Comparative analysis of codon usage bias and codon context patterns between dipteran and hymenopteran sequenced genomes.

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Susanta K Behura

Full Text Available BACKGROUND: Codon bias is a phenomenon of non-uniform usage of codons whereas codon context generally refers to sequential pair of codons in a gene. Although genome sequencing of multiple species of dipteran and hymenopteran insects have been completed only a few of these species have been analyzed for codon usage bias. METHODS AND PRINCIPAL FINDINGS: Here, we use bioinformatics approaches to analyze codon usage bias and codon context patterns in a genome-wide manner among 15 dipteran and 7 hymenopteran insect species. Results show that GAA is the most frequent codon in the dipteran species whereas GAG is the most frequent codon in the hymenopteran species. Data reveals that codons ending with C or G are frequently used in the dipteran genomes whereas codons ending with A or T are frequently used in the hymenopteran genomes. Synonymous codon usage orders (SCUO vary within genomes in a pattern that seems to be distinct for each species. Based on comparison of 30 one-to-one orthologous genes among 17 species, the fruit fly Drosophila willistoni shows the least codon usage bias whereas the honey bee (Apis mellifera shows the highest bias. Analysis of codon context patterns of these insects shows that specific codons are frequently used as the 3'- and 5'-context of start and stop codons, respectively. CONCLUSIONS: Codon bias pattern is distinct between dipteran and hymenopteran insects. While codon bias is favored by high GC content of dipteran genomes, high AT content of genes favors biased usage of synonymous codons in the hymenopteran insects. Also, codon context patterns vary among these species largely according to their phylogeny.

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Data Analysis and Visualization (IDAV) and the Department of Computer Science, University of California, Davis, One Shields Avenue, Davis CA 95616, USA,; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,' ' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA; Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA,; Computer Science Division,University of California, Berkeley, CA, USA,; Computer Science Department, University of California, Irvine, CA, USA,; All authors are with the Berkeley Drosophila Transcription Network Project, Lawrence Berkeley National Laboratory,; Rubel, Oliver; Weber, Gunther H.; Huang, Min-Yu; Bethel, E. Wes; Biggin, Mark D.; Fowlkes, Charless C.; Hendriks, Cris L. Luengo; Keranen, Soile V. E.; Eisen, Michael B.; Knowles, David W.; Malik, Jitendra; Hagen, Hans; Hamann, Bernd

2008-05-12

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

Needs Analysis and English Teaching in Professional Contexts

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Orlando Vian Jr.

2011-07-01

Full Text Available Based on the concept of needs analysis as proposed by Hutchinson and Waters (1987, this article discusses some aspects of English teaching in professional contexts in Brazil. We start with a brief historical view of needs analysis in order to discuss its application to teaching English for specific business purposes in professional contexts and its role for the instructor teaching in-company classes. We also aim to discuss the importance of needs analysis and its relation to the business area, as well as other features related to teaching in these contexts and its relevance to the professionals involved with business English teaching.

Genome-wide comparative analysis of codon usage bias and codon context patterns among cyanobacterial genomes.

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Prabha, Ratna; Singh, Dhananjaya P; Sinha, Swati; Ahmad, Khurshid; Rai, Anil

2017-04-01

With the increasing accumulation of genomic sequence information of prokaryotes, the study of codon usage bias has gained renewed attention. The purpose of this study was to examine codon selection pattern within and across cyanobacterial species belonging to diverse taxonomic orders and habitats. We performed detailed comparative analysis of cyanobacterial genomes with respect to codon bias. Our analysis reflects that in cyanobacterial genomes, A- and/or T-ending codons were used predominantly in the genes whereas G- and/or C-ending codons were largely avoided. Variation in the codon context usage of cyanobacterial genes corresponded to the clustering of cyanobacteria as per their GC content. Analysis of codon adaptation index (CAI) and synonymous codon usage order (SCUO) revealed that majority of genes are associated with low codon bias. Codon selection pattern in cyanobacterial genomes reflected compositional constraints as major influencing factor. It is also identified that although, mutational constraint may play some role in affecting codon usage bias in cyanobacteria, compositional constraint in terms of genomic GC composition coupled with environmental factors affected codon selection pattern in cyanobacterial genomes. Copyright © 2016 Elsevier B.V. All rights reserved.

