChip5.1 microarray capture probes. The microarray detected all of the tested viruses and differentiated between many closely related viruses such as members of the dengue, Japanese encephalitis, and Semliki Forest virus clades. Laboratory infected mosquitoes were used to simulate field samples and to determine the limits of detection. Additionally, we identified dengue virus type 3, Japanese encephalitis virus, Tembusu virus, Culex flavivirus, and a Quang Binh-like virus from mosquitoes collected in Thailand in 2011 and 2012. CONCLUSIONS/SIGNIFICANCE: We demonstrated that the described assay can be utilized in a comprehensive field surveillance program by the broad-range amplification and specific identification of arboviruses from infected mosquitoes. Furthermore, the microarray platform can be deployed in the field and viral RNA extraction to data analysis can occur in as little as 12 h. The information derived from the ArboChip5.1 microarray can help to establish

  18. Harshlight: a "corrective make-up" program for microarray chips

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    Wittkowski Knut M

    2005-12-01

    Full Text Available Abstract Background Microscopists are familiar with many blemishes that fluorescence images can have due to dust and debris, glass flaws, uneven distribution of fluids or surface coatings, etc. Microarray scans do show similar artifacts, which might affect subsequent analysis. Although all but the starkest blemishes are hard to find by the unaided eye, particularly in high-density oligonucleotide arrays (HDONAs, few tools are available to help with the detection of those defects. Results We develop a novel tool, Harshlight, for the automatic detection and masking of blemishes in HDONA microarray chips. Harshlight uses a combination of statistic and image processing methods to identify three different types of defects: localized blemishes affecting a few probes, diffuse defects affecting larger areas, and extended defects which may invalidate an entire chip. Conclusion We demonstrate the use of Harshlight can materially improve analysis of HDONA chips, especially for experiments with subtle changes between samples. For the widely used MAS5 algorithm, we show that compact blemishes cause an average of 8 gene expression values per chip to change by more than 50%, two of them by more than twofold; our masking algorithm restores about two thirds of this damage. Large-scale artifacts are successfully detected and eliminated.

  19. geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research

    Directory of Open Access Journals (Sweden)

    Fdez-Riverola Florentino

    2009-06-01

    Full Text Available Abstract Background Bioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine. Results In addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can

  20. Normalization and gene p-value estimation: issues in microarray data processing.

    Science.gov (United States)

    Fundel, Katrin; Küffner, Robert; Aigner, Thomas; Zimmer, Ralf

    2008-05-28

    Numerous methods exist for basic processing, e.g. normalization, of microarray gene expression data. These methods have an important effect on the final analysis outcome. Therefore, it is crucial to select methods appropriate for a given dataset in order to assure the validity and reliability of expression data analysis. Furthermore, biological interpretation requires expression values for genes, which are often represented by several spots or probe sets on a microarray. How to best integrate spot/probe set values into gene values has so far been a somewhat neglected problem. We present a case study comparing different between-array normalization methods with respect to the identification of differentially expressed genes. Our results show that it is feasible and necessary to use prior knowledge on gene expression measurements to select an adequate normalization method for the given data. Furthermore, we provide evidence that combining spot/probe set p-values into gene p-values for detecting differentially expressed genes has advantages compared to combining expression values for spots/probe sets into gene expression values. The comparison of different methods suggests to use Stouffer's method for this purpose. The study has been conducted on gene expression experiments investigating human joint cartilage samples of osteoarthritis related groups: a cDNA microarray (83 samples, four groups) and an Affymetrix (26 samples, two groups) data set. The apparently straight forward steps of gene expression data analysis, e.g. between-array normalization and detection of differentially regulated genes, can be accomplished by numerous different methods. We analyzed multiple methods and the possible effects and thereby demonstrate the importance of the single decisions taken during data processing. We give guidelines for evaluating normalization outcomes. An overview of these effects via appropriate measures and plots compared to prior knowledge is essential for the biological

  1. SiPM as miniaturised optical biosensor for DNA-microarray applications

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    M.F. Santangelo

    2015-12-01

    Full Text Available A miniaturized optical biosensor for low-level fluorescence emitted by DNA strands labelled with CY5 is showed. Aim of this work is to demonstrate that a Si-based photodetector, having a low noise and a high sensitivity, can replace traditional detection systems in DNA-microarray applications. The photodetector used is a photomultiplier (SiPM, with 25 pixels. It exhibits a higher sensitivity than commercial optical readers and we experimentally found a detection limit for spotted dried samples of ∼1 nM. We measured the fluorescence signal in different operating conditions (angle of analysis, fluorophores concentrations, solution volumes and support. Once fixed the angle of analysis, for samples spotted on Al-TEOS slide dried, the system is proportional to the concentration of the analyte in the sample and is linear in the range 1 nM–1 μM. For solutions, the range of linearity ranges from 100 fM to 10 nM. The system potentialities and the device low costs suggest it as basic component for the design and fabrication of a cheap, easy and portable optical system. Keywords: Optical Biosensor, SiPM, DNA microarray, Fluorophore detection

  2. Biocompatible Hydrogels for Microarray Cell Printing and Encapsulation

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    Akshata Datar

    2015-10-01

    Full Text Available Conventional drug screening processes are a time-consuming and expensive endeavor, but highly rewarding when they are successful. To identify promising lead compounds, millions of compounds are traditionally screened against therapeutic targets on human cells grown on the surface of 96-wells. These two-dimensional (2D cell monolayers are physiologically irrelevant, thus, often providing false-positive or false-negative results, when compared to cells grown in three-dimensional (3D structures such as hydrogel droplets. However, 3D cell culture systems are not easily amenable to high-throughput screening (HTS, thus inherently low throughput, and requiring relatively large volume for cell-based assays. In addition, it is difficult to control cellular microenvironments and hard to obtain reliable cell images due to focus position and transparency issues. To overcome these problems, miniaturized 3D cell cultures in hydrogels were developed via cell printing techniques where cell spots in hydrogels can be arrayed on the surface of glass slides or plastic chips by microarray spotters and cultured in growth media to form cells encapsulated 3D droplets for various cell-based assays. These approaches can dramatically reduce assay volume, provide accurate control over cellular microenvironments, and allow us to obtain clear 3D cell images for high-content imaging (HCI. In this review, several hydrogels that are compatible to microarray printing robots are discussed for miniaturized 3D cell cultures.

  3. Microarray of DNA probes on carboxylate functional beads surface

    Institute of Scientific and Technical Information of China (English)

    黄承志; 李原芳; 黄新华; 范美坤

    2000-01-01

    The microarray of DNA probes with 5’ -NH2 and 5’ -Tex/3’ -NH2 modified terminus on 10 um carboxylate functional beads surface in the presence of 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) is characterized in the preseni paper. it was found that the microarray capacity of DNA probes on the beads surface depends on the pH of the aqueous solution, the concentra-tion of DNA probe and the total surface area of the beads. On optimal conditions, the minimum distance of 20 mer single-stranded DNA probe microarrayed on beads surface is about 14 nm, while that of 20 mer double-stranded DNA probes is about 27 nm. If the probe length increases from 20 mer to 35 mer, its microarray density decreases correspondingly. Mechanism study shows that the binding mode of DNA probes on the beads surface is nearly parallel to the beads surface.

  4. Microarray of DNA probes on carboxylate functional beads surface

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The microarray of DNA probes with 5′-NH2 and 5′-Tex/3′-NH2 modified terminus on 10 m m carboxylate functional beads surface in the presence of 1-ethyl-3-(3-dimethylaminopropyl)- carbodiimide (EDC) is characterized in the present paper. It was found that the microarray capacity of DNA probes on the beads surface depends on the pH of the aqueous solution, the concentration of DNA probe and the total surface area of the beads. On optimal conditions, the minimum distance of 20 mer single-stranded DNA probe microarrayed on beads surface is about 14 nm, while that of 20 mer double-stranded DNA probes is about 27 nm. If the probe length increases from 20 mer to 35 mer, its microarray density decreases correspondingly. Mechanism study shows that the binding mode of DNA probes on the beads surface is nearly parallel to the beads surface.

  5. Lipid Microarray Biosensor for Biotoxin Detection.

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Anup K.; Throckmorton, Daniel J.; Moran-Mirabal, Jose C.; Edel, Joshua B.; Meyer, Grant D.; Craighead, Harold G.

    2006-05-01

    We present the use of micron-sized lipid domains, patterned onto planar substrates and within microfluidic channels, to assay the binding of bacterial toxins via total internal reflection fluorescence microscopy (TIRFM). The lipid domains were patterned using a polymer lift-off technique and consisted of ganglioside-populated DSPC:cholesterol supported lipid bilayers (SLBs). Lipid patterns were formed on the substrates by vesicle fusion followed by polymer lift-off, which revealed micron-sized SLBs containing either ganglioside GT1b or GM1. The ganglioside-populated SLB arrays were then exposed to either Cholera toxin subunit B (CTB) or Tetanus toxin fragment C (TTC). Binding was assayed on planar substrates by TIRFM down to 1 nM concentration for CTB and 100 nM for TTC. Apparent binding constants extracted from three different models applied to the binding curves suggest that binding of a protein to a lipid-based receptor is strongly affected by the lipid composition of the SLB and by the substrate on which the bilayer is formed. Patterning of SLBs inside microfluidic channels also allowed the preparation of lipid domains with different compositions on a single device. Arrays within microfluidic channels were used to achieve segregation and selective binding from a binary mixture of the toxin fragments in one device. The binding and segregation within the microfluidic channels was assayed with epifluorescence as proof of concept. We propose that the method used for patterning the lipid microarrays on planar substrates and within microfluidic channels can be easily adapted to proteins or nucleic acids and can be used for biosensor applications and cell stimulation assays under different flow conditions. KEYWORDS. Microarray, ganglioside, polymer lift-off, cholera toxin, tetanus toxin, TIRFM, binding constant.4

  6. ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping

    DEFF Research Database (Denmark)

    Hansen, Christian Skjødt; Østerbye, Thomas; Marcatili, Paolo

    2017-01-01

    -reactivity. B cell epitopes are typically classified as either linear epitopes, i.e. short consecutive segments from the protein sequence or conformational epitopes adapted through native protein folding. Recent advances in high-density peptide microarrays enable high-throughput, high-resolution identification...

