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

SmD1 Modulates the miRNA Pathway Independently of Its Pre-mRNA Splicing Function.

Directory of Open Access Journals (Sweden)

Xiao-Peng Xiong

2015-08-01

Full Text Available microRNAs (miRNAs are a class of endogenous regulatory RNAs that play a key role in myriad biological processes. Upon transcription, primary miRNA transcripts are sequentially processed by Drosha and Dicer ribonucleases into ~22-24 nt miRNAs. Subsequently, miRNAs are incorporated into the RNA-induced silencing complexes (RISCs that contain Argonaute (AGO family proteins and guide RISC to target RNAs via complementary base pairing, leading to post-transcriptional gene silencing by a combination of translation inhibition and mRNA destabilization. Select pre-mRNA splicing factors have been implicated in small RNA-mediated gene silencing pathways in fission yeast, worms, flies and mammals, but the underlying molecular mechanisms are not well understood. Here, we show that SmD1, a core component of the Drosophila small nuclear ribonucleoprotein particle (snRNP implicated in splicing, is required for miRNA biogenesis and function. SmD1 interacts with both the microprocessor component Pasha and pri-miRNAs, and is indispensable for optimal miRNA biogenesis. Depletion of SmD1 impairs the assembly and function of the miRISC without significantly affecting the expression of major canonical miRNA pathway components. Moreover, SmD1 physically and functionally associates with components of the miRISC, including AGO1 and GW182. Notably, miRNA defects resulting from SmD1 silencing can be uncoupled from defects in pre-mRNA splicing, and the miRNA and splicing machineries are physically and functionally distinct entities. Finally, photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP analysis identifies numerous SmD1-binding events across the transcriptome and reveals direct SmD1-miRNA interactions. Our study suggests that SmD1 plays a direct role in miRNA-mediated gene silencing independently of its pre-mRNA splicing activity and indicates that the dual roles of splicing factors in post-transcriptional gene regulation may be

Database Description - RED | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available ase Description General information of database Database name RED Alternative name Rice Expression Database...enome Research Unit Shoshi Kikuchi E-mail : Database classification Plant databases - Rice Database classifi...cation Microarray, Gene Expression Organism Taxonomy Name: Oryza sativa Taxonomy ID: 4530 Database descripti... Article title: Rice Expression Database: the gateway to rice functional genomics...nt Science (2002) Dec 7 (12):563-564 External Links: Original website information Database maintenance site

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.

Database Description - RMOS | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available base Description General information of database Database name RMOS Alternative nam...arch Unit Shoshi Kikuchi E-mail : Database classification Plant databases - Rice Microarray Data and other Gene Expression Database...s Organism Taxonomy Name: Oryza sativa Taxonomy ID: 4530 Database description The Ric...19&lang=en Whole data download - Referenced database Rice Expression Database (RED) Rice full-length cDNA Database... (KOME) Rice Genome Integrated Map Database (INE) Rice Mutant Panel Database (Tos17) Rice Genome Annotation Database

An Aggregated Optimization Model for Multi-Head SMD Placements

NARCIS (Netherlands)

Ashayeri, J.; Ma, N.; Sotirov, R.

2010-01-01

In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads,

An aggregated optimization model for multi-head SMD placements

NARCIS (Netherlands)

Ashayeri, J.; Ma, N.; Sotirov, R.

2011-01-01

In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads,

Amniotic Fluid Soluble Myeloid Differentiation-2 (sMD-2) as Regulator of Intra-amniotic Inflammation in Infection-induced Preterm Birth.

Science.gov (United States)

Dulay, Antonette T; Buhimschi, Catalin S; Zhao, Guomao; Oliver, Emily A; Abdel-Razeq, Sonya S; Shook, Lydia L; Bahtiyar, Mert O; Buhimschi, Irina A

2015-06-01

TLR4 mediates host responses to pathogens through a mechanism that involves protein myeloid differentiation-2 (MD-2) and its soluble form sMD-2. The role of sMD2 in intra-amniotic inflammation-induced preterm birth has not been previously explored. Human amniotic fluid (AF) sMD-2 was studied by Western blotting in 152 AF samples of patients who had an amniocentesis to rule-out infection (yes infection, n = 50; no infection, n = 50) or women with normal pregnancy outcome (second trimester genetic karyotyping, n = 26; third trimester lung maturity testing, n = 26). Histological localization and mRNA expression of MD2 in fetal membranes were studied by immunohistochemistry and RT-PCR. The ability of fetal membrane to release sMD-2 and inflammatory cytokines was studied in vitro. Human AF contains three sMD-2 proteoforms whose levels of expression were lower at term. Intra-amniotic infection upregulated sMD-2. MD-2 mRNA and immunohistochemistry findings concurred. In vitro, LPS and monensin increased, while cycloheximide decreased sMD-2 production. Recombinant sMD-2 modulated TNF-α and IL-6 levels in a dose- and time-dependent fashion. sMD2 proteoforms are constitutively present in human AF. The intensity of the intra-amniotic inflammatory response to bacteria or perhaps to other TLR4 ligands may be facilitated through synthesis and release of sMD2 by the amniochorion. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

MicroArray Facility: a laboratory information management system with extended support for Nylon based technologies

Directory of Open Access Journals (Sweden)

Beaudoing Emmanuel

2006-09-01

Full Text Available Abstract Background High throughput gene expression profiling (GEP is becoming a routine technique in life science laboratories. With experimental designs that repeatedly span thousands of genes and hundreds of samples, relying on a dedicated database infrastructure is no longer an option. GEP technology is a fast moving target, with new approaches constantly broadening the field diversity. This technology heterogeneity, compounded by the informatics complexity of GEP databases, means that software developments have so far focused on mainstream techniques, leaving less typical yet established techniques such as Nylon microarrays at best partially supported. Results MAF (MicroArray Facility is the laboratory database system we have developed for managing the design, production and hybridization of spotted microarrays. Although it can support the widely used glass microarrays and oligo-chips, MAF was designed with the specific idiosyncrasies of Nylon based microarrays in mind. Notably single channel radioactive probes, microarray stripping and reuse, vector control hybridizations and spike-in controls are all natively supported by the software suite. MicroArray Facility is MIAME supportive and dynamically provides feedback on missing annotations to help users estimate effective MIAME compliance. Genomic data such as clone identifiers and gene symbols are also directly annotated by MAF software using standard public resources. The MAGE-ML data format is implemented for full data export. Journalized database operations (audit tracking, data anonymization, material traceability and user/project level confidentiality policies are also managed by MAF. Conclusion MicroArray Facility is a complete data management system for microarray producers and end-users. Particular care has been devoted to adequately model Nylon based microarrays. The MAF system, developed and implemented in both private and academic environments, has proved a robust solution for

Discovery of possible gene relationships through the application of self-organizing maps to DNA microarray databases.

Science.gov (United States)

Chavez-Alvarez, Rocio; Chavoya, Arturo; Mendez-Vazquez, Andres

2014-01-01

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques--an unsupervised artificial neural network called a Self-Organizing Map (SOM)-which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.

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.

Database Description - DGBY | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available base Description General information of database Database name DGBY Alternative name Database...EL: +81-29-838-8066 E-mail: Database classification Microarray Data and other Gene Expression Databases Orga...nism Taxonomy Name: Saccharomyces cerevisiae Taxonomy ID: 4932 Database descripti...-called phenomics). We uploaded these data on this website which is designated DGBY(Database for Gene expres...ma J, Ando A, Takagi H. Journal: Yeast. 2008 Mar;25(3):179-90. External Links: Original website information Database

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.

[Implementation and evaluation of case management in Catalonia: the ISP-SMD program].

Science.gov (United States)

Balsera Gómez, J; Rodríguez Medina, C; Caba Calvet, R; Vega Prada, R; Ruiz Ureña, H; Berruezo Ortiz, L; Clusa Gironella, D; Rodríguez Montes, M J; Haro Abad, J M

2002-01-01

The pilot study of the Individualized Service Program for people with Severe Mental Disorders (ISP-SMD) consists of the implementation of case management services in Catalonia. The ISP-SMD has been implemented in two health care sectors and will be expanded to the rest of Catalonia in the next years. The program serves people with persistent mental disorders who have serious social or family problems and/or who have inadequate mental health service use (high use of inpatient services, no use of community services). The ISP-SMD is a community intervention program that focuses its activities on direct care and coordination between services. Thirty patients have been included in the evaluation. The results of the pilot study have shown that, compared to the year before entering the program, the patients show better clinical status, they decrease their unmet need level, they have more appropriate use of health services and have lower treatment costs. Satisfaction of the patients, family members and professionals with the program is very high. It is possible to adapt and implement case management services in Catalonia. When implemented, they improve patient quality of life.

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....

Tools for Scientist Engagement in E/PO: NASA SMD Community Workspace and Online Resources

Science.gov (United States)

Dalton, H.; Shipp, S. S.; Grier, J.; Gross, N. A.; Buxner, S.; Bartolone, L.; Peticolas, L. M.; Woroner, M.; Schwerin, T. G.

2014-12-01

The Science Mission Directorate (SMD) Science Education and Public Outreach (E/PO) Forums are here to help you get involved in E/PO! The Forums have been developing several online resources to support scientists who are - or who are interested in becoming - involved in E/PO. These include NASA Wavelength, EarthSpace, and the SMD E/PO online community workspace. NASA Wavelength is the one-stop shop of all peer-reviewed NASA education resources to find materials you - or your audiences - can use. Browse by audience (pre-K through 12, higher education, and informal education) or topic, or choose to search for something specific by keyword and audience. http://nasawavelength.org. EarthSpace, an online clearinghouse of Earth and space materials for use in the higher education classroom, is driven by a powerful search engine that allows you to browse the collection of resources by science topic, audience, type of material or key terms. All materials are peer-reviewed before posting, and because all submissions receive a digital object identifier (doi), submitted materials can be listed as publications. http://www.lpi.usra.edu/earthspace. The SMD E/PO online community workspace contains many resources for scientists. These include one-page guides on how to get involved, tips on how to make the most of your time spent on E/PO, and sample activities, as well as news on funding, policy, and what's happening in the E/PO community. The workspace also provides scientists and the public pathways to find opportunities for participation in E/PO, to learn about SMD E/PO projects and their impacts, to connect with SMD E/PO practitioners, and to explore resources to improve professional E/PO practice, including literature reviews, information about the Next Generation Science Standards, and best practices in evaluation and engaging diverse audiences. http://smdepo.org.

NASA SMD STEM Activation: Enabling NASA Science Experts and Content into the Learning Environment

Science.gov (United States)

Hasan, Hashima; Erickson, Kristen

2018-01-01

The NASA Science Mission Directorate (SMD) restructured its efforts to enhance learning in science, technology, engineering, and mathematics (STEM) content areas through a cooperative agreement notice issued in 2015. This effort resulted in the competitive selection of 27 organizations to implement a strategic approach that leverages SMD’s unique assets. Three of these are exclusively directed towards Astrophysics. These unique assets include SMD’s science and engineering content and Science Discipline Subject Matter Experts. Awardees began their work during 2016 and span all areas of Earth and space science and the audiences NASA SMD intends to reach. The goal of the restructured STEM Activation program is to further enable NASA science experts and content into the learning environment more effectively and efficiently with learners of all ages. The objectives are to enable STEM education, improve US scientific literacy, advance national educational goals, and leverage efforts through partnerships. This presentation will provide an overview of the NASA SMD STEM Activation landscape and its commitment to meeting user needs.

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

Directory of Open Access Journals (Sweden)

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.

SMD-based numerical stochastic perturbation theory

Science.gov (United States)

Dalla Brida, Mattia; Lüscher, Martin

2017-05-01

The viability of a variant of numerical stochastic perturbation theory, where the Langevin equation is replaced by the SMD algorithm, is examined. In particular, the convergence of the process to a unique stationary state is rigorously established and the use of higher-order symplectic integration schemes is shown to be highly profitable in this context. For illustration, the gradient-flow coupling in finite volume with Schrödinger functional boundary conditions is computed to two-loop (i.e. NNL) order in the SU(3) gauge theory. The scaling behaviour of the algorithm turns out to be rather favourable in this case, which allows the computations to be driven close to the continuum limit.

SMD-based numerical stochastic perturbation theory

Energy Technology Data Exchange (ETDEWEB)

Dalla Brida, Mattia [Universita di Milano-Bicocca, Dipartimento di Fisica, Milan (Italy); INFN, Sezione di Milano-Bicocca (Italy); Luescher, Martin [CERN, Theoretical Physics Department, Geneva (Switzerland); AEC, Institute for Theoretical Physics, University of Bern (Switzerland)

2017-05-15

The viability of a variant of numerical stochastic perturbation theory, where the Langevin equation is replaced by the SMD algorithm, is examined. In particular, the convergence of the process to a unique stationary state is rigorously established and the use of higher-order symplectic integration schemes is shown to be highly profitable in this context. For illustration, the gradient-flow coupling in finite volume with Schroedinger functional boundary conditions is computed to two-loop (i.e. NNL) order in the SU(3) gauge theory. The scaling behaviour of the algorithm turns out to be rather favourable in this case, which allows the computations to be driven close to the continuum limit. (orig.)

SMD-based numerical stochastic perturbation theory

International Nuclear Information System (INIS)

Dalla Brida, Mattia; Luescher, Martin

2017-01-01

The viability of a variant of numerical stochastic perturbation theory, where the Langevin equation is replaced by the SMD algorithm, is examined. In particular, the convergence of the process to a unique stationary state is rigorously established and the use of higher-order symplectic integration schemes is shown to be highly profitable in this context. For illustration, the gradient-flow coupling in finite volume with Schroedinger functional boundary conditions is computed to two-loop (i.e. NNL) order in the SU(3) gauge theory. The scaling behaviour of the algorithm turns out to be rather favourable in this case, which allows the computations to be driven close to the continuum limit. (orig.)

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.

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

Science.gov (United States)

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.

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.

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.

ELISA-BASE: an integrated bioinformatics tool for analyzing and tracking ELISA microarray data

OpenAIRE

White, Amanda M.; Collett, James R.; Seurynck-Servoss, Shannon L.; Daly, Don S.; Zangar, Richard C.

2009-01-01

Summary:ELISA-BASE is an open source database for capturing, organizing and analyzing enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Software Environment (BASE) database system.

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....

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.

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.

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

analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

A Java-based tool for the design of classification microarrays

Directory of Open Access Journals (Sweden)

Broschat Shira L

2008-08-01

generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

Autoantibodies from patients with systemic lupus erythematosus bind a shared sequence of SmD and Epstein-Barr virus-encoded nuclear antigen EBNA I.

Science.gov (United States)

Sabbatini, A; Bombardieri, S; Migliorini, P

1993-05-01

SmD is one of the small nuclear ribonucleoproteins frequently targeted by autoantibodies in systemic lupus erythematosus. We isolated and characterized the antibodies present in lupus sera that are specific for the C-terminal region of SmD (sequence 95-119). This region is highly homologous to sequence 35-58 of the EBNA I antigen, one of the nuclear antigens induced by infection with Epstein-Barr virus. Antibodies affinity purified over a peptide 95-119 column were able to recognize this sequence in the context of the whole SmD molecule, as they reacted with blotted recombinant SmD. Anti-SmD 95-119 antibodies bound also the EBNA I 35-58 peptide and detected the EBNA I molecule in a total cell extract from Epstein-Barr virus-infected lines. A population of anti-SmD antibodies is, therefore, able to bind an epitope shared by the autoantigen and the viral antigen EBNA I. To investigate the involvement of this shared epitope in the generation of anti-SmD antibodies, we immunized mice with the EBNA I 35-58 peptide. Sera from immunized animals displayed the same pattern of reactivity of spontaneously produced anti-SmD antibodies. They reacted in fact with the EBNA peptide as well as with SmD 95-119 and recombinant SmD. These data suggest that molecular mimicry may play a role in the induction of anti-SmD autoantibodies.

Resources from the NASA SMD Astrophysics Forum: Addressing the needs of the higher education community (Invited)

Science.gov (United States)

Meinke, B. K.; Schultz, G. R.; Smith, D.; Bianchi, L.; Blair, W. P.; Fraknoi, A.

2013-12-01

Four NASA Science Mission Directorate (SMD) Science Education and Public Outreach Forums organize individual SMD-funded E/PO projects and their teams into a coordinated effort. The Forums assist scientists and educators with becoming involved in SMD E/PO and make SMD E/PO resources and expertise accessible to the science and education communities. The Astrophysics Forum and the Astrophysics E/PO community have focused efforts to support and engage the higher education community on enhancing awareness of the resources available to them. To ensure Astrophysics higher education efforts are grounded in audience needs, we held informal conversations with instructors of introductory astronomy courses, convened sessions with higher education faculty and E/PO professionals at conferences, and examined existing literature and findings of the SMD Higher Education Working Group. This work indicates that most Astronomy 101 instructors are not specialists in areas of astrophysics where rapid progress is being made, older textbooks are out of date, and ideas are challenging for students. Instructors are seeking resources and training that support them in effectively teaching the latest science and are in need both basic material and information on new results. In this session, we will discuss our efforts to address these expressed needs, namely through Resource Guides and Slide Sets, and how these are applicable to topics in Heliophysics and Planetary Science. We have collaborated with the Astrophysics E/PO community, researchers, and Astronomy 101 instructors to create two Resource Guides on the topics of cosmology and exoplanets. These fields are ripe with scientific developments that college instructors have told us they find challenging to stay current. Each guide includes a wide variety of sources of background information, links to animations/simulations, classroom activities, and references on teaching each topic. Feedback from Astronomy 101 instructors indicated that the

An algorithm for finding biologically significant features in microarray data based on a priori manifold learning.

Directory of Open Access Journals (Sweden)

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.

NASA SMD Airborne Science Capabilities for Development and Testing of New Instruments

Science.gov (United States)

Fladeland, Matthew

2015-01-01

The SMD NASA Airborne Science Program operates and maintains a fleet of highly modified aircraft to support instrument development, satellite instrument calibration, data product validation and earth science process studies. This poster will provide an overview of aircraft available to NASA researchers including performance specifications and modifications for instrument support, processes for requesting aircraft time and developing cost estimates for proposals, and policies and procedures required to ensure safety of flight.

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

AMDA: an R package for the automated microarray data analysis

Directory of Open Access Journals (Sweden)

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/

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.

Dosimetric analysis of SMD phototransistor in dental phantom of different geometries

International Nuclear Information System (INIS)

Belinato, W.; Magalhaes, C. M. S.; Souza, D. N.; Santos, L. A. P.

2009-10-01

A commercial surface mount device (SMD) phototransistor, OP520, was inserted in two dental phantoms for dosimetric analysis. The irradiations were accomplished in a dental x-ray equipment of 80 kV using different exposition times. A standard ionization chamber was irradiated at the same conditions and the air kerma measured with it was compared with the electrical charge evaluated by the phototransistor. The results showed satisfactory correspondence among the detectors readings. Moreover, the phototransistor showed up quite sensitively for dental applications, allowing verifying the variations for the different phantoms configurations. (Author)

Engaging Scientists in Meaningful E/PO: How the NASA SMD E/PO Community Addresses Informal Educators' Preferences for PD and Materials

Science.gov (United States)

Bartolone, Lindsay; Nelson, Andi; Smith, Denise A.; NASA SMD Astrophysics E/PO Community

2015-01-01

The NASA Astrophysics Science Education and Public Outreach Forum (SEPOF) coordinates the work of NASA Science Mission Directorate (SMD) Astrophysics EPO projects. These teams work together to capitalize on the cutting-edge discoveries of NASA Astrophysics missions to support educators in Science, Technology, Engineering, and Math (STEM) and to enable youth to engage in doing STEM inside and outside of school. The Astrophysics Forum assists scientists and educators with becoming involved in SMD E/PO, which is uniquely poised to foster collaboration between scientists with content expertise and educators with pedagogy expertise, and makes SMD E/PO resources and expertise accessible to the science and education communities. Informal educators participated in a recent nationally-distributed survey from the NASA SMD SEPOF Informal Education Working Group. The results show the preferences of staff from museums, parks, public libraries, community/afterschool centers, and others with regard to professional development and material resources. The results of the survey will be presented during this session.In addition, we present opportunities for the astronomy community to participate in collaborations supporting the NASA SMD efforts in K-12 Formal Education, Informal Science Education, and Outreach. These efforts focus on enhancing instruction, as well as youth and public engagement, in STEM via use of research-based best practices, collaborations with libraries, partnerships with local and national organizations, and remote engagement of audiences. The Forums' efforts for the Formal, Informal Science Education and Outreach communities include a literature review, appraisal of informal educators' needs, coordination of audience-based NASA resources and opportunities, professional development, plus support with the Next Generation Science Standards. Learn how to join in our collaborative efforts to support the K-12 Formal Education community and to reach the informal

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.

Construction of an Ostrea edulis database from genomic and expressed sequence tags (ESTs) obtained from Bonamia ostreae infected haemocytes: Development of an immune-enriched oligo-microarray.

Science.gov (United States)

Pardo, Belén G; Álvarez-Dios, José Antonio; Cao, Asunción; Ramilo, Andrea; Gómez-Tato, Antonio; Planas, Josep V; Villalba, Antonio; Martínez, Paulino

2016-12-01

The flat oyster, Ostrea edulis, is one of the main farmed oysters, not only in Europe but also in the United States and Canada. Bonamiosis due to the parasite Bonamia ostreae has been associated with high mortality episodes in this species. This parasite is an intracellular protozoan that infects haemocytes, the main cells involved in oyster defence. Due to the economical and ecological importance of flat oyster, genomic data are badly needed for genetic improvement of the species, but they are still very scarce. The objective of this study is to develop a sequence database, OedulisDB, with new genomic and transcriptomic resources, providing new data and convenient tools to improve our knowledge of the oyster's immune mechanisms. Transcriptomic and genomic sequences were obtained using 454 pyrosequencing and compiled into an O. edulis database, OedulisDB, consisting of two sets of 10,318 and 7159 unique sequences that represent the oyster's genome (WG) and de novo haemocyte transcriptome (HT), respectively. The flat oyster transcriptome was obtained from two strains (naïve and tolerant) challenged with B. ostreae, and from their corresponding non-challenged controls. Approximately 78.5% of 5619 HT unique sequences were successfully annotated by Blast search using public databases. A total of 984 sequences were identified as being related to immune response and several key immune genes were identified for the first time in flat oyster. Additionally, transcriptome information was used to design and validate the first oligo-microarray in flat oyster enriched with immune sequences from haemocytes. Our transcriptomic and genomic sequencing and subsequent annotation have largely increased the scarce resources available for this economically important species and have enabled us to develop an OedulisDB database and accompanying tools for gene expression analysis. This study represents the first attempt to characterize in depth the O. edulis haemocyte transcriptome in

Tools You Can Use! E/PO Resources for Scientists and Faculty to Use and Contribute To: EarthSpace and the NASA SMD Scientist Speaker’s Bureau

Science.gov (United States)

Buxner, Sanlyn; Shupla, C.; CoBabe-Ammann, E.; Dalton, H.; Shipp, S.

2013-10-01

The Planetary Science Education and Public Outreach (E/PO) Forum has helped to create two tools that are designed to help scientists and higher-education science faculty make stronger connections with their audiences: EarthSpace, an education clearinghouse for the undergraduate classroom; and NASA SMD Scientist Speaker’s Bureau, an online portal to help bring science - and scientists - to the public. Are you looking for Earth and space science higher education resources and materials? Come explore EarthSpace, a searchable database of undergraduate classroom materials for faculty teaching Earth and space sciences at both the introductory and upper division levels! In addition to classroom materials, EarthSpace provides news and information about educational research, best practices, and funding opportunities. All materials submitted to EarthSpace are peer reviewed, ensuring that the quality of the EarthSpace materials is high and also providing important feedback to authors. Your submission is a reviewed publication! Learn more, search for resources, join the listserv, sign up to review materials, and submit your own at http://www.lpi.usra.edu/earthspace. Join the new NASA SMD Scientist Speaker’s Bureau, an online portal to connect scientists interested in getting involved in E/PO projects (e.g., giving public talks, classroom visits, and virtual connections) with audiences! The Scientist Speaker’s Bureau helps educators and institutions connect with NASA scientists who are interested in giving presentations, based upon the topic, logistics, and audience. The information input into the database will be used to help match scientists (you!) with the requests being placed by educators. All Earth and space scientists funded by NASA - and/or engaged in active research using NASA’s science - are invited to become part of the Scientist Speaker’s Bureau. Submit your information into the short form at http://www.lpi.usra.edu/education/speaker.

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.

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

Science.gov (United States)

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.

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.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

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.

Solar Electric Propulsion (SEP) Systems for SMD Mission Needs. Technology Infusion Study.

Science.gov (United States)

Anderson, David

2014-01-01

Two presentations for SBAG and OPAG meetings: 1) Solar Electric Propulsion Systems for SMD Missions, and 2) Technology Infusion Study - Draft Findings Recommendation Small Bodies Assessment Group (SBAG) meeting is January 9th in Washington D.C., and the Outer Planets Assessment Group (OPAG) meeting is January 23-14 in Tucson, AZ. NASA sponsors these assessment groups, through the NRC, for the science community to assess and provide advice. These talks are to provide a status of 2 NASA activities, and to seek feedback from the respective science communities.

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.

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.

Creating databases for biological information: an introduction.

Science.gov (United States)

Stein, Lincoln

2013-06-01

The essence of bioinformatics is dealing with large quantities of information. Whether it be sequencing data, microarray data files, mass spectrometric data (e.g., fingerprints), the catalog of strains arising from an insertional mutagenesis project, or even large numbers of PDF files, there inevitably comes a time when the information can simply no longer be managed with files and directories. This is where databases come into play. This unit briefly reviews the characteristics of several database management systems, including flat file, indexed file, relational databases, and NoSQL databases. It compares their strengths and weaknesses and offers some general guidelines for selecting an appropriate database management system. Copyright 2013 by JohnWiley & Sons, Inc.

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.

Improved catalytic efficiency, thermophilicity, anti-salt and detergent tolerance of keratinase KerSMD by partially truncation of PPC domain.

Science.gov (United States)

Fang, Zhen; Zhang, Juan; Du, Guocheng; Chen, Jian

2016-06-14

The keratinase from Stenotrophomonas maltophilia (KerSMD) is known for its high activity and pH stability in keratin degradation. However, catalytic efficiency and detergent tolerability need to be improved in order to be used for industrial application. In this work, we obtained several keratinase variants with enhanced catalytic efficiency, thermophilicity, and anti-salt and detergent tolerability by partially truncating the PPC domain of KerSMD. The variants all showed improved catalytic efficiency to synthetic substrate AAPF, with the V355 variant having the highest kcat /Km value of 143.6 s(-1) mM(-1). The truncation of keratinase had little effect on alkaline stability but obviously decreased collagenase activity, developing its potential application in leather treatment. The variants V380, V370, and V355 were thermophilic, with a 1.7-fold enhancement of keratinlytic activity at 60 °C when compared to the wild type. The entire truncation of PPC domain obtained the variant V355 with improved tolerance to alkalinity, salt, chaotropic agents, and detergents. The V355 variant showed more than a 40% improvement in activity under 15% (w/v) NaCl or 4% (w/v) SDS solution, showing excellent stability under harsh washing and unhairing conditions. Our work investigated how protein engineering affects the function of PPC domain of KerSMD.