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.

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Boris P Hejblum

2015-06-01

Full Text Available Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial, and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.

Characterizing genes with distinct methylation patterns in the context of protein-protein interaction network: application to human brain tissues.

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Li, Yongsheng; Xu, Juan; Chen, Hong; Zhao, Zheng; Li, Shengli; Bai, Jing; Wu, Aiwei; Jiang, Chunjie; Wang, Yuan; Su, Bin; Li, Xia

2013-01-01

DNA methylation is an essential epigenetic mechanism involved in transcriptional control. However, how genes with different methylation patterns are assembled in the protein-protein interaction network (PPIN) remains a mystery. In the present study, we systematically dissected the characterization of genes with different methylation patterns in the PPIN. A negative association was detected between the methylation levels in the brain tissues and topological centralities. By focusing on two classes of genes with considerably different methylation levels in the brain tissues, namely the low methylated genes (LMGs) and high methylated genes (HMGs), we found that their organizing principles in the PPIN are distinct. The LMGs tend to be the center of the PPIN, and attacking them causes a more deleterious effect on the network integrity. Furthermore, the LMGs express their functions in a modular pattern and substantial differences in functions are observed between the two types of genes. The LMGs are enriched in the basic biological functions, such as binding activity and regulation of transcription. More importantly, cancer genes, especially recessive cancer genes, essential genes, and aging-related genes were all found more often in the LMGs. Additionally, our analysis presented that the intra-classes communications are enhanced, but inter-classes communications are repressed. Finally, a functional complementation was revealed between methylation and miRNA regulation in the human genome. We have elucidated the assembling principles of genes with different methylation levels in the context of the PPIN, providing key insights into the complex epigenetic regulation mechanisms.

Effect of the absolute statistic on gene-sampling gene-set analysis methods.

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Nam, Dougu

2017-06-01

Gene-set enrichment analysis and its modified versions have commonly been used for identifying altered functions or pathways in disease from microarray data. In particular, the simple gene-sampling gene-set analysis methods have been heavily used for datasets with only a few sample replicates. The biggest problem with this approach is the highly inflated false-positive rate. In this paper, the effect of absolute gene statistic on gene-sampling gene-set analysis methods is systematically investigated. Thus far, the absolute gene statistic has merely been regarded as a supplementary method for capturing the bidirectional changes in each gene set. Here, it is shown that incorporating the absolute gene statistic in gene-sampling gene-set analysis substantially reduces the false-positive rate and improves the overall discriminatory ability. Its effect was investigated by power, false-positive rate, and receiver operating curve for a number of simulated and real datasets. The performances of gene-set analysis methods in one-tailed (genome-wide association study) and two-tailed (gene expression data) tests were also compared and discussed.

MAGMA: generalized gene-set analysis of GWAS data.

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de Leeuw, Christiaan A; Mooij, Joris M; Heskes, Tom; Posthuma, Danielle

2015-04-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

Recovery and evolutionary analysis of complete integron gene cassette arrays from Vibrio