  7. Uses of Dendrimers for DNA Microarrays

    Directory of Open Access Journals (Sweden)

    Jean-Pierre Majoral

    2006-08-01

    Full Text Available Biosensors such as DNA microarrays and microchips are gaining an increasingimportance in medicinal, forensic, and environmental analyses. Such devices are based onthe detection of supramolecular interactions called hybridizations that occur betweencomplementary oligonucleotides, one linked to a solid surface (the probe, and the other oneto be analyzed (the target. This paper focuses on the improvements that hyperbranched andperfectly defined nanomolecules called dendrimers can provide to this methodology. Twomain uses of dendrimers for such purpose have been described up to now; either thedendrimer is used as linker between the solid surface and the probe oligonucleotide, or thedendrimer is used as a multilabeled entity linked to the target oligonucleotide. In the firstcase the dendrimer generally induces a higher loading of probes and an easier hybridization,due to moving away the solid phase. In the second case the high number of localized labels(generally fluorescent induces an increased sensitivity, allowing the detection of smallquantities of biological entities.

  8. In silico design and performance of peptide microarrays for breast cancer tumour-auto-antibody testing

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    Andreas Weinhäusel

    2012-06-01

    Full Text Available The simplicity and potential of minimally invasive testing using sera from patients makes auto-antibody based biomarkers a very promising tool for use in cancer diagnostics. Protein microarrays have been used for the identification of such auto-antibody signatures. Because high throughput protein expression and purification is laborious, synthetic peptides might be a good alternative for microarray generation and multiplexed analyses. In this study, we designed 1185 antigenic peptides, deduced from proteins expressed by 642 cDNA expression clones found to be sero-reactive in both breast tumour patients and controls. The sero-reactive proteins and the corresponding peptides were used for the production of protein and peptide microarrays. Serum samples from females with benign and malignant breast tumours and healthy control sera (n=16 per group were then analysed. Correct classification of the serum samples on peptide microarrays were 78% for discrimination of ‘malignant versus healthy controls’, 72% for ‘benign versus malignant’ and 94% for ‘benign versus controls’. On protein arrays, correct classification for these contrasts was 69%, 59% and 59%, respectively. The over-representation analysis of the classifiers derived from class prediction showed enrichment of genes associated with ribosomes, spliceosomes, endocytosis and the pentose phosphate pathway. Sequence analyses of the peptides with the highest sero-reactivity demonstrated enrichment of the zinc-finger domain. Peptides’ sero-reactivities were found negatively correlated with hydrophobicity and positively correlated with positive charge, high inter-residue protein contact energies and a secondary structure propensity bias. This study hints at the possibility of using in silico designed antigenic peptide microarrays as an alternative to protein microarrays for the improvement of tumour auto-antibody based diagnostics.

  9. Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning

    Science.gov (United States)

    Weitzeal, A. J.; Wyatt, S. E.; Parsons-Wingerter, P.

    2016-01-01

    Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photoassimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASAs GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be upregulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS like protein (potentially affecting cell elongation in the leaves), and an F-boxkelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm upregulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASAs VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

  10. Development of a Feature and Template-Assisted Assembler and Application to the Analysis of a Foot-and-Mouth Disease Virus Genotyping Microarray.

    Directory of Open Access Journals (Sweden)

    Roger W Barrette

    Full Text Available Several RT-PCR and genome sequencing strategies exist for the resolution of Foot-and-Mouth Disease virus (FMDV. While these approaches are relatively straightforward, they can be vulnerable to failure due to the unpredictable nature of FMDV genome sequence variations. Sequence independent single primer amplification (SISPA followed by genotyping microarray offers an attractive unbiased approach to FMDV characterization. Here we describe a custom FMDV microarray and a companion feature and template-assisted assembler software (FAT-assembler capable of resolving virus genome sequence using a moderate number of conserved microarray features. The results demonstrate that this approach may be used to rapidly characterize naturally occurring FMDV as well as an engineered chimeric strain of FMDV. The FAT-assembler, while applied to resolving FMDV genomes, represents a new bioinformatics approach that should be broadly applicable to interpreting microarray genotyping data for other viruses or target organisms.

  11. High-density polymer microarrays: identifying synthetic polymers that control human embryonic stem cell growth.

    Science.gov (United States)

    Hansen, Anne; Mjoseng, Heidi K; Zhang, Rong; Kalloudis, Michail; Koutsos, Vasileios; de Sousa, Paul A; Bradley, Mark

    2014-06-01

    The fabrication of high-density polymer microarray is described, allowing the simultaneous and efficient evaluation of more than 7000 different polymers in a single-cellular-based screen. These high-density polymer arrays are applied in the search for synthetic substrates for hESCs culture. Up-scaling of the identified hit polymers enables long-term cellular cultivation and promoted successful stem-cell maintenance. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Microarray MAPH: accurate array-based detection of relative copy number in genomic DNA

    Directory of Open Access Journals (Sweden)

    Chan Alan

    2006-06-01

    Full Text Available Abstract Background Current methods for measurement of copy number do not combine all the desirable qualities of convenience, throughput, economy, accuracy and resolution. In this study, to improve the throughput associated with Multiplex Amplifiable Probe Hybridisation (MAPH we aimed to develop a modification based on the 3-Dimensional, Flow-Through Microarray Platform from PamGene International. In this new method, electrophoretic analysis of amplified products is replaced with photometric analysis of a probed oligonucleotide array. Copy number analysis of hybridised probes is based on a dual-label approach by comparing the intensity of Cy3-labelled MAPH probes amplified from test samples co-hybridised with similarly amplified Cy5-labelled reference MAPH probes. The key feature of using a hybridisation-based end point with MAPH is that discrimination of amplified probes is based on sequence and not fragment length. Results In this study we showed that microarray MAPH measurement of PMP22 gene dosage correlates well with PMP22 gene dosage determined by capillary MAPH and that copy number was accurately reported in analyses of DNA from 38 individuals, 12 of which were known to have Charcot-Marie-Tooth disease type 1A (CMT1A. Conclusion Measurement of microarray-based endpoints for MAPH appears to be of comparable accuracy to electrophoretic methods, and holds the prospect of fully exploiting the potential multiplicity of MAPH. The technology has the potential to simplify copy number assays for genes with a large number of exons, or of expanded sets of probes from dispersed genomic locations.

  13. Microarray MAPH: accurate array-based detection of relative copy number in genomic DNA.

    Science.gov (United States)

    Gibbons, Brian; Datta, Parikkhit; Wu, Ying; Chan, Alan; Al Armour, John

    2006-06-30

    Current methods for measurement of copy number do not combine all the desirable qualities of convenience, throughput, economy, accuracy and resolution. In this study, to improve the throughput associated with Multiplex Amplifiable Probe Hybridisation (MAPH) we aimed to develop a modification based on the 3-Dimensional, Flow-Through Microarray Platform from PamGene International. In this new method, electrophoretic analysis of amplified products is replaced with photometric analysis of a probed oligonucleotide array. Copy number analysis of hybridised probes is based on a dual-label approach by comparing the intensity of Cy3-labelled MAPH probes amplified from test samples co-hybridised with similarly amplified Cy5-labelled reference MAPH probes. The key feature of using a hybridisation-based end point with MAPH is that discrimination of amplified probes is based on sequence and not fragment length. In this study we showed that microarray MAPH measurement of PMP22 gene dosage correlates well with PMP22 gene dosage determined by capillary MAPH and that copy number was accurately reported in analyses of DNA from 38 individuals, 12 of which were known to have Charcot-Marie-Tooth disease type 1A (CMT1A). Measurement of microarray-based endpoints for MAPH appears to be of comparable accuracy to electrophoretic methods, and holds the prospect of fully exploiting the potential multiplicity of MAPH. The technology has the potential to simplify copy number assays for genes with a large number of exons, or of expanded sets of probes from dispersed genomic locations.

  14. Sensitivity and fidelity of DNA microarray improved with integration of Amplified Differential Gene Expression (ADGE

    Directory of Open Access Journals (Sweden)

    Ile Kristina E

    2003-07-01

    Full Text Available Abstract Background The ADGE technique is a method designed to magnify the ratios of gene expression before detection. It improves the detection sensitivity to small change of gene expression and requires small amount of starting material. However, the throughput of ADGE is low. We integrated ADGE with DNA microarray (ADGE microarray and compared it with regular microarray. Results When ADGE was integrated with DNA microarray, a quantitative relationship of a power function between detected and input ratios was found. Because of ratio magnification, ADGE microarray was better able to detect small changes in gene expression in a drug resistant model cell line system. The PCR amplification of templates and efficient labeling reduced the requirement of starting material to as little as 125 ng of total RNA for one slide hybridization and enhanced the signal intensity. Integration of ratio magnification, template amplification and efficient labeling in ADGE microarray reduced artifacts in microarray data and improved detection fidelity. The results of ADGE microarray were less variable and more reproducible than those of regular microarray. A gene expression profile generated with ADGE microarray characterized the drug resistant phenotype, particularly with reference to glutathione, proliferation and kinase pathways. Conclusion ADGE microarray magnified the ratios of differential gene expression in a power function, improved the detection sensitivity and fidelity and reduced the requirement for starting material while maintaining high throughput. ADGE microarray generated a more informative expression pattern than regular microarray.