Performance of the SMD and SM8 models for predicting solvation free energy of neutral solutes in methanol, dimethyl sulfoxide and acetonitrile

Science.gov (United States)

Zanith, Caroline C.; Pliego, Josefredo R.

2015-03-01

The continuum solvation models SMD and SM8 were developed using 2,346 solvation free energy values for 318 neutral molecules in 91 solvents as reference. However, no solvation data of neutral solutes in methanol was used in the parametrization, while only few solvation free energy values of solutes in dimethyl sulfoxide and acetonitrile were used. In this report, we have tested the performance of the models for these important solvents. Taking data from literature, we have generated solvation free energy, enthalpy and entropy values for 37 solutes in methanol, 21 solutes in dimethyl sulfoxide and 19 solutes in acetonitrile. Both SMD and SM8 models have presented a good performance in methanol and acetonitrile, with mean unsigned error equal or less than 0.66 and 0.55 kcal mol-1 in methanol and acetonitrile, respectively. However, the correlation is worse in dimethyl sulfoxide, where the SMD and SM8 methods present mean unsigned error of 1.02 and 0.95 kcal mol-1, respectively. Our results point out the SMx family of models need be improved for dimethyl sulfoxide solvent.

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...

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.

Differential diagnosis of sensory modulation dysfunction (SMD and attention deficit hyperactivity disorder (ADHD: participation, sensation and attention

Directory of Open Access Journals (Sweden)

Aviva eYochman

2013-12-01

Full Text Available Differential diagnosis between sensory modulation disorder (SMD and attention deficit hyperactivity disorder (ADHD is often challenging, since these disorders occur at a high rate of co-morbidity and share several clinical characteristics. Preliminary studies providing evidence that these are distinct disorders have focused solely on body functions, using sophisticated laboratory measurements. Moreover, no studies have compared participation profiles of these populations. This study is the first to compare the profiles of these populations regarding both ‘body functions’(attention and sensation and ‘participation,’ using measures applicable for clinical use. The study included 19 children with ADHD without SMD and 19 with SMD without ADHD (diagnosed by both pediatric neurologists and occupational therapists, aged 6 to 9, and matched by age and gender. All children underwent a broad battery of evaluations: The Evaluation of Sensory Processing, Fabric Prickliness Test and Von Frey Test to evaluate sensory processing, and Test of Everyday Attention to evaluate attention components. The Participation in Childhood Occupations Questionnaire was used to evaluate participation. Results support significant group differences in all sensory components, including pain intensity to suprathreshold stimuli and pain 'after sensation', as well as in tactile, vestibular, taste and olfactory processing. No differences were found in attention components and participation. This study has both theoretical and clinical importance, inter alia, providing further evidence of two distinct disorders as well as indications of specific clinical instruments that might enable clinicians to implement differential diagnoses. In addition, results accord with other previous statements, which indicate that the clinical diagnosis of children with disabilities may not be a major factor in determining their participation profile.

A new optimization approach for nozzle selection and component allocation in multi-head beam-type SMD placement machines

NARCIS (Netherlands)

Torabi, S.A.; Hamedi, M.; Ashayeri, J.

2013-01-01

This paper addresses a highly challenging scheduling problem faced in multi-head beam-type surface mounting devices (SMD) machines. An integrated mathematical model is formulated aiming to balance workloads over multiple heads as well as improving the traveling speed of the robotic arm by

Supplementing High-Density SNP Microarrays for Additional Coverage of Disease-Related Genes: Addiction as a Paradigm

Energy Technology Data Exchange (ETDEWEB)

SacconePhD, Scott F [Washington University, St. Louis; Chesler, Elissa J [ORNL; Bierut, Laura J [Washington University, St. Louis; Kalivas, Peter J [Medical College of South Carolina, Charleston; Lerman, Caryn [University of Pennsylvania; Saccone, Nancy L [Washington University, St. Louis; Uhl, George R [Johns Hopkins University; Li, Chuan-Yun [Peking University; Philip, Vivek M [ORNL; Edenberg, Howard [Indiana University; Sherry, Steven [National Center for Biotechnology Information; Feolo, Michael [National Center for Biotechnology Information; Moyzis, Robert K [Johns Hopkins University; Rutter, Joni L [National Institute of Drug Abuse

2009-01-01

Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.

Construction and evaluation of yeast expression networks by database-guided predictions

Directory of Open Access Journals (Sweden)

Katharina Papsdorf

2016-05-01

Full Text Available DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.

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

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

Science.gov (United States)

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

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

Transcriptome sequencing of the Microarray Quality Control (MAQC RNA reference samples using next generation sequencing

Directory of Open Access Journals (Sweden)

Thierry-Mieg Danielle

2009-06-01

Full Text Available Abstract Background Transcriptome sequencing using next-generation sequencing platforms will soon be competing with DNA microarray technologies for global gene expression analysis. As a preliminary evaluation of these promising technologies, we performed deep sequencing of cDNA synthesized from the Microarray Quality Control (MAQC reference RNA samples using Roche's 454 Genome Sequencer FLX. Results We generated more that 3.6 million sequence reads of average length 250 bp for the MAQC A and B samples and introduced a data analysis pipeline for translating cDNA read counts into gene expression levels. Using BLAST, 90% of the reads mapped to the human genome and 64% of the reads mapped to the RefSeq database of well annotated genes with e-values ≤ 10-20. We measured gene expression levels in the A and B samples by counting the numbers of reads that mapped to individual RefSeq genes in multiple sequencing runs to evaluate the MAQC quality metrics for reproducibility, sensitivity, specificity, and accuracy and compared the results with DNA microarrays and Quantitative RT-PCR (QRTPCR from the MAQC studies. In addition, 88% of the reads were successfully aligned directly to the human genome using the AceView alignment programs with an average 90% sequence similarity to identify 137,899 unique exon junctions, including 22,193 new exon junctions not yet contained in the RefSeq database. Conclusion Using the MAQC metrics for evaluating the performance of gene expression platforms, the ExpressSeq results for gene expression levels showed excellent reproducibility, sensitivity, and specificity that improved systematically with increasing shotgun sequencing depth, and quantitative accuracy that was comparable to DNA microarrays and QRTPCR. In addition, a careful mapping of the reads to the genome using the AceView alignment programs shed new light on the complexity of the human transcriptome including the discovery of thousands of new splice variants.

The laboratory-clinician team: a professional call to action to improve communication and collaboration for optimal patient care in chromosomal microarray testing.

Science.gov (United States)

Wain, Karen E; Riggs, Erin; Hanson, Karen; Savage, Melissa; Riethmaier, Darlene; Muirhead, Andrea; Mitchell, Elyse; Packard, Bethanny Smith; Faucett, W Andrew

2012-10-01

The International Standards for Cytogenomic Arrays (ISCA) Consortium is a worldwide collaborative effort dedicated to optimizing patient care by improving the quality of chromosomal microarray testing. The primary effort of the ISCA Consortium has been the development of a database of copy number variants (CNVs) identified during the course of clinical microarray testing. This database is a powerful resource for clinicians, laboratories, and researchers, and can be utilized for a variety of applications, such as facilitating standardized interpretations of certain CNVs across laboratories or providing phenotypic information for counseling purposes when published data is sparse. A recognized limitation to the clinical utility of this database, however, is the quality of clinical information available for each patient. Clinical genetic counselors are uniquely suited to facilitate the communication of this information to the laboratory by virtue of their existing clinical responsibilities, case management skills, and appreciation of the evolving nature of scientific knowledge. We intend to highlight the critical role that genetic counselors play in ensuring optimal patient care through contributing to the clinical utility of the ISCA Consortium's database, as well as the quality of individual patient microarray reports provided by contributing laboratories. Current tools, paper and electronic forms, created to maximize this collaboration are shared. In addition to making a professional commitment to providing complete clinical information, genetic counselors are invited to become ISCA members and to become involved in the discussions and initiatives within the Consortium.

miRNAs modified by dietary lipids in Caco-2 cells. A microarray screening

Directory of Open Access Journals (Sweden)

Lidia Daimiel

2015-09-01

Full Text Available We performed a screening of miRNAs regulated by dietary lipids in a cellular model of enterocytes, Caco-2 cells. Our aim was to describe new lipid-modified miRNAs with an implication in lipid homeostasis and cardiovascular disease [1,2]. For that purpose, we treated differentiated Caco-2 cells with micelles containing the assayed lipids (cholesterol, conjugated linoleic acid and docosahexaenoic acid and the screening of miRNAs was carried out by microarray using the μParaflo®Microfluidic Biochip Technology of LC Sciences (Huston, TX, USA. Experimental design, microarray description and raw data have been made available in the GEO database with the reference number of GSE59153. Here we described in detail the experimental design and methods used to obtain the relative expression data.

A salmonid EST genomic study: genes, duplications, phylogeny and microarrays

Directory of Open Access Journals (Sweden)

Brahmbhatt Sonal

2008-11-01

Full Text Available Abstract Background Salmonids are of interest because of their relatively recent genome duplication, and their extensive use in wild fisheries and aquaculture. A comprehensive gene list and a comparison of genes in some of the different species provide valuable genomic information for one of the most widely studied groups of fish. Results 298,304 expressed sequence tags (ESTs from Atlantic salmon (69% of the total, 11,664 chinook, 10,813 sockeye, 10,051 brook trout, 10,975 grayling, 8,630 lake whitefish, and 3,624 northern pike ESTs were obtained in this study and have been deposited into the public databases. Contigs were built and putative full-length Atlantic salmon clones have been identified. A database containing ESTs, assemblies, consensus sequences, open reading frames, gene predictions and putative annotation is available. The overall similarity between Atlantic salmon ESTs and those of rainbow trout, chinook, sockeye, brook trout, grayling, lake whitefish, northern pike and rainbow smelt is 93.4, 94.2, 94.6, 94.4, 92.5, 91.7, 89.6, and 86.2% respectively. An analysis of 78 transcript sets show Salmo as a sister group to Oncorhynchus and Salvelinus within Salmoninae, and Thymallinae as a sister group to Salmoninae and Coregoninae within Salmonidae. Extensive gene duplication is consistent with a genome duplication in the common ancestor of salmonids. Using all of the available EST data, a new expanded salmonid cDNA microarray of 32,000 features was created. Cross-species hybridizations to this cDNA microarray indicate that this resource will be useful for studies of all 68 salmonid species. Conclusion An extensive collection and analysis of salmonid RNA putative transcripts indicate that Pacific salmon, Atlantic salmon and charr are 94–96% similar while the more distant whitefish, grayling, pike and smelt are 93, 92, 89 and 86% similar to salmon. The salmonid transcriptome reveals a complex history of gene duplication that is

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...

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.

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

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

Radioactive cDNA microarray in neurospsychiatry

Energy Technology Data Exchange (ETDEWEB)

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

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...

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 ...

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...

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.

MAGIC Database and Interfaces: An Integrated Package for Gene Discovery and Expression

Directory of Open Access Journals (Sweden)

Lee H. Pratt

2006-03-01

Full Text Available The rapidly increasing rate at which biological data is being produced requires a corresponding growth in relational databases and associated tools that can help laboratories contend with that data. With this need in mind, we describe here a Modular Approach to a Genomic, Integrated and Comprehensive (MAGIC Database. This Oracle 9i database derives from an initial focus in our laboratory on gene discovery via production and analysis of expressed sequence tags (ESTs, and subsequently on gene expression as assessed by both EST clustering and microarrays. The MAGIC Gene Discovery portion of the database focuses on information derived from DNA sequences and on its biological relevance. In addition to MAGIC SEQ-LIMS, which is designed to support activities in the laboratory, it contains several additional subschemas. The latter include MAGIC Admin for database administration, MAGIC Sequence for sequence processing as well as sequence and clone attributes, MAGIC Cluster for the results of EST clustering, MAGIC Polymorphism in support of microsatellite and single-nucleotide-polymorphism discovery, and MAGIC Annotation for electronic annotation by BLAST and BLAT. The MAGIC Microarray portion is a MIAME-compliant database with two components at present. These are MAGIC Array-LIMS, which makes possible remote entry of all information into the database, and MAGIC Array Analysis, which provides data mining and visualization. Because all aspects of interaction with the MAGIC Database are via a web browser, it is ideally suited not only for individual research laboratories but also for core facilities that serve clients at any distance.

Genomotyping of Pseudomonas putida strains using P. putida KT2440-based high-density DNA microarrays: Implications for transcriptomics studies

NARCIS (Netherlands)

Ballerstedt, H.; Volkers, R.J.M.; Mars, A.E.; Hallsworth, J.E.; Santos, V.A.M.D.; Puchalka, J.; Duuren, J. van; Eggink, G.; Timmis, K.N.; Bont, J.A.M. de; Wery, J.

2007-01-01

Pseudomonas putida KT2440 is the only fully sequenced P. putida strain. Thus, for transcriptomics and proteomics studies with other P. putida strains, the P. putida KT2440 genomic database serves as standard reference. The utility of KT2440 whole-genome, high-density oligonucleotide microarrays for

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

Evaluation of current and temperature effects on optical performance of InGaAlP thin-film SMD LED mounted on different substrate packages

International Nuclear Information System (INIS)

Raypah, Muna E.; Devarajan, Mutharasu; Sulaiman, Fauziah

2017-01-01

The relationship between the photometric, electric, and thermal parameters of light-emitting diodes (LEDs) is important for optimizing the LED illumination design. Indium gallium aluminium phosphide (InGaAlP)-based thin-film surface-mounted device (SMD) LEDs have attracted wide attention in research and development due to their portability and miniaturization. We report the optical characterization of InGaAlP thin-film SMD LED mounted on FR4, 2 W, and 5 W aluminum (Al) packages. The optical and thermal parameters of LED are determined at different injection currents and ambient temperatures by combining the T3ster (thermal transient tester) and TeraLED (thermal and radiometric characterization of power LEDs) systems. Analysis shows that LED on a 5 W Al substrate package obtains the highest luminous and optical efficiency. (paper)

Arabidopsis Gene Family Profiler (aGFP) - user-oriented transcriptomic database with easy-to-use graphic interface

Czech Academy of Sciences Publication Activity Database

Dupľáková, Nikoleta; Reňák, David; Hovanec, P.; Honysová, Barbora; Twell, D.; Honys, David

2007-01-01

Roč. 7, - (2007), Article Number: 39 ISSN 1471-2229 R&D Projects: GA MŠk(CZ) LC06004; GA ČR GA522/06/0896 Institutional research plan: CEZ:AV0Z50380511 Source of funding: V - iné verejné zdroje ; V - iné verejné zdroje Keywords : STANFORD MICROARRAY DATABASE * EXPRESSION ANALYSIS * DNA MICROARRAYS Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.232, year: 2007

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

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

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

Directory of Open Access Journals (Sweden)

Alina Sîrbu

2015-05-01

Full Text Available Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions. Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks.

Science.gov (United States)

Sîrbu, Alina; Crane, Martin; Ruskin, Heather J

2015-05-14

Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.

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.

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

Engaging Scientists in Meaningful E/PO: How the NASA SMD E/PO Community Addresses the needs of Underrepresented Audiences through NASA Science4Girls and Their Families

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Meinke, Bonnie K.; Smith, Denise A.; Bleacher, Lora; Hauck, Karin; Soeffing, Cassie; NASA SMD E/PO Community

2015-01-01

The NASA Astrophysics Science Education and Public Outreach Forum (SEPOF) coordinates the work of individual NASA Science Mission Directorate (SMD) Astrophysics EPO projects and their teams to bring the NASA science education resources and expertise to libraries nationwide. The Astrophysics Forum assists scientists and educators with becoming involved in SMD E/PO (which is uniquely poised to foster collaboration between scientists with content expertise and educators with pedagogy expertise) and makes SMD E/PO resources and expertise accessible to the science and education communities. The NASA Science4Girls and Their Families initiative partners NASA science education programs with public libraries to provide NASA-themed hands-on education activities for girls and their families. As such, the initiative engages girls in all four NASA science discipline areas (Astrophysics, Earth Science, Planetary Science, and Heliophysics), which enables audiences to experience the full range of NASA science topics and the different career skills each requires. The events focus on engaging this particular underserved and underrepresented audience in Science, Technology, Engineering, and Mathematics (STEM) via use of research-based best practices, collaborations with libraries, partnerships with local and national organizations, and remote engagement of audiences.

Design of an Enterobacteriaceae Pan-genome Microarray Chip

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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...

Assessing Bacterial Interactions Using Carbohydrate-Based Microarrays

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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.

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

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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.

An expression database for roots of the model legume Medicago truncatula under salt stress.

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Li, Daofeng; Su, Zhen; Dong, Jiangli; Wang, Tao

2009-11-11

Medicago truncatula is a model legume whose genome is currently being sequenced by an international consortium. Abiotic stresses such as salt stress limit plant growth and crop productivity, including those of legumes. We anticipate that studies on M. truncatula will shed light on other economically important legumes across the world. Here, we report the development of a database called MtED that contains gene expression profiles of the roots of M. truncatula based on time-course salt stress experiments using the Affymetrix Medicago GeneChip. Our hope is that MtED will provide information to assist in improving abiotic stress resistance in legumes. The results of our microarray experiment with roots of M. truncatula under 180 mM sodium chloride were deposited in the MtED database. Additionally, sequence and annotation information regarding microarray probe sets were included. MtED provides functional category analysis based on Gene and GeneBins Ontology, and other Web-based tools for querying and retrieving query results, browsing pathways and transcription factor families, showing metabolic maps, and comparing and visualizing expression profiles. Utilities like mapping probe sets to genome of M. truncatula and In-Silico PCR were implemented by BLAT software suite, which were also available through MtED database. MtED was built in the PHP script language and as a MySQL relational database system on a Linux server. It has an integrated Web interface, which facilitates ready examination and interpretation of the results of microarray experiments. It is intended to help in selecting gene markers to improve abiotic stress resistance in legumes. MtED is available at http://bioinformatics.cau.edu.cn/MtED/.

An expression database for roots of the model legume Medicago truncatula under salt stress

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Dong Jiangli

2009-11-01

Full Text Available Abstract Background Medicago truncatula is a model legume whose genome is currently being sequenced by an international consortium. Abiotic stresses such as salt stress limit plant growth and crop productivity, including those of legumes. We anticipate that studies on M. truncatula will shed light on other economically important legumes across the world. Here, we report the development of a database called MtED that contains gene expression profiles of the roots of M. truncatula based on time-course salt stress experiments using the Affymetrix Medicago GeneChip. Our hope is that MtED will provide information to assist in improving abiotic stress resistance in legumes. Description The results of our microarray experiment with roots of M. truncatula under 180 mM sodium chloride were deposited in the MtED database. Additionally, sequence and annotation information regarding microarray probe sets were included. MtED provides functional category analysis based on Gene and GeneBins Ontology, and other Web-based tools for querying and retrieving query results, browsing pathways and transcription factor families, showing metabolic maps, and comparing and visualizing expression profiles. Utilities like mapping probe sets to genome of M. truncatula and In-Silico PCR were implemented by BLAT software suite, which were also available through MtED database. Conclusion MtED was built in the PHP script language and as a MySQL relational database system on a Linux server. It has an integrated Web interface, which facilitates ready examination and interpretation of the results of microarray experiments. It is intended to help in selecting gene markers to improve abiotic stress resistance in legumes. MtED is available at http://bioinformatics.cau.edu.cn/MtED/.

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.

Engaging Scientists in Meaningful E/PO: How the NASA SMD E/PO Community Addresses the Needs of the Higher Ed Community

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Manning, James; Meinke, Bonnie K.; Schultz, Gregory R.; Smith, Denise A.; Lawton, Brandon L.; Gurton, Suzanne; NASA Astrophysics E/PO Community

2015-01-01

The NASA Astrophysics Science Education and Public Outreach Forum (SEPOF) coordinates the work of NASA Science Mission Directorate (SMD) Astrophysics EPO projects and their teams to bring cutting-edge discoveries of NASA missions to the introductory astronomy college classroom. The Astrophysics Forum assists scientist and educator involvement in SMD E/PO (uniquely poised to foster collaboration between scientists with content expertise and educators with pedagogy expertise) and makes SMD E/PO resources and expertise accessible to the science and education communities. We present three new opportunities for college instructors to bring the latest NASA discoveries in Astrophysics into their classrooms.To address the expressed needs of the higher education community, the Astrophysics Forum collaborated with the Astrophysics E/PO community, researchers, and Astronomy 101 instructors to place individual science discoveries and learning resources into context for higher education audiences. Among these resources are two Resource Guides on the topics of cosmology and exoplanets, each including a variety of accessible sources.The Astrophysics Forum also coordinates the development of the Astro 101 slide set series--5 to 7-slide presentations on new discoveries from NASA Astrophysics missions relevant to topics in introductory astronomy courses. These sets enable Astronomy 101 instructors to include new discoveries not yet in their textbooks into the broader context of the course: http://www.astrosociety.org/education/astronomy-resource-guides/.The Astrophysics Forum also coordinated the development of 12 monthly Universe Discovery Guides, each featuring a theme and a representative object well-placed for viewing, with an accompanying interpretive story, strategies for conveying the topics, and supporting NASA-approved education activities and background information from a spectrum of NASA missions and programs: http://nightsky.jpl.nasa.gov/news-display.cfm?News_ID=611

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

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

Plant-pathogen interactions: what microarray tells about it?

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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.

Nanotechnology: moving from microarrays toward nanoarrays.

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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.

DNA Microarray Technology

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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 ...

Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data.

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Greenfeld, Max; van de Meent, Jan-Willem; Pavlichin, Dmitri S; Mabuchi, Hideo; Wiggins, Chris H; Gonzalez, Ruben L; Herschlag, Daniel

2015-01-16

Single-molecule techniques have emerged as incisive approaches for addressing a wide range of questions arising in contemporary biological research [Trends Biochem Sci 38:30-37, 2013; Nat Rev Genet 14:9-22, 2013; Curr Opin Struct Biol 2014, 28C:112-121; Annu Rev Biophys 43:19-39, 2014]. The analysis and interpretation of raw single-molecule data benefits greatly from the ongoing development of sophisticated statistical analysis tools that enable accurate inference at the low signal-to-noise ratios frequently associated with these measurements. While a number of groups have released analysis toolkits as open source software [J Phys Chem B 114:5386-5403, 2010; Biophys J 79:1915-1927, 2000; Biophys J 91:1941-1951, 2006; Biophys J 79:1928-1944, 2000; Biophys J 86:4015-4029, 2004; Biophys J 97:3196-3205, 2009; PLoS One 7:e30024, 2012; BMC Bioinformatics 288 11(8):S2, 2010; Biophys J 106:1327-1337, 2014; Proc Int Conf Mach Learn 28:361-369, 2013], it remains difficult to compare analysis for experiments performed in different labs due to a lack of standardization. Here we propose a standardized single-molecule dataset (SMD) file format. SMD is designed to accommodate a wide variety of computer programming languages, single-molecule techniques, and analysis strategies. To facilitate adoption of this format we have made two existing data analysis packages that are used for single-molecule analysis compatible with this format. Adoption of a common, standard data file format for sharing raw single-molecule data and analysis outcomes is a critical step for the emerging and powerful single-molecule field, which will benefit both sophisticated users and non-specialists by allowing standardized, transparent, and reproducible analysis practices.

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.

Discovering biological progression underlying microarray samples.

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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

Principles of gene microarray data analysis.

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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.

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.

The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data

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Hyunseok P Kang

2010-01-01

Full Text Available Background: Tissue microarrays (TMAs are enormously useful tools for translational research, but incompatibilities in database systems between various researchers and institutions prevent the efficient sharing of data that could help realize their full potential. Resource Description Framework (RDF provides a flexible method to represent knowledge in triples, which take the form Subject- Predicate-Object. All data resources are described using Uniform Resource Identifiers (URIs, which are global in scope. We present an OWL (Web Ontology Language schema that expands upon the TMA data exchange specification to address this issue and assist in data sharing and integration. Methods: A minimal OWL schema was designed containing only concepts specific to TMA experiments. More general data elements were incorporated from predefined ontologies such as the NCI thesaurus. URIs were assigned using the Linked Data format. Results: We present examples of files utilizing the schema and conversion of XML data (similar to the TMA DES to OWL. Conclusion: By utilizing predefined ontologies and global unique identifiers, this OWL schema provides a solution to the limitations of XML, which represents concepts defined in a localized setting. This will help increase the utilization of tissue resources, facilitating collaborative translational research efforts.

Cell-Based Microarrays for In Vitro Toxicology

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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.

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

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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.

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.

Evaluation of DNA microarray results in the Toxicogenomics Project (TGP) consortium in Japan.

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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.

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.

CycleBase.org - a comprehensive multi-organism online database of cell-cycle experiments

DEFF Research Database (Denmark)

Gauthier, Nicholas Paul; Larsen, Malene Erup; Wernersson, Rasmus

2007-01-01

The past decade has seen the publication of a large number of cell-cycle microarray studies and many more are in the pipeline. However, data from these experiments are not easy to access, combine and evaluate. We have developed a centralized database with an easy-to-use interface, Cyclebase...

Microintaglio Printing for Soft Lithography-Based in Situ Microarrays

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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.

RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina

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Léveillard Thierry

2008-05-01

Full Text Available Abstract Background The retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina. Description RETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed

Fabrication of Biomolecule Microarrays for Cell Immobilization Using Automated Microcontact Printing.

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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.

Shared probe design and existing microarray reanalysis using PICKY

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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.

Emerging use of gene expression microarrays in plant physiology.

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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.

The use of microarrays in microbial ecology

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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

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/.

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

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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).

Cohen syndrome diagnosed using microarray comparative genomic hibridization

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Saldarriaga-Gil, Wilmar

2017-10-01

Full Text Available Cohen syndrome (CS is an uncommon autosomal recessive genetic disorder attributed to damage on VPS13B gene, locus 8q22-q23. Characteristic phenotype consists of intellectual disability, microcephaly, facial dysmorphism, ophthalmic abnormalities, truncal obesity and hipotony. Worldwide, around 150 cases have been published, mostly in Finish patients. We report the case of a 3 year-old male, with short height, craniosynostosis, facial dysmorphism, hipotony, and developmental delay. He was diagnosed with Cohen syndrome using Microarray Comparative Genomic Hibridization (aCGH that showed homozygous deletion of 0.153 Mb on 8q22.2 including VPS13B gene, OMIM #216550. With this report we contribute to enlarge epidemiological databases on an uncommon genetic disorder. Besides, we illustrate on the contribution of aCGH to the etiological diagnosis of patients with unexplained intellectual disability, delayed psychomotor development, language difficulties, autism and multiple congenital anomalies.

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

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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.

Emerging Use of Gene Expression Microarrays in Plant Physiology

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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.

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.

Microarray-based screening of heat shock protein inhibitors.