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Gillings Michael R

2006-01-01

Full Text Available Abstract Background Integrons are genetic elements capable of the acquisition, rearrangement and expression of genes contained in gene cassettes. Gene cassettes generally consist of a promoterless gene associated with a recombination site known as a 59-base element (59-be. Multiple insertion events can lead to the assembly of large integron-associated cassette arrays. The most striking examples are found in Vibrio, where such cassette arrays are widespread and can range from 30 kb to 150 kb. Besides those found in completely sequenced genomes, no such array has yet been recovered in its entirety. We describe an approach to systematically isolate, sequence and annotate large integron gene cassette arrays from bacterial strains. Results The complete Vibrio sp. DAT722 integron cassette array was determined through the streamlined approach described here. To place it in an evolutionary context, we compare the DAT722 array to known vibrio arrays and performed phylogenetic analyses for all of its components (integrase, 59-be sites, gene cassette encoded genes. It differs extensively in terms of genomic context as well as gene cassette content and organization. The phylogenetic tree of the 59-be sites collectively found in the Vibrio gene cassette pool suggests frequent transfer of cassettes within and between Vibrio species, with slower transfer rates between more phylogenetically distant relatives. We also identify multiple cases where non-integron chromosomal genes seem to have been assembled into gene cassettes and others where cassettes have been inserted into chromosomal locations outside integrons. Conclusion Our systematic approach greatly facilitates the isolation and annotation of large integrons gene cassette arrays. Comparative analysis of the Vibrio sp. DAT722 integron obtained through this approach to those found in other vibrios confirms the role of this genetic element in promoting lateral gene transfer and suggests a high rate of gene

Deriving Trading Rules Using Gene Expression Programming

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Adrian VISOIU

2011-01-01

Full Text Available This paper presents how buy and sell trading rules are generated using gene expression programming with special setup. Market concepts are presented and market analysis is discussed with emphasis on technical analysis and quantitative methods. The use of genetic algorithms in deriving trading rules is presented. Gene expression programming is applied in a form where multiple types of operators and operands are used. This gives birth to multiple gene contexts and references between genes in order to keep the linear structure of the gene expression programming chromosome. The setup of multiple gene contexts is presented. The case study shows how to use the proposed gene setup to derive trading rules encoded by Boolean expressions, using a dataset with the reference exchange rates between the Euro and the Romanian leu. The conclusions highlight the positive results obtained in deriving useful trading rules.

Understanding context in knowledge translation: a concept analysis study protocol.

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Squires, Janet E; Graham, Ian D; Hutchinson, Alison M; Linklater, Stefanie; Brehaut, Jamie C; Curran, Janet; Ivers, Noah; Lavis, John N; Michie, Susan; Sales, Anne E; Fiander, Michelle; Fenton, Shannon; Noseworthy, Thomas; Vine, Jocelyn; Grimshaw, Jeremy M

2015-05-01

To conduct a concept analysis of clinical practice contexts (work environments) that facilitate or militate against the uptake of research evidence by healthcare professionals in clinical practice. This will involve developing a clear definition of context by describing its features, domains and defining characteristics. The context where clinical care is delivered influences that care. While research shows that context is important to knowledge translation (implementation), we lack conceptual clarity on what is context, which contextual factors probably modify the effect of knowledge translation interventions (and hence should be considered when designing interventions) and which contextual factors themselves could be targeted as part of a knowledge translation intervention (context modification). Concept analysis. The Walker and Avant concept analysis method, comprised of eight systematic steps, will be used: (1) concept selection; (2) determination of aims; (3) identification of uses of context; (4) determination of defining attributes of context; (5) identification/construction of a model case of context; (6) identification/construction of additional cases of context; (7) identification/construction of antecedents and consequences of context; and (8) definition of empirical referents of context. This study is funded by the Canadian Institutes of Health Research (January 2014). This study will result in a much needed framework of context for knowledge translation, which identifies specific elements that, if assessed and used to tailor knowledge translation activities, will result in increased research use by nurses and other healthcare professionals in clinical practice, ultimately leading to better patient care. © 2014 John Wiley & Sons Ltd.

Function analysis of unknown genes

DEFF Research Database (Denmark)

Rogowska-Wrzesinska, A.

2002-01-01

  This thesis entitled "Function analysis of unknown genes" presents the use of proteome analysis for the characterisation of yeast (Saccharomyces cerevisiae) genes and their products (proteins especially those of unknown function). This study illustrates that proteome analysis can be used...... to describe different aspects of molecular biology of the cell, to study changes that occur in the cell due to overexpression or deletion of a gene and to identify various protein modifications. The biological questions and the results of the described studies show the diversity of the information that can...... genes and proteins. It reports the first global proteome database collecting 36 yeast single gene deletion mutants and selecting over 650 differences between analysed mutants and the wild type strain. The obtained results show that two-dimensional gel electrophoresis and mass spectrometry based proteome...