  15. Exploring matrix factorization techniques for significant genes identification of Alzheimer’s disease microarray gene expression data

    Directory of Open Access Journals (Sweden)

    Hu Xiaohua

    2011-07-01

    Full Text Available Abstract Background The wide use of high-throughput DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. Although biomedical researchers typically design microarray experiments to explore specific biological contexts, the relationships between genes are hard to identified because they are complex and noisy high-dimensional data and are often hindered by low statistical power. The main challenge now is to extract valuable biological information from the colossal amount of data to gain insight into biological processes and the mechanisms of human disease. To overcome the challenge requires mathematical and computational methods that are versatile enough to capture the underlying biological features and simple enough to be applied efficiently to large datasets. Methods Unsupervised machine learning approaches provide new and efficient analysis of gene expression profiles. In our study, two unsupervised knowledge-based matrix factorization methods, independent component analysis (ICA and nonnegative matrix factorization (NMF are integrated to identify significant genes and related pathways in microarray gene expression dataset of Alzheimer’s disease. The advantage of these two approaches is they can be performed as a biclustering method by which genes and conditions can be clustered simultaneously. Furthermore, they can group genes into different categories for identifying related diagnostic pathways and regulatory networks. The difference between these two method lies in ICA assume statistical independence of the expression modes, while NMF need positivity constrains to generate localized gene expression profiles. Results In our work, we performed FastICA and non-smooth NMF methods on DNA microarray gene expression data of Alzheimer’s disease respectively. The simulation results shows that both of the methods can clearly classify severe AD samples from control samples, and

  16. Significance of genomic instability in breast cancer in atomic bomb survivors: analysis of microarray-comparative genomic hybridization

    Directory of Open Access Journals (Sweden)

    Oikawa Masahiro

    2011-12-01

    Full Text Available Abstract Background It has been postulated that ionizing radiation induces breast cancers among atomic bomb (A-bomb survivors. We have reported a higher incidence of HER2 and C-MYC oncogene amplification in breast cancers from A-bomb survivors. The purpose of this study was to clarify the effect of A-bomb radiation exposure on genomic instability (GIN, which is an important hallmark of carcinogenesis, in archival formalin-fixed paraffin-embedded (FFPE tissues of breast cancer by using microarray-comparative genomic hybridization (aCGH. Methods Tumor DNA was extracted from FFPE tissues of invasive ductal cancers from 15 survivors who were exposed at 1.5 km or less from the hypocenter and 13 calendar year-matched non-exposed patients followed by aCGH analysis using a high-density oligonucleotide microarray. The total length of copy number aberrations (CNA was used as an indicator of GIN, and correlation with clinicopathological factors were statistically tested. Results The mean of the derivative log ratio spread (DLRSpread, which estimates the noise by calculating the spread of log ratio differences between consecutive probes for all chromosomes, was 0.54 (range, 0.26 to 1.05. The concordance of results between aCGH and fluorescence in situ hybridization (FISH for HER2 gene amplification was 88%. The incidence of HER2 amplification and histological grade was significantly higher in the A-bomb survivors than control group (P = 0.04, respectively. The total length of CNA tended to be larger in the A-bomb survivors (P = 0.15. Correlation analysis of CNA and clinicopathological factors revealed that DLRSpread was negatively correlated with that significantly (P = 0.034, r = -0.40. Multivariate analysis with covariance revealed that the exposure to A-bomb was a significant (P = 0.005 independent factor which was associated with larger total length of CNA of breast cancers. Conclusions Thus, archival FFPE tissues from A-bomb survivors are useful for

  17. Significance of genomic instability in breast cancer in atomic bomb survivors: analysis of microarray-comparative genomic hybridization

    International Nuclear Information System (INIS)

    Oikawa, Masahiro; Yoshiura, Koh-ichiro; Kondo, Hisayoshi; Miura, Shiro; Nagayasu, Takeshi; Nakashima, Masahiro

    2011-01-01

    It has been postulated that ionizing radiation induces breast cancers among atomic bomb (A-bomb) survivors. We have reported a higher incidence of HER2 and C-MYC oncogene amplification in breast cancers from A-bomb survivors. The purpose of this study was to clarify the effect of A-bomb radiation exposure on genomic instability (GIN), which is an important hallmark of carcinogenesis, in archival formalin-fixed paraffin-embedded (FFPE) tissues of breast cancer by using microarray-comparative genomic hybridization (aCGH). Tumor DNA was extracted from FFPE tissues of invasive ductal cancers from 15 survivors who were exposed at 1.5 km or less from the hypocenter and 13 calendar year-matched non-exposed patients followed by aCGH analysis using a high-density oligonucleotide microarray. The total length of copy number aberrations (CNA) was used as an indicator of GIN, and correlation with clinicopathological factors were statistically tested. The mean of the derivative log ratio spread (DLRSpread), which estimates the noise by calculating the spread of log ratio differences between consecutive probes for all chromosomes, was 0.54 (range, 0.26 to 1.05). The concordance of results between aCGH and fluorescence in situ hybridization (FISH) for HER2 gene amplification was 88%. The incidence of HER2 amplification and histological grade was significantly higher in the A-bomb survivors than control group (P = 0.04, respectively). The total length of CNA tended to be larger in the A-bomb survivors (P = 0.15). Correlation analysis of CNA and clinicopathological factors revealed that DLRSpread was negatively correlated with that significantly (P = 0.034, r = -0.40). Multivariate analysis with covariance revealed that the exposure to A-bomb was a significant (P = 0.005) independent factor which was associated with larger total length of CNA of breast cancers. Thus, archival FFPE tissues from A-bomb survivors are useful for genome-wide aCGH analysis. Our results suggested that A

  18. Significance of genomic instability in breast cancer in atomic bomb survivors: analysis of microarray-comparative genomic hybridization.

    Science.gov (United States)

    Oikawa, Masahiro; Yoshiura, Koh-ichiro; Kondo, Hisayoshi; Miura, Shiro; Nagayasu, Takeshi; Nakashima, Masahiro

    2011-12-07

    It has been postulated that ionizing radiation induces breast cancers among atomic bomb (A-bomb) survivors. We have reported a higher incidence of HER2 and C-MYC oncogene amplification in breast cancers from A-bomb survivors. The purpose of this study was to clarify the effect of A-bomb radiation exposure on genomic instability (GIN), which is an important hallmark of carcinogenesis, in archival formalin-fixed paraffin-embedded (FFPE) tissues of breast cancer by using microarray-comparative genomic hybridization (aCGH). Tumor DNA was extracted from FFPE tissues of invasive ductal cancers from 15 survivors who were exposed at 1.5 km or less from the hypocenter and 13 calendar year-matched non-exposed patients followed by aCGH analysis using a high-density oligonucleotide microarray. The total length of copy number aberrations (CNA) was used as an indicator of GIN, and correlation with clinicopathological factors were statistically tested. The mean of the derivative log ratio spread (DLRSpread), which estimates the noise by calculating the spread of log ratio differences between consecutive probes for all chromosomes, was 0.54 (range, 0.26 to 1.05). The concordance of results between aCGH and fluorescence in situ hybridization (FISH) for HER2 gene amplification was 88%. The incidence of HER2 amplification and histological grade was significantly higher in the A-bomb survivors than control group (P = 0.04, respectively). The total length of CNA tended to be larger in the A-bomb survivors (P = 0.15). Correlation analysis of CNA and clinicopathological factors revealed that DLRSpread was negatively correlated with that significantly (P = 0.034, r = -0.40). Multivariate analysis with covariance revealed that the exposure to A-bomb was a significant (P = 0.005) independent factor which was associated with larger total length of CNA of breast cancers. Thus, archival FFPE tissues from A-bomb survivors are useful for genome-wide aCGH analysis. Our results suggested that A

  19. Feature selection and classification of MAQC-II breast cancer and multiple myeloma microarray gene expression data.

    Directory of Open Access Journals (Sweden)

    Qingzhong Liu

    Full Text Available Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA, which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE, Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS, Gradient based Leave-one-out Gene Selection (GLGS. To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and

  20. Microarray analysis on human neuroblastoma cells exposed to aluminum, β(1-42-amyloid or the β(1-42-amyloid aluminum complex.

    Directory of Open Access Journals (Sweden)

    Valentina Gatta

    Full Text Available BACKGROUND: A typical pathological feature of Alzheimer's disease (AD is the appearance in the brain of senile plaques made up of β-amyloid (Aβ and neurofibrillary tangles. AD is also associated with an abnormal accumulation of some metal ions, and we have recently shown that one of these, aluminum (Al, plays a relevant role in affecting Aβ aggregation and neurotoxicity. METHODOLOGY: In this study, employing a microarray analysis of 35,129 genes, we investigated the effects induced by the exposure to the Aβ(1-42-Al (Aβ-Al complex on the gene expression profile of the neuronal-like cell line, SH-SY5Y. PRINCIPAL FINDINGS: The microarray assay indicated that, compared to Aβ or Al alone, exposure to Aβ-Al complex produced selective changes in gene expression. Some of the genes selectively over or underexpressed are directly related to AD. A further evaluation performed with Ingenuity Pathway analysis revealed that these genes are nodes of networks and pathways that are involved in the modulation of Ca(2+ homeostasis as well as in the regulation of glutamatergic transmission and synaptic plasticity. CONCLUSIONS AND SIGNIFICANCE: Aβ-Al appears to be largely involved in the molecular machinery that regulates neuronal as well as synaptic dysfunction and loss. Aβ-Al seems critical in modulating key AD-related pathways such as glutamatergic transmission, Ca(2+ homeostasis, oxidative stress, inflammation, and neuronal apoptosis.

  1. Evaluation of DNA microarray results in the Toxicogenomics Project (TGP) consortium in Japan.

    Science.gov (United States)

    Noriyuki, Nakatsu; Igarashi, Yoshinobu; Ono, Atsushi; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro

    2012-01-01

    An important technology used in toxicogenomic drug discovery research is the microarray, which enables researchers to simultaneously analyze the expression of a large number of genes. To build a database and data analysis system for use in assessing the safety of drugs and drug candidates, in 2002 we conducted a 5-year collaborative study in the Toxicogenomics Project (TGP1) in Japan. Experimental data generated by such studies must be validated by different laboratories for robust and accurate analysis. For this purpose, we conducted intra- and inter-laboratory validation studies with participating companies in the second collaborative study in the Toxicogenomics Project (TGP2). Gene expression in the liver of rats treated with acetaminophen (APAP) was independently examined by the participating companies using Affymetrix GeneChip microarrays. The intra- and inter-laboratory reproducibility of the data was evaluated using hierarchical clustering analysis. The toxicogenomics results were highly reproducible, indicating that the gene expression data generated in our TGP1 project is reliable and compatible with the data generated by the participating laboratories.