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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.

Spot detection and image segmentation in DNA microarray data.

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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.

Implementation of mutual information and bayes theorem for classification microarray data

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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%.

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...

Universal Reference RNA as a standard for microarray experiments

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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

Protein microarray: sensitive and effective immunodetection for drug residues

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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.

A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

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Lan Chung-Yu

2008-09-01

Full Text Available Abstract Background Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs such as NF-κB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin. Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network

GeneBins: a database for classifying gene expression data, with application to plant genome arrays

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Weiller Georg

2007-03-01

Full Text Available Abstract Background To interpret microarray experiments, several ontological analysis tools have been developed. However, current tools are limited to specific organisms. Results We developed a bioinformatics system to assign the probe set sequences of any organism to a hierarchical functional classification modelled on KEGG ontology. The GeneBins database currently supports the functional classification of expression data from four Affymetrix arrays; Arabidopsis thaliana, Oryza sativa, Glycine max and Medicago truncatula. An online analysis tool to identify relevant functions is also provided. Conclusion GeneBins provides resources to interpret gene expression results from microarray experiments. It is available at http://bioinfoserver.rsbs.anu.edu.au/utils/GeneBins/

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.

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

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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.

The application of DNA microarrays in gene expression analysis.

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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.

Systematic gene microarray analysis of the lncRNA expression profiles in human uterine cervix carcinoma.

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Chen, Jie; Fu, Ziyi; Ji, Chenbo; Gu, Pingqing; Xu, Pengfei; Yu, Ningzhu; Kan, Yansheng; Wu, Xiaowei; Shen, Rong; Shen, Yan

2015-05-01

The human uterine cervix carcinoma is one of the most well-known malignancy reproductive system cancers, which threatens women health globally. However, the mechanisms of the oncogenesis and development process of cervix carcinoma are not yet fully understood. Long non-coding RNAs (lncRNAs) have been proved to play key roles in various biological processes, especially development of cancer. The function and mechanism of lncRNAs on cervix carcinoma is still rarely reported. We selected 3 cervix cancer and normal cervix tissues separately, then performed lncRNA microarray to detect the differentially expressed lncRNAs. Subsequently, we explored the potential function of these dysregulated lncRNAs through online bioinformatics databases. Finally, quantity real-time PCR was carried out to confirm the expression levels of these dysregulated lncRNAs in cervix cancer and normal tissues. We uncovered the profiles of differentially expressed lncRNAs between normal and cervix carcinoma tissues by using the microarray techniques, and found 1622 upregulated and 3026 downregulated lncRNAs (fold-change>2.0) in cervix carcinoma compared to the normal cervical tissue. Furthermore, we found HOXA11-AS might participate in cervix carcinogenesis by regulating HOXA11, which is involved in regulating biological processes of cervix cancer. This study afforded expression profiles of lncRNAs between cervix carcinoma tissue and normal cervical tissue, which could provide database for further research about the function and mechanism of key-lncRNAs in cervix carcinoma, and might be helpful to explore potential diagnosis factors and therapeutic targets for cervix carcinoma. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote.

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Strakova, Eva; Zikova, Alice; Vohradsky, Jiri

2014-01-01

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

Microarray-based analysis of plasma cirDNA epigenetic modification profiling in xenografted mice exposed to intermittent hypoxia

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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.

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

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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

Biological data warehousing system for identifying transcriptional regulatory sites from gene expressions of microarray data.

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Tsou, Ann-Ping; Sun, Yi-Ming; Liu, Chia-Lin; Huang, Hsien-Da; Horng, Jorng-Tzong; Tsai, Meng-Feng; Liu, Baw-Juine

2006-07-01

Identification of transcriptional regulatory sites plays an important role in the investigation of gene regulation. For this propose, we designed and implemented a data warehouse to integrate multiple heterogeneous biological data sources with data types such as text-file, XML, image, MySQL database model, and Oracle database model. The utility of the biological data warehouse in predicting transcriptional regulatory sites of coregulated genes was explored using a synexpression group derived from a microarray study. Both of the binding sites of known transcription factors and predicted over-represented (OR) oligonucleotides were demonstrated for the gene group. The potential biological roles of both known nucleotides and one OR nucleotide were demonstrated using bioassays. Therefore, the results from the wet-lab experiments reinforce the power and utility of the data warehouse as an approach to the genome-wide search for important transcription regulatory elements that are the key to many complex biological systems.

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.

Identifying Fishes through DNA Barcodes and Microarrays.

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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.

Clinical relevance of DNA microarray analyses using archival formalin-fixed paraffin-embedded breast cancer specimens

International Nuclear Information System (INIS)

Sadi, Al Muktafi; Wang, Dong-Yu; Youngson, Bruce J; Miller, Naomi; Boerner, Scott; Done, Susan J; Leong, Wey L

2011-01-01

The ability of gene profiling to predict treatment response and prognosis in breast cancers has been demonstrated in many studies using DNA microarray analyses on RNA from fresh frozen tumor specimens. In certain clinical and research situations, performing such analyses on archival formalin fixed paraffin-embedded (FFPE) surgical specimens would be advantageous as large libraries of such specimens with long-term follow-up data are widely available. However, FFPE tissue processing can cause fragmentation and chemical modifications of the RNA. A number of recent technical advances have been reported to overcome these issues. Our current study evaluates whether or not the technology is ready for clinical applications. A modified RNA extraction method and a recent DNA microarray technique, cDNA-mediated annealing, selection, extension and ligation (DASL, Illumina Inc) were evaluated. The gene profiles generated from FFPE specimens were compared to those obtained from paired fresh fine needle aspiration biopsies (FNAB) of 25 breast cancers of different clinical subtypes (based on ER and Her2/neu status). Selected RNA levels were validated using RT-qPCR, and two public databases were used to demonstrate the prognostic significance of the gene profiles generated from FFPE specimens. Compared to FNAB, RNA isolated from FFPE samples was relatively more degraded, nonetheless, over 80% of the RNA samples were deemed suitable for subsequent DASL assay. Despite a higher noise level, a set of genes from FFPE specimens correlated very well with the gene profiles obtained from FNAB, and could differentiate breast cancer subtypes. Expression levels of these genes were validated using RT-qPCR. Finally, for the first time we correlated gene expression profiles from FFPE samples to survival using two independent microarray databases. Specifically, over-expression of ANLN and KIF2C, and under-expression of MAPT strongly correlated with poor outcomes in breast cancer patients. We

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...

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.

Density based pruning for identification of differentially expressed genes from microarray data

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Xu Jia

2010-11-01

Full Text Available Abstract Motivation Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes. Results We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB Pruning is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change. Conclusions Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://mleg.cse.sc.edu/degprune

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

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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.

VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine).

Science.gov (United States)

Wong, Darren C J; Sweetman, Crystal; Drew, Damian P; Ford, Christopher M

2013-12-16

Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera. The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis

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.

Microarray of DNA probes on carboxylate functional beads surface

Institute of Scientific and Technical Information of China (English)

无

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.

Classification across gene expression microarray studies

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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

High-density rhesus macaque oligonucleotide microarray design using early-stage rhesus genome sequence information and human genome annotations

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Magness Charles L

2007-01-01

Full Text Available Abstract Background Until recently, few genomic reagents specific for non-human primate research have been available. To address this need, we have constructed a macaque-specific high-density oligonucleotide microarray by using highly fragmented low-pass sequence contigs from the rhesus genome project together with the detailed sequence and exon structure of the human genome. Using this method, we designed oligonucleotide probes to over 17,000 distinct rhesus/human gene orthologs and increased by four-fold the number of available genes relative to our first-generation expressed sequence tag (EST-derived array. Results We constructed a database containing 248,000 exon sequences from 23,000 human RefSeq genes and compared each human exon with its best matching sequence in the January 2005 version of the rhesus genome project list of 486,000 DNA contigs. Best matching rhesus exon sequences for each of the 23,000 human genes were then concatenated in the proper order and orientation to produce a rhesus "virtual transcriptome." Microarray probes were designed, one per gene, to the region closest to the 3' untranslated region (UTR of each rhesus virtual transcript. Each probe was compared to a composite rhesus/human transcript database to test for cross-hybridization potential yielding a final probe set representing 18,296 rhesus/human gene orthologs, including transcript variants, and over 17,000 distinct genes. We hybridized mRNA from rhesus brain and spleen to both the EST- and genome-derived microarrays. Besides four-fold greater gene coverage, the genome-derived array also showed greater mean signal intensities for genes present on both arrays. Genome-derived probes showed 99.4% identity when compared to 4,767 rhesus GenBank sequence tag site (STS sequences indicating that early stage low-pass versions of complex genomes are of sufficient quality to yield valuable functional genomic information when combined with finished genome information from

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....

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.

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.

Sensitivity and fidelity of DNA microarray improved with integration of Amplified Differential Gene Expression (ADGE

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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.

BBGD: an online database for blueberry genomic data

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Matthews Benjamin F

2007-01-01

Full Text Available Abstract Background Blueberry is a member of the Ericaceae family, which also includes closely related cranberry and more distantly related rhododendron, azalea, and mountain laurel. Blueberry is a major berry crop in the United States, and one that has great nutritional and economical value. Extreme low temperatures, however, reduce crop yield and cause major losses to US farmers. A better understanding of the genes and biochemical pathways that are up- or down-regulated during cold acclimation is needed to produce blueberry cultivars with enhanced cold hardiness. To that end, the blueberry genomics database (BBDG was developed. Along with the analysis tools and web-based query interfaces, the database serves both the broader Ericaceae research community and the blueberry research community specifically by making available ESTs and gene expression data in searchable formats and in elucidating the underlying mechanisms of cold acclimation and freeze tolerance in blueberry. Description BBGD is the world's first database for blueberry genomics. BBGD is both a sequence and gene expression database. It stores both EST and microarray data and allows scientists to correlate expression profiles with gene function. BBGD is a public online database. Presently, the main focus of the database is the identification of genes in blueberry that are significantly induced or suppressed after low temperature exposure. Conclusion By using the database, researchers have developed EST-based markers for mapping and have identified a number of "candidate" cold tolerance genes that are highly expressed in blueberry flower buds after exposure to low temperatures.

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...

Oxidative stress and sodium methyldithiocarbamate-induced modulation of the macrophage response to lipopolysaccharide in vivo.

Science.gov (United States)

Pruett, Stephen B; Cheng, Bing; Fan, Ruping; Tan, Wei; Sebastian, Thomas

2009-06-01

Sodium methyldithiocarbamate (SMD) is the third most abundantly used conventional pesticide in the United States, and hundreds of thousands of persons are exposed to this compound or its major breakdown product, methylisothiocyanate, at levels greater than recommended by the Environmental Protection Agency. A previous study suggests three mechanisms of action involved to some degree in the inhibition of inflammation and decreased resistance to infection caused by exposure of mice to the compound. One of these mechanisms is oxidative stress. The purpose of the present study was to confirm that this mechanism is involved in the effects of SMD on cytokine production by peritoneal macrophages and to further characterize its role in altered cytokine production. Results indicated that SMD significantly decreased the intracellular concentration of reduced glutathione (GSH), suggesting oxidative stress. This was further indicated by the upregulation of genes involved in the "response to oxidative stress" as determined by microarray analysis. These effects were associated with the inhibition of lipopolysaccharide (LPS)-induced production of several proinflammatory cytokines. Experimental depletion of GSH with buthionine sulfoximine (BSO) partially prevented the decrease in LPS-induced interleukin (IL)-6 production caused by SMD and completely prevented the decrease in IL-12. In contrast, BSO plus SMD substantially enhanced the production of IL-10. These results along with results from a previous study are consistent with the hypothesis that SMD causes oxidative stress, which contributes to modulation of cytokine production. However, oxidative stress alone cannot explain the increased IL-10 production caused by SMD.

Dimension reduction methods for microarray data: a review

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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.

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.

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

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...

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

MALDI imaging mass spectrometry profiling of N-glycans in formalin-fixed paraffin embedded clinical tissue blocks and tissue microarrays.

Science.gov (United States)

Powers, Thomas W; Neely, Benjamin A; Shao, Yuan; Tang, Huiyuan; Troyer, Dean A; Mehta, Anand S; Haab, Brian B; Drake, Richard R

2014-01-01

A recently developed matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) method to spatially profile the location and distribution of multiple N-linked glycan species in frozen tissues has been extended and improved for the direct analysis of glycans in clinically derived formalin-fixed paraffin-embedded (FFPE) tissues. Formalin-fixed tissues from normal mouse kidney, human pancreatic and prostate cancers, and a human hepatocellular carcinoma tissue microarray were processed by antigen retrieval followed by on-tissue digestion with peptide N-glycosidase F. The released N-glycans were detected by MALDI-IMS analysis, and the structural composition of a subset of glycans could be verified directly by on-tissue collision-induced fragmentation. Other structural assignments were confirmed by off-tissue permethylation analysis combined with multiple database comparisons. Imaging of mouse kidney tissue sections demonstrates specific tissue distributions of major cellular N-linked glycoforms in the cortex and medulla. Differential tissue distribution of N-linked glycoforms was also observed in the other tissue types. The efficacy of using MALDI-IMS glycan profiling to distinguish tumor from non-tumor tissues in a tumor microarray format is also demonstrated. This MALDI-IMS workflow has the potential to be applied to any FFPE tissue block or tissue microarray to enable higher throughput analysis of the global changes in N-glycosylation associated with cancers.

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

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

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

A fisheye viewer for microarray-based gene expression data

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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.

Second Sound Measurement using SMD resistors to simulate Quench locations on the 704 MHZ Single-Cell Cavity at CERN

CERN Document Server

Liao, K; Ciapala, E; Junginger, T; Weingarten, W

2012-01-01

Oscillating Superleak Transducers (OSTs) containing flexible porous membranes are widely used to detect the so-called second sound temperature wave when a quench event occurs in a superconducting RF cavity. In principle, from the measured speed of this wave and the travel time between the quench event and several OSTs, the location of the quench sites can be derived by triangulation. Second sound behaviour has been simulated through different surface mount (SMD) resistors setups on a Superconducting Proton Linac (SPL) test cavity, to help understanding the underlying physics and improve quench localisation. Experiments are described that have been conducted to search for explanation of heat transfer during cavity quench that causes contradictory triangulation results.

Array patterns and clones - RMOS | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us RMOS Array patterns and clones Data detail Data name Array patterns and clones DOI 10.18908/...lsdba.nbdc00194-002 Description of data contents Static files of array patterns and cDNA clones. Data file F...h rice cDNA comprises a pair of glass slides. The microarray patterns are shown i...escription Download License Update History of This Database Site Policy | Contact Us Array patterns and clones - RMOS | LSDB Archive ...

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.

AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

Directory of Open Access Journals (Sweden)

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.

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.

The Immunome of Colon Cancer: Functional In Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling

Directory of Open Access Journals (Sweden)

Johana A. Luna Coronell

2018-02-01

Full Text Available Characterization of the colon cancer immunome and its autoantibody signature from differentially-reactive antigens (DIRAGs could provide insights into aberrant cellular mechanisms or enriched networks associated with diseases. The purpose of this study was to characterize the antibody profile of plasma samples from 32 colorectal cancer (CRC patients and 32 controls using proteins isolated from 15,417 human cDNA expression clones on microarrays. 671 unique DIRAGs were identified and 632 were more highly reactive in CRC samples. Bioinformatics analyses reveal that compared to control samples, the immunoproteomic IgG profiling of CRC samples is mainly associated with cell death, survival, and proliferation pathways, especially proteins involved in EIF2 and mTOR signaling. Ribosomal proteins (e.g., RPL7, RPL22, and RPL27A and CRC-related genes such as APC, AXIN1, E2F4, MSH2, PMS2, and TP53 were highly enriched. In addition, differential pathways were observed between the CRC and control samples. Furthermore, 103 DIRAGs were reported in the SEREX antigen database, demonstrating our ability to identify known and new reactive antigens. We also found an overlap of 7 antigens with 48 “CRC genes.” These data indicate that immunomics profiling on protein microarrays is able to reveal the complexity of immune responses in cancerous diseases and faithfully reflects the underlying pathology. Keywords: Autoantibody tumor biomarker, Cancer immunology, Colorectal cancer, Immunomics, Protein microarray

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.

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.

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.

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

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.

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

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.

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.

High quality protein microarray using in situ protein purification

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Fleischmann Robert D

2009-08-01

Full Text Available Abstract Background In the postgenomic era, high throughput protein expression and protein microarray technologies have progressed markedly permitting screening of therapeutic reagents and discovery of novel protein functions. Hexa-histidine is one of the most commonly used fusion tags for protein expression due to its small size and convenient purification via immobilized metal ion affinity chromatography (IMAC. This purification process has been adapted to the protein microarray format, but the quality of in situ His-tagged protein purification on slides has not been systematically evaluated. We established methods to determine the level of purification of such proteins on metal chelate-modified slide surfaces. Optimized in situ purification of His-tagged recombinant proteins has the potential to become the new gold standard for cost-effective generation of high-quality and high-density protein microarrays. Results Two slide surfaces were examined, chelated Cu2+ slides suspended on a polyethylene glycol (PEG coating and chelated Ni2+ slides immobilized on a support without PEG coating. Using PEG-coated chelated Cu2+ slides, consistently higher purities of recombinant proteins were measured. An optimized wash buffer (PBST composed of 10 mM phosphate buffer, 2.7 mM KCl, 140 mM NaCl and 0.05% Tween 20, pH 7.4, further improved protein purity levels. Using Escherichia coli cell lysates expressing 90 recombinant Streptococcus pneumoniae proteins, 73 proteins were successfully immobilized, and 66 proteins were in situ purified with greater than 90% purity. We identified several antigens among the in situ-purified proteins via assays with anti-S. pneumoniae rabbit antibodies and a human patient antiserum, as a demonstration project of large scale microarray-based immunoproteomics profiling. The methodology is compatible with higher throughput formats of in vivo protein expression, eliminates the need for resin-based purification and circumvents

EchoBASE: an integrated post-genomic database for Escherichia coli.

Science.gov (United States)

Misra, Raju V; Horler, Richard S P; Reindl, Wolfgang; Goryanin, Igor I; Thomas, Gavin H

2005-01-01

EchoBASE (http://www.ecoli-york.org) is a relational database designed to contain and manipulate information from post-genomic experiments using the model bacterium Escherichia coli K-12. Its aim is to collate information from a wide range of sources to provide clues to the functions of the approximately 1500 gene products that have no confirmed cellular function. The database is built on an enhanced annotation of the updated genome sequence of strain MG1655 and the association of experimental data with the E.coli genes and their products. Experiments that can be held within EchoBASE include proteomics studies, microarray data, protein-protein interaction data, structural data and bioinformatics studies. EchoBASE also contains annotated information on 'orphan' enzyme activities from this microbe to aid characterization of the proteins that catalyse these elusive biochemical reactions.

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

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.

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.

Gene selection for microarray data classification via subspace learning and manifold regularization.

Science.gov (United States)

Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui

2017-12-19

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

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.

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.

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

Directory of Open Access Journals (Sweden)

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

Serious limitations of the QTL/Microarray approach for QTL gene discovery

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Warden Craig H

2010-07-01

Full Text Available Abstract Background It has been proposed that the use of gene expression microarrays in nonrecombinant parental or congenic strains can accelerate the process of isolating individual genes underlying quantitative trait loci (QTL. However, the effectiveness of this approach has not been assessed. Results Thirty-seven studies that have implemented the QTL/microarray approach in rodents were reviewed. About 30% of studies showed enrichment for QTL candidates, mostly in comparisons between congenic and background strains. Three studies led to the identification of an underlying QTL gene. To complement the literature results, a microarray experiment was performed using three mouse congenic strains isolating the effects of at least 25 biometric QTL. Results show that genes in the congenic donor regions were preferentially selected. However, within donor regions, the distribution of differentially expressed genes was homogeneous once gene density was accounted for. Genes within identical-by-descent (IBD regions were less likely to be differentially expressed in chromosome 2, but not in chromosomes 11 and 17. Furthermore, expression of QTL regulated in cis (cis eQTL showed higher expression in the background genotype, which was partially explained by the presence of single nucleotide polymorphisms (SNP. Conclusions The literature shows limited successes from the QTL/microarray approach to identify QTL genes. Our own results from microarray profiling of three congenic strains revealed a strong tendency to select cis-eQTL over trans-eQTL. IBD regions had little effect on rate of differential expression, and we provide several reasons why IBD should not be used to discard eQTL candidates. In addition, mismatch probes produced false cis-eQTL that could not be completely removed with the current strains genotypes and low probe density microarrays. The reviewed studies did not account for lack of coverage from the platforms used and therefore removed genes

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

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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.

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.

Improved microarray-based decision support with graph encoded interactome data.

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Anneleen Daemen

Full Text Available In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG, protein-protein interactions (OPHID and miRNA-gene targeting (microRNA.org outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method.

Fluorescent microarray for multiplexed quantification of environmental contaminants in seawater samples

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The development of a fluorescent multiplexed microarray platform able to detect and quantify a wide variety of pollutants in seawater is reported. The microarray platform has been manufactured by spotting 6 different bioconjugate competitors and it uses a cocktail of 6 monoclonal and polyclonal anti...

Calling biomarkers in milk using a protein microarray on your smartphone

NARCIS (Netherlands)

Ludwig, S.K.J.; Tokarski, Christian; Lang, Stefan N.; Ginkel, Van L.A.; Zhu, Hongying; Ozcan, Aydogan; Nielen, M.W.F.

2015-01-01

Here we present the concept of a protein microarray-based fluorescence immunoassay for multiple biomarker detection in milk extracts by an ordinary smartphone. A multiplex immunoassay was designed on a microarray chip, having built-in positive and negative quality controls. After the immunoassay

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

International Nuclear Information System (INIS)

Ovacik, Meric A.; Sen, Banalata; Euling, Susan Y.; Gaido, Kevin W.; Ierapetritou, Marianthi G.; Androulakis, Ioannis P.

2013-01-01

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

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Ovacik, Meric A. [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Sen, Banalata [National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27709 (United States); Euling, Susan Y. [National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC 20460 (United States); Gaido, Kevin W. [U.S. Food and Drug Administration, Center for Veterinary Medicine, Office of New Animal Drug Evaluation, Division of Human Food Safety, Rockville, MD 20855 (United States); Ierapetritou, Marianthi G. [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Androulakis, Ioannis P., E-mail: yannis@rci.rutgers.edu [Chemical and Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854 (United States); Biomedical Engineering Department, Rutgers University, NJ 08854 (United States)

2013-09-15

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.

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

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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

The MGED Ontology: a resource for semantics-based description of microarray experiments.

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Whetzel, Patricia L; Parkinson, Helen; Causton, Helen C; Fan, Liju; Fostel, Jennifer; Fragoso, Gilberto; Game, Laurence; Heiskanen, Mervi; Morrison, Norman; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Taylor, Chris; White, Joseph; Stoeckert, Christian J

2006-04-01

The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). Stoeckrt@pcbi.upenn.edu Supplementary data are available at Bioinformatics online.

Multi-task feature selection in microarray data by binary integer programming.

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Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

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

Seeded Bayesian Networks: Constructing genetic networks from microarray data

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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.

Exploiting fluorescence for multiplex immunoassays on protein microarrays

International Nuclear Information System (INIS)

Herbáth, Melinda; Balogh, Andrea; Matkó, János; Papp, Krisztián; Prechl, József

2014-01-01

Protein microarray technology is becoming the method of choice for identifying protein interaction partners, detecting specific proteins, carbohydrates and lipids, or for characterizing protein interactions and serum antibodies in a massively parallel manner. Availability of the well-established instrumentation of DNA arrays and development of new fluorescent detection instruments promoted the spread of this technique. Fluorescent detection has the advantage of high sensitivity, specificity, simplicity and wide dynamic range required by most measurements. Fluorescence through specifically designed probes and an increasing variety of detection modes offers an excellent tool for such microarray platforms. Measuring for example the level of antibodies, their isotypes and/or antigen specificity simultaneously can offer more complex and comprehensive information about the investigated biological phenomenon, especially if we take into consideration that hundreds of samples can be measured in a single assay. Not only body fluids, but also cell lysates, extracted cellular components, and intact living cells can be analyzed on protein arrays for monitoring functional responses to printed samples on the surface. As a rapidly evolving area, protein microarray technology offers a great bulk of information and new depth of knowledge. These are the features that endow protein arrays with wide applicability and robust sample analyzing capability. On the whole, protein arrays are emerging new tools not just in proteomics, but glycomics, lipidomics, and are also important for immunological research. In this review we attempt to summarize the technical aspects of planar fluorescent microarray technology along with the description of its main immunological applications. (topical review)

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

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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.

Online Analytical Processing (OLAP: A Fast and Effective Data Mining Tool for Gene Expression Databases

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Alkharouf Nadim W.

2005-01-01

Full Text Available Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD. A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.

Online analytical processing (OLAP): a fast and effective data mining tool for gene expression databases.

Science.gov (United States)

Alkharouf, Nadim W; Jamison, D Curtis; Matthews, Benjamin F

2005-06-30

Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.

Testing a Microarray to Detect and Monitor Toxic Microalgae in Arcachon Bay in France

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Linda K. Medlin

2013-03-01

Full Text Available Harmful algal blooms (HABs occur worldwide, causing health problems and economic damages to fisheries and tourism. Monitoring agencies are therefore essential, yet monitoring is based only on time-consuming light microscopy, a level at which a correct identification can be limited by insufficient morphological characters. The project MIDTAL (Microarray Detection of Toxic Algae—an FP7-funded EU project—used rRNA genes (SSU and LSU as a target on microarrays to identify toxic species. Furthermore, toxins were detected with a newly developed multiplex optical Surface Plasmon Resonance biosensor (Multi SPR and compared with an enzyme-linked immunosorbent assay (ELISA. In this study, we demonstrate the latest generation of MIDTAL microarrays (version 3 and show the correlation between cell counts, detected toxin and microarray signals from field samples taken in Arcachon Bay in France in 2011. The MIDTAL microarray always detected more potentially toxic species than those detected by microscopic counts. The toxin detection was even more sensitive than both methods. Because of the universal nature of both toxin and species microarrays, they can be used to detect invasive species. Nevertheless, the MIDTAL microarray is not completely universal: first, because not all toxic species are on the chip, and second, because invasive species, such as Ostreopsis, already influence European coasts.

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

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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.

THE MAQC PROJECT: ESTABLISHING QC METRICS AND THRESHOLDS FOR MICROARRAY QUALITY CONTROL

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Microarrays represent a core technology in pharmacogenomics and toxicogenomics; however, before this technology can successfully and reliably be applied in clinical practice and regulatory decision-making, standards and quality measures need to be developed. The Microarray Qualit...