In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene

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Raghunath Satpathy

2015-01-01

Full Text Available Context: Paracoccidioides brasiliensis, a dimorphic fungus is the causative agent of paracoccidioidomycosis, a disease globally affecting millions of people. The haloacid dehalogenase (HAD superfamily hydrolases enzyme in the fungi, in particular, is known to be responsible in the pathogenesis by adhering to the tissue. Hence, identification of novel drug targets is essential. Aims: In-silico based identification of co-expressed genes along with HAD superfamily hydrolase in P. brasiliensis during the morphogenesis from mycelium to yeast to identify possible genes as drug targets. Materials and Methods: In total, four datasets were retrieved from the NCBI-gene expression omnibus (GEO database, each containing 4340 genes, followed by gene filtration expression of the data set. Further co-expression (CE study was performed individually and then a combination these genes were visualized in the Cytoscape 2. 8.3. Statistical Analysis Used: Mean and standard deviation value of the HAD superfamily hydrolase gene was obtained from the expression data and this value was subsequently used for the CE calculation purpose by selecting specific correlation power and filtering threshold. Results: The 23 genes that were thus obtained are common with respect to the HAD superfamily hydrolase gene. A significant network was selected from the Cytoscape network visualization that contains total 7 genes out of which 5 genes, which do not have significant protein hits, obtained from gene annotation of the expressed sequence tags by BLAST X. For all the protein PSI-BLAST was performed against human genome to find the homology. Conclusions: The gene co-expression network was obtained with respect to HAD superfamily dehalogenase gene in P. Brasiliensis.

Environmental context-dependent memory: a review and meta-analysis.

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Smith, S M; Vela, E

2001-06-01

To address questions about human memory's dependence on the coincidental environmental contexts in which events occur, we review studies of incidental environmental context-dependent memory in humans and report a meta-analysis. Our theoretical approach to the issue stems from Glenberg's (1997) contention that introspective thought (e.g., remembering, conceptualizing) requires cognitive resources normally used to represent the immediate environment. We propose that if tasks encourage processing of noncontextual information (i.e., introspective thought) at input and/or at test, then both learning and memory will be less dependent on the ambient environmental contexts in which those activities occur. The meta-analysis showed that across all studies, environmental context effects were reliable, and furthermore, that the use of noncontextual cues during learning (overshadowing) and at test (outshining), as well as mental reinstatement of appropriate context cues at test, all reduce the effect of environmental manipulations. We conclude that environmental context-dependent memory effects are less likely to occur under conditions in which the immediate environment is likely to be suppressed.

Microblog sentiment analysis using social and topic context.

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Zou, Xiaomei; Yang, Jing; Zhang, Jianpei

2018-01-01

Analyzing massive user-generated microblogs is very crucial in many fields, attracting many researchers to study. However, it is very challenging to process such noisy and short microblogs. Most prior works only use texts to identify sentiment polarity and assume that microblogs are independent and identically distributed, which ignore microblogs are networked data. Therefore, their performance is not usually satisfactory. Inspired by two sociological theories (sentimental consistency and emotional contagion), in this paper, we propose a new method combining social context and topic context to analyze microblog sentiment. In particular, different from previous work using direct user relations, we introduce structure similarity context into social contexts and propose a method to measure structure similarity. In addition, we also introduce topic context to model the semantic relations between microblogs. Social context and topic context are combined by the Laplacian matrix of the graph built by these contexts and Laplacian regularization are added into the microblog sentiment analysis model. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods

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Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E.; Re, Matteo

2014-01-01

Objective In the context of “network medicine”, gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. Materials and methods We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. Results The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different “informativeness” embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Conclusions Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further

ComPath: comparative enzyme analysis and annotation in pathway/subsystem contexts