  2. 3D Biomaterial Microarrays for Regenerative Medicine

    DEFF Research Database (Denmark)

    Gaharwar, Akhilesh K.; Arpanaei, Ayyoob; Andresen, Thomas Lars

    2015-01-01

    Three dimensional (3D) biomaterial microarrays hold enormous promise for regenerative medicine because of their ability to accelerate the design and fabrication of biomimetic materials. Such tissue-like biomaterials can provide an appropriate microenvironment for stimulating and controlling stem...... for tissue engineering and drug screening applications....... cell differentiation into tissue-specifi c lineages. The use of 3D biomaterial microarrays can, if optimized correctly, result in a more than 1000-fold reduction in biomaterials and cells consumption when engineering optimal materials combinations, which makes these miniaturized systems very attractive...

  3. Dimension reduction methods for microarray data: a review

    Directory of Open Access Journals (Sweden)

    Rabia Aziz

    2017-03-01

    Full Text Available Dimension reduction has become inevitable for pre-processing of high dimensional data. “Gene expression microarray data” is an instance of such high dimensional data. Gene expression microarray data displays the maximum number of genes (features simultaneously at a molecular level with a very small number of samples. The copious numbers of genes are usually provided to a learning algorithm for producing a complete characterization of the classification task. However, most of the times the majority of the genes are irrelevant or redundant to the learning task. It will deteriorate the learning accuracy and training speed as well as lead to the problem of overfitting. Thus, dimension reduction of microarray data is a crucial preprocessing step for prediction and classification of disease. Various feature selection and feature extraction techniques have been proposed in the literature to identify the genes, that have direct impact on the various machine learning algorithms for classification and eliminate the remaining ones. This paper describes the taxonomy of dimension reduction methods with their characteristics, evaluation criteria, advantages and disadvantages. It also presents a review of numerous dimension reduction approaches for microarray data, mainly those methods that have been proposed over the past few years.

  4. The detection and differentiation of canine respiratory pathogens using oligonucleotide microarrays.

    Science.gov (United States)

    Wang, Lih-Chiann; Kuo, Ya-Ting; Chueh, Ling-Ling; Huang, Dean; Lin, Jiunn-Horng

    2017-05-01

    Canine respiratory diseases are commonly seen in dogs along with co-infections with multiple respiratory pathogens, including viruses and bacteria. Virus infections in even vaccinated dogs were also reported. The clinical signs caused by different respiratory etiological agents are similar, which makes differential diagnosis imperative. An oligonucleotide microarray system was developed in this study. The wild type and vaccine strains of canine distemper virus (CDV), influenza virus, canine herpesvirus (CHV), Bordetella bronchiseptica and Mycoplasma cynos were detected and differentiated simultaneously on a microarray chip. The detection limit is 10, 10, 100, 50 and 50 copy numbers for CDV, influenza virus, CHV, B. bronchiseptica and M. cynos, respectively. The clinical test results of nasal swab samples showed that the microarray had remarkably better efficacy than the multiplex PCR-agarose gel method. The positive detection rate of microarray and agarose gel was 59.0% (n=33) and 41.1% (n=23) among the 56 samples, respectively. CDV vaccine strain and pathogen co-infections were further demonstrated by the microarray but not by the multiplex PCR-agarose gel. The oligonucleotide microarray provides a highly efficient diagnosis alternative that could be applied to clinical usage, greatly assisting in disease therapy and control. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. The Importance of Normalization on Large and Heterogeneous Microarray Datasets

    Science.gov (United States)

    DNA microarray technology is a powerful functional genomics tool increasingly used for investigating global gene expression in environmental studies. Microarrays can also be used in identifying biological networks, as they give insight on the complex gene-to-gene interactions, ne...

  6. Practice guideline: joint CCMG-SOGC recommendations for the use of chromosomal microarray analysis for prenatal diagnosis and assessment of fetal loss in Canada

    Science.gov (United States)

    Armour, Christine M; Dougan, Shelley Danielle; Brock, Jo-Ann; Chari, Radha; Chodirker, Bernie N; DeBie, Isabelle; Evans, Jane A; Gibson, William T; Kolomietz, Elena; Nelson, Tanya N; Tihy, Frédérique; Thomas, Mary Ann; Stavropoulos, Dimitri J

    2018-01-01

    Background The aim of this guideline is to provide updated recommendations for Canadian genetic counsellors, medical geneticists, maternal fetal medicine specialists, clinical laboratory geneticists and other practitioners regarding the use of chromosomal microarray analysis (CMA) for prenatal diagnosis. This guideline replaces the 2011 Society of Obstetricians and Gynaecologists of Canada (SOGC)-Canadian College of Medical Geneticists (CCMG) Joint Technical Update. Methods A multidisciplinary group consisting of medical geneticists, genetic counsellors, maternal fetal medicine specialists and clinical laboratory geneticists was assembled to review existing literature and guidelines for use of CMA in prenatal care and to make recommendations relevant to the Canadian context. The statement was circulated for comment to the CCMG membership-at-large for feedback and, following incorporation of feedback, was approved by the CCMG Board of Directors on 5 June 2017 and the SOGC Board of Directors on 19 June 2017. Results and conclusions Recommendations include but are not limited to: (1) CMA should be offered following a normal rapid aneuploidy screen when multiple fetal malformations are detected (II-1A) or for nuchal translucency (NT) ≥3.5 mm (II-2B) (recommendation 1); (2) a professional with expertise in prenatal chromosomal microarray analysis should provide genetic counselling to obtain informed consent, discuss the limitations of the methodology, obtain the parental decisions for return of incidental findings (II-2A) (recommendation 4) and provide post-test counselling for reporting of test results (III-A) (recommendation 9); (3) the resolution of chromosomal microarray analysis should be similar to postnatal microarray platforms to ensure small pathogenic variants are detected. To minimise the reporting of uncertain findings, it is recommended that variants of unknown significance (VOUS) smaller than 500 Kb deletion or 1 Mb duplication not be routinely

  7. Practice guideline: joint CCMG-SOGC recommendations for the use of chromosomal microarray analysis for prenatal diagnosis and assessment of fetal loss in Canada.

    Science.gov (United States)

    Armour, Christine M; Dougan, Shelley Danielle; Brock, Jo-Ann; Chari, Radha; Chodirker, Bernie N; DeBie, Isabelle; Evans, Jane A; Gibson, William T; Kolomietz, Elena; Nelson, Tanya N; Tihy, Frédérique; Thomas, Mary Ann; Stavropoulos, Dimitri J

    2018-04-01

    The aim of this guideline is to provide updated recommendations for Canadian genetic counsellors, medical geneticists, maternal fetal medicine specialists, clinical laboratory geneticists and other practitioners regarding the use of chromosomal microarray analysis (CMA) for prenatal diagnosis. This guideline replaces the 2011 Society of Obstetricians and Gynaecologists of Canada (SOGC)-Canadian College of Medical Geneticists (CCMG) Joint Technical Update. A multidisciplinary group consisting of medical geneticists, genetic counsellors, maternal fetal medicine specialists and clinical laboratory geneticists was assembled to review existing literature and guidelines for use of CMA in prenatal care and to make recommendations relevant to the Canadian context. The statement was circulated for comment to the CCMG membership-at-large for feedback and, following incorporation of feedback, was approved by the CCMG Board of Directors on 5 June 2017 and the SOGC Board of Directors on 19 June 2017. Recommendations include but are not limited to: (1) CMA should be offered following a normal rapid aneuploidy screen when multiple fetal malformations are detected (II-1A) or for nuchal translucency (NT) ≥3.5 mm (II-2B) (recommendation 1); (2) a professional with expertise in prenatal chromosomal microarray analysis should provide genetic counselling to obtain informed consent, discuss the limitations of the methodology, obtain the parental decisions for return of incidental findings (II-2A) (recommendation 4) and provide post-test counselling for reporting of test results (III-A) (recommendation 9); (3) the resolution of chromosomal microarray analysis should be similar to postnatal microarray platforms to ensure small pathogenic variants are detected. To minimise the reporting of uncertain findings, it is recommended that variants of unknown significance (VOUS) smaller than 500 Kb deletion or 1 Mb duplication not be routinely reported in the prenatal context. Additionally

  8. Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning. Analysis of Space Grown Arabidopsis with Microarray Data from GeneLab: Identification of Genes Important in Vascular Patterning

    Science.gov (United States)

    Weitzel, A. J.; Wyatt, S. E.; Parsons-Wingerter, P.

    2016-01-01

    Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photo-assimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASA's GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be up-regulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS (Auxin-Regulated Gene Involved in Organ Size)-like protein (potentially affecting cell elongation in the leaves), and an F-box/kelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm up-regulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASA's VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

  9. Microarray-based genomic surveying of gene polymorphisms in Chlamydia trachomatis

    OpenAIRE

    Brunelle, Brian W; Nicholson, Tracy L; Stephens, Richard S

    2004-01-01

    By comparing two fully sequenced genomes of Chlamydia trachomatis using competitive hybridization on DNA microarrays, a logarithmic correlation was demonstrated between the signal ratio of the arrays and the 75-99% range of nucleotide identities of the genes. Variable genes within 14 uncharacterized strains of C. trachomatis were identified by array analysis and verified by DNA sequencing. These genes may be crucial for understanding chlamydial virulence and pathogenesis.