Software Design of SMD LEDs for Homogeneous Distribution of Irradiation in the Model of Dark Room

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Andrej Liner

2014-01-01

Full Text Available This article describes wireless optical data networks using visible spectra of optical radiation with a focus on interior areas with direct line of sight LOS (line-of-sight. This type of network represents progressively evolving area of information technologies. Development of lightning technologies based on white power LED was the impulse for wireless optical data networks based on visible spectra of optical radiation (VLC development. Its basic advantage is the flexibility of users. Users don’t have to stay on one place during the data sharing anymore. Wireless optical data networks represent an alternative solution for metallic and fiber networks [1], [2]. This paper deals with the software simulation of homogeneous distribution of optical irradiation in dark room model, carrying out in LightTools software. First, in previous simulations, the optical source composed from 9 SMD LED’s type LW G6SP-EAFA-JKQL-1 was designed. In various simulations, various numbers and distributions of LED’s were used. These were placed at the ceiling of the dark room. At last, the results of optical irradiation homogeneity are compared.

Genome rearrangements detected by SNP microarrays in individuals with intellectual disability referred with possible Williams syndrome.

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Ariel M Pani

2010-08-01

Full Text Available Intellectual disability (ID affects 2-3% of the population and may occur with or without multiple congenital anomalies (MCA or other medical conditions. Established genetic syndromes and visible chromosome abnormalities account for a substantial percentage of ID diagnoses, although for approximately 50% the molecular etiology is unknown. Individuals with features suggestive of various syndromes but lacking their associated genetic anomalies pose a formidable clinical challenge. With the advent of microarray techniques, submicroscopic genome alterations not associated with known syndromes are emerging as a significant cause of ID and MCA.High-density SNP microarrays were used to determine genome wide copy number in 42 individuals: 7 with confirmed alterations in the WS region but atypical clinical phenotypes, 31 with ID and/or MCA, and 4 controls. One individual from the first group had the most telomeric gene in the WS critical region deleted along with 2 Mb of flanking sequence. A second person had the classic WS deletion and a rearrangement on chromosome 5p within the Cri du Chat syndrome (OMIM:123450 region. Six individuals from the ID/MCA group had large rearrangements (3 deletions, 3 duplications, one of whom had a large inversion associated with a deletion that was not detected by the SNP arrays.Combining SNP microarray analyses and qPCR allowed us to clone and sequence 21 deletion breakpoints in individuals with atypical deletions in the WS region and/or ID or MCA. Comparison of these breakpoints to databases of genomic variation revealed that 52% occurred in regions harboring structural variants in the general population. For two probands the genomic alterations were flanked by segmental duplications, which frequently mediate recurrent genome rearrangements; these may represent new genomic disorders. While SNP arrays and related technologies can identify potentially pathogenic deletions and duplications, obtaining sequence information

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

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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.

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.

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

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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.

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.

Cross-platform comparison of SYBR® Green real-time PCR with TaqMan PCR, microarrays and other gene expression measurement technologies evaluated in the MicroArray Quality Control (MAQC study

Directory of Open Access Journals (Sweden)

Dial Stacey L

2008-07-01

Full Text Available Abstract Background The MicroArray Quality Control (MAQC project evaluated the inter- and intra-platform reproducibility of seven microarray platforms and three quantitative gene expression assays in profiling the expression of two commercially available Reference RNA samples (Nat Biotechnol 24:1115-22, 2006. The tested microarrays were the platforms from Affymetrix, Agilent Technologies, Applied Biosystems, GE Healthcare, Illumina, Eppendorf and the National Cancer Institute, and quantitative gene expression assays included TaqMan® Gene Expression PCR Assay, Standardized (Sta RT-PCR™ and QuantiGene®. The data showed great consistency in gene expression measurements across different microarray platforms, different technologies and test sites. However, SYBR® Green real-time PCR, another common technique utilized by half of all real-time PCR users for gene expression measurement, was not addressed in the MAQC study. In the present study, we compared the performance of SYBR Green PCR with TaqMan PCR, microarrays and other quantitative technologies using the same two Reference RNA samples as the MAQC project. We assessed SYBR Green real-time PCR using commercially available RT2 Profiler™ PCR Arrays from SuperArray, containing primer pairs that have been experimentally validated to ensure gene-specificity and high amplification efficiency. Results The SYBR Green PCR Arrays exhibit good reproducibility among different users, PCR instruments and test sites. In addition, the SYBR Green PCR Arrays have the highest concordance with TaqMan PCR, and a high level of concordance with other quantitative methods and microarrays that were evaluated in this study in terms of fold-change correlation and overlap of lists of differentially expressed genes. Conclusion These data demonstrate that SYBR Green real-time PCR delivers highly comparable results in gene expression measurement with TaqMan PCR and other high-density microarrays.

LNA-modified isothermal oligonucleotide microarray for ...

Indian Academy of Sciences (India)

2014-10-20

Oct 20, 2014 ... the advent of DNA microarray techniques (Lee et al. 2007). ... atoms of ribose to form a bicyclic ribosyl structure. It is the .... 532 nm and emission at 570 nm. The signal ..... sis and validation using real-time PCR. Nucleic Acids ...

Transcriptome analysis of zebrafish embryogenesis using microarrays.

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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.

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

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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

Dynamic, electronically switchable surfaces for membrane protein microarrays.

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Tang, C S; Dusseiller, M; Makohliso, S; Heuschkel, M; Sharma, S; Keller, B; Vörös, J

2006-02-01

Microarray technology is a powerful tool that provides a high throughput of bioanalytical information within a single experiment. These miniaturized and parallelized binding assays are highly sensitive and have found widespread popularity especially during the genomic era. However, as drug diagnostics studies are often targeted at membrane proteins, the current arraying technologies are ill-equipped to handle the fragile nature of the protein molecules. In addition, to understand the complex structure and functions of proteins, different strategies to immobilize the probe molecules selectively onto a platform for protein microarray are required. We propose a novel approach to create a (membrane) protein microarray by using an indium tin oxide (ITO) microelectrode array with an electronic multiplexing capability. A polycationic, protein- and vesicle-resistant copolymer, poly(l-lysine)-grafted-poly(ethylene glycol) (PLL-g-PEG), is exposed to and adsorbed uniformly onto the microelectrode array, as a passivating adlayer. An electronic stimulation is then applied onto the individual ITO microelectrodes resulting in the localized release of the polymer thus revealing a bare ITO surface. Different polymer and biological moieties are specifically immobilized onto the activated ITO microelectrodes while the other regions remain protein-resistant as they are unaffected by the induced electrical potential. The desorption process of the PLL-g-PEG is observed to be highly selective, rapid, and reversible without compromising on the integrity and performance of the conductive ITO microelectrodes. As such, we have successfully created a stable and heterogeneous microarray of biomolecules by using selective electronic addressing on ITO microelectrodes. Both pharmaceutical diagnostics and biomedical technology are expected to benefit directly from this unique method.

Chromosomal microarrays testing in children with developmental disabilities and congenital anomalies

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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.

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

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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.

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....

Applications of nanotechnology, next generation sequencing and microarrays in biomedical research.

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Elingaramil, Sauli; Li, Xiaolong; He, Nongyue

2013-07-01

Next-generation sequencing technologies, microarrays and advances in bio nanotechnology have had an enormous impact on research within a short time frame. This impact appears certain to increase further as many biomedical institutions are now acquiring these prevailing new technologies. Beyond conventional sampling of genome content, wide-ranging applications are rapidly evolving for next-generation sequencing, microarrays and nanotechnology. To date, these technologies have been applied in a variety of contexts, including whole-genome sequencing, targeted re sequencing and discovery of transcription factor binding sites, noncoding RNA expression profiling and molecular diagnostics. This paper thus discusses current applications of nanotechnology, next-generation sequencing technologies and microarrays in biomedical research and highlights the transforming potential these technologies offer.

Gene Expression and Microarray Investigation of Dendrobium ...

African Journals Online (AJOL)

blood glucose > 16.7 mmol/L were used as the model group and treated with Dendrobium mixture. (DEN ... Keywords: Diabetes, Gene expression, Dendrobium mixture, Microarray testing ..... homeostasis in airway smooth muscle. Am J.

Detection of NASBA amplified bacterial tmRNA molecules on SLICSel designed microarray probes

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Toome Kadri

2011-02-01

Full Text Available Abstract Background We present a comprehensive technological solution for bacterial diagnostics using tmRNA as a marker molecule. A robust probe design algorithm for microbial detection microarray is implemented. The probes were evaluated for specificity and, combined with NASBA (Nucleic Acid Sequence Based Amplification amplification, for sensitivity. Results We developed a new web-based program SLICSel for the design of hybridization probes, based on nearest-neighbor thermodynamic modeling. A SLICSel minimum binding energy difference criterion of 4 kcal/mol was sufficient to design of Streptococcus pneumoniae tmRNA specific microarray probes. With lower binding energy difference criteria, additional hybridization specificity tests on the microarray were needed to eliminate non-specific probes. Using SLICSel designed microarray probes and NASBA we were able to detect S. pneumoniae tmRNA from a series of total RNA dilutions equivalent to the RNA content of 0.1-10 CFU. Conclusions The described technological solution and both its separate components SLICSel and NASBA-microarray technology independently are applicative for many different areas of microbial diagnostics.

Detection of NASBA amplified bacterial tmRNA molecules on SLICSel designed microarray probes

LENUS (Irish Health Repository)

Scheler, Ott

2011-02-28

Abstract Background We present a comprehensive technological solution for bacterial diagnostics using tmRNA as a marker molecule. A robust probe design algorithm for microbial detection microarray is implemented. The probes were evaluated for specificity and, combined with NASBA (Nucleic Acid Sequence Based Amplification) amplification, for sensitivity. Results We developed a new web-based program SLICSel for the design of hybridization probes, based on nearest-neighbor thermodynamic modeling. A SLICSel minimum binding energy difference criterion of 4 kcal\\/mol was sufficient to design of Streptococcus pneumoniae tmRNA specific microarray probes. With lower binding energy difference criteria, additional hybridization specificity tests on the microarray were needed to eliminate non-specific probes. Using SLICSel designed microarray probes and NASBA we were able to detect S. pneumoniae tmRNA from a series of total RNA dilutions equivalent to the RNA content of 0.1-10 CFU. Conclusions The described technological solution and both its separate components SLICSel and NASBA-microarray technology independently are applicative for many different areas of microbial diagnostics.

Microarray labeling extension values: laboratory signatures for Affymetrix GeneChips

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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

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

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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.

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

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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.

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

On the classification techniques in data mining for microarray data classification

Science.gov (United States)

Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Immobilization Techniques for Microarray: Challenges and Applications

Directory of Open Access Journals (Sweden)

Satish Balasaheb Nimse

2014-11-01

Full Text Available The highly programmable positioning of molecules (biomolecules, nanoparticles, nanobeads, nanocomposites materials on surfaces has potential applications in the fields of biosensors, biomolecular electronics, and nanodevices. However, the conventional techniques including self-assembled monolayers fail to position the molecules on the nanometer scale to produce highly organized monolayers on the surface. The present article elaborates different techniques for the immobilization of the biomolecules on the surface to produce microarrays and their diagnostic applications. The advantages and the drawbacks of various methods are compared. This article also sheds light on the applications of the different technologies for the detection and discrimination of viral/bacterial genotypes and the detection of the biomarkers. A brief survey with 115 references covering the last 10 years on the biological applications of microarrays in various fields is also provided.

Mining meiosis and gametogenesis with DNA microarrays.

Science.gov (United States)

Schlecht, Ulrich; Primig, Michael

2003-04-01

Gametogenesis is a key developmental process that involves complex transcriptional regulation of numerous genes including many that are conserved between unicellular eukaryotes and mammals. Recent expression-profiling experiments using microarrays have provided insight into the co-ordinated transcription of several hundred genes during mitotic growth and meiotic development in budding and fission yeast. Furthermore, microarray-based studies have identified numerous loci that are regulated during the cell cycle or expressed in a germ-cell specific manner in eukaryotic model systems like Caenorhabditis elegans, Mus musculus as well as Homo sapiens. The unprecedented amount of information produced by post-genome biology has spawned novel approaches to organizing biological knowledge using currently available information technology. This review outlines experiments that contribute to an emerging comprehensive picture of the molecular machinery governing sexual reproduction in eukaryotes.

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.

SegMine workflows for semantic microarray data analysis in Orange4WS

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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.

"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.

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

Science.gov (United States)

2012-01-01

Background The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. Results We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. Conclusion T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In

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.

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

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.

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.

Fluorescent labeling of NASBA amplified tmRNA molecules for microarray applications

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Kaplinski Lauris

2009-05-01

Full Text Available Abstract Background Here we present a novel promising microbial diagnostic method that combines the sensitivity of Nucleic Acid Sequence Based Amplification (NASBA with the high information content of microarray technology for the detection of bacterial tmRNA molecules. The NASBA protocol was modified to include aminoallyl-UTP (aaUTP molecules that were incorporated into nascent RNA during the NASBA reaction. Post-amplification labeling with fluorescent dye was carried out subsequently and tmRNA hybridization signal intensities were measured using microarray technology. Significant optimization of the labeled NASBA protocol was required to maintain the required sensitivity of the reactions. Results Two different aaUTP salts were evaluated and optimum final concentrations were identified for both. The final 2 mM concentration of aaUTP Li-salt in NASBA reaction resulted in highest microarray signals overall, being twice as high as the strongest signals with 1 mM aaUTP Na-salt. Conclusion We have successfully demonstrated efficient combination of NASBA amplification technology with microarray based hybridization detection. The method is applicative for many different areas of microbial diagnostics including environmental monitoring, bio threat detection, industrial process monitoring and clinical microbiology.

Position dependent mismatch discrimination on DNA microarrays – experiments and model

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Michel Wolfgang

2008-12-01

Full Text Available Abstract Background The propensity of oligonucleotide strands to form stable duplexes with complementary sequences is fundamental to a variety of biological and biotechnological processes as various as microRNA signalling, microarray hybridization and PCR. Yet our understanding of oligonucleotide hybridization, in particular in presence of surfaces, is rather limited. Here we use oligonucleotide microarrays made in-house by optically controlled DNA synthesis to produce probe sets comprising all possible single base mismatches and base bulges for each of 20 sequence motifs under study. Results We observe that mismatch discrimination is mostly determined by the defect position (relative to the duplex ends as well as by the sequence context. We investigate the thermodynamics of the oligonucleotide duplexes on the basis of double-ended molecular zipper. Theoretical predictions of defect positional influence as well as long range sequence influence agree well with the experimental results. Conclusion Molecular zipping at thermodynamic equilibrium explains the binding affinity of mismatched DNA duplexes on microarrays well. The position dependent nearest neighbor model (PDNN can be inferred from it. Quantitative understanding of microarray experiments from first principles is in reach.

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

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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.

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

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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.

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.

Improvement in the amine glass platform by bubbling method for a DNA microarray.

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Jee, Seung Hyun; Kim, Jong Won; Lee, Ji Hyeong; Yoon, Young Soo

2015-01-01

A glass platform with high sensitivity for sexually transmitted diseases microarray is described here. An amino-silane-based self-assembled monolayer was coated on the surface of a glass platform using a novel bubbling method. The optimized surface of the glass platform had highly uniform surface modifications using this method, as well as improved hybridization properties with capture probes in the DNA microarray. On the basis of these results, the improved glass platform serves as a highly reliable and optimal material for the DNA microarray. Moreover, in this study, we demonstrated that our glass platform, manufactured by utilizing the bubbling method, had higher uniformity, shorter processing time, lower background signal, and higher spot signal than the platforms manufactured by the general dipping method. The DNA microarray manufactured with a glass platform prepared using bubbling method can be used as a clinical diagnostic tool.

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

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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.

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...

Microarray expression profiling of human dental pulp from single subject.

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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.

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

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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.

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

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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.

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

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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.

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.

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/

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...

Comparing transformation methods for DNA microarray data

NARCIS (Netherlands)

Thygesen, Helene H.; Zwinderman, Aeilko H.

2004-01-01

Background: When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include

Constructing Tissue Microarrays: Protocols and Methods Considering Potential Advantages and Disadvantages for Downstream Use.

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Bingle, Lynne; Fonseca, Felipe P; Farthing, Paula M

2017-01-01

Tissue microarrays were first constructed in the 1980s but were used by only a limited number of researchers for a considerable period of time. In the last 10 years there has been a dramatic increase in the number of publications describing the successful use of tissue microarrays in studies aimed at discovering and validating biomarkers. This, along with the increased availability of both manual and automated microarray builders on the market, has encouraged even greater use of this novel and powerful tool. This chapter describes the basic techniques required to build a tissue microarray using a manual method in order that the theory behind the practical steps can be fully explained. Guidance is given to ensure potential disadvantages of the technique are fully considered.

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data.

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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

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.

DNA Microarrays: a Powerful Genomic Tool for Biomedical and Clinical Research

OpenAIRE

Trevino, Victor; Falciani, Francesco; Barrera-Saldaña, Hugo A

2007-01-01

Among the many benefits of the Human Genome Project are new and powerful tools such as the genome-wide hybridization devices referred to as microarrays. Initially designed to measure gene transcriptional levels, microarray technologies are now used for comparing other genome features among individuals and their tissues and cells. Results provide valuable information on disease subcategories, disease prognosis, and treatment outcome. Likewise, they reveal differences in genetic makeup, regulat...

BIOPHYSICAL PROPERTIES OF NUCLEIC ACIDS AT SURFACES RELEVANT TO MICROARRAY PERFORMANCE

OpenAIRE

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 surface...

Precision grinding of microarray lens molding die with 4-axes controlled microwheel

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Yuji Yamamoto, Hirofumi Suzuki, Takashi Onishi1, Tadashi Okino and Toshimichi Moriwaki

2007-01-01

Full Text Available This paper deals with precision grinding of microarray lens (fly eye molding die by using a resinoid bonded diamond wheel. An ultra-precision grinding system of microarray lens molding die and new truing method of resinoid bonded diamond wheel were developed. In this system, a grinding wheel was four-dimensionally controlled with 1 nm resolution by linear scale feedback system and scanned on the workpiece surface. New truing method by using a vanadium alloy tool was developed and its performance was obtained with high preciseness and low wheel wear. Finally, the microarray lens molding dies of fine grain tungsten carbide (WC was tested with the resinoid bonded diamond wheel to evaluate grinding performance.

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....

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

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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.

Homogeneous versus heterogeneous probes for microbial ecological microarrays.

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Bae, Jin-Woo; Park, Yong-Ha

2006-07-01

Microbial ecological microarrays have been developed for investigating the composition and functions of microorganism communities in environmental niches. These arrays include microbial identification microarrays, which use oligonucleotides, gene fragments or microbial genomes as probes. In this article, the advantages and disadvantages of each type of probe are reviewed. Oligonucleotide probes are currently useful for probing uncultivated bacteria that are not amenable to gene fragment probing, whereas the functional gene fragments amplified randomly from microbial genomes require phylogenetic and hierarchical categorization before use as microbial identification probes, despite their high resolution for both specificity and sensitivity. Until more bacteria are sequenced and gene fragment probes are thoroughly validated, heterogeneous bacterial genome probes will provide a simple, sensitive and quantitative tool for exploring the ecosystem structure.

CONFIRMING MICROARRAY DATA--IS IT REALLY NECESSARY?

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The generation of corroborative data has become a commonly used approach for ensuring the veracity of microarray data. Indeed, the need to conduct corroborative studies has now become official editorial policy for at least two journals, and several more are considering introducin...

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

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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...

Plasmonically amplified fluorescence bioassay with microarray format

Science.gov (United States)

Gogalic, S.; Hageneder, S.; Ctortecka, C.; Bauch, M.; Khan, I.; Preininger, Claudia; Sauer, U.; Dostalek, J.

2015-05-01

Plasmonic amplification of fluorescence signal in bioassays with microarray detection format is reported. A crossed relief diffraction grating was designed to couple an excitation laser beam to surface plasmons at the wavelength overlapping with the absorption and emission bands of fluorophore Dy647 that was used as a label. The surface of periodically corrugated sensor chip was coated with surface plasmon-supporting gold layer and a thin SU8 polymer film carrying epoxy groups. These groups were employed for the covalent immobilization of capture antibodies at arrays of spots. The plasmonic amplification of fluorescence signal on the developed microarray chip was tested by using interleukin 8 sandwich immunoassay. The readout was performed ex situ after drying the chip by using a commercial scanner with high numerical aperture collecting lens. Obtained results reveal the enhancement of fluorescence signal by a factor of 5 when compared to a regular glass chip.

Improvement in the amine glass platform by bubbling method for a DNA microarray

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Jee SH

2015-10-01

Full Text Available Seung Hyun Jee,1 Jong Won Kim,2 Ji Hyeong Lee,2 Young Soo Yoon11Department of Chemical and Biological Engineering, Gachon University, Seongnam, Gyeonggi, Republic of Korea; 2Genomics Clinical Research Institute, LabGenomics Co., Ltd., Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of KoreaAbstract: A glass platform with high sensitivity for sexually transmitted diseases microarray is described here. An amino-silane-based self-assembled monolayer was coated on the surface of a glass platform using a novel bubbling method. The optimized surface of the glass platform had highly uniform surface modifications using this method, as well as improved hybridization properties with capture probes in the DNA microarray. On the basis of these results, the improved glass platform serves as a highly reliable and optimal material for the DNA microarray. Moreover, in this study, we demonstrated that our glass platform, manufactured by utilizing the bubbling method, had higher uniformity, shorter processing time, lower background signal, and higher spot signal than the platforms manufactured by the general dipping method. The DNA microarray manufactured with a glass platform prepared using bubbling method can be used as a clinical diagnostic tool. Keywords: DNA microarray, glass platform, bubbling method, self-assambled monolayer

Fuzzy support vector machine for microarray imbalanced data classification

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Ladayya, Faroh; Purnami, Santi Wulan; Irhamah

2017-11-01

DNA microarrays are data containing gene expression with small sample sizes and high number of features. Furthermore, imbalanced classes is a common problem in microarray data. This occurs when a dataset is dominated by a class which have significantly more instances than the other minority classes. Therefore, it is needed a classification method that solve the problem of high dimensional and imbalanced data. Support Vector Machine (SVM) is one of the classification methods that is capable of handling large or small samples, nonlinear, high dimensional, over learning and local minimum issues. SVM has been widely applied to DNA microarray data classification and it has been shown that SVM provides the best performance among other machine learning methods. However, imbalanced data will be a problem because SVM treats all samples in the same importance thus the results is bias for minority class. To overcome the imbalanced data, Fuzzy SVM (FSVM) is proposed. This method apply a fuzzy membership to each input point and reformulate the SVM such that different input points provide different contributions to the classifier. The minority classes have large fuzzy membership so FSVM can pay more attention to the samples with larger fuzzy membership. Given DNA microarray data is a high dimensional data with a very large number of features, it is necessary to do feature selection first using Fast Correlation based Filter (FCBF). In this study will be analyzed by SVM, FSVM and both methods by applying FCBF and get the classification performance of them. Based on the overall results, FSVM on selected features has the best classification performance compared to SVM.

Transcriptome profiling in conifers and the PiceaGenExpress database show patterns of diversification within gene families and interspecific conservation in vascular gene expression

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Raherison Elie

2012-08-01

Full Text Available Abstract Background Conifers have very large genomes (13 to 30 Gigabases that are mostly uncharacterized although extensive cDNA resources have recently become available. This report presents a global overview of transcriptome variation in a conifer tree and documents conservation and diversity of gene expression patterns among major vegetative tissues. Results An oligonucleotide microarray was developed from Picea glauca and P. sitchensis cDNA datasets. It represents 23,853 unique genes and was shown to be suitable for transcriptome profiling in several species. A comparison of secondary xylem and phelloderm tissues showed that preferential expression in these vascular tissues was highly conserved among Picea spp. RNA-Sequencing strongly confirmed tissue preferential expression and provided a robust validation of the microarray design. A small database of transcription profiles called PiceaGenExpress was developed from over 150 hybridizations spanning eight major tissue types. In total, transcripts were detected for 92% of the genes on the microarray, in at least one tissue. Non-annotated genes were predominantly expressed at low levels in fewer tissues than genes of known or predicted function. Diversity of expression within gene families may be rapidly assessed from PiceaGenExpress. In conifer trees, dehydrins and late embryogenesis abundant (LEA osmotic regulation proteins occur in large gene families compared to angiosperms. Strong contrasts and low diversity was observed in the dehydrin family, while diverse patterns suggested a greater degree of diversification among LEAs. Conclusion Together, the oligonucleotide microarray and the PiceaGenExpress database represent the first resource of this kind for gymnosperm plants. The spruce transcriptome analysis reported here is expected to accelerate genetic studies in the large and important group comprised of conifer trees.

Transcriptional profiling of endocrine cerebro-osteodysplasia using microarray and next-generation sequencing.

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Piya Lahiry

Full Text Available BACKGROUND: Transcriptome profiling of patterns of RNA expression is a powerful approach to identify networks of genes that play a role in disease. To date, most mRNA profiling of tissues has been accomplished using microarrays, but next-generation sequencing can offer a richer and more comprehensive picture. METHODOLOGY/PRINCIPAL FINDINGS: ECO is a rare multi-system developmental disorder caused by a homozygous mutation in ICK encoding intestinal cell kinase. We performed gene expression profiling using both cDNA microarrays and next-generation mRNA sequencing (mRNA-seq of skin fibroblasts from ECO-affected subjects. We then validated a subset of differentially expressed transcripts identified by each method using quantitative reverse transcription-polymerase chain reaction (qRT-PCR. Finally, we used gene ontology (GO to identify critical pathways and processes that were abnormal according to each technical platform. Methodologically, mRNA-seq identifies a much larger number of differentially expressed genes with much better correlation to qRT-PCR results than the microarray (r² = 0.794 and 0.137, respectively. Biologically, cDNA microarray identified functional pathways focused on anatomical structure and development, while the mRNA-seq platform identified a higher proportion of genes involved in cell division and DNA replication pathways. CONCLUSIONS/SIGNIFICANCE: Transcriptome profiling with mRNA-seq had greater sensitivity, range and accuracy than the microarray. The two platforms generated different but complementary hypotheses for further evaluation.

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.

Bacterial identification and subtyping using DNA microarray and DNA sequencing.

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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.

Label and Label-Free Detection Techniques for Protein Microarrays

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Amir Syahir

2015-04-01

Full Text Available Protein microarray technology has gone through numerous innovative developments in recent decades. In this review, we focus on the development of protein detection methods embedded in the technology. Early microarrays utilized useful chromophores and versatile biochemical techniques dominated by high-throughput illumination. Recently, the realization of label-free techniques has been greatly advanced by the combination of knowledge in material sciences, computational design and nanofabrication. These rapidly advancing techniques aim to provide data without the intervention of label molecules. Here, we present a brief overview of this remarkable innovation from the perspectives of label and label-free techniques in transducing nano‑biological events.

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

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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.

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...

Microarray-based RNA profiling of breast cancer

DEFF Research Database (Denmark)

Larsen, Martin J; Thomassen, Mads; Tan, Qihua

2014-01-01

analyzed the same 234 breast cancers on two different microarray platforms. One dataset contained known batch-effects associated with the fabrication procedure used. The aim was to assess the significance of correcting for systematic batch-effects when integrating data from different platforms. We here...

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

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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.

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

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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.