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Kim Sun

2008-03-01

Full Text Available Abstract Background Once a new genome is sequenced, one of the important questions is to determine the presence and absence of biological pathways. Analysis of biological pathways in a genome is a complicated task since a number of biological entities are involved in pathways and biological pathways in different organisms are not identical. Computational pathway identification and analysis thus involves a number of computational tools and databases and typically done in comparison with pathways in other organisms. This computational requirement is much beyond the capability of biologists, so information systems for reconstructing, annotating, and analyzing biological pathways are much needed. We introduce a new comparative pathway analysis workbench, ComPath, which integrates various resources and computational tools using an interactive spreadsheet-style web interface for reliable pathway analyses. Results ComPath allows users to compare biological pathways in multiple genomes using a spreadsheet style web interface where various sequence-based analysis can be performed either to compare enzymes (e.g. sequence clustering and pathways (e.g. pathway hole identification, to search a genome for de novo prediction of enzymes, or to annotate a genome in comparison with reference genomes of choice. To fill in pathway holes or make de novo enzyme predictions, multiple computational methods such as FASTA, Whole-HMM, CSR-HMM (a method of our own introduced in this paper, and PDB-domain search are integrated in ComPath. Our experiments show that FASTA and CSR-HMM search methods generally outperform Whole-HMM and PDB-domain search methods in terms of sensitivity, but FASTA search performs poorly in terms of specificity, detecting more false positive as E-value cutoff increases. Overall, CSR-HMM search method performs best in terms of both sensitivity and specificity. Gene neighborhood and pathway neighborhood (global network visualization tools can be used

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis

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Akira Ishikawa

2017-11-01

Full Text Available Large numbers of quantitative trait loci (QTL affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

A Strategy for Identifying Quantitative Trait Genes Using Gene Expression Analysis and Causal Analysis.

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Ishikawa, Akira

2017-11-27

Large numbers of quantitative trait loci (QTL) affecting complex diseases and other quantitative traits have been reported in humans and model animals. However, the genetic architecture of these traits remains elusive due to the difficulty in identifying causal quantitative trait genes (QTGs) for common QTL with relatively small phenotypic effects. A traditional strategy based on techniques such as positional cloning does not always enable identification of a single candidate gene for a QTL of interest because it is difficult to narrow down a target genomic interval of the QTL to a very small interval harboring only one gene. A combination of gene expression analysis and statistical causal analysis can greatly reduce the number of candidate genes. This integrated approach provides causal evidence that one of the candidate genes is a putative QTG for the QTL. Using this approach, I have recently succeeded in identifying a single putative QTG for resistance to obesity in mice. Here, I outline the integration approach and discuss its usefulness using my studies as an example.

Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy.

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Tejaswini Narayanan

Full Text Available Duchenne Muscular Dystrophy (DMD is an important pathology associated with the human skeletal muscle and has been studied extensively. Gene expression measurements on skeletal muscle of patients afflicted with DMD provides the opportunity to understand the underlying mechanisms that lead to the pathology. Community structure analysis is a useful computational technique for understanding and modeling genetic interaction networks. In this paper, we leverage this technique in combination with gene expression measurements from normal and DMD patient skeletal muscle tissue to study the structure of genetic interactions in the context of DMD. We define a novel framework for transforming a raw dataset of gene expression measurements into an interaction network, and subsequently apply algorithms for community structure analysis for the extraction of topological communities. The emergent communities are analyzed from a biological standpoint in terms of their constituent biological pathways, and an interpretation that draws correlations between functional and structural organization of the genetic interactions is presented. We also compare these communities and associated functions in pathology against those in normal human skeletal muscle. In particular, differential enhancements are observed in the following pathways between pathological and normal cases: Metabolic, Focal adhesion, Regulation of actin cytoskeleton and Cell adhesion, and implication of these mechanisms are supported by prior work. Furthermore, our study also includes a gene-level analysis to identify genes that are involved in the coupling between the pathways of interest. We believe that our results serve to highlight important distinguishing features in the structural/functional organization of constituent biological pathways, as it relates to normal and DMD cases, and provide the mechanistic basis for further biological investigations into specific pathways differently regulated

MAGMA: Generalized Gene-Set Analysis of GWAS Data

NARCIS (Netherlands)

de Leeuw, C.A.; Mooij, J.M.; Heskes, T.; Posthuma, D.