  10. Microarray-based DNA methylation study of Ewing's sarcoma of the bone.

    Science.gov (United States)

    Park, Hye-Rim; Jung, Woon-Won; Kim, Hyun-Sook; Park, Yong-Koo

    2014-10-01

    Alterations in DNA methylation patterns are a hallmark of malignancy. However, the majority of epigenetic studies of Ewing's sarcoma have focused on the analysis of only a few candidate genes. Comprehensive studies are thus lacking and are required. The aim of the present study was to identify novel methylation markers in Ewing's sarcoma using microarray analysis. The current study reports the microarray-based DNA methylation study of 1,505 CpG sites of 807 cancer-related genes from 69 Ewing's sarcoma samples. The Illumina GoldenGate Methylation Cancer Panel I microarray was used, and with the appropriate controls (n=14), a total of 92 hypermethylated genes were identified in the Ewing's sarcoma samples. The majority of the hypermethylated genes were associated with cell adhesion, cell regulation, development and signal transduction. The overall methylation mean values were compared between patients who survived and those that did not. The overall methylation mean was significantly higher in the patients who did not survive (0.25±0.03) than in those who did (0.22±0.05) (P=0.0322). However, the overall methylation mean was not found to significantly correlate with age, gender or tumor location. GDF10 , OSM , APC and HOXA11 were the most significant differentially-methylated genes, however, their methylation levels were not found to significantly correlate with the survival rate. The DNA methylation profile of Ewing's sarcoma was characterized and 92 genes that were significantly hypermethylated were detected. A trend towards a more aggressive behavior was identified in the methylated group. The results of this study indicated that methylation may be significant in the development of Ewing's sarcoma.

  11. Microarray analysis of pancreatic gene expression during biotin repletion in biotin-deficient rats.

    Science.gov (United States)

    Dakshinamurti, Krishnamurti; Bagchi, Rushita A; Abrenica, Bernard; Czubryt, Michael P

    2015-12-01

    Biotin is a B vitamin involved in multiple metabolic pathways. In humans, biotin deficiency is relatively rare but can cause dermatitis, alopecia, and perosis. Low biotin levels occur in individuals with type-2 diabetes, and supplementation with biotin plus chromium may improve blood sugar control. The acute effect on pancreatic gene expression of biotin repletion following chronic deficiency is unclear, therefore we induced biotin deficiency in adult male rats by feeding them a 20% raw egg white diet for 6 weeks. Animals were then randomized into 2 groups: one group received a single biotin supplement and returned to normal chow lacking egg white, while the second group remained on the depletion diet. After 1 week, pancreata were removed from biotin-deficient (BD) and biotin-repleted (BR) animals and RNA was isolated for microarray analysis. Biotin depletion altered gene expression in a manner indicative of inflammation, fibrosis, and defective pancreatic function. Conversely, biotin repletion activated numerous repair and anti-inflammatory pathways, reduced fibrotic gene expression, and induced multiple genes involved in pancreatic endocrine and exocrine function. A subset of the results was confirmed by quantitative real-time PCR analysis, as well as by treatment of pancreatic AR42J cells with biotin. The results indicate that biotin repletion, even after lengthy deficiency, results in the rapid induction of repair processes in the pancreas.

  12. An automated microfluidic DNA microarray platform for genetic variant detection in inherited arrhythmic diseases.

    Science.gov (United States)

    Huang, Shu-Hong; Chang, Yu-Shin; Juang, Jyh-Ming Jimmy; Chang, Kai-Wei; Tsai, Mong-Hsun; Lu, Tzu-Pin; Lai, Liang-Chuan; Chuang, Eric Y; Huang, Nien-Tsu

    2018-03-12

    In this study, we developed an automated microfluidic DNA microarray (AMDM) platform for point mutation detection of genetic variants in inherited arrhythmic diseases. The platform allows for automated and programmable reagent sequencing under precise conditions of hybridization flow and temperature control. It is composed of a commercial microfluidic control system, a microfluidic microarray device, and a temperature control unit. The automated and rapid hybridization process can be performed in the AMDM platform using Cy3 labeled oligonucleotide exons of SCN5A genetic DNA, which produces proteins associated with sodium channels abundant in the heart (cardiac) muscle cells. We then introduce a graphene oxide (GO)-assisted DNA microarray hybridization protocol to enable point mutation detection. In this protocol, a GO solution is added after the staining step to quench dyes bound to single-stranded DNA or non-perfectly matched DNA, which can improve point mutation specificity. As proof-of-concept we extracted the wild-type and mutant of exon 12 and exon 17 of SCN5A genetic DNA from patients with long QT syndrome or Brugada syndrome by touchdown PCR and performed a successful point mutation discrimination in the AMDM platform. Overall, the AMDM platform can greatly reduce laborious and time-consuming hybridization steps and prevent potential contamination. Furthermore, by introducing the reciprocating flow into the microchannel during the hybridization process, the total assay time can be reduced to 3 hours, which is 6 times faster than the conventional DNA microarray. Given the automatic assay operation, shorter assay time, and high point mutation discrimination, we believe that the AMDM platform has potential for low-cost, rapid and sensitive genetic testing in a simple and user-friendly manner, which may benefit gene screening in medical practice.

  13. Design, construction and validation of a Plasmodium vivax microarray for the transcriptome profiling of clinical isolates

    KAUST Repository

    Boopathi, Pon Arunachalam

    2016-10-09

    High density oligonucleotide microarrays have been used on Plasmodium vivax field isolates to estimate whole genome expression. However, no microarray platform has been experimentally optimized for studying the transcriptome of field isolates. In the present study, we adopted both bioinformatics and experimental testing approaches to select best optimized probes suitable for detecting parasite transcripts from field samples and included them in designing a custom 15K P. vivax microarray. This microarray has long oligonucleotide probes (60 mer) that were in-situ synthesized onto glass slides using Agilent SurePrint technology and has been developed into an 8X15K format (8 identical arrays on a single slide). Probes in this array were experimentally validated and represents 4180 P. vivax genes in sense orientation, of which 1219 genes have also probes in antisense orientation. Validation of the 15K array by using field samples (n =14) has shown 99% of parasite transcript detection from any of the samples. Correlation analysis between duplicate probes (n = 85) present in the arrays showed perfect correlation (r(2) = 0.98) indicating the reproducibility. Multiple probes representing the same gene exhibited similar kind of expression pattern across the samples (positive correlation, r >= 0.6). Comparison of hybridization data with the previous studies and quantitative real-time PCR experiments were performed to highlight the microarray validation procedure. This array is unique in its design, and results indicate that the array is sensitive and reproducible. Hence, this microarray could be a valuable functional genomics tool to generate reliable expression data from P. vivax field isolates. (C) 2016 Published by Elsevier B.V.

  14. Design, construction and validation of a Plasmodium vivax microarray for the transcriptome profiling of clinical isolates

    KAUST Repository

    Boopathi, Pon Arunachalam; Subudhi, Amit; Middha, Sheetal; Acharya, Jyoti; Mugasimangalam, Raja Chinnadurai; Kochar, Sanjay Kumar; Kochar, Dhanpat Kumar; Das, Ashis

    2016-01-01

    High density oligonucleotide microarrays have been used on Plasmodium vivax field isolates to estimate whole genome expression. However, no microarray platform has been experimentally optimized for studying the transcriptome of field isolates. In the present study, we adopted both bioinformatics and experimental testing approaches to select best optimized probes suitable for detecting parasite transcripts from field samples and included them in designing a custom 15K P. vivax microarray. This microarray has long oligonucleotide probes (60 mer) that were in-situ synthesized onto glass slides using Agilent SurePrint technology and has been developed into an 8X15K format (8 identical arrays on a single slide). Probes in this array were experimentally validated and represents 4180 P. vivax genes in sense orientation, of which 1219 genes have also probes in antisense orientation. Validation of the 15K array by using field samples (n =14) has shown 99% of parasite transcript detection from any of the samples. Correlation analysis between duplicate probes (n = 85) present in the arrays showed perfect correlation (r(2) = 0.98) indicating the reproducibility. Multiple probes representing the same gene exhibited similar kind of expression pattern across the samples (positive correlation, r >= 0.6). Comparison of hybridization data with the previous studies and quantitative real-time PCR experiments were performed to highlight the microarray validation procedure. This array is unique in its design, and results indicate that the array is sensitive and reproducible. Hence, this microarray could be a valuable functional genomics tool to generate reliable expression data from P. vivax field isolates. (C) 2016 Published by Elsevier B.V.

  15. Mutation Analysis of Consanguineous Moroccan Patients with Parkinson’s Disease Combining Microarray and Gene Panel

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    Ahmed Bouhouche

    2017-10-01

    Full Text Available During the last two decades, 15 different genes have been reported to be responsible for the monogenic form of Parkinson’s disease (PD, representing a worldwide frequency of 5–10%. Among them, 10 genes have been associated with autosomal recessive PD, with PRKN and PINK1 being the most frequent. In a cohort of 145 unrelated Moroccan PD patients enrolled since 2013, 19 patients were born from a consanguineous marriage, of which 15 were isolated cases and 4 familial. One patient was homozygous for the common LRRK2 G2019S mutation and the 18 others who did not carry this mutation were screened for exon rearrangements in the PRKN gene using Affymetrix Cytoscan HD microarray. Two patients were determined homozygous for PRKN exon-deletions, while another patient presented with compound heterozygous inheritance (3/18, 17%. Two other patients showed a region of homozygosity covering the 1p36.12 locus and were sequenced for the candidate PINK1 gene, which revealed two homozygous point mutations: the known Q456X mutation in exon 7 and a novel L539F variation in exon 8. The 13 remaining patients were subjected to next-generation sequencing (NGS that targeted a panel of 22 PD-causing genes and overlapping phenotypes. NGS data showed that two unrelated consanguineous patients with juvenile-onset PD (12 and 13 years carried the same homozygous stop mutation W258X in the ATP13A2 gene, possibly resulting from a founder effect; and one patient with late onset (76 years carried a novel heterozygous frameshift mutation in SYNJ1. Clinical analysis showed that patients with the ATP13A2 mutation developed juvenile-onset PD with a severe phenotype, whereas patients having either PRKN or PINK1 mutations displayed early-onset PD with a relatively mild phenotype. By identifying pathogenic mutations in 45% (8/18 of our consanguineous Moroccan PD series, we demonstrate that the combination of chromosomal microarray analysis and NGS is a powerful approach to

  16. Drop drying on surfaces determines chemical reactivity - the specific case of immobilization of oligonucleotides on microarrays

    Science.gov (United States)