Nanomedicine, microarrays and their applications in clinical microbiology

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Özcan Deveci

2010-12-01

Full Text Available Growing interest in the future medical applications of nanotechnology is leading to the emergence of a new scientific field that called as “nanomedicine”. Nanomedicine may be defined as the investigating, treating, reconstructing and controlling human biology and health at the molecular level, using engineered nanodevices and nanostructures. Microarray technology is a revolutionary tool for elucidating roles of genes in infectious diseases, shifting from traditional methods of research to integrated approaches. This technology has great potential to provide medical diagnosis, monitor treatment and help in the development of new tools for infectious disease prevention and/or management. The aim of this paper is to provide an overview of the current application of microarray platforms and nanomedicine in the study of experimental microbiology and the impact of this technology in clinical settings.

Gene Expression Analysis Using Agilent DNA Microarrays

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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 ...

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

OpenAIRE

Yamada, Yoichi; Sawada, Hiroki; Hirotani, Ken-ichi; Oshima, Masanobu; Satou, Kenji

2012-01-01

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...

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

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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.

Polysaccharide microarray technology for the detection of Burkholderia pseudomallei and Burkholderia mallei antibodies.

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Parthasarathy, Narayanan; DeShazer, David; England, Marilyn; Waag, David M

2006-11-01

A polysaccharide microarray platform was prepared by immobilizing Burkholderia pseudomallei and Burkholderia mallei polysaccharides. This polysaccharide array was tested with success for detecting B. pseudomallei and B. mallei serum (human and animal) antibodies. The advantages of this microarray technology over the current serodiagnosis of the above bacterial infections were discussed.

Rapid Diagnosis of Bacterial Meningitis Using a Microarray

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Ren-Jy Ben

2008-06-01

Conclusion: The microarray method provides a more accurate and rapid diagnostic tool for bacterial meningitis compared to traditional culture methods. Clinical application of this new technique may reduce the potential risk of delay in treatment.

A Lateral Flow Protein Microarray for Rapid and Sensitive Antibody Assays

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Helene Andersson-Svahn

2011-11-01

Full Text Available Protein microarrays are useful tools for highly multiplexed determination of presence or levels of clinically relevant biomarkers in human tissues and biofluids. However, such tools have thus far been restricted to laboratory environments. Here, we present a novel 384-plexed easy to use lateral flow protein microarray device capable of sensitive (< 30 ng/mL determination of antigen-specific antibodies in ten minutes of total assay time. Results were developed with gold nanobeads and could be recorded by a cell-phone camera or table top scanner. Excellent accuracy with an area under curve (AUC of 98% was achieved in comparison with an established glass microarray assay for 26 antigen-specific antibodies. We propose that the presented framework could find use in convenient and cost-efficient quality control of antibody production, as well as in providing a platform for multiplexed affinity-based assays in low-resource or mobile settings.

Microfluidic extraction and microarray detection of biomarkers from cancer tissue slides

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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.

Microarray-based ultra-high resolution discovery of genomic deletion mutations

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2014-01-01

Background Oligonucleotide microarray-based comparative genomic hybridization (CGH) offers an attractive possible route for the rapid and cost-effective genome-wide discovery of deletion mutations. CGH typically involves comparison of the hybridization intensities of genomic DNA samples with microarray chip representations of entire genomes, and has widespread potential application in experimental research and medical diagnostics. However, the power to detect small deletions is low. Results Here we use a graduated series of Arabidopsis thaliana genomic deletion mutations (of sizes ranging from 4 bp to ~5 kb) to optimize CGH-based genomic deletion detection. We show that the power to detect smaller deletions (4, 28 and 104 bp) depends upon oligonucleotide density (essentially the number of genome-representative oligonucleotides on the microarray chip), and determine the oligonucleotide spacings necessary to guarantee detection of deletions of specified size. Conclusions Our findings will enhance a wide range of research and clinical applications, and in particular will aid in the discovery of genomic deletions in the absence of a priori knowledge of their existence. PMID:24655320

Universal ligation-detection-reaction microarray applied for compost microbes

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Romantschuk Martin

2008-12-01

Full Text Available Abstract Background Composting is one of the methods utilised in recycling organic communal waste. The composting process is dependent on aerobic microbial activity and proceeds through a succession of different phases each dominated by certain microorganisms. In this study, a ligation-detection-reaction (LDR based microarray method was adapted for species-level detection of compost microbes characteristic of each stage of the composting process. LDR utilises the specificity of the ligase enzyme to covalently join two adjacently hybridised probes. A zip-oligo is attached to the 3'-end of one probe and fluorescent label to the 5'-end of the other probe. Upon ligation, the probes are combined in the same molecule and can be detected in a specific location on a universal microarray with complementary zip-oligos enabling equivalent hybridisation conditions for all probes. The method was applied to samples from Nordic composting facilities after testing and optimisation with fungal pure cultures and environmental clones. Results Probes targeted for fungi were able to detect 0.1 fmol of target ribosomal PCR product in an artificial reaction mixture containing 100 ng competing fungal ribosomal internal transcribed spacer (ITS area or herring sperm DNA. The detection level was therefore approximately 0.04% of total DNA. Clone libraries were constructed from eight compost samples. The LDR microarray results were in concordance with the clone library sequencing results. In addition a control probe was used to monitor the per-spot hybridisation efficiency on the array. Conclusion This study demonstrates that the LDR microarray method is capable of sensitive and accurate species-level detection from a complex microbial community. The method can detect key species from compost samples, making it a basis for a tool for compost process monitoring in industrial facilities.

Detection of selected plant viruses by microarrays

OpenAIRE

HRABÁKOVÁ, Lenka

2013-01-01

The main aim of this master thesis was the simultaneous detection of four selected plant viruses ? Apple mosaic virus, Plum pox virus, Prunus necrotic ringspot virus and Prune harf virus, by microarrays. The intermediate step in the process of the detection was optimizing of multiplex polymerase chain reaction (PCR).

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

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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.

Development and application of a fluorescence protein microarray for detecting serum alpha-fetoprotein in patients with hepatocellular carcinoma.

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Zhang, Aiying; Yin, Chengzeng; Wang, Zhenshun; Zhang, Yonghong; Zhao, Yuanshun; Li, Ang; Sun, Huanqin; Lin, Dongdong; Li, Ning

2016-12-01

Objective To develop a simple, effective, time-saving and low-cost fluorescence protein microarray method for detecting serum alpha-fetoprotein (AFP) in patients with hepatocellular carcinoma (HCC). Method Non-contact piezoelectric print techniques were applied to fluorescence protein microarray to reduce the cost of prey antibody. Serum samples from patients with HCC and healthy control subjects were collected and evaluated for the presence of AFP using a novel fluorescence protein microarray. To validate the fluorescence protein microarray, serum samples were tested for AFP using an enzyme-linked immunosorbent assay (ELISA). Results A total of 110 serum samples from patients with HCC ( n = 65) and healthy control subjects ( n = 45) were analysed. When the AFP cut-off value was set at 20 ng/ml, the fluorescence protein microarray had a sensitivity of 91.67% and a specificity of 93.24% for detecting serum AFP. Serum AFP quantified via fluorescence protein microarray had a similar diagnostic performance compared with ELISA in distinguishing patients with HCC from healthy control subjects (area under receiver operating characteristic curve: 0.906 for fluorescence protein microarray; 0.880 for ELISA). Conclusion A fluorescence protein microarray method was developed for detecting serum AFP in patients with HCC.

Exploring the use of internal and externalcontrols for assessing microarray technical performance

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Game Laurence

2010-12-01

Full Text Available Abstract Background The maturing of gene expression microarray technology and interest in the use of microarray-based applications for clinical and diagnostic applications calls for quantitative measures of quality. This manuscript presents a retrospective study characterizing several approaches to assess technical performance of microarray data measured on the Affymetrix GeneChip platform, including whole-array metrics and information from a standard mixture of external spike-in and endogenous internal controls. Spike-in controls were found to carry the same information about technical performance as whole-array metrics and endogenous "housekeeping" genes. These results support the use of spike-in controls as general tools for performance assessment across time, experimenters and array batches, suggesting that they have potential for comparison of microarray data generated across species using different technologies. Results A layered PCA modeling methodology that uses data from a number of classes of controls (spike-in hybridization, spike-in polyA+, internal RNA degradation, endogenous or "housekeeping genes" was used for the assessment of microarray data quality. The controls provide information on multiple stages of the experimental protocol (e.g., hybridization, RNA amplification. External spike-in, hybridization and RNA labeling controls provide information related to both assay and hybridization performance whereas internal endogenous controls provide quality information on the biological sample. We find that the variance of the data generated from the external and internal controls carries critical information about technical performance; the PCA dissection of this variance is consistent with whole-array quality assessment based on a number of quality assurance/quality control (QA/QC metrics. Conclusions These results provide support for the use of both external and internal RNA control data to assess the technical quality of microarray

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

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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.

Assessing the Clinical Utility of SNP Microarray for Prader-Willi Syndrome due to Uniparental Disomy.

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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.

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

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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.

Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model

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Diaz-Rodriguez, Sebastian; Bozada, Samantha M.; Phifer, Jeremy R.; Paluch, Andrew S.

2016-11-01

We present blind predictions using the solubility parameter based method MOSCED submitted for the SAMPL5 challenge on calculating cyclohexane/water distribution coefficients at 298 K. Reference data to parameterize MOSCED was generated with knowledge only of chemical structure by performing solvation free energy calculations using electronic structure calculations in the SMD continuum solvent. To maintain simplicity and use only a single method, we approximate the distribution coefficient with the partition coefficient of the neutral species. Over the final SAMPL5 set of 53 compounds, we achieved an average unsigned error of 2.2± 0.2 log units (ranking 15 out of 62 entries), the correlation coefficient ( R) was 0.6± 0.1 (ranking 35), and 72± 6 % of the predictions had the correct sign (ranking 30). While used here to predict cyclohexane/water distribution coefficients at 298 K, MOSCED is broadly applicable, allowing one to predict temperature dependent infinite dilution activity coefficients in any solvent for which parameters exist, and provides a means by which an excess Gibbs free energy model may be parameterized to predict composition dependent phase-equilibrium.

Development and evaluation of a high-throughput, low-cost genotyping platform based on oligonucleotide microarrays in rice

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Liu Bin

2008-05-01

Full Text Available Abstract Background We report the development of a microarray platform for rapid and cost-effective genetic mapping, and its evaluation using rice as a model. In contrast to methods employing whole-genome tiling microarrays for genotyping, our method is based on low-cost spotted microarray production, focusing only on known polymorphic features. Results We have produced a genotyping microarray for rice, comprising 880 single feature polymorphism (SFP elements derived from insertions/deletions identified by aligning genomic sequences of the japonica cultivar Nipponbare and the indica cultivar 93-11. The SFPs were experimentally verified by hybridization with labeled genomic DNA prepared from the two cultivars. Using the genotyping microarrays, we found high levels of polymorphism across diverse rice accessions, and were able to classify all five subpopulations of rice with high bootstrap support. The microarrays were used for mapping of a gene conferring resistance to Magnaporthe grisea, the causative organism of rice blast disease, by quantitative genotyping of samples from a recombinant inbred line population pooled by phenotype. Conclusion We anticipate this microarray-based genotyping platform, based on its low cost-per-sample, to be particularly useful in applications requiring whole-genome molecular marker coverage across large numbers of individuals.

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

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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.

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

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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.

Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

Science.gov (United States)

Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

2017-08-07

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier

Application of a New Genetic Deafness Microarray for Detecting Mutations in the Deaf in China.

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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.

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

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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.

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.

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...

Evaluation of an expanded microarray for detecting antibiotic resistance genes in a broad range of gram-negative bacterial pathogens.

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Card, Roderick; Zhang, Jiancheng; Das, Priya; Cook, Charlotte; Woodford, Neil; Anjum, Muna F

2013-01-01

A microarray capable of detecting genes for resistance to 75 clinically relevant antibiotics encompassing 19 different antimicrobial classes was tested on 132 Gram-negative bacteria. Microarray-positive results correlated >91% with antimicrobial resistance phenotypes, assessed using British Society for Antimicrobial Chemotherapy clinical breakpoints; the overall test specificity was >83%. Microarray-positive results without a corresponding resistance phenotype matched 94% with PCR results, indicating accurate detection of genes present in the respective bacteria by microarray when expression was low or absent and, hence, undetectable by susceptibility testing. The low sensitivity and negative predictive values of the microarray results for identifying resistance to some antimicrobial resistance classes are likely due to the limited number of resistance genes present on the current microarray for those antimicrobial agents or to mutation-based resistance mechanisms. With regular updates, this microarray can be used for clinical diagnostics to help accurate therapeutic options to be taken following infection with multiple-antibiotic-resistant Gram-negative bacteria and prevent treatment failure.

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.

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

Development and validation of a flax (Linum usitatissimum L.) gene expression oligo microarray.

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Fenart, Stéphane; Ndong, Yves-Placide Assoumou; Duarte, Jorge; Rivière, Nathalie; Wilmer, Jeroen; van Wuytswinkel, Olivier; Lucau, Anca; Cariou, Emmanuelle; Neutelings, Godfrey; Gutierrez, Laurent; Chabbert, Brigitte; Guillot, Xavier; Tavernier, Reynald; Hawkins, Simon; Thomasset, Brigitte

2010-10-21

Flax (Linum usitatissimum L.) has been cultivated for around 9,000 years and is therefore one of the oldest cultivated species. Today, flax is still grown for its oil (oil-flax or linseed cultivars) and its cellulose-rich fibres (fibre-flax cultivars) used for high-value linen garments and composite materials. Despite the wide industrial use of flax-derived products, and our actual understanding of the regulation of both wood fibre production and oil biosynthesis more information must be acquired in both domains. Recent advances in genomics are now providing opportunities to improve our fundamental knowledge of these complex processes. In this paper we report the development and validation of a high-density oligo microarray platform dedicated to gene expression analyses in flax. Nine different RNA samples obtained from flax inner- and outer-stems, seeds, leaves and roots were used to generate a collection of 1,066,481 ESTs by massive parallel pyrosequencing. Sequences were assembled into 59,626 unigenes and 48,021 sequences were selected for oligo design and high-density microarray (Nimblegen 385K) fabrication with eight, non-overlapping 25-mers oligos per unigene. 18 independent experiments were used to evaluate the hybridization quality, precision, specificity and accuracy and all results confirmed the high technical quality of our microarray platform. Cross-validation of microarray data was carried out using quantitative qRT-PCR. Nine target genes were selected on the basis of microarray results and reflected the whole range of fold change (both up-regulated and down-regulated genes in different samples). A statistically significant positive correlation was obtained comparing expression levels for each target gene across all biological replicates both in qRT-PCR and microarray results. Further experiments illustrated the capacity of our arrays to detect differential gene expression in a variety of flax tissues as well as between two contrasted flax varieties

Development and validation of a flax (Linum usitatissimum L. gene expression oligo microarray

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Gutierrez Laurent

2010-10-01

Full Text Available Abstract Background Flax (Linum usitatissimum L. has been cultivated for around 9,000 years and is therefore one of the oldest cultivated species. Today, flax is still grown for its oil (oil-flax or linseed cultivars and its cellulose-rich fibres (fibre-flax cultivars used for high-value linen garments and composite materials. Despite the wide industrial use of flax-derived products, and our actual understanding of the regulation of both wood fibre production and oil biosynthesis more information must be acquired in both domains. Recent advances in genomics are now providing opportunities to improve our fundamental knowledge of these complex processes. In this paper we report the development and validation of a high-density oligo microarray platform dedicated to gene expression analyses in flax. Results Nine different RNA samples obtained from flax inner- and outer-stems, seeds, leaves and roots were used to generate a collection of 1,066,481 ESTs by massive parallel pyrosequencing. Sequences were assembled into 59,626 unigenes and 48,021 sequences were selected for oligo design and high-density microarray (Nimblegen 385K fabrication with eight, non-overlapping 25-mers oligos per unigene. 18 independent experiments were used to evaluate the hybridization quality, precision, specificity and accuracy and all results confirmed the high technical quality of our microarray platform. Cross-validation of microarray data was carried out using quantitative qRT-PCR. Nine target genes were selected on the basis of microarray results and reflected the whole range of fold change (both up-regulated and down-regulated genes in different samples. A statistically significant positive correlation was obtained comparing expression levels for each target gene across all biological replicates both in qRT-PCR and microarray results. Further experiments illustrated the capacity of our arrays to detect differential gene expression in a variety of flax tissues as well

A cDNA microarray, UniShrimpChip, for identification of genes relevant to testicular development in the black tiger shrimp (Penaeus monodon

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Klinbunga Sirawut

2011-04-01

Full Text Available Abstract Background Poor reproductive maturation in captive male broodstock of the black tiger shrimp (Penaeus monodon is one of the serious problems to the farming industries. Without genome sequence, EST libraries of P. monodon were previously constructed to identify transcripts with important biological functions. In this study, a new version of cDNA microarray, UniShrimpChip, was constructed from the Peneaus monodon EST libraries of 12 tissues, containing 5,568 non-redundant cDNA clones from 10,536 unique cDNA in the P. monodon EST database. UniShrimpChip was used to study testicular development by comparing gene expression levels of wild brooders from the West and East coasts of Thailand and domesticated brooders with different ages (10-, 14-, 18-month-old. Results The overall gene expression patterns from the microarray experiments revealed distinct transcriptomic patterns between the wild and domesticated groups. Moreover, differentially expressed genes from the microarray comparisons were identified, and the expression patterns of eight selected transcripts were subsequently confirmed by reverse-transcriptase quantitative PCR (RT-qPCR. Among these, expression levels of six subunits (CSN2, 4, 5, 6, 7a, and 8 of the COP9 signalosome (CSN gene family in wild and different ages of domesticated brooders were examined by RT-qPCR. Among the six subunits, CSN5 and CSN6 were most highly expressed in wild brooders and least expressed in the 18-month-old domesticated group; therefore, their full-length cDNA sequences were characterized. Conclusions This study is the first report to employ cDNA microarray to study testicular development in the black tiger shrimp. We show that there are obvious differences between the wild and domesticated shrimp at the transcriptomic level. Furthermore, our study is the first to investigate the feasibility that the CSN gene family might have involved in reproduction and development of this economically important

Value of apparent diffusion coefficient (ADC) in assessing radiotherapy and chemotherapy success in cervical cancer.

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Fu, Zhan-Zhao; Peng, Yong; Cao, Li-Yan; Chen, Yan-Sheng; Li, Kun; Fu, Bao-Hong

2015-06-01

We investigated the clinical significance of apparent diffusion coefficient (ADC) values in diffusion-weighted magnetic resonance imaging (DWI) in monitoring the efficacy of radiotherapy (RT) and chemotherapy (CT) treatments in cervical cancer. In order to identify relevant high quality clinical cohort studies reporting the use of DWI in cervical cancers, the following electronic databases in English and Chinese languages were comprehensively searched: MEDLINE, Science Citation Index database, Cochrane Library Database, PubMed, Embase, CINAHL, and Current Contents Index; Chinese Biomedical Database, Chinese Journal Full-Text Database. All selected studies were published prior to March 2014, and data extracted from these studies were analyzed using STATA 12.0 statistical software. We initially retrieved 196 articles (79 Chinese articles and 117 English articles) through database searches and finally selected sixteen cohort studies for this meta-analysis. The 16 studies contained a combined total of 517 subjects, and all selected studies reported the mean ADC value (10(-3) mm(2)/s) in DWI in cervical cancer patients treated with RT and CT. Combined standardized mean difference (SMD) suggested that the mean post-RT and mean post-CT ADC values were significantly higher than the mean pre-RT and mean pre-CT ADC values, respectively, in cervical cancer patients (SMD=2.95, 95% CI=2.19-3.72, P900 (SMD=2.55, 95% CI=1.78-3.32, P<0.001). The mean ADC value in patients without residual tumor post-RT and post-CT treatment was significantly higher than seen in patients with residual tumors (SMD=0.80, 95% CI=0.49-1.12, P<0.001). Our meta-analysis revealed a significant correlation between mean ADC values and the clinical response to RT and CT treatment. Thus, ADC values in DWI may be effective in evaluating the clinical outcome of treatments in cervical cancer patients. Copyright © 2015 Elsevier Inc. All rights reserved.

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

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...

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.

Printing Proteins as Microarrays for High-Throughput Function Determination

Science.gov (United States)

MacBeath, Gavin; Schreiber, Stuart L.

2000-09-01

Systematic efforts are currently under way to construct defined sets of cloned genes for high-throughput expression and purification of recombinant proteins. To facilitate subsequent studies of protein function, we have developed miniaturized assays that accommodate extremely low sample volumes and enable the rapid, simultaneous processing of thousands of proteins. A high-precision robot designed to manufacture complementary DNA microarrays was used to spot proteins onto chemically derivatized glass slides at extremely high spatial densities. The proteins attached covalently to the slide surface yet retained their ability to interact specifically with other proteins, or with small molecules, in solution. Three applications for protein microarrays were demonstrated: screening for protein-protein interactions, identifying the substrates of protein kinases, and identifying the protein targets of small molecules.

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

Dye-Doped Silica Nanoparticle Labels/Protein Microarray for Detection of Protein Biomarkers

OpenAIRE

Wu, Hong; Huo, Qisheng; Varnum, Susan; Wang, Jun; Liu, Guodong; Nie, Zimin; Liu, Jun; Lin, Yuehe

2008-01-01

We report a dye-encapsulated silica nanoparticle as a label, with the advantages of high fluorescence intensity, photostability, and biocompatibility, in conjunction with microarray technology for sensitive immunoassay of a biomarker, Interleukin-6 (IL-6), on a microarray format. The tris (2,2’-bipyridyl)ruthenium (II)chloride hexahydrate (Rubpy) dye was incorporated into silica nanoparticles using a simple one-step microemulsion synthesis. In this synthesis process, Igepal CA520 was used as ...

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...

Novel R pipeline for analyzing Biolog Phenotypic MicroArray data.

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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.

Aligning ontologies and integrating textual evidence for pathway analysis of microarray data

Energy Technology Data Exchange (ETDEWEB)

Gopalan, Banu; Posse, Christian; Sanfilippo, Antonio P.; Stenzel-Poore, Mary; Stevens, S.L.; Castano, Jose; Beagley, Nathaniel; Riensche, Roderick M.; Baddeley, Bob; Simon, R.P.; Pustejovsky, James

2006-10-08

Expression arrays are introducing a paradigmatic change in biology by shifting experimental approaches from single gene studies to genome-level analysis, monitoring the ex-pression levels of several thousands of genes in parallel. The massive amounts of data obtained from the microarray data needs to be integrated and interpreted to infer biological meaning within the context of information-rich pathways. In this paper, we present a methodology that integrates textual information with annotations from cross-referenced ontolo-gies to map genes to pathways in a semi-automated way. We illustrate this approach and compare it favorably to other tools by analyzing the gene expression changes underlying the biological phenomena related to stroke. Stroke is the third leading cause of death and a major disabler in the United States. Through years of study, researchers have amassed a significant knowledge base about stroke, and this knowledge, coupled with new technologies, is providing a wealth of new scientific opportunities. The potential for neu-roprotective stroke therapy is enormous. However, the roles of neurogenesis, angiogenesis, and other proliferative re-sponses in the recovery process following ischemia and the molecular mechanisms that lead to these processes still need to be uncovered. Improved annotation of genomic and pro-teomic data, including annotation of pathways in which genes and proteins are involved, is required to facilitate their interpretation and clinical application. While our approach is not aimed at replacing existing curated pathway databases, it reveals multiple hidden relationships that are not evident with the way these databases analyze functional groupings of genes from the Gene Ontology.

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.

Fabrication of protein microarrays for alpha fetoprotein detection by using a rapid photo-immobilization process

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Sirasa Yodmongkol

2016-03-01

Full Text Available In this study, protein microarrays based on sandwich immunoassays are generated to quantify the amount of alpha fetoprotein (AFP in blood serum. For chip generation a mixture of capture antibody and a photoactive copolymer consisting of N,N-dimethylacrylamide (DMAA, methacryloyloxy benzophenone (MaBP, and Na-4-styrenesulfonate (SSNa was spotted onto unmodified polymethyl methacrylate (PMMA substrates. Subsequently to printing of the microarray, the polymer and protein were photochemically cross-linked and the forming, biofunctionalized hydrogels simultaneously bound to the chip surface by short UV- irradiation. The obtained biochip was incubated with AFP antigen, followed by biotinylated AFP antibody and streptavidin-Cy5 and the fluorescence signal read-out. The developed microarray biochip covers the range of AFP in serum samples such as maternal serum in the range of 5 and 100 ng/ml. The chip production process is based on a fast and simple immobilization process, which can be applied to conventional plastic surfaces. Therefore, this protein microarray production process is a promising method to fabricate biochips for AFP screening processes. Keywords: Photo-immobilization, Protein microarray, Alpha fetoprotein, Hydrogel, 3D surface, Down syndrome

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

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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.

Microarray-based genotyping of Salmonella: Inter-laboratory evaluation of reproducibility and standardization potential

DEFF Research Database (Denmark)

Grønlund, Hugo Ahlm; Riber, Leise; Vigre, Håkan

2011-01-01

Bacterial food-borne infections in humans caused by Salmonella spp. are considered a crucial food safety issue. Therefore, it is important for the risk assessments of Salmonella to consider the genomic variationamong different isolates in order to control pathogen-induced infections. Microarray...... critical methodology parameters that differed between the two labs were identified. These related to printing facilities, choice of hybridization buffer,wash buffers used following the hybridization and choice of procedure for purifying genomic DNA. Critical parameters were randomized in a four......DNA and different wash buffers. However, less agreement (Kappa=0.2–0.6) between microarray results were observed when using different hybridization buffers, indicating this parameter as being highly criticalwhen transferring a standard microarray assay between laboratories. In conclusion, this study indicates...

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

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

Application of broad-spectrum resequencing microarray for genotyping rhabdoviruses.

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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.

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

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

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

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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

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

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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.

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.

Development of a genotyping microarray for Usher syndrome.

Science.gov (United States)

Cremers, Frans P M; Kimberling, William J; Külm, Maigi; de Brouwer, Arjan P; van Wijk, Erwin; te Brinke, Heleen; Cremers, Cor W R J; Hoefsloot, Lies H; Banfi, Sandro; Simonelli, Francesca; Fleischhauer, Johannes C; Berger, Wolfgang; Kelley, Phil M; Haralambous, Elene; Bitner-Glindzicz, Maria; Webster, Andrew R; Saihan, Zubin; De Baere, Elfride; Leroy, Bart P; Silvestri, Giuliana; McKay, Gareth J; Koenekoop, Robert K; Millan, Jose M; Rosenberg, Thomas; Joensuu, Tarja; Sankila, Eeva-Marja; Weil, Dominique; Weston, Mike D; Wissinger, Bernd; Kremer, Hannie

2007-02-01

Usher syndrome, a combination of retinitis pigmentosa (RP) and sensorineural hearing loss with or without vestibular dysfunction, displays a high degree of clinical and genetic heterogeneity. Three clinical subtypes can be distinguished, based on the age of onset and severity of the hearing impairment, and the presence or absence of vestibular abnormalities. Thus far, eight genes have been implicated in the syndrome, together comprising 347 protein-coding exons. To improve DNA diagnostics for patients with Usher syndrome, we developed a genotyping microarray based on the arrayed primer extension (APEX) method. Allele-specific oligonucleotides corresponding to all 298 Usher syndrome-associated sequence variants known to date, 76 of which are novel, were arrayed. Approximately half of these variants were validated using original patient DNAs, which yielded an accuracy of >98%. The efficiency of the Usher genotyping microarray was tested using DNAs from 370 unrelated European and American patients with Usher syndrome. Sequence variants were identified in 64/140 (46%) patients with Usher syndrome type I, 45/189 (24%) patients with Usher syndrome type II, 6/21 (29%) patients with Usher syndrome type III and 6/20 (30%) patients with atypical Usher syndrome. The chip also identified two novel sequence variants, c.400C>T (p.R134X) in PCDH15 and c.1606T>C (p.C536S) in USH2A. The Usher genotyping microarray is a versatile and affordable screening tool for Usher syndrome. Its efficiency will improve with the addition of novel sequence variants with minimal extra costs, making it a very useful first-pass screening tool.

Does Ramadan fasting alter body weight and blood lipids and fasting blood glucose in a healthy population? A meta-analysis.

Science.gov (United States)

Kul, Seval; Savaş, Esen; Öztürk, Zeynel Abidin; Karadağ, Gülendam

2014-06-01

In this study, we conducted a meta-analysis of self-controlled cohort studies comparing body weights, blood levels of lipids and fasting blood glucose levels before and after Ramadan taking into account gender differences. Several databases were searched up to June 2012 for studies showing an effect of Ramadan fasting in healthy subjects, yielding 30 articles. The primary finding of this meta-analysis was that after Ramadan fasting, low-density lipoprotein (SMD = -1.67, 95 % CI = -2.48 to -0.86) and fasting blood glucose levels (SMD = -1.10, 95 % CI = -1.62 to -0.58) were decreased in both sex groups and also in the entire group compared to levels prior to Ramadan. In addition, in the female subgroup, body weight (SMD = -0.04, 95 % CI = -0.20, 0.12), total cholesterol (SMD = 0.05, 95 % CI = -0.51 to 0.60), and triglyceride levels (SMD = 0.03, 95 % CI = -0.31, 0.36) remained unchanged, while HDL levels (SMD = 0.86, 95 % CI = 0.11 to 1.61, p = 0.03) were increased. In males, Ramadan fasting resulted in weight loss (SMD = -0.24, 95 % CI = -0.36, -0.12, p = 0.001). Also, a substantial reduction in total cholesterol (SMD = -0.44, 95 % CI = -0.77 to -0.11) and LDL levels (SMD = -2.22, 95 % CI = -3.47 to -0.96) and a small decrease in triglyceride levels (SMD = -0.35, 95 % CI = -0.67 to -0.02) were observed in males. In conclusion, by looking at this data, it is evident that Ramadan fasting can effectively change body weight and some biochemical parameters in healthy subjects especially in males compared to pre-Ramadan period.

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

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Laurenzi Ian J

2009-12-01

Full Text Available Abstract Background Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches. Results In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay. Conclusions By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at http://www.laurenzi.net.

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

Science.gov (United States)

Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

2016-03-01

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics

The microarray detecting six fruit-tree viruses

Czech Academy of Sciences Publication Activity Database

Lenz, Ondřej; Petrzik, Karel; Špak, Josef

2009-01-01

Roč. 148, July (2009), s. 27 ISSN 1866-590X. [International Conference on Virus and other Graft Transmissible Diseases of Fruit Crops /21./. 05.07.2009-10.07.2009, Neustadt] R&D Projects: GA MŠk OC 853.001 Institutional research plan: CEZ:AV0Z50510513 Keywords : microarray * detection * virus Subject RIV: EE - Microbiology, Virology

Microarrays (DNA Chips) for the Classroom Laboratory

Science.gov (United States)

Barnard, Betsy; Sussman, Michael; BonDurant, Sandra Splinter; Nienhuis, James; Krysan, Patrick

2006-01-01

We have developed and optimized the necessary laboratory materials to make DNA microarray technology accessible to all high school students at a fraction of both cost and data size. The primary component is a DNA chip/array that students "print" by hand and then analyze using research tools that have been adapted for classroom use. The…

OntologyWidget – a reusable, embeddable widget for easily locating ontology terms

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Skene JH Pate

2007-09-01

Full Text Available Abstract Background Biomedical ontologies are being widely used to annotate biological data in a computer-accessible, consistent and well-defined manner. However, due to their size and complexity, annotating data with appropriate terms from an ontology is often challenging for experts and non-experts alike, because there exist few tools that allow one to quickly find relevant ontology terms to easily populate a web form. Results We have produced a tool, OntologyWidget, which allows users to rapidly search for and browse ontology terms. OntologyWidget can easily be embedded in other web-based applications. OntologyWidget is written using AJAX (Asynchronous JavaScript and XML and has two related elements. The first is a dynamic auto-complete ontology search feature. As a user enters characters into the search box, the appropriate ontology is queried remotely for terms that match the typed-in text, and the query results populate a drop-down list with all potential matches. Upon selection of a term from the list, the user can locate this term within a generic and dynamic ontology browser, which comprises the second element of the tool. The ontology browser shows the paths from a selected term to the root as well as parent/child tree hierarchies. We have implemented web services at the Stanford Microarray Database (SMD, which provide the OntologyWidget with access to over 40 ontologies from the Open Biological Ontology (OBO website 1. Each ontology is updated weekly. Adopters of the OntologyWidget can either use SMD's web services, or elect to rely on their own. Deploying the OntologyWidget can be accomplished in three simple steps: (1 install Apache Tomcat 2 on one's web server, (2 download and install the OntologyWidget servlet stub that provides access to the SMD ontology web services, and (3 create an html (HyperText Markup Language file that refers to the OntologyWidget using a simple, well-defined format. Conclusion We have developed Ontology

SNP typing on the NanoChip electronic microarray

DEFF Research Database (Denmark)

Børsting, Claus; Sanchez Sanchez, Juan Jose; Morling, Niels

2005-01-01

We describe a single nucleotide polymorphism (SNP) typing protocol developed for the NanoChip electronic microarray. The NanoChip array consists of 100 electrodes covered by a thin hydrogel layer containing streptavidin. An electric currency can be applied to one, several, or all electrodes...

Protein-protein interactions: an application of Tus-Ter mediated protein microarray system.

Science.gov (United States)

Sitaraman, Kalavathy; Chatterjee, Deb K

2011-01-01

In this chapter, we present a novel, cost-effective microarray strategy that utilizes expression-ready plasmid DNAs to generate protein arrays on-demand and its use to validate protein-protein interactions. These expression plasmids were constructed in such a way so as to serve a dual purpose of synthesizing the protein of interest as well as capturing the synthesized protein. The microarray system is based on the high affinity binding of Escherichia coli "Tus" protein to "Ter," a 20 bp DNA sequence involved in the regulation of DNA replication. The protein expression is carried out in a cell-free protein synthesis system, with rabbit reticulocyte lysates, and the target proteins are detected either by labeled incorporated tag specific or by gene-specific antibodies. This microarray system has been successfully used for the detection of protein-protein interaction because both the target protein and the query protein can be transcribed and translated simultaneously in the microarray slides. The utility of this system for detecting protein-protein interaction is demonstrated by a few well-known examples: Jun/Fos, FRB/FKBP12, p53/MDM2, and CDK4/p16. In all these cases, the presence of protein complexes resulted in the localization of fluorophores at the specific sites of the immobilized target plasmids. Interestingly, during our interactions studies we also detected a previously unknown interaction between CDK2 and p16. Thus, this Tus-Ter based system of protein microarray can be used for the validation of known protein interactions as well as for identifying new protein-protein interactions. In addition, it can be used to examine and identify targets of nucleic acid-protein, ligand-receptor, enzyme-substrate, and drug-protein interactions.

Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

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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 %.

Theoretical prediction of pKa in methanol: testing SM8 and SMD models for carboxylic acids, phenols, and amines.

Science.gov (United States)

Miguel, Elizabeth L M; Silva, Poliana L; Pliego, Josefredo R

2014-05-29

Methanol is a widely used solvent for chemical reactions and has solvation properties similar to those of water. However, the performance of continuum solvation models in this solvent has not been tested yet. In this report, we have investigated the performance of the SM8 and SMD models for pKa prediction of 26 carboxylic acids, 24 phenols, and 23 amines in methanol. The gas phase contribution was included at the X3LYP/TZVPP+diff//X3LYP/DZV+P(d) level. Using the proton exchange reaction with acetic acid, phenol, and ammonia as reference species leads to RMS error in the range of 1.4 to 3.6 pKa units. This finding suggests that the performance of the continuum models for methanol is similar to that found for aqueous solvent. Application of simple empirical correction through a linear equation leads to accurate pKa prediction, with uncertainty less than 0.8 units with the SM8 method. Testing with the less expensive PBE1PBE/6-311+G** method results in a slight improvement in the results.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

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Ashlock Daniel

2009-08-01

Full Text Available Abstract Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.

Science.gov (United States)

Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

2009-08-22

Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

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.

Multi-gene detection and identification of mosquito-borne RNA viruses using an oligonucleotide microarray.

Directory of Open Access Journals (Sweden)

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

SSHscreen and SSHdb, generic software for microarray based gene discovery: application to the stress response in cowpea

Directory of Open Access Journals (Sweden)

Oelofse Dean

2010-04-01

Full Text Available Abstract Background Suppression subtractive hybridization is a popular technique for gene discovery from non-model organisms without an annotated genome sequence, such as cowpea (Vigna unguiculata (L. Walp. We aimed to use this method to enrich for genes expressed during drought stress in a drought tolerant cowpea line. However, current methods were inefficient in screening libraries and management of the sequence data, and thus there was a need to develop software tools to facilitate the process. Results Forward and reverse cDNA libraries enriched for cowpea drought response genes were screened on microarrays, and the R software package SSHscreen 2.0.1 was developed (i to normalize the data effectively using spike-in control spot normalization, and (ii to select clones for sequencing based on the calculation of enrichment ratios with associated statistics. Enrichment ratio 3 values for each clone showed that 62% of the forward library and 34% of the reverse library clones were significantly differentially expressed by drought stress (adjusted p value 88% of the clones in both libraries were derived from rare transcripts in the original tester samples, thus supporting the notion that suppression subtractive hybridization enriches for rare transcripts. A set of 118 clones were chosen for sequencing, and drought-induced cowpea genes were identified, the most interesting encoding a late embryogenesis abundant Lea5 protein, a glutathione S-transferase, a thaumatin, a universal stress protein, and a wound induced protein. A lipid transfer protein and several components of photosynthesis were down-regulated by the drought stress. Reverse transcriptase quantitative PCR confirmed the enrichment ratio values for the selected cowpea genes. SSHdb, a web-accessible database, was developed to manage the clone sequences and combine the SSHscreen data with sequence annotations derived from BLAST and Blast2GO. The self-BLAST function within SSHdb grouped

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

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...

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...

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.

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

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Arpita Nagpal

2017-01-01

Full Text Available Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure uses a firefly optimization algorithm in the local search optimzation phase of the greedy randomized adaptive search procedure (GRASP. Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FFGRASP performs better on seven out of nine datasets.

Hybridization chain reaction amplification for highly sensitive fluorescence detection of DNA with dextran coated microarrays.

Science.gov (United States)

Chao, Jie; Li, Zhenhua; Li, Jing; Peng, Hongzhen; Su, Shao; Li, Qian; Zhu, Changfeng; Zuo, Xiaolei; Song, Shiping; Wang, Lianhui; Wang, Lihua

2016-07-15

Microarrays of biomolecules hold great promise in the fields of genomics, proteomics, and clinical assays on account of their remarkably parallel and high-throughput assay capability. However, the fluorescence detection used in most conventional DNA microarrays is still limited by sensitivity. In this study, we have demonstrated a novel universal and highly sensitive platform for fluorescent detection of sequence specific DNA at the femtomolar level by combining dextran-coated microarrays with hybridization chain reaction (HCR) signal amplification. Three-dimensional dextran matrix was covalently coated on glass surface as the scaffold to immobilize DNA recognition probes to increase the surface binding capacity and accessibility. DNA nanowire tentacles were formed on the matrix surface for efficient signal amplification by capturing multiple fluorescent molecules in a highly ordered way. By quantifying microscopic fluorescent signals, the synergetic effects of dextran and HCR greatly improved sensitivity of DNA microarrays, with a detection limit of 10fM (1×10(5) molecules). This detection assay could recognize one-base mismatch with fluorescence signals dropped down to ~20%. This cost-effective microarray platform also worked well with samples in serum and thus shows great potential for clinical diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

DNA microarray technology in nutraceutical and food safety.

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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.

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...

A DNA microarray-based methylation-sensitive (MS)-AFLP hybridization method for genetic and epigenetic analyses.

Science.gov (United States)

Yamamoto, F; Yamamoto, M

2004-07-01

We previously developed a PCR-based DNA fingerprinting technique named the Methylation Sensitive (MS)-AFLP method, which permits comparative genome-wide scanning of methylation status with a manageable number of fingerprinting experiments. The technique uses the methylation sensitive restriction enzyme NotI in the context of the existing Amplified Fragment Length Polymorphism (AFLP) method. Here we report the successful conversion of this gel electrophoresis-based DNA fingerprinting technique into a DNA microarray hybridization technique (DNA Microarray MS-AFLP). By performing a total of 30 (15 x 2 reciprocal labeling) DNA Microarray MS-AFLP hybridization experiments on genomic DNA from two breast and three prostate cancer cell lines in all pairwise combinations, and Southern hybridization experiments using more than 100 different probes, we have demonstrated that the DNA Microarray MS-AFLP is a reliable method for genetic and epigenetic analyses. No statistically significant differences were observed in the number of differences between the breast-prostate hybridization experiments and the breast-breast or prostate-prostate comparisons.

High throughput screening of starch structures using carbohydrate microarrays

DEFF Research Database (Denmark)

Tanackovic, Vanja; Rydahl, Maja Gro; Pedersen, Henriette Lodberg

2016-01-01

In this study we introduce the starch-recognising carbohydrate binding module family 20 (CBM20) from Aspergillus niger for screening biological variations in starch molecular structure using high throughput carbohydrate microarray technology. Defined linear, branched and phosphorylated...

Exploring Lactobacillus plantarum genome diversity by using microarrays

NARCIS (Netherlands)

Molenaar, D.; Bringel, F.; Schuren, F.H.; Vos, de W.M.; Siezen, R.J.; Kleerebezem, M.

2005-01-01

Lactobacillus plantarum is a versatile and flexible species that is encountered in a variety of niches and can utilize a broad range of fermentable carbon sources. To assess if this versatility is linked to a variable gene pool, microarrays containing a subset of small genomic fragments of L.

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

Microarray analysis of Arabidopsis under gold exposure to identify putative genes involved in the synthesis of gold nanoparticles (AuNPs).

Science.gov (United States)

Shukla, Devesh; Krishnamurthy, Sneha; Sahi, Shivendra V

2015-03-01

Very little is known about the genes responsible for Au uptake, reduction and detoxification in plants, which indeed essential to understand the complex trait of AuNP biosynthesis. We designed a targeted experiment to elucidate the response of plant at transcriptional level under Au exposure, and a microarray was performed on root tissue treated with AuCl4 (-) in the absence of nutrient media to record specific gene expression signature. Here, we describe the experimental procedures and data analysis in detail to reproduce the results (available at GEO database under GSE55436) published by Shukla et al. (2014) [1] in the Frontiers in Plant Sciences. The data produced from this study provide significant information of genes which may be used to enhance the AuNP biosynthesis.

Development of a genotyping microarray for Usher syndrome

Science.gov (United States)

Cremers, Frans P M; Kimberling, William J; Külm, Maigi; de Brouwer, Arjan P; van Wijk, Erwin; te Brinke, Heleen; Cremers, Cor W R J; Hoefsloot, Lies H; Banfi, Sandro; Simonelli, Francesca; Fleischhauer, Johannes C; Berger, Wolfgang; Kelley, Phil M; Haralambous, Elene; Bitner‐Glindzicz, Maria; Webster, Andrew R; Saihan, Zubin; De Baere, Elfride; Leroy, Bart P; Silvestri, Giuliana; McKay, Gareth J; Koenekoop, Robert K; Millan, Jose M; Rosenberg, Thomas; Joensuu, Tarja; Sankila, Eeva‐Marja; Weil, Dominique; Weston, Mike D; Wissinger, Bernd; Kremer, Hannie

2007-01-01

Background Usher syndrome, a combination of retinitis pigmentosa (RP) and sensorineural hearing loss with or without vestibular dysfunction, displays a high degree of clinical and genetic heterogeneity. Three clinical subtypes can be distinguished, based on the age of onset and severity of the hearing impairment, and the presence or absence of vestibular abnormalities. Thus far, eight genes have been implicated in the syndrome, together comprising 347 protein‐coding exons. Methods: To improve DNA diagnostics for patients with Usher syndrome, we developed a genotyping microarray based on the arrayed primer extension (APEX) method. Allele‐specific oligonucleotides corresponding to all 298 Usher syndrome‐associated sequence variants known to date, 76 of which are novel, were arrayed. Results Approximately half of these variants were validated using original patient DNAs, which yielded an accuracy of >98%. The efficiency of the Usher genotyping microarray was tested using DNAs from 370 unrelated European and American patients with Usher syndrome. Sequence variants were identified in 64/140 (46%) patients with Usher syndrome type I, 45/189 (24%) patients with Usher syndrome type II, 6/21 (29%) patients with Usher syndrome type III and 6/20 (30%) patients with atypical Usher syndrome. The chip also identified two novel sequence variants, c.400C>T (p.R134X) in PCDH15 and c.1606T>C (p.C536S) in USH2A. Conclusion The Usher genotyping microarray is a versatile and affordable screening tool for Usher syndrome. Its efficiency will improve with the addition of novel sequence variants with minimal extra costs, making it a very useful first‐pass screening tool. PMID:16963483

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.

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.

Detection of the specific binding on protein microarrays by oblique-incidence reflectivity difference method

International Nuclear Information System (INIS)

Lu, Heng; Wen, Juan; Wang, Xu; Yuan, Kun; Lu, Huibin; Zhou, Yueliang; Jin, Kuijuan; Yang, Guozhen; Li, Wei; Ruan, Kangcheng

2010-01-01

The specific binding between Cy5-labeled goat anti-mouse Immunoglobulin G (IgG) and mouse IgG with a concentration range from 625 to 10 4 µg ml −1 has been detected successfully by the oblique-incidence reflectivity difference (OI-RD) method in each procedure of microarray fabrication. The experimental data prove that the OI-RD method can be employed not only to distinguish the different concentrations in label-free fashion but also to detect the antibody–antigen capture. In addition, the differential treatment of the OI-RD signals can decrease the negative influences of glass slide as the microarray upholder. Therefore the OI-RD technique has promising applications for the label-free and high-throughput detection of protein microarrays

A DNA Microarray-Based Assay to Detect Dual Infection with Two Dengue Virus Serotypes

Directory of Open Access Journals (Sweden)

Alvaro Díaz-Badillo

2014-04-01

Full Text Available Here; we have described and tested a microarray based-method for the screening of dengue virus (DENV serotypes. This DNA microarray assay is specific and sensitive and can detect dual infections with two dengue virus serotypes and single-serotype infections. Other methodologies may underestimate samples containing more than one serotype. This technology can be used to discriminate between the four DENV serotypes. Single-stranded DNA targets were covalently attached to glass slides and hybridised with specific labelled probes. DENV isolates and dengue samples were used to evaluate microarray performance. Our results demonstrate that the probes hybridized specifically to DENV serotypes; with no detection of unspecific signals. This finding provides evidence that specific probes can effectively identify single and double infections in DENV samples.

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

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

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.

Novel Protein Microarray Technology to Examine Men with Prostate Cancer

National Research Council Canada - National Science Library

Lilja, Hans

2005-01-01

The authors developed a novel macro and nanoporous silicon surface for protein microarrays to facilitate high-throughput biomarker discovery, and high-density protein-chip array analyses of complex biological samples...

Calling Biomarkers in Milk Using a Protein Microarray on Your Smartphone

Science.gov (United States)

Ludwig, Susann K. J.; Tokarski, Christian; Lang, Stefan N.; van Ginkel, Leendert A.; Zhu, Hongying; Ozcan, Aydogan; Nielen, Michel W. F.

2015-01-01

Here we present the concept of a protein microarray-based fluorescence immunoassay for multiple biomarker detection in milk extracts by an ordinary smartphone. A multiplex immunoassay was designed on a microarray chip, having built-in positive and negative quality controls. After the immunoassay procedure, the 48 microspots were labelled with Quantum Dots (QD) depending on the protein biomarker levels in the sample. QD-fluorescence was subsequently detected by the smartphone camera under UV light excitation from LEDs embedded in a simple 3D-printed opto-mechanical smartphone attachment. The somewhat aberrant images obtained under such conditions, were corrected by newly developed Android-based software on the same smartphone, and protein biomarker profiles were calculated. The indirect detection of recombinant bovine somatotropin (rbST) in milk extracts based on altered biomarker profile of anti-rbST antibodies was selected as a real-life challenge. RbST-treated and untreated cows clearly showed reproducible treatment-dependent biomarker profiles in milk, in excellent agreement with results from a flow cytometer reference method. In a pilot experiment, anti-rbST antibody detection was multiplexed with the detection of another rbST-dependent biomarker, insulin-like growth factor 1 (IGF-1). Milk extract IGF-1 levels were found to be increased after rbST treatment and correlated with the results obtained from the reference method. These data clearly demonstrate the potential of the portable protein microarray concept towards simultaneous detection of multiple biomarkers. We envisage broad application of this ‘protein microarray on a smartphone’-concept for on-site testing, e.g., in food safety, environment and health monitoring. PMID:26308444

Calling Biomarkers in Milk Using a Protein Microarray on Your Smartphone.

Directory of Open Access Journals (Sweden)

Susann K J Ludwig

Full Text Available Here we present the concept of a protein microarray-based fluorescence immunoassay for multiple biomarker detection in milk extracts by an ordinary smartphone. A multiplex immunoassay was designed on a microarray chip, having built-in positive and negative quality controls. After the immunoassay procedure, the 48 microspots were labelled with Quantum Dots (QD depending on the protein biomarker levels in the sample. QD-fluorescence was subsequently detected by the smartphone camera under UV light excitation from LEDs embedded in a simple 3D-printed opto-mechanical smartphone attachment. The somewhat aberrant images obtained under such conditions, were corrected by newly developed Android-based software on the same smartphone, and protein biomarker profiles were calculated. The indirect detection of recombinant bovine somatotropin (rbST in milk extracts based on altered biomarker profile of anti-rbST antibodies was selected as a real-life challenge. RbST-treated and untreated cows clearly showed reproducible treatment-dependent biomarker profiles in milk, in excellent agreement with results from a flow cytometer reference method. In a pilot experiment, anti-rbST antibody detection was multiplexed with the detection of another rbST-dependent biomarker, insulin-like growth factor 1 (IGF-1. Milk extract IGF-1 levels were found to be increased after rbST treatment and correlated with the results obtained from the reference method. These data clearly demonstrate the potential of the portable protein microarray concept towards simultaneous detection of multiple biomarkers. We envisage broad application of this 'protein microarray on a smartphone'-concept for on-site testing, e.g., in food safety, environment and health monitoring.

permGPU: Using graphics processing units in RNA microarray association studies

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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.

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

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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.

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

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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

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

Effect of Massage Therapy on Labor Pain Reduction in Primiparous Women: A Systematic Review and Meta-analysis of Randomized Controlled Clinical Trials in Iran.

Science.gov (United States)

Ranjbaran, Mehdi; Khorsandi, Maahboobeh; Matourypour, Pegah; Shamsi, Mohsen

2017-01-01

Pain is a common experience for women during labor. Therefore, pain relief care for mothers during labor is very important. This meta-analysis was conducted to evaluate the efficacy of massage therapy on labor pain reduction in primiparous women. In this meta-analysis, the databases of Web of Knowledge, PubMed, Scopus, Cochrane, Iranmedex, Scientific Information Database (SID), and Magiran were searched for published articles in English and Persian language up to January 2016. Among the studies, with regard to the inclusion and exclusion criteria, 10 studies were selected. Data were analyzed by using Stata software version 11, and standard mean difference (SMD) of effects of massage therapy was calculated. The heterogeneity among studies was evaluated by the Chi-square based Q-test and I 2 statistics. The results of Chi-square based on Q-test and I 2 statistics showed heterogeneity among studies in the latent phase ( Q = 63.52, P value massage therapy reduces labor pain in the latent phase (SMD = -1.23, 95% CI: -1.73 to -0.74), active phase (SMD = -1.59, 95% CI: -2.06 to -1.12), and transitional phase (SMD = -1.90, 95% CI: -3.09 to -0.71). This study provides valid evidence for the effect of massage therapy in Iran for labor pain relief. Therefore, the use of massage therapy can be recommended in the primiparous women.

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.

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)

Mining biological databases for candidate disease genes

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Braun, Terry A.; Scheetz, Todd; Webster, Gregg L.; Casavant, Thomas L.

2001-07-01

The publicly-funded effort to sequence the complete nucleotide sequence of the human genome, the Human Genome Project (HGP), has currently produced more than 93% of the 3 billion nucleotides of the human genome into a preliminary `draft' format. In addition, several valuable sources of information have been developed as direct and indirect results of the HGP. These include the sequencing of model organisms (rat, mouse, fly, and others), gene discovery projects (ESTs and full-length), and new technologies such as expression analysis and resources (micro-arrays or gene chips). These resources are invaluable for the researchers identifying the functional genes of the genome that transcribe and translate into the transcriptome and proteome, both of which potentially contain orders of magnitude more complexity than the genome itself. Preliminary analyses of this data identified approximately 30,000 - 40,000 human `genes.' However, the bulk of the effort still remains -- to identify the functional and structural elements contained within the transcriptome and proteome, and to associate function in the transcriptome and proteome to genes. A fortuitous consequence of the HGP is the existence of hundreds of databases containing biological information that may contain relevant data pertaining to the identification of disease-causing genes. The task of mining these databases for information on candidate genes is a commercial application of enormous potential. We are developing a system to acquire and mine data from specific databases to aid our efforts to identify disease genes. A high speed cluster of Linux of workstations is used to analyze sequence and perform distributed sequence alignments as part of our data mining and processing. This system has been used to mine GeneMap99 sequences within specific genomic intervals to identify potential candidate disease genes associated with Bardet-Biedle Syndrome (BBS).

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.

Systematic spatial bias in DNA microarray hybridization is caused by probe spot position-dependent variability in lateral diffusion.