    2013-01-01

    Background Drop drying is a key factor in a wide range of technical applications, including spotted microarrays. The applied nL liquid volume provides specific reaction conditions for the immobilization of probe molecules to a chemically modified surface. Results We investigated the influence of nL and μL liquid drop volumes on the process of probe immobilization and compare the results obtained to the situation in liquid solution. In our data, we observe a strong relationship between drop drying effects on immobilization and surface chemistry. In this work, we present results on the immobilization of dye labeled 20mer oligonucleotides with and without an activating 5′-aminoheptyl linker onto a 2D epoxysilane and a 3D NHS activated hydrogel surface. Conclusions Our experiments identified two basic processes determining immobilization. First, the rate of drop drying that depends on the drop volume and the ambient relative humidity. Oligonucleotides in a dried spot react unspecifically with the surface and long reaction times are needed. 3D hydrogel surfaces allow for immobilization in a liquid environment under diffusive conditions. Here, oligonucleotide immobilization is much faster and a specific reaction with the reactive linker group is observed. Second, the effect of increasing probe concentration as a result of drop drying. On a 3D hydrogel, the increasing concentration of probe molecules in nL spotting volumes accelerates immobilization dramatically. In case of μL volumes, immobilization depends on whether the drop is allowed to dry completely. At non-drying conditions, very limited immobilization is observed due to the low oligonucleotide concentration used in microarray spotting solutions. The results of our study provide a general guideline for microarray assay development. They allow for the initial definition and further optimization of reaction conditions for the immobilization of oligonucleotides and other probe molecule classes to different

  17. Gene expression analysis of the biocontrol fungus Trichoderma harzianum in the presence of tomato plants, chitin, or glucose using a high-density oligonucleotide microarray.

    Science.gov (United States)

    Samolski, Ilanit; de Luis, Alberto; Vizcaíno, Juan Antonio; Monte, Enrique; Suárez, M Belén

    2009-10-13

    It has recently been shown that the Trichoderma fungal species used for biocontrol of plant diseases are capable of interacting with plant roots directly, behaving as symbiotic microorganisms. With a view to providing further information at transcriptomic level about the early response of Trichoderma to a host plant, we developed a high-density oligonucleotide (HDO) microarray encompassing 14,081 Expressed Sequence Tag (EST)-based transcripts from eight Trichoderma spp. and 9,121 genome-derived transcripts of T. reesei, and we have used this microarray to examine the gene expression of T. harzianum either alone or in the presence of tomato plants, chitin, or glucose. Global microarray analysis revealed 1,617 probe sets showing differential expression in T. harzianum mycelia under at least one of the culture conditions tested as compared with one another. Hierarchical clustering and heat map representation showed that the expression patterns obtained in glucose medium clustered separately from the expression patterns observed in the presence of tomato plants and chitin. Annotations using the Blast2GO suite identified 85 of the 257 transcripts whose probe sets afforded up-regulated expression in response to tomato plants. Some of these transcripts were predicted to encode proteins related to Trichoderma-host (fungus or plant) associations, such as Sm1/Elp1 protein, proteases P6281 and PRA1, enchochitinase CHIT42, or QID74 protein, although previously uncharacterized genes were also identified, including those responsible for the possible biosynthesis of nitric oxide, xenobiotic detoxification, mycelium development, or those related to the formation of infection structures in plant tissues. The effectiveness of the Trichoderma HDO microarray to detect different gene responses under different growth conditions in the fungus T. harzianum strongly indicates that this tool should be useful for further assays that include different stages of plant colonization, as well as

  18. Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

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    Cheung Leo

    2007-02-01

    Full Text Available Abstract Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make

  19. PERBANDINGAN ANALISIS LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR DAN PARTIAL LEAST SQUARES (Studi Kasus: Data Microarray

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    KADEK DWI FARMANI

    2012-09-01

    Full Text Available Linear regression analysis is one of the parametric statistical methods which utilize the relationship between two or more quantitative variables. In linear regression analysis, there are several assumptions that must be met that is normal distribution of errors, there is no correlation between the error and error variance is constant and homogent. There are some constraints that caused the assumption can not be met, for example, the correlation between independent variables (multicollinearity, constraints on the number of data and independent variables are obtained. When the number of samples obtained less than the number of independent variables, then the data is called the microarray data. Least Absolute shrinkage and Selection Operator (LASSO and Partial Least Squares (PLS is a statistical method that can be used to overcome the microarray, overfitting, and multicollinearity. From the above description, it is necessary to study with the intention of comparing LASSO and PLS method. This study uses coronary heart and stroke patients data which is a microarray data and contain multicollinearity. With these two characteristics of the data that most have a weak correlation between independent variables, LASSO method produces a better model than PLS seen from the large RMSEP.

  20. AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

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    V.G. Biju

    2015-11-01

    Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

  1. Gene expression profiling in gill tissues of White spot syndrome virus infected black tiger shrimp Penaeus monodon by DNA microarray.

    Science.gov (United States)

    Shekhar, M S; Gomathi, A; Gopikrishna, G; Ponniah, A G

    2015-06-01

    White spot syndrome virus (WSSV) continues to be the most devastating viral pathogen infecting penaeid shrimp the world over. The genome of WSSV has been deciphered and characterized from three geographical isolates and significant progress has been made in developing various molecular diagnostic methods to detect the virus. However, the information on host immune gene response to WSSV pathogenesis is limited. Microarray analysis was carried out as an approach to analyse the gene expression in black tiger shrimp Penaeus monodon in response to WSSV infection. Gill tissues collected from the WSSV infected shrimp at 6, 24, 48 h and moribund stage were analysed for differential gene expression. Shrimp cDNAs of 40,059 unique sequences were considered for designing the microarray chip. The Cy3-labeled cRNA derived from healthy and WSSV-infected shrimp was subjected to hybridization with all the DNA spots in the microarray which revealed 8,633 and 11,147 as up- and down-regulated genes respectively at different time intervals post infection. The altered expression of these numerous genes represented diverse functions such as immune response, osmoregulation, apoptosis, nucleic acid binding, energy and metabolism, signal transduction, stress response and molting. The changes in gene expression profiles observed by microarray analysis provides molecular insights and framework of genes which are up- and down-regulated at different time intervals during WSSV infection in shrimp. The microarray data was validated by Real Time analysis of four differentially expressed genes involved in apoptosis (translationally controlled tumor protein, inhibitor of apoptosis protein, ubiquitin conjugated enzyme E2 and caspase) for gene expression levels. The role of apoptosis related genes in WSSV infected shrimp is discussed herein.

  2. Prediction of transcriptional regulatory elements for plant hormone responses based on microarray data

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    Yamaguchi-Shinozaki Kazuko

    2011-02-01

    Full Text Available Abstract Background Phytohormones organize plant development and environmental adaptation through cell-to-cell signal transduction, and their action involves transcriptional activation. Recent international efforts to establish and maintain public databases of Arabidopsis microarray data have enabled the utilization of this data in the analysis of various phytohormone responses, providing genome-wide identification of promoters targeted by phytohormones. Results We utilized such microarray data for prediction of cis-regulatory elements with an octamer-based approach. Our test prediction of a drought-responsive RD29A promoter with the aid of microarray data for response to drought, ABA and overexpression of DREB1A, a key regulator of cold and drought response, provided reasonable results that fit with the experimentally identified regulatory elements. With this succession, we expanded the prediction to various phytohormone responses, including those for abscisic acid, auxin, cytokinin, ethylene, brassinosteroid, jasmonic acid, and salicylic acid, as well as for hydrogen peroxide, drought and DREB1A overexpression. Totally 622 promoters that are activated by phytohormones were subjected to the prediction. In addition, we have assigned putative functions to 53 octamers of the Regulatory Element Group (REG that have been extracted as position-dependent cis-regulatory elements with the aid of their feature of preferential appearance in the promoter region. Conclusions Our prediction of Arabidopsis cis-regulatory elements for phytohormone responses provides guidance for experimental analysis of promoters to reveal the basis of the transcriptional network of phytohormone responses.

  3. Systematic validation and atomic force microscopy of non-covalent short oligonucleotide barcode microarrays.

    Directory of Open Access Journals (Sweden)

    Michael A Cook

    Full Text Available BACKGROUND: Molecular barcode arrays provide a powerful means to analyze cellular phenotypes in parallel through detection of short (20-60 base unique sequence tags, or "barcodes", associated with each strain or clone in a collection. However, costs of current methods for microarray construction, whether by in situ oligonucleotide synthesis or ex situ coupling of modified oligonucleotides to the slide surface are often prohibitive to large-scale analyses. METHODOLOGY/PRINCIPAL FINDINGS: Here we demonstrate that unmodified 20mer oligonucleotide probes printed on conventional surfaces show comparable hybridization signals to covalently linked 5'-amino-modified probes. As a test case, we undertook systematic cell size analysis of the budding yeast Saccharomyces cerevisiae genome-wide deletion collection by size separation of the deletion pool followed by determination of strain abundance in size fractions by barcode arrays. We demonstrate that the properties of a 13K unique feature spotted 20 mer oligonucleotide barcode microarray compare favorably with an analogous covalently-linked oligonucleotide array. Further, cell size profiles obtained with the size selection/barcode array approach recapitulate previous cell size measurements of individual deletion strains. Finally, through atomic force microscopy (AFM, we characterize the mechanism of hybridization to unmodified barcode probes on the slide surface. CONCLUSIONS/SIGNIFICANCE: These studies push the lower limit of probe size in genome-scale unmodified oligonucleotide microarray construction and demonstrate a versatile, cost-effective and reliable method for molecular barcode analysis.

  4. DNA microarray global gene expression analysis of influenza virus-infected chicken and duck cells

    Directory of Open Access Journals (Sweden)

    Suresh V. Kuchipudi

    2015-06-01

    Full Text Available The data described in this article pertain to the article by Kuchipudi et al. (2014 titled “Highly Pathogenic Avian Influenza Virus Infection in Chickens But Not Ducks Is Associated with Elevated Host Immune and Pro-inflammatory Responses” [1]. While infection of chickens with highly pathogenic avian influenza (HPAI H5N1 virus subtypes often leads to 100% mortality within 1 to 2 days, infection of ducks in contrast causes mild or no clinical signs. The rapid onset of fatal disease in chickens, but with no evidence of severe clinical symptoms in ducks, suggests underlying differences in their innate immune mechanisms. We used Chicken Genechip microarrays (Affymetrix to analyse the gene expression profiles of primary chicken and duck lung cells infected with a low pathogenic avian influenza (LPAI H2N3 virus and two HPAI H5N1 virus subtypes to understand the molecular basis of host susceptibility and resistance in chickens and ducks. Here, we described the experimental design, quality control and analysis that were performed on the data set. The data are publicly available through the Gene Expression Omnibus (GEOdatabase with accession number GSE33389, and the analysis and interpretation of these data are included in Kuchipudi et al. (2014 [1].

  5. Identification of differentially expressed genes in cutaneous squamous cell carcinoma by microarray expression profiling

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    Sterry Wolfram

    2006-08-01

    Full Text Available Abstract Background Carcinogenesis is a multi-step process indicated by several genes up- or down-regulated during tumor progression. This study examined and identified differentially expressed genes in cutaneous squamous cell carcinoma (SCC. Results Three different biopsies of 5 immunosuppressed organ-transplanted recipients each normal skin (all were pooled, actinic keratosis (AK (two were pooled, and invasive SCC and additionally 5 normal skin tissues from immunocompetent patients were analyzed. Thus, total RNA of 15 specimens were used for hybridization with Affymetrix HG-U133A microarray technology containing 22,283 genes. Data analyses were performed by prediction analysis of microarrays using nearest shrunken centroids with the threshold 3.5 and ANOVA analysis was independently performed in order to identify differentially expressed genes (p vs. AK and SCC were observed for 118 genes. Conclusion The majority of identified differentially expressed genes in cutaneous SCC were previously not described.

  6. Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray

    Directory of Open Access Journals (Sweden)

    Lan Shu

    2008-07-01

    Full Text Available Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLE’s KNN algorithm. In addition, kernel method based support vector machine (SVM will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method.

  7. Facilitating RNA structure prediction with microarrays.

    Science.gov (United States)

    Kierzek, Elzbieta; Kierzek, Ryszard; Turner, Douglas H; Catrina, Irina E

    2006-01-17

    Determining RNA secondary structure is important for understanding structure-function relationships and identifying potential drug targets. This paper reports the use of microarrays with heptamer 2'-O-methyl oligoribonucleotides to probe the secondary structure of an RNA and thereby improve the prediction of that secondary structure. When experimental constraints from hybridization results are added to a free-energy minimization algorithm, the prediction of the secondary structure of Escherichia coli 5S rRNA improves from 27 to 92% of the known canonical base pairs. Optimization of buffer conditions for hybridization and application of 2'-O-methyl-2-thiouridine to enhance binding and improve discrimination between AU and GU pairs are also described. The results suggest that probing RNA with oligonucleotide microarrays can facilitate determination of secondary structure.

  8. Utility of the pooling approach as applied to whole genome association scans with high-density Affymetrix microarrays

    Directory of Open Access Journals (Sweden)

    Gray Joanna

    2010-11-01

    Full Text Available Abstract Background We report an attempt to extend the previously successful approach of combining SNP (single nucleotide polymorphism microarrays and DNA pooling (SNP-MaP employing high-density microarrays. Whereas earlier studies employed a range of Affymetrix SNP microarrays comprising from 10 K to 500 K SNPs, this most recent investigation used the 6.0 chip which displays 906,600 SNP probes and 946,000 probes for the interrogation of CNVs (copy number variations. The genotyping assay using the Affymetrix SNP 6.0 array is highly demanding on sample quality due to the small feature size, low redundancy, and lack of mismatch probes. Findings In the first study published so far using this microarray on pooled DNA, we found that pooled cheek swab DNA could not accurately predict real allele frequencies of the samples that comprised the pools. In contrast, the allele frequency estimates using blood DNA pools were reasonable, although inferior compared to those obtained with previously employed Affymetrix microarrays. However, it might be possible to improve performance by developing improved analysis methods. Conclusions Despite the decreasing costs of genome-wide individual genotyping, the pooling approach may have applications in very large-scale case-control association studies. In such cases, our study suggests that high-quality DNA preparations and lower density platforms should be preferred.

  9. ArraySolver: an algorithm for colour-coded graphical display and Wilcoxon signed-rank statistics for comparing microarray gene expression data.

    Science.gov (United States)

    Khan, Haseeb Ahmad

    2004-01-01

    The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann-Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n < or = 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform.

  10. Potential upstream regulators of cannabinoid receptor 1 signaling in prostate cancer: a Bayesian network analysis of data from a tissue microarray.

    Science.gov (United States)

    Häggström, Jenny; Cipriano, Mariateresa; Forshell, Linus Plym; Persson, Emma; Hammarsten, Peter; Stella, Nephi; Fowler, Christopher J

    2014-08-01

    The endocannabinoid system regulates cancer cell proliferation, and in prostate cancer a high cannabinoid CB1 receptor expression is associated with a poor prognosis. Down-stream mediators of CB1 receptor signaling in prostate cancer are known, but information on potential upstream regulators is lacking. Data from a well-characterized tumor tissue microarray were used for a Bayesian network analysis using the max-min hill-climbing method. In non-malignant tissue samples, a directionality of pEGFR (the phosphorylated form of the epidermal growth factor receptor) → CB1 receptors were found regardless as to whether the endocannabinoid metabolizing enzyme fatty acid amide hydrolase (FAAH) was included as a parameter. A similar result was found in the tumor tissue, but only when FAAH was included in the analysis. A second regulatory pathway, from the growth factor receptor ErbB2 → FAAH was also identified in the tumor samples. Transfection of AT1 prostate cancer cells with CB1 receptors induced a sensitivity to the growth-inhibiting effects of the CB receptor agonist CP55,940. The sensitivity was not dependent upon the level of receptor expression. Thus a high CB1 receptor expression alone does not drive the cells towards a survival phenotype in the presence of a CB receptor agonist. The data identify two potential regulators of the endocannabinoid system in prostate cancer and allow the construction of a model of a dysregulated endocannabinoid signaling network in this tumor. Further studies should be designed to test the veracity of the predictions of the network analysis in prostate cancer and other solid tumors. © 2014 The Authors. The Prostate published by Wiley Periodicals, Inc.

  11. See what you eat--broad GMO screening with microarrays.

    Science.gov (United States)

    von Götz, Franz

    2010-03-01

    Despite the controversy of whether genetically modified organisms (GMOs) are beneficial or harmful for humans, animals, and/or ecosystems, the number of cultivated GMOs is increasing every year. Many countries and federations have implemented safety and surveillance systems for GMOs. Potent testing technologies need to be developed and implemented to monitor the increasing number of GMOs. First, these GMO tests need to be comprehensive, i.e., should detect all, or at least the most important, GMOs on the market. This type of GMO screening requires a high degree of parallel tests or multiplexing. To date, DNA microarrays have the highest number of multiplexing capabilities when nucleic acids are analyzed. This trend article focuses on the evolution of DNA microarrays for GMO testing. Over the last 7 years, combinations of multiplex PCR detection and microarray detection have been developed to qualitatively assess the presence of GMOs. One example is the commercially available DualChip GMO (Eppendorf, Germany; http://www.eppendorf-biochip.com), which is the only GMO screening system successfully validated in a multicenter study. With use of innovative amplification techniques, promising steps have recently been taken to make GMO detection with microarrays quantitative.

  12. Microarray analysis reveals genetic pathways modulated by tipifarnib in acute myeloid leukemia

    International Nuclear Information System (INIS)

    Raponi, Mitch; Belly, Robert T; Karp, Judith E; Lancet, Jeffrey E; Atkins, David; Wang, Yixin

    2004-01-01

    Farnesyl protein transferase inhibitors (FTIs) were originally developed to inhibit oncogenic ras, however it is now clear that there are several other potential targets for this drug class. The FTI tipifarnib (ZARNESTRA™, R115777) has recently demonstrated clinical responses in adults with refractory and relapsed acute leukemias. This study was conducted to identify genetic markers and pathways that are regulated by tipifarnib in acute myeloid leukemia (AML). Tipifarnib-mediated gene expression changes in 3 AML cell lines and bone marrow samples from two patients with AML were analyzed on a cDNA microarray containing approximately 7000 human genes. Pathways associated with these expression changes were identified using the Ingenuity Pathway Analysis tool. The expression analysis identified a common set of genes that were regulated by tipifarnib in three leukemic cell lines and in leukemic blast cells isolated from two patients who had been treated with tipifarnib. Association of modulated genes with biological functional groups identified several pathways affected by tipifarnib including cell signaling, cytoskeletal organization, immunity, and apoptosis. Gene expression changes were verified in a subset of genes using real time RT-PCR. Additionally, regulation of apoptotic genes was found to correlate with increased Annexin V staining in the THP-1 cell line but not in the HL-60 cell line. The genetic networks derived from these studies illuminate some of the biological pathways affected by FTI treatment while providing a proof of principle for identifying candidate genes that might be used as surrogate biomarkers of drug activity

  13. Fuzzy C-means method for clustering microarray data.

    Science.gov (United States)

    Dembélé, Doulaye; Kastner, Philippe

    2003-05-22

    Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/

  14. Recommendations for the use of microarrays in prenatal diagnosis.

    Science.gov (United States)

    Suela, Javier; López-Expósito, Isabel; Querejeta, María Eugenia; Martorell, Rosa; Cuatrecasas, Esther; Armengol, Lluis; Antolín, Eugenia; Domínguez Garrido, Elena; Trujillo-Tiebas, María José; Rosell, Jordi; García Planells, Javier; Cigudosa, Juan Cruz

    2017-04-07

    Microarray technology, recently implemented in international prenatal diagnosis systems, has become one of the main techniques in this field in terms of detection rate and objectivity of the results. This guideline attempts to provide background information on this technology, including technical and diagnostic aspects to be considered. Specifically, this guideline defines: the different prenatal sample types to be used, as well as their characteristics (chorionic villi samples, amniotic fluid, fetal cord blood or miscarriage tissue material); variant reporting policies (including variants of uncertain significance) to be considered in informed consents and prenatal microarray reports; microarray limitations inherent to the technique and which must be taken into account when recommending microarray testing for diagnosis; a detailed clinical algorithm recommending the use of microarray testing and its introduction into routine clinical practice within the context of other genetic tests, including pregnancies in families with a genetic history or specific syndrome suspicion, first trimester increased nuchal translucency or second trimester heart malformation and ultrasound findings not related to a known or specific syndrome. This guideline has been coordinated by the Spanish Association for Prenatal Diagnosis (AEDP, «Asociación Española de Diagnóstico Prenatal»), the Spanish Human Genetics Association (AEGH, «Asociación Española de Genética Humana») and the Spanish Society of Clinical Genetics and Dysmorphology (SEGCyD, «Sociedad Española de Genética Clínica y Dismorfología»). Copyright © 2017 Elsevier España, S.L.U. All rights reserved.