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Steger, Doris; Berry, David; Haider, Susanne; Horn, Matthias; Wagner, Michael; Stocker, Roman; Loy, Alexander

2011-01-01

The hybridization of nucleic acid targets with surface-immobilized probes is a widely used assay for the parallel detection of multiple targets in medical and biological research. Despite its widespread application, DNA microarray technology still suffers from several biases and lack of reproducibility, stemming in part from an incomplete understanding of the processes governing surface hybridization. In particular, non-random spatial variations within individual microarray hybridizations are often observed, but the mechanisms underpinning this positional bias remain incompletely explained. This study identifies and rationalizes a systematic spatial bias in the intensity of surface hybridization, characterized by markedly increased signal intensity of spots located at the boundaries of the spotted areas of the microarray slide. Combining observations from a simplified single-probe block array format with predictions from a mathematical model, the mechanism responsible for this bias is found to be a position-dependent variation in lateral diffusion of target molecules. Numerical simulations reveal a strong influence of microarray well geometry on the spatial bias. Reciprocal adjustment of the size of the microarray hybridization chamber to the area of surface-bound probes is a simple and effective measure to minimize or eliminate the diffusion-based bias, resulting in increased uniformity and accuracy of quantitative DNA microarray hybridization.

Development and application of an oligonucleotide microarray and real-time quantitative PCR for detection of wastewater bacterial pathogens

Energy Technology Data Exchange (ETDEWEB)

Lee, Dae-Young [National Water Research Institute, Environment Canada, 867 Lakeshore Road, Burlington, Ontario, L7R 4A6 (Canada)], E-mail: daeyoung.lee@ec.gc.ca; Lauder, Heather; Cruwys, Heather; Falletta, Patricia [National Water Research Institute, Environment Canada, 867 Lakeshore Road, Burlington, Ontario, L7R 4A6 (Canada); Beaudette, Lee A. [Environmental Science and Technology Centre, Environment Canada, 335 River Road South, Ottawa, Ontario, K1A 0H3 (Canada)], E-mail: lee.beaudette@ec.gc.ca

2008-07-15

Conventional microbial water quality test methods are well known for their technical limitations, such as lack of direct pathogen detection capacity and low throughput capability. The microarray assay has recently emerged as a promising alternative for environmental pathogen monitoring. In this study, bacterial pathogens were detected in municipal wastewater using a microarray equipped with short oligonucleotide probes targeting 16S rRNA sequences. To date, 62 probes have been designed against 38 species, 4 genera, and 1 family of pathogens. The detection sensitivity of the microarray for a waterborne pathogen Aeromonas hydrophila was determined to be approximately 1.0% of the total DNA, or approximately 10{sup 3}A. hydrophila cells per sample. The efficacy of the DNA microarray was verified in a parallel study where pathogen genes and E. coli cells were enumerated using real-time quantitative PCR (qPCR) and standard membrane filter techniques, respectively. The microarray and qPCR successfully detected multiple wastewater pathogen species at different stages of the disinfection process (i.e. secondary effluents vs. disinfected final effluents) and at two treatment plants employing different disinfection methods (i.e. chlorination vs. UV irradiation). This result demonstrates the effectiveness of the DNA microarray as a semi-quantitative, high throughput pathogen monitoring tool for municipal wastewater.

Systematic interpretation of microarray data using experiment annotations

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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.

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.

Comparison of Comparative Genomic Hybridization Technologies across Microarray Platforms

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In the 2007 Association of Biomolecular Resource Facilities (ABRF) Microarray Research Group (MARG) project, we analyzed HL-60 DNA with five platforms: Agilent, Affymetrix 500K, Affymetrix U133 Plus 2.0, Illumina, and RPCI 19K BAC arrays. Copy number variation (CNV) was analyzed ...

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.

CoryneRegNet 4.0 – A reference database for corynebacterial gene regulatory networks

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Baumbach Jan

2007-11-01

Full Text Available Abstract Background Detailed information on DNA-binding transcription factors (the key players in the regulation of gene expression and on transcriptional regulatory interactions of microorganisms deduced from literature-derived knowledge, computer predictions and global DNA microarray hybridization experiments, has opened the way for the genome-wide analysis of transcriptional regulatory networks. The large-scale reconstruction of these networks allows the in silico analysis of cell behavior in response to changing environmental conditions. We previously published CoryneRegNet, an ontology-based data warehouse of corynebacterial transcription factors and regulatory networks. Initially, it was designed to provide methods for the analysis and visualization of the gene regulatory network of Corynebacterium glutamicum. Results Now we introduce CoryneRegNet release 4.0, which integrates data on the gene regulatory networks of 4 corynebacteria, 2 mycobacteria and the model organism Escherichia coli K12. As the previous versions, CoryneRegNet provides a web-based user interface to access the database content, to allow various queries, and to support the reconstruction, analysis and visualization of regulatory networks at different hierarchical levels. In this article, we present the further improved database content of CoryneRegNet along with novel analysis features. The network visualization feature GraphVis now allows the inter-species comparisons of reconstructed gene regulatory networks and the projection of gene expression levels onto that networks. Therefore, we added stimulon data directly into the database, but also provide Web Service access to the DNA microarray analysis platform EMMA. Additionally, CoryneRegNet now provides a SOAP based Web Service server, which can easily be consumed by other bioinformatics software systems. Stimulons (imported from the database, or uploaded by the user can be analyzed in the context of known

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.

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.

Study of hepatitis B virus gene mutations with enzymatic colorimetry-based DNA microarray.

Science.gov (United States)

Mao, Hailei; Wang, Huimin; Zhang, Donglei; Mao, Hongju; Zhao, Jianlong; Shi, Jian; Cui, Zhichu

2006-01-01

To establish a modified microarray method for detecting HBV gene mutations in the clinic. Site-specific oligonucleotide probes were immobilized to microarray slides and hybridized to biotin-labeled HBV gene fragments amplified from two-step PCR. Hybridized targets were transferred to nitrocellulose membranes, followed by intensity measurement using BCIP/NBT colorimetry. HBV genes from 99 Hepatitis B patients and 40 healthy blood donors were analyzed. Mutation frequencies of HBV pre-core/core and basic core promoter (BCP) regions were found to be significantly higher in the patient group (42%, 40% versus 2.5%, 5%, P colorimetry method exhibited the same level of sensitivity and reproducibility. An enzymatic colorimetry-based DNA microarray assay was successfully established to monitor HBV mutations. Pre-core/core and BCP mutations of HBV genes could be major causes of HBV infection in HBeAg-negative patients and could also be relevant to chronicity and aggravation of hepatitis B.

Database Description - Trypanosomes Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Trypanosomes Database Database Description General information of database Database name Trypanosomes Database...stitute of Genetics Research Organization of Information and Systems Yata 1111, Mishima, Shizuoka 411-8540, JAPAN E mail: Database...y Name: Trypanosoma Taxonomy ID: 5690 Taxonomy Name: Homo sapiens Taxonomy ID: 9606 Database description The... Article title: Author name(s): Journal: External Links: Original website information Database maintenance s...DB (Protein Data Bank) KEGG PATHWAY Database DrugPort Entry list Available Query search Available Web servic

Comparing transformation methods for DNA microarray data

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Zwinderman Aeilko H

2004-06-01

Full Text Available Abstract Background When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects, and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer. Results We used the ratio between biological variance and measurement variance (which is an F-like statistic as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters. Conclusions The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method.

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.

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.

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.

Detection of Alicyclobacillus species in fruit juice using a random genomic DNA microarray chip.

Science.gov (United States)

Jang, Jun Hyeong; Kim, Sun-Joong; Yoon, Bo Hyun; Ryu, Jee-Hoon; Gu, Man Bock; Chang, Hyo-Ihl

2011-06-01

This study describes a method using a DNA microarray chip to rapidly and simultaneously detect Alicyclobacillus species in orange juice based on the hybridization of genomic DNA with random probes. Three food spoilage bacteria were used in this study: Alicyclobacillus acidocaldarius, Alicyclobacillus acidoterrestris, and Alicyclobacillus cycloheptanicus. The three Alicyclobacillus species were adjusted to 2 × 10(3) CFU/ml and inoculated into pasteurized 100% pure orange juice. Cy5-dCTP labeling was used for reference signals, and Cy3-dCTP was labeled for target genomic DNA. The molar ratio of 1:1 of Cy3-dCTP and Cy5-dCTP was used. DNA microarray chips were fabricated using randomly fragmented DNA of Alicyclobacillus spp. and were hybridized with genomic DNA extracted from Bacillus spp. Genomic DNA extracted from Alicyclobacillus spp. showed a significantly higher hybridization rate compared with DNA of Bacillus spp., thereby distinguishing Alicyclobacillus spp. from Bacillus spp. The results showed that the microarray DNA chip containing randomly fragmented genomic DNA was specific and clearly identified specific food spoilage bacteria. This microarray system is a good tool for rapid and specific detection of thermophilic spoilage bacteria, mainly Alicyclobacillus spp., and is useful and applicable to the fruit juice industry.

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....

Database Description - SKIP Stemcell Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us SKIP Stemcell Database Database Description General information of database Database name SKIP Stemcell Database...rsity Journal Search: Contact address http://www.skip.med.keio.ac.jp/en/contact/ Database classification Human Genes and Diseases Dat...abase classification Stemcell Article Organism Taxonomy Name: Homo sapiens Taxonomy ID: 9606 Database...ks: Original website information Database maintenance site Center for Medical Genetics, School of medicine, ...lable Web services Not available URL of Web services - Need for user registration Not available About This Database Database

Correction of technical bias in clinical microarray data improves concordance with known biological information

DEFF Research Database (Denmark)

Eklund, Aron Charles; Szallasi, Zoltan Imre

2008-01-01

The performance of gene expression microarrays has been well characterized using controlled reference samples, but the performance on clinical samples remains less clear. We identified sources of technical bias affecting many genes in concert, thus causing spurious correlations in clinical data...... sets and false associations between genes and clinical variables. We developed a method to correct for technical bias in clinical microarray data, which increased concordance with known biological relationships in multiple data sets....

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....

Linking probe thermodynamics to microarray quantification

International Nuclear Information System (INIS)

Li, Shuzhao; Pozhitkov, Alexander; Brouwer, Marius

2010-01-01

Understanding the difference in probe properties holds the key to absolute quantification of DNA microarrays. So far, Langmuir-like models have failed to link sequence-specific properties to hybridization signals in the presence of a complex hybridization background. Data from washing experiments indicate that the post-hybridization washing has no major effect on the specifically bound targets, which give the final signals. Thus, the amount of specific targets bound to probes is likely determined before washing, by the competition against nonspecific binding. Our competitive hybridization model is a viable alternative to Langmuir-like models. (comment)

Database Description - Arabidopsis Phenome Database | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us Arabidopsis Phenome Database Database Description General information of database Database n... BioResource Center Hiroshi Masuya Database classification Plant databases - Arabidopsis thaliana Organism T...axonomy Name: Arabidopsis thaliana Taxonomy ID: 3702 Database description The Arabidopsis thaliana phenome i...heir effective application. We developed the new Arabidopsis Phenome Database integrating two novel database...seful materials for their experimental research. The other, the “Database of Curated Plant Phenome” focusing

DNA microarrays immobilized on unmodified plastics in a microfluidic biochip for rapid typing of Avian Influenza Virus

DEFF Research Database (Denmark)

Yi, Sun; Perch-Nielsen, Ivan R.; Dufva, Martin

2011-01-01

Polymers are widely used for microfluidic systems, but fabrication of microarrays on such materials often requires complicated chemical surface modifications, which hinders the integration of microarrays into microfluidic systems. In this paper, we demonstrate that UV irradiation can be used to d...

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.

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.

aeGEPUCI: a database of gene expression in the dengue vector mosquito, Aedes aegypti

Directory of Open Access Journals (Sweden)

James Anthony A

2010-10-01

Full Text Available Abstract Background Aedes aegypti is the principal vector of dengue and yellow fever viruses. The availability of the sequenced and annotated genome enables genome-wide analyses of gene expression in this mosquito. The large amount of data resulting from these analyses requires efficient cataloguing before it becomes useful as the basis for new insights into gene expression patterns and studies of the underlying molecular mechanisms for generating these patterns. Findings We provide a publicly-accessible database and data-mining tool, aeGEPUCI, that integrates 1 microarray analyses of sex- and stage-specific gene expression in Ae. aegypti, 2 functional gene annotation, 3 genomic sequence data, and 4 computational sequence analysis tools. The database can be used to identify genes expressed in particular stages and patterns of interest, and to analyze putative cis-regulatory elements (CREs that may play a role in coordinating these patterns. The database is accessible from the address http://www.aegep.bio.uci.edu. Conclusions The combination of gene expression, function and sequence data coupled with integrated sequence analysis tools allows for identification of expression patterns and streamlines the development of CRE predictions and experiments to assess how patterns of expression are coordinated at the molecular level.

Preparation of Biomolecule Microstructures and Microarrays by Thiol–ene Photoimmobilization

NARCIS (Netherlands)

Weinrich, Dirk; Köhn, Maja; Jonkheijm, Pascal; Westerlind, Ulrika; Dehmelt, Leif; Engelkamp, Hans; Christianen, Peter C.M.; Kuhlmann, Jürgen; Maan, Jan C.; Nüsse, Dirk; Schröder, Hendrik; Wacker, Ron; Voges, Edgar; Breinbauer, Rolf; Kunz, Horst; Niemeyer, Christof M.; Waldmann, Herbert

2010-01-01

A mild, fast and flexible method for photoimmobilization of biomolecules based on the light-initiated thiol–ene reaction has been developed. After investigation and optimization of various surface materials, surface chemistries and reaction parameters, microstructures and microarrays of biotin,

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.

Quantitative inference of dynamic regulatory pathways via microarray data

Directory of Open Access Journals (Sweden)

Chen Bor-Sen

2005-03-01

Full Text Available Abstract Background The cellular signaling pathway (network is one of the main topics of organismic investigations. The intracellular interactions between genes in a signaling pathway are considered as the foundation of functional genomics. Thus, what genes and how much they influence each other through transcriptional binding or physical interactions are essential problems. Under the synchronous measures of gene expression via a microarray chip, an amount of dynamic information is embedded and remains to be discovered. Using a systematically dynamic modeling approach, we explore the causal relationship among genes in cellular signaling pathways from the system biology approach. Results In this study, a second-order dynamic model is developed to describe the regulatory mechanism of a target gene from the upstream causality point of view. From the expression profile and dynamic model of a target gene, we can estimate its upstream regulatory function. According to this upstream regulatory function, we would deduce the upstream regulatory genes with their regulatory abilities and activation delays, and then link up a regulatory pathway. Iteratively, these regulatory genes are considered as target genes to trace back their upstream regulatory genes. Then we could construct the regulatory pathway (or network to the genome wide. In short, we can infer the genetic regulatory pathways from gene-expression profiles quantitatively, which can confirm some doubted paths or seek some unknown paths in a regulatory pathway (network. Finally, the proposed approach is validated by randomly reshuffling the time order of microarray data. Conclusion We focus our algorithm on the inference of regulatory abilities of the identified causal genes, and how much delay before they regulate the downstream genes. With this information, a regulatory pathway would be built up using microarray data. In the present study, two signaling pathways, i.e. circadian regulatory

Rapid and reliable detection and identification of GM events using multiplex PCR coupled with oligonucleotide microarray.

Science.gov (United States)

Xu, Xiaodan; Li, Yingcong; Zhao, Heng; Wen, Si-yuan; Wang, Sheng-qi; Huang, Jian; Huang, Kun-lun; Luo, Yun-bo

2005-05-18

To devise a rapid and reliable method for the detection and identification of genetically modified (GM) events, we developed a multiplex polymerase chain reaction (PCR) coupled with a DNA microarray system simultaneously aiming at many targets in a single reaction. The system included probes for screening gene, species reference gene, specific gene, construct-specific gene, event-specific gene, and internal and negative control genes. 18S rRNA was combined with species reference genes as internal controls to assess the efficiency of all reactions and to eliminate false negatives. Two sets of the multiplex PCR system were used to amplify four and five targets, respectively. Eight different structure genes could be detected and identified simultaneously for Roundup Ready soybean in a single microarray. The microarray specificity was validated by its ability to discriminate two GM maizes Bt176 and Bt11. The advantages of this method are its high specificity and greatly reduced false-positives and -negatives. The multiplex PCR coupled with microarray technology presented here is a rapid and reliable tool for the simultaneous detection of GM organism ingredients.

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

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.

Clinical value of miR-198-5p in lung squamous cell carcinoma assessed using microarray and RT-qPCR.

Science.gov (United States)

Liang, Yue-Ya; Huang, Jia-Cheng; Tang, Rui-Xue; Chen, Wen-Jie; Chen, Peng; Cen, Wei-Luan; Shi, Ke; Gao, Li; Gao, Xiang; Liu, An-Gui; Peng, Xiao-Tong; Chen, Gang; Huang, Su-Ning; Fang, Ye-Ying; Gu, Yong-Yao

2018-02-02

To examine the clinical value of miR-198-5p in lung squamous cell carcinoma (LUSC). Gene Expression Omnibus (GEO) microarray datasets were used to explore the miR-198-5p expression and its diagnostic value in LUSC. Real-time reverse transcription quantitative polymerase chain reaction was used to evaluate the expression of miR-198-5p in 23 formalin-fixed, paraffin-embedded (FFPE) LUSC tissues and corresponding non-cancerous tissues. The correlation between miR-198-5p expression and clinic pathological features was assessed. Meanwhile, putative target messenger RNAs of miR-198-5p were identified based on the analysis of differentially expressed genes in the Cancer Genome Atlas (TCGA) and 12 miRNA prediction tools. Subsequently, the putative target genes were sent to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. MiR-198-5p was low expressed in LUSC tissues. The combined standard mean difference (SMD) values of miR-198-5p expression based on GEO datasets were - 0.30 (95% confidence interval (CI) - 0.54, - 0.06) and - 0.39 (95% CI - 0.83, 0.05) using fixed effect model and random effect model, respectively. The sensitivity and specificity were not sufficiently high, as the area under the curve (AUC) was 0.7749 (Q* = 0.7143) based on summarized receiver operating characteristic (SROC) curves constructed using GEO datasets. Based on the in-house RT-qPCR, miR-198-5p expression was 4.3826 ± 1.7660 in LUSC tissues and 4.4522 ± 1.8263 in adjacent normal tissues (P = 0.885). The expression of miR-198-5p was significantly higher in patients with early TNM stages (I-II) than that in cases with advanced TNM stages (III-IV) (5.4400 ± 1.5277 vs 3.5690 ± 1.5228, P = 0.008). Continuous variable-based meta-analysis of GEO and PCR data displayed the SMD values of - 0.26 (95% CI - 0.48, - 0.04) and - 0.34 (95% CI - 0.71, 0.04) based on fixed and random effect models, respectively. As for the diagnostic

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.

Screening hybridomas for anabolic androgenic steroids by steroid analog antigen microarray.

Science.gov (United States)

Du, Hongwu; Chen, Guangyu; Bian, Yongzhong; Xing, Cenzan; Ding, Xue; Zhu, Mengliang; Xun, Yiping; Chen, Peng; Zhou, Yabin; Li, Shaoxu

2015-01-01

Currently, dozens of anabolic androgenic steroids (AAS) are forbidden in the World Anti-Doping Agency Prohibited List, however, despite extensive investigation, there are still lots of AAS without corresponding monoclonal antibodies. A steroid analog antigen microarray made up of ten AAS was fabricated to screen the hybridoma and it was found an original unsuccessful clone turned out to be a candidate anti-boldenone antibody, without any cross-reactions with endogenous AAS or 44 different AAS standard reference materials tested. Our findings suggested that steroid analog antigen microarray could be a promising tool to screen and characterize new applications of antibodies for structure analogs, and this also exhibits the potential to fast identify effective epitopes of hybridomas in a single assay.

Exergaming as a viable therapeutic tool to improve static and dynamic balance among older adults and people with idiopathic Parkinson’s disease: a systematic review and meta-analysis

Directory of Open Access Journals (Sweden)

Dale Michael Harris

2015-09-01

Full Text Available The use of virtual reality games (known as ‘exergaming’ as a neurorehabilitation tool is gaining interest. Therefore, we aim to collate evidence for the effects of exergaming on the balance and postural control of older adults and people with idiopathic Parkinson’s disease (IPD. Six electronic databases were searched, from inception to April 2015, to identify relevant studies. Standardised mean differences (SMD and 95% confidence intervals (CI were used to calculate effect sizes between experimental and control groups. I2 statistics were used to determine levels of heterogeneity. 309 older adults and 74 people with IPD were assessed across eleven studies. The results showed that exergaming improved static balance (SMD 1.069, 95% CI 0.563 to 1.576, postural control (SMD 0.826, 95% CI 0.481 to 1.170 and dynamic balance (SMD -0.808, 95% CI -1.192 to -0.424 in healthy older adults. Two PD studies showed an improvement in static balance (SMD 0.124, 95% CI -0.581 to 0.828 and postural control (SMD 2.576, 95% CI 1.534 to 3.599. Our findings suggest that exergaming might be an appropriate therapeutic tool for improving balance and postural control in older adults, but more large-scale trials are needed to determine if the same is true for people with IPD.

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.

Carbohydrate Microarrays in Plant Science

DEFF Research Database (Denmark)

Fangel, Jonatan Ulrik; Pedersen, H.L.; Vidal-Melgosa, S.

2012-01-01

Almost all plant cells are surrounded by glycan-rich cell walls, which form much of the plant body and collectively are the largest source of biomass on earth. Plants use polysaccharides for support, defense, signaling, cell adhesion, and as energy storage, and many plant glycans are also important...... industrially and nutritionally. Understanding the biological roles of plant glycans and the effective exploitation of their useful properties requires a detailed understanding of their structures, occurrence, and molecular interactions. Microarray technology has revolutionized the massively high...... for plant research and can be used to map glycan populations across large numbers of samples to screen antibodies, carbohydrate binding proteins, and carbohydrate binding modules and to investigate enzyme activities....

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.

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

Development and application of an antibody-based protein microarray to assess physiological stress in grizzly bears (Ursus arctos).

Science.gov (United States)

Carlson, Ruth I; Cattet, Marc R L; Sarauer, Bryan L; Nielsen, Scott E; Boulanger, John; Stenhouse, Gordon B; Janz, David M

2016-01-01

A novel antibody-based protein microarray was developed that simultaneously determines expression of 31 stress-associated proteins in skin samples collected from free-ranging grizzly bears (Ursus arctos) in Alberta, Canada. The microarray determines proteins belonging to four broad functional categories associated with stress physiology: hypothalamic-pituitary-adrenal axis proteins, apoptosis/cell cycle proteins, cellular stress/proteotoxicity proteins and oxidative stress/inflammation proteins. Small skin samples (50-100 mg) were collected from captured bears using biopsy punches. Proteins were isolated and labelled with fluorescent dyes, with labelled protein homogenates loaded onto microarrays to hybridize with antibodies. Relative protein expression was determined by comparison with a pooled standard skin sample. The assay was sensitive, requiring 80 µg of protein per sample to be run in triplicate on the microarray. Intra-array and inter-array coefficients of variation for individual proteins were generally bears. This suggests that remotely delivered biopsy darts could be used in future sampling. Using generalized linear mixed models, certain proteins within each functional category demonstrated altered expression with respect to differences in year, season, geographical sampling location within Alberta and bear biological parameters, suggesting that these general variables may influence expression of specific proteins in the microarray. Our goal is to apply the protein microarray as a conservation physiology tool that can detect, evaluate and monitor physiological stress in grizzly bears and other species at risk over time in response to environmental change.

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.

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

Baseline and postoperative levels of C-reactive protein and interleukins as inflammatory predictors of atrial fibrillation following cardiac surgery: a systematic review and meta-analysis.

Science.gov (United States)

Weymann, Alexander; Popov, Aron-Frederik; Sabashnikov, Anton; Ali-Hasan-Al-Saegh, Sadeq; Ryazanov, Mikhail; Tse, Gary; Mirhosseini, Seyed Jalil; Liu, Tong; Lotfaliani, Mohammadreza; Sedaghat, Meghdad; Baker, William L; Ghanei, Azam; Yavuz, Senol; Zeriouh, Mohamed; Izadpanah, Payman; Dehghan, Hamidreza; Testa, Luca; Nikfard, Maryam; Sá, Michel Pompeu Barros de Oliveira; Mashhour, Ahmed; Nombela-Franco, Luis; Rezaeisadrabadi, Mohammad; D'Ascenzo, Fabrizio; Zhigalov, Konstantin; Benedetto, Umberto; Aminolsharieh Najafi, Soroosh; Szczechowicz, Marcin; Roever, Leonardo; Meng, Lei; Gong, Mengqi; Deshmukh, Abhishek J; Palmerini, Tullio; Linde, Cecilia; Filipiak, Krzysztof J; Stone, Gregg W; Biondi-Zoccai, Giuseppe; Calkins, Hugh

2018-01-01

Postoperative atrial fibrillation (POAF) is a leading arrhythmia with high incidence and serious clinical implications after cardiac surgery. Cardiac surgery is associated with systemic inflammatory response including increase in cytokines and activation of endothelial and leukocyte responses. This systematic review and meta-analysis aimed to determine the strength of evidence for evaluating the association of inflammatory markers, such as C-reactive protein (CRP) and interleukins (IL), with POAF following isolated coronary artery bypass grafting (CABG), isolated valvular surgery, or a combination of these procedures. We conducted a meta-analysis of studies evaluating measured baseline (from one week before surgical procedures) and postoperative levels (until one week after surgical procedures) of inflammatory markers in patients with POAF. A compre-hensive search was performed in electronic medical databases (Medline/PubMed, Web of Science, Embase, Science Direct, and Google Scholar) from their inception through May 2017 to identify relevant studies. A comprehensive subgroup analysis was performed to explore potential sources of heterogeneity. A literature search of all major databases retrieved 1014 studies. After screening, 42 studies were analysed including a total of 8398 patients. Pooled analysis showed baseline levels of CRP (standard mean difference [SMD] 0.457 mg/L, p < 0.001), baseline levels of IL-6 (SMD 0.398 pg/mL, p < 0.001), postoperative levels of CRP (SMD 0.576 mg/L, p < 0.001), postoperative levels of IL-6 (SMD 1.66 pg/mL, p < 0.001), postoperative levels of IL-8 (SMD 0.839 pg/mL, p < 0.001), and postoperative levels of IL-10 (SMD 0.590 pg/mL, p < 0.001) to be relevant inflammatory parameters significantly associated with POAF. Perioperative inflammation is proposed to be involved in the pathogenesis of POAF. Therefore, perioperative assessment of CRP, IL-6, IL-8, and IL-10 can help clinicians in terms of predicting and monitoring for POAF.

Effectiveness of Aquatic Exercise in Improving Lower Limb Strength in Musculoskeletal Conditions: A Systematic Review and Meta-Analysis.