  15. Screening for viral extraneous agents in live-attenuated avian vaccines by using a microbial microarray and sequencing

    DEFF Research Database (Denmark)

    Olesen, Majken Lindholm; Jørgensen, Lotte Leick; Blixenkrone-Møller, Merete

    2018-01-01

    The absence of extraneous agents (EA) in the raw material used for production and in finished products is one of the principal safety elements related to all medicinal products of biological origin, such as live-attenuated vaccines. The aim of this study was to investigate the applicability...... of the Lawrence Livermore Microbial detection array version 2 (LLMDAv2) combined with whole genome amplification and sequencing for screening for viral EAs in live-attenuated vaccines and specific pathogen-free (SPF) eggs.We detected positive microarray signals for avian endogenous retrovirus EAV-HP and several...... viruses belonging to the Alpharetrovirus genus in all analyzed vaccines and SPF eggs. We used a microarray probe mapping approach to evaluate the presence of intact retroviral genomes, which in addition to PCR analysis revealed that several of the positive microarray signals were most likely due to cross...

  16. Uropathogenic Escherichia coli virulence genes: invaluable approaches for designing DNA microarray probes.

    Science.gov (United States)

    Jahandeh, Nadia; Ranjbar, Reza; Behzadi, Payam; Behzadi, Elham

    2015-01-01

    The pathotypes of uropathogenic Escherichia coli (UPEC) cause different types of urinary tract infections (UTIs). The presence of a wide range of virulence genes in UPEC enables us to design appropriate DNA microarray probes. These probes, which are used in DNA microarray technology, provide us with an accurate and rapid diagnosis and definitive treatment in association with UTIs caused by UPEC pathotypes. The main goal of this article is to introduce the UPEC virulence genes as invaluable approaches for designing DNA microarray probes. Main search engines such as Google Scholar and databases like NCBI were searched to find and study several original pieces of literature, review articles, and DNA gene sequences. In parallel with in silico studies, the experiences of the authors were helpful for selecting appropriate sources and writing this review article. There is a significant variety of virulence genes among UPEC strains. The DNA sequences of virulence genes are fabulous patterns for designing microarray probes. The location of virulence genes and their sequence lengths influence the quality of probes. The use of selected virulence genes for designing microarray probes gives us a wide range of choices from which the best probe candidates can be chosen. DNA microarray technology provides us with an accurate, rapid, cost-effective, sensitive, and specific molecular diagnostic method which is facilitated by designing microarray probes. Via these tools, we are able to have an accurate diagnosis and a definitive treatment regarding UTIs caused by UPEC pathotypes.

  17. ESTs, cDNA microarrays, and gene expression profiling: tools for dissecting plant physiology and development.

    Science.gov (United States)

    Alba, Rob; Fei, Zhangjun; Payton, Paxton; Liu, Yang; Moore, Shanna L; Debbie, Paul; Cohn, Jonathan; D'Ascenzo, Mark; Gordon, Jeffrey S; Rose, Jocelyn K C; Martin, Gregory; Tanksley, Steven D; Bouzayen, Mondher; Jahn, Molly M; Giovannoni, Jim

    2004-09-01

    Gene expression profiling holds tremendous promise for dissecting the regulatory mechanisms and transcriptional networks that underlie biological processes. Here we provide details of approaches used by others and ourselves for gene expression profiling in plants with emphasis on cDNA microarrays and discussion of both experimental design and downstream analysis. We focus on methods and techniques emphasizing fabrication of cDNA microarrays, fluorescent labeling, cDNA hybridization, experimental design, and data processing. We include specific examples that demonstrate how this technology can be used to further our understanding of plant physiology and development (specifically fruit development and ripening) and for comparative genomics by comparing transcriptome activity in tomato and pepper fruit.

  18. Microarray-based analysis of plasma cirDNA epigenetic modification profiling in xenografted mice exposed to intermittent hypoxia

    Directory of Open Access Journals (Sweden)

    Rene Cortese

    2015-09-01

    Full Text Available Intermittent hypoxia (IH during sleep is one of the major abnormalities occurring in patients suffering from obstructive sleep apnea (OSA, a highly prevalent disorder affecting 6–15% of the general population, particularly among obese people. IH has been proposed as a major determinant of oncogenetically-related processes such as tumor growth, invasion and metastasis. During the growth and expansion of tumors, fragmented DNA is released into the bloodstream and enters the circulation. Circulating tumor DNA (cirDNA conserves the genetic and epigenetic profiles from the tumor of origin and can be isolated from the plasma fraction. Here we report a microarray-based epigenetic profiling of cirDNA isolated from blood samples of mice engrafted with TC1 epithelial lung cancer cells and controls, which were exposed to IH during sleep (XenoIH group, n = 3 or control conditions, (i.e., room air (RA; XenoRA group, n = 3 conditions. To prepare the targets for microarray hybridization, we applied a previously developed method that enriches the modified fraction of the cirDNA without amplification of genomic DNA. Regions of differential cirDNA modification between the two groups were identified by hybridizing the enriched fractions for each sample to Affymetrix GeneChip Human Promoter Arrays 1.0R. Microarray raw and processed data were deposited in NCBI's Gene Expression Omnibus (GEO database (accession number: GSE61070.

  19. Microarray analysis reveals key genes and pathways in Tetralogy of Fallot

    Science.gov (United States)

    He, Yue-E; Qiu, Hui-Xian; Jiang, Jian-Bing; Wu, Rong-Zhou; Xiang, Ru-Lian; Zhang, Yuan-Hai

    2017-01-01

    The aim of the present study was to identify key genes that may be involved in the pathogenesis of Tetralogy of Fallot (TOF) using bioinformatics methods. The GSE26125 microarray dataset, which includes cardiovascular tissue samples derived from 16 children with TOF and five healthy age-matched control infants, was downloaded from the Gene Expression Omnibus database. Differential expression analysis was performed between TOF and control samples to identify differentially expressed genes (DEGs) using Student's t-test, and the R/limma package, with a log2 fold-change of >2 and a false discovery rate of <0.01 set as thresholds. The biological functions of DEGs were analyzed using the ToppGene database. The ReactomeFIViz application was used to construct functional interaction (FI) networks, and the genes in each module were subjected to pathway enrichment analysis. The iRegulon plugin was used to identify transcription factors predicted to regulate the DEGs in the FI network, and the gene-transcription factor pairs were then visualized using Cytoscape software. A total of 878 DEGs were identified, including 848 upregulated genes and 30 downregulated genes. The gene FI network contained seven function modules, which were all comprised of upregulated genes. Genes enriched in Module 1 were enriched in the following three neurological disorder-associated signaling pathways: Parkinson's disease, Alzheimer's disease and Huntington's disease. Genes in Modules 0, 3 and 5 were dominantly enriched in pathways associated with ribosomes and protein translation. The Xbox binding protein 1 transcription factor was demonstrated to be involved in the regulation of genes encoding the subunits of cytoplasmic and mitochondrial ribosomes, as well as genes involved in neurodegenerative disorders. Therefore, dysfunction of genes involved in signaling pathways associated with neurodegenerative disorders, ribosome function and protein translation may contribute to the pathogenesis of TOF

  20. Microarray analysis of toxicogenomic effects of Ortho-phenylphenol in Staphylococcus aureus

    Directory of Open Access Journals (Sweden)

    Toghrol Freshteh

    2008-09-01

    Full Text Available Abstract Background Staphylococcus aureus (S. aureus, is responsible for many infectious diseases, ranging from benign skin infections to life-threatening endocarditis and toxic shock syndrome. Ortho-phenylphenol (OPP is an antimicrobial agent and an active ingredient of EPA-registered disinfectants with wide human exposure in various agricultural, hospital and veterinary disinfectant products. Despite many uses, an understanding of a cellular response to OPP and it's mechanism of action, targeted genes, and the connectivity between targeted genes and the rest of cell metabolism remains obscure. Results Herein, we performed a genome-wide transcriptome analysis of the cellular responses of S. aureus when exposed to 0.82 mM of OPP for 20 and 60 min. Our data indicated that OPP downregulated the biosynthesis of many amino acids, which are required for protein synthesis. In particular, the genes encoding the enzymes of the diaminopimelate (DAP pathway which results in lysine biosynthesis were significantly downregualted. Intriguingly, we revealed that the transcription of genes encoding ribosomal proteins was upregulated by OPP and at the same time, the genes encoding iron acquisition and transport were downregulated. The genes encoding virulence factors were upregulated and genes encoding phospholipids were downregulated upon 20 min exposure to OPP. Conclusion By using microarray analysis that enables us to simultaneously and globally examine the complete transcriptome during cellular responses, we have revealed novel information regarding the mode of action of OPP on Staphylococcus: OPP inhibits anabolism of many amino acids and highly downregulates the genes that encode the enzymes involved in the DAP pathway. Lysine and DAP are essential for building up the peptidoglycan cell wall. It was concluded that the mode of action of OPP is similar to the mechanism of action of some antibiotics. The discovery of this phenomenon provides useful