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Heywood, Sophie; McClelland, Jodie; Mentiplay, Benjamin; Geigle, Paula; Rahmann, Ann; Clark, Ross

2017-01-01

To investigate the effectiveness of aquatic exercise in improving lower limb strength in people with musculoskeletal conditions. A systematic search used 5 databases, including MEDLINE, CINAHL, Embase, SPORTDiscus, and The Cochrane Library. Randomized controlled trials evaluating aquatic exercise with a resistance training component for adults with musculoskeletal conditions compared with no intervention or land-based exercise were identified. Fifteen studies from the initial yield of 1214 met these criteria. Data related to participant demographics, study design, and methods, interventions, and outcomes, including numerical means and SDs, were extracted independently by 2 reviewers. Nine of the 15 studies were of high quality, scoring at least 6 on the Physiotherapy Evidence Database Scale. Limited consideration of the prescription of resistance in the aquatic exercise and application of resistance training principles existed. Low- or very low-quality evidence indicates there was no difference in average effect between aquatic exercise and no exercise in improving hip abductor strength (standardized mean difference [SMD], .28; 95% confidence interval [CI], -.04 to .59), knee extensor strength (SMD, .18; 95% CI, -.03 to .40), knee flexor strength (SMD, .13; 95% CI, -.20 to .45), or lower limb endurance (SMD, .35; 95% CI, -.06 to .77). Low-quality evidence indicates no difference in average effect between aquatic and land exercise for knee extensor (SMD, -.24; 95% CI, -.49 to .02) or flexor strength (SMD, -.15; 95% CI, -.53 to .22). It is likely that the inadequate application of resistance in water is a significant contributor to the limited effectiveness of aquatic exercise interventions in improving hip and knee muscle strength in people with musculoskeletal conditions. Future research is needed to quantify resistance with aquatic exercises and to determine if using opportunities for greater resistance in aquatic rehabilitation and appropriate resistance

A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays.

Science.gov (United States)

Lee, Mei-Ling Ting; Bulyk, Martha L; Whitmore, G A; Church, George M

2002-12-01

There is considerable scientific interest in knowing the probability that a site-specific transcription factor will bind to a given DNA sequence. Microarray methods provide an effective means for assessing the binding affinities of a large number of DNA sequences as demonstrated by Bulyk et al. (2001, Proceedings of the National Academy of Sciences, USA 98, 7158-7163) in their study of the DNA-binding specificities of Zif268 zinc fingers using microarray technology. In a follow-up investigation, Bulyk, Johnson, and Church (2002, Nucleic Acid Research 30, 1255-1261) studied the interdependence of nucleotides on the binding affinities of transcription proteins. Our article is motivated by this pair of studies. We present a general statistical methodology for analyzing microarray intensity measurements reflecting DNA-protein interactions. The log probability of a protein binding to a DNA sequence on an array is modeled using a linear ANOVA model. This model is convenient because it employs familiar statistical concepts and procedures and also because it is effective for investigating the probability structure of the binding mechanism.

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

A random-access microarray for programmable droplet storage, retrieval and manipulation

International Nuclear Information System (INIS)

Tseng, Yi-Ming; Wang, Jhih-Jhe; Su, Yu-Chuan

2012-01-01

This article presents an integrated microfluidic system that is capable of programmably metering, entrapping, coalescing, addressably storing, retrieving and manipulating emulsion droplets. A multilayer, flexible PDMS chip with specially designed fluidic channels dynamically reconfigured by pneumatically actuated diaphragms is utilized to integrate a variety of droplet manipulation schemes. Once droplets are formed, their motions are coordinated by a 2D multiplexing scheme, which exploits the bidirectional movement of diaphragms to implement a random-access microarray. In the prototype demonstration, a PDMS molding and bonding process is used to fabricate the proposed microfluidic system. Emulsion droplets with desired volumes and compositions are produced, addressably stored, manipulated and retrieved from a 4 × 4 array, which employs just 4 (= 2 × log 2 4) control inputs for the operation. It has been demonstrated that (1) the integration of droplet manipulation and 2D multiplexing schemes can be achieved readily using bidirectional diaphragm valves, (2) multiplexing of an N × N array could be realized utilizing only 2 × log 2 N control inputs and (3) a multifunctional, random-access microarray can be accomplished employing a multilayer PDMS chip. As such, the demonstrated random-access microarray could potentially serve as a platform for continuous tracking and multistep processing of emulsion droplets, which is desired for various biological and chemical applications. (paper)

Radioactive cDNA microarray (II): Gene expression profiling of antidepressant treatment by human cDNA microarray

Energy Technology Data Exchange (ETDEWEB)

Lee, Ji Hye; Kang, Rhee Hun; Ham, Byung Joo; Lee, Min Su; Shin, Kyung Ho; Choe, Jae Gol; Kim, Meyoung Kon [College of Medicine, Univ. of Korea, Seoul (Korea, Republic of)

2003-07-01

Major depressive disorder is a prevalent psychiatric disorder in primary care, associated with impaired patient functioning and well-being. Fluoxetine is a selective serotonin-reuptake inhibitors (SSRIs) and is a commonly prescribed antidepressant compound. Its action is primarily attributed to selective inhibition of the reuptake of serotonin (5-hydroxytryptamine) in the central nervous system. Objectives ; the aims of this study were two-fold: (1) to determine the usefulness for investigation of the transcription profiles in depression patients, and (2) to assess the differences in gene expression profiles between positive response group and negative response groups by fluoxetine treatment. This study included 53 patients with major depression (26 in positive response group with antidepressant treatment, 27 in negative response group with antidepressant treatment), and 53 healthy controls. To examine the difference of gene expression profile in depression patients, radioactive complementary DNA microarrays were used to evaluate changes in the expression of 1,152 genes in total. Using 33p-labeled probes, this method provided highly sensitive gene expression profiles including brain receptors, drug metabolism, and cellular signaling. Gene transcription profiles were classified into several categories in accordance with the antidepressant gene-regulation. The gene profiles were significantly up-(22 genes) and down-(16 genes) regulated in the positive response group when compared to the control group. Also, in the negative response group, 35 genes were up-regulated and 8 genes were down-regulated when compared to the control group. Consequently, we demonstrated that radioactive human cDNA microarray is highly likely to be an efficient technology for evaluating the gene regulation of antidepressants, such as selective serotonin-reuptake inhibitors (SSRIs), by using high-throughput biotechnology.

Radioactive cDNA microarray (II): Gene expression profiling of antidepressant treatment by human cDNA microarray

International Nuclear Information System (INIS)

Lee, Ji Hye; Kang, Rhee Hun; Ham, Byung Joo; Lee, Min Su; Shin, Kyung Ho; Choe, Jae Gol; Kim, Meyoung Kon

2003-01-01

Major depressive disorder is a prevalent psychiatric disorder in primary care, associated with impaired patient functioning and well-being. Fluoxetine is a selective serotonin-reuptake inhibitors (SSRIs) and is a commonly prescribed antidepressant compound. Its action is primarily attributed to selective inhibition of the reuptake of serotonin (5-hydroxytryptamine) in the central nervous system. Objectives ; the aims of this study were two-fold: (1) to determine the usefulness for investigation of the transcription profiles in depression patients, and (2) to assess the differences in gene expression profiles between positive response group and negative response groups by fluoxetine treatment. This study included 53 patients with major depression (26 in positive response group with antidepressant treatment, 27 in negative response group with antidepressant treatment), and 53 healthy controls. To examine the difference of gene expression profile in depression patients, radioactive complementary DNA microarrays were used to evaluate changes in the expression of 1,152 genes in total. Using 33p-labeled probes, this method provided highly sensitive gene expression profiles including brain receptors, drug metabolism, and cellular signaling. Gene transcription profiles were classified into several categories in accordance with the antidepressant gene-regulation. The gene profiles were significantly up-(22 genes) and down-(16 genes) regulated in the positive response group when compared to the control group. Also, in the negative response group, 35 genes were up-regulated and 8 genes were down-regulated when compared to the control group. Consequently, we demonstrated that radioactive human cDNA microarray is highly likely to be an efficient technology for evaluating the gene regulation of antidepressants, such as selective serotonin-reuptake inhibitors (SSRIs), by using high-throughput biotechnology

Carbohydrate Microarray on Glass: a Tool for Carbohydrate-Lectin Interactions

NARCIS (Netherlands)

Tetala, K.K.R.; Giesbers, M.; Visser, G.M.; Sudhölter, E.J.R.; Beek, van T.A.

2007-01-01

A simple method to immobilize carbohydrates on a glass surface to obtain a carbohydrate microarray is described. The array was used to study carbohydrate-lectin interactions. The glass surface was modified with aldehyde terminated linker groups of various chain lengths. Coupling of carbohydrates

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

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.

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.

Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes.

Science.gov (United States)

Santos, Alberto; Wernersson, Rasmus; Jensen, Lars Juhl

2015-01-01

The eukaryotic cell division cycle is a highly regulated process that consists of a complex series of events and involves thousands of proteins. Researchers have studied the regulation of the cell cycle in several organisms, employing a wide range of high-throughput technologies, such as microarray-based mRNA expression profiling and quantitative proteomics. Due to its complexity, the cell cycle can also fail or otherwise change in many different ways if important genes are knocked out, which has been studied in several microscopy-based knockdown screens. The data from these many large-scale efforts are not easily accessed, analyzed and combined due to their inherent heterogeneity. To address this, we have created Cyclebase--available at http://www.cyclebase.org--an online database that allows users to easily visualize and download results from genome-wide cell-cycle-related experiments. In Cyclebase version 3.0, we have updated the content of the database to reflect changes to genome annotation, added new mRNA and protein expression data, and integrated cell-cycle phenotype information from high-content screens and model-organism databases. The new version of Cyclebase also features a new web interface, designed around an overview figure that summarizes all the cell-cycle-related data for a gene. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Establishment and Application of a Visual DNA Microarray for the Detection of Food-borne Pathogens.

Science.gov (United States)

Li, Yongjin

2016-01-01

The accurate detection and identification of food-borne pathogenic microorganisms is critical for food safety nowadays. In the present work, a visual DNA microarray was established and applied to detect pathogens commonly found in food, including Salmonella enterica, Shigella flexneri, E. coli O157:H7 and Listeria monocytogenes in food samples. Multiplex PCR (mPCR) was employed to simultaneously amplify specific gene fragments, fimY for Salmonella, ipaH for Shigella, iap for L. monocytogenes and ECs2841 for E. coli O157:H7, respectively. Biotinylated PCR amplicons annealed to the microarray probes were then reacted with a streptavidin-alkaline phosphatase conjugate and nitro blue tetrazolium/5-bromo-4-chloro-3'-indolylphosphate, p-toluidine salt (NBT/BCIP); the positive results were easily visualized as blue dots formatted on the microarray surface. The performance of a DNA microarray was tested against 14 representative collection strains and mock-contamination food samples. The combination of mPCR and a visual micro-plate chip specifically and sensitively detected Salmonella enterica, Shigella flexneri, E. coli O157:H7 and Listeria monocytogenes in standard strains and food matrices with a sensitivity of ∼10(2) CFU/mL of bacterial culture. Thus, the developed method is advantageous because of its high throughput, cost-effectiveness and ease of use.

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

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Atiyeh Mortazavi

2016-01-01

Full Text Available High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation of the proposed approach is to employ Qualitative Mutual Information (QMI for this purpose. The idea of Qualitative Mutual Information causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance and scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The performance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum redundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification accuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability compared to other approaches.

Complete gene expression profiling of Saccharopolyspora erythraea using GeneChip DNA microarrays

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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

Comparison of small n statistical tests of differential expression applied to microarrays

Directory of Open Access Journals (Sweden)

Lee Anna Y

2009-02-01

Full Text Available Abstract Background DNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data. Results Three Empirical Bayes methods (CyberT, BRB, and limma t-statistics were the most effective statistical tests across simulated and both 2-colour cDNA and Affymetrix experimental data. The CyberT regularized t-statistic in particular was able to maintain expected false positive rates with simulated data showing high variances at low gene intensities, although at the cost of low true positive rates. The Local Pooled Error (LPE test introduced a bias that lowered false positive rates below theoretically expected values and had lower power relative to the top performers. The standard two-sample t-test and fold change were also found to be sub-optimal for detecting differentially expressed genes. The generalized log transformation was shown to be beneficial in improving results with certain data sets, in particular high variance cDNA data. Conclusion Pre-processing of data influences performance and the proper combination of pre-processing and statistical testing is necessary for obtaining the best results. All three Empirical Bayes methods assessed in our study are good choices for statistical tests for small n microarray studies for both Affymetrix and cDNA data. Choice of method for a particular study will depend on software and normalization preferences.

Systematic review and meta-analysis shows a specific micronutrient profile in people with Down Syndrome: Lower blood calcium, selenium and zinc, higher red blood cell copper and zinc, and higher salivary calcium and sodium.

Directory of Open Access Journals (Sweden)

Amene Saghazadeh

Full Text Available Different metabolic profiles as well as comorbidities are common in people with Down Syndrome (DS. Therefore it is relevant to know whether micronutrient levels in people with DS are also different. This systematic review was designed to review the literature on micronutrient levels in people with DS compared to age and sex-matched controls without DS. We identified sixty nine studies from January 1967 to April 2016 through main electronic medical databases PubMed, Scopus, and Web of knowledge. We carried out meta-analysis of the data on four essential trace elements (Cu, Fe, Se, and Zn, six minerals (Ca, Cl, K, Mg, Na, and P, and five vitamins (vitamin A, B9, B12, D, and E. People with DS showed lower blood levels of Ca (standard mean difference (SMD = -0.63; 95% confidence interval (CI: -1.16 to -0.09, Se (SMD = -0.99; 95% CI: -1.55 to -0.43, and Zn (SMD = -1.30; 95% CI: -1.75 to -0.84, while red cell levels of Zn (SMD = 1.88; 95% CI: 0.48 to 3.28 and Cu (SMD = 2.77; 95% CI: 1.96 to 3.57 were higher. They had also higher salivary levels of Ca (SMD = 0.85; 95% CI: 0.38 to 1.33 and Na (SMD = 1.04; 95% CI: 0.39 to 1.69. Our findings that micronutrient levels are different in people with DS raise the question whether these differences are related to the different metabolic profiles, the common comorbidities or merely reflect DS.

The Immunomodulatory Effect of Acupoint Application for Childhood Asthma: A Systematic Review and Meta-Analysis

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Xiao Cun Yang

2015-01-01

Full Text Available Objective. To evaluate the evidence on the immunomodulatory effect of acupoint application for childhood asthma. Methods. Five electronic databases through October 2014 were searched. The risk of bias in eligible studies was assessed using the Cochrane Collaboration tool. Standardised mean difference (SMD and 95% confidence intervals (CI of random-effects model were calculated. And heterogeneity was assessed using the Cochran Q statistic and quantified with the I2 index. Results. Six studies were included in our review. The aggregated results suggested that acupoint application showed the beneficial effect for childhood asthma in improving IgA (SMD, −0.83; 95% CI −1.14 to −0.52; P<0.00001, IgE (SMD, −0.52; 95% CI −0.76 to −0.29; P<0.001, IgG (SMD, −1.17; 95% CI −1.61 to −0.74; P<0.0001, IL-4 (SMD, −0.57; 95% CI −0.91 to −0.23; P=0.0009, and IFN-γ (SMD, −0.38; 95% CI −0.71 to −0.04; P=0.03 but not IgM (SMD, −0.40; 95% CI −0.98 to 0.18; P=0.18. And the effective dose of acupoint application may be 2–6 hours/time and a total of 3 times within 4 weeks. Conclusions. This review showed the positive evidence that acupoint application had the favorable immunomodulatory effect for childhood asthma. However, more studies with long follow-up are warrant to confirm the current findings.

The Effectiveness of Aromatherapy in Reducing Pain: A Systematic Review and Meta-Analysis

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Shaheen E. Lakhan

2016-01-01

Full Text Available Background. Aromatherapy refers to the medicinal or therapeutic use of essential oils absorbed through the skin or olfactory system. Recent literature has examined the effectiveness of aromatherapy in treating pain. Methods. 12 studies examining the use of aromatherapy for pain management were identified through an electronic database search. A meta-analysis was performed to determine the effects of aromatherapy on pain. Results. There is a significant positive effect of aromatherapy (compared to placebo or treatments as usual controls in reducing pain reported on a visual analog scale (SMD = −1.18, 95% CI: −1.33, −1.03; p<0.0001. Secondary analyses found that aromatherapy is more consistent for treating nociceptive (SMD = −1.57, 95% CI: −1.76, −1.39, p<0.0001 and acute pain (SMD = −1.58, 95% CI: −1.75, −1.40, p<0.0001 than inflammatory (SMD = −0.53, 95% CI: −0.77, −0.29, p<0.0001 and chronic pain (SMD = −0.22, 95% CI: −0.49, 0.05, p=0.001, respectively. Based on the available research, aromatherapy is most effective in treating postoperative pain (SMD = −1.79, 95% CI: −2.08, −1.51, p<0.0001 and obstetrical and gynecological pain (SMD = −1.14, 95% CI: −2.10, −0.19, p<0.0001. Conclusion. The findings of this study indicate that aromatherapy can successfully treat pain when combined with conventional treatments.

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.

Glycoprofiling of Early Gastric Cancer Using Lectin Microarray Technology.

Science.gov (United States)

Li, Taijie; Mo, Cuiju; Qin, Xue; Li, Shan; Liu, Yinkun; Liu, Zhiming

2018-01-01

Recently, studies have reported that protein glycosylation plays an important role in the occurrence and development of cancer. Gastric cancer is a common cancer with high morbidity and mortality owing to most gastric cancers are discovered only at an advanced stage. Here, we aim to discover novel specific serum glycanbased biomarkers for gastric cancer. A lectin microarray with 50 kinds of tumor-associated lectin was used to detect the glycan profiles of serum samples between early gastric cancer and healthy controls. Then lectin blot was performed to validate the differences. The result of the lectin microarray showed that the signal intensities of 13 lectins showed significant differences between the healthy controls and early gastric cancer. Compared to the healthy, the normalized fluorescent intensities of the lectins PWA, LEL, and STL were significantly increased, and it implied that their specifically recognized GlcNAc showed an especially elevated expression in early gastric cancer. Moreover, the binding affinity of the lectins EEL, RCA-II, RCA-I, VAL, DSA, PHA-L, UEA, and CAL were higher in the early gastric cancer than in healthy controls. These glycan structures containing GalNAc, terminal Galβ 1-4 GlcNAc, Tri/tetraantennary N-glycan, β-1, 6GlcNAc branching structure, α-linked fucose residues, and Tn antigen were elevated in gastric cancer. While the two lectins CFL GNL reduced their binding ability. In addition, their specifically recognized N-acetyl-D-galactosamine structure and (α-1,3) mannose residues were decreased in early gastric cancer. Furthermore, lectin blot results of LEL, STL, PHA-L, RCA-I were consistent with the results of the lectin microarray. The findings of our study clarify the specific alterations for glycosylation during the pathogenesis of gastric cancer. The specific high expression of GlcNAc structure may act as a potential early diagnostic marker for gastric cancer.

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

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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

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

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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

Utility of the pooling approach as applied to whole genome association scans with high-density Affymetrix microarrays

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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.

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

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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.

Screening for C3 deficiency in newborns using microarrays.

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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.

Development of a cell microarray chip for detection of circulating tumor cells

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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.

Focused Screening of ECM-Selective Adhesion Peptides on Cellulose-Bound Peptide Microarrays.

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Kanie, Kei; Kondo, Yuto; Owaki, Junki; Ikeda, Yurika; Narita, Yuji; Kato, Ryuji; Honda, Hiroyuki

2016-11-19

The coating of surfaces with bio-functional proteins is a promising strategy for the creation of highly biocompatible medical implants. Bio-functional proteins from the extracellular matrix (ECM) provide effective surface functions for controlling cellular behavior. We have previously screened bio-functional tripeptides for feasibility of mass production with the aim of identifying those that are medically useful, such as cell-selective peptides. In this work, we focused on the screening of tripeptides that selectively accumulate collagen type IV (Col IV), an ECM protein that accelerates the re-endothelialization of medical implants. A SPOT peptide microarray was selected for screening owing to its unique cellulose membrane platform, which can mimic fibrous scaffolds used in regenerative medicine. However, since the library size on the SPOT microarray was limited, physicochemical clustering was used to provide broader variation than that of random peptide selection. Using the custom focused microarray of 500 selected peptides, we assayed the relative binding rates of tripeptides to Col IV, collagen type I (Col I), and albumin. We discovered a cluster of Col IV-selective adhesion peptides that exhibit bio-safety with endothelial cells. The results from this study can be used to improve the screening of regeneration-enhancing peptides.

Focused Screening of ECM-Selective Adhesion Peptides on Cellulose-Bound Peptide Microarrays

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Kei Kanie

2016-11-01

Full Text Available The coating of surfaces with bio-functional proteins is a promising strategy for the creation of highly biocompatible medical implants. Bio-functional proteins from the extracellular matrix (ECM provide effective surface functions for controlling cellular behavior. We have previously screened bio-functional tripeptides for feasibility of mass production with the aim of identifying those that are medically useful, such as cell-selective peptides. In this work, we focused on the screening of tripeptides that selectively accumulate collagen type IV (Col IV, an ECM protein that accelerates the re-endothelialization of medical implants. A SPOT peptide microarray was selected for screening owing to its unique cellulose membrane platform, which can mimic fibrous scaffolds used in regenerative medicine. However, since the library size on the SPOT microarray was limited, physicochemical clustering was used to provide broader variation than that of random peptide selection. Using the custom focused microarray of 500 selected peptides, we assayed the relative binding rates of tripeptides to Col IV, collagen type I (Col I, and albumin. We discovered a cluster of Col IV-selective adhesion peptides that exhibit bio-safety with endothelial cells. The results from this study can be used to improve the screening of regeneration-enhancing peptides.

Integrated database for identifying candidate genes for Aspergillus flavus resistance in maize.

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Kelley, Rowena Y; Gresham, Cathy; Harper, Jonathan; Bridges, Susan M; Warburton, Marilyn L; Hawkins, Leigh K; Pechanova, Olga; Peethambaran, Bela; Pechan, Tibor; Luthe, Dawn S; Mylroie, J E; Ankala, Arunkanth; Ozkan, Seval; Henry, W B; Williams, W P

2010-10-07

Aspergillus flavus Link:Fr, an opportunistic fungus that produces aflatoxin, is pathogenic to maize and other oilseed crops. Aflatoxin is a potent carcinogen, and its presence markedly reduces the value of grain. Understanding and enhancing host resistance to A. flavus infection and/or subsequent aflatoxin accumulation is generally considered an efficient means of reducing grain losses to aflatoxin. Different proteomic, genomic and genetic studies of maize (Zea mays L.) have generated large data sets with the goal of identifying genes responsible for conferring resistance to A. flavus, or aflatoxin. In order to maximize the usage of different data sets in new studies, including association mapping, we have constructed a relational database with web interface integrating the results of gene expression, proteomic (both gel-based and shotgun), Quantitative Trait Loci (QTL) genetic mapping studies, and sequence data from the literature to facilitate selection of candidate genes for continued investigation. The Corn Fungal Resistance Associated Sequences Database (CFRAS-DB) (http://agbase.msstate.edu/) was created with the main goal of identifying genes important to aflatoxin resistance. CFRAS-DB is implemented using MySQL as the relational database management system running on a Linux server, using an Apache web server, and Perl CGI scripts as the web interface. The database and the associated web-based interface allow researchers to examine many lines of evidence (e.g. microarray, proteomics, QTL studies, SNP data) to assess the potential role of a gene or group of genes in the response of different maize lines to A. flavus infection and subsequent production of aflatoxin by the fungus. CFRAS-DB provides the first opportunity to integrate data pertaining to the problem of A. flavus and aflatoxin resistance in maize in one resource and to support queries across different datasets. The web-based interface gives researchers different query options for mining the database

Microarray assessment of virulence, antibiotic, and heavy metal resistance in an agricultural watershed creek.

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Unc, Adrian; Zurek, Ludek; Peterson, Greg; Narayanan, Sanjeev; Springthorpe, Susan V; Sattar, Syed A

2012-01-01

Potential risks associated with impaired surface water quality have commonly been evaluated by indirect description of potential sources using various fecal microbial indicators and derived source-tracking methods. These approaches are valuable for assessing and monitoring the impacts of land-use changes and changes in management practices at the source of contamination. A more detailed evaluation of putative etiologically significant genetic determinants can add value to these assessments. We evaluated the utility of using a microarray that integrates virulence genes with antibiotic and heavy metal resistance genes to describe and discriminate among spatially and seasonally distinct water samples from an agricultural watershed creek in Eastern Ontario. Because microarray signals may be analyzed as binomial distributions, the significance of ambiguous signals can be easily evaluated by using available off-the-shelf software. The FAMD software was used to evaluate uncertainties in the signal data. Analysis of multilocus fingerprinting data sets containing missing data has shown that, for the tested system, any variability in microarray signals had a marginal effect on data interpretation. For the tested watershed, results suggest that in general the wet fall season increased the downstream detection of virulence and resistance genes. Thus, the tested microarray technique has the potential to rapidly describe the quality of surface waters and thus to provide a qualitative tool to augment quantitative microbial risk assessments. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

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

A protein microarray for the rapid screening of patients suspected of infection with various food-borne helminthiases.

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Jia-Xu Chen

Full Text Available BACKGROUND: Food-borne helminthiases (FBHs have become increasingly important due to frequent occurrence and worldwide distribution. There is increasing demand for developing more sensitive, high-throughput techniques for the simultaneous detection of multiple parasitic diseases due to limitations in differential clinical diagnosis of FBHs with similar symptoms. These infections are difficult to diagnose correctly by conventional diagnostic approaches including serological approaches. METHODOLOGY/PRINCIPAL FINDINGS: In this study, antigens obtained from 5 parasite species, namely Cysticercus cellulosae, Angiostrongylus cantonensis, Paragonimus westermani, Trichinella spiralis and Spirometra sp., were semi-purified after immunoblotting. Sera from 365 human cases of helminthiasis and 80 healthy individuals were assayed with semi-purified antigens by both a protein microarray and the enzyme-linked immunosorbent assay (ELISA. The sensitivity, specificity and simplicity of each test for the end-user were evaluated. The specificity of the tests ranged from 97.0% (95% confidence interval (CI: 95.3-98.7% to 100.0% (95% CI: 100.0% in the protein microarray and from 97.7% (95% CI: 96.2-99.2% to 100.0% (95% CI: 100.0% in ELISA. The sensitivity varied from 85.7% (95% CI: 75.1-96.3% to 92.1% (95% CI: 83.5-100.0% in the protein microarray, while the corresponding values for ELISA were 82.0% (95% CI: 71.4-92.6% to 92.1% (95% CI: 83.5-100.0%. Furthermore, the Youden index spanned from 0.83 to 0.92 in the protein microarray and from 0.80 to 0.92 in ELISA. For each parasite, the Youden index from the protein microarray was often slightly higher than the one from ELISA even though the same antigen was used. CONCLUSIONS/SIGNIFICANCE: The protein microarray platform is a convenient, versatile, high-throughput method that can easily be adapted to massive FBH screening.

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