2015-01-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical

MAGMA: generalized gene-set analysis of GWAS data.

NARCIS (Netherlands)

de Leeuw, C.A.; Mooij, J.M.; Heskes, T.; Posthuma, D.

2015-01-01

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical

Concept analysis: patient autonomy in a caring context.

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Lindberg, Catharina; Fagerström, Cecilia; Sivberg, Bengt; Willman, Ania

2014-10-01

This paper is a report of an analysis of the concept of patient autonomy Many problems regarding patient autonomy in healthcare contexts derive from the patient's dependent condition as well as the traditional authoritarian position of healthcare professionals. Existing knowledge and experience reveal a lack of consensus among nurses regarding the meaning of this ethical concept. Concept analysis. Medline, CINAHL, The Cochrane Library and PsycINFO were searched (2005-June 2013) using the search blocks 'autonomy', 'patient' and 'nursing/caring'. A total of 41 articles were retrieved. The Evolutionary Method of Concept Analysis by Rodgers was used to identify and construct the meaning of the concept of patient autonomy in a caring context. Five attributes were identified, thus creating the following descriptive definition: 'Patient autonomy is a gradual, time-changing process of (re-)constructing autonomy through the interplay of to be seen as a person, the capacity to act and the obligation to take responsibility for one's actions'. Patient vulnerability was shown to be the antecedent of patient autonomy and arises due to an impairment of a person's physical and/or mental state. The consequences of patient autonomy were discussed in relation to preserving control and freedom. Patient autonomy in a caring context does not need to be the same before, during and after a care episode. A tentative model has been constructed, thus extending the understanding of this ethical concept in a caring context. © 2014 John Wiley & Sons Ltd.

The Drosophila Perlecan gene trol regulates multiple signaling pathways in different developmental contexts

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Perry Trinity L

2007-11-01

Full Text Available Abstract Background Heparan sulfate proteoglycans modulate signaling by a variety of growth factors. The mammalian proteoglycan Perlecan binds and regulates signaling by Sonic Hedgehog, Fibroblast Growth Factors (FGFs, Vascular Endothelial Growth Factor (VEGF and Platelet Derived Growth Factor (PDGF, among others, in contexts ranging from angiogenesis and cardiovascular development to cancer progression. The Drosophila Perlecan homolog trol has been shown to regulate the activity of Hedgehog and Branchless (an FGF homolog to control the onset of stem cell proliferation in the developing brain during first instar. Here we extend analysis of trol mutant phenotypes to show that trol is required for a variety of developmental events and modulates signaling by multiple growth factors in different situations. Results Different mutations in trol allow developmental progression to varying extents, suggesting that trol is involved in multiple cell-fate and patterning decisions. Analysis of the initiation of neuroblast proliferation at second instar demonstrated that trol regulates this event by modulating signaling by Hedgehog and Branchless, as it does during first instar. Trol protein is distributed over the surface of the larval brain, near the regulated neuroblasts that reside on the cortical surface. Mutations in trol also decrease the number of circulating plasmatocytes. This is likely to be due to decreased expression of pointed, the response gene for VEGF/PDGF signaling that is required for plasmatocyte proliferation. Trol is found on plasmatocytes, where it could regulate VEGF/PDGF signaling. Finally, we show that in second instar brains but not third instar brain lobes and eye discs, mutations in trol affect signaling by Decapentaplegic (a Transforming Growth Factor family member, Wingless (a Wnt growth factor and Hedgehog. Conclusion These studies extend the known functions of the Drosophila Perlecan homolog trol in both developmental and

An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.

Science.gov (United States)

Valentini, Giorgio; Paccanaro, Alberto; Caniza, Horacio; Romero, Alfonso E; Re, Matteo

2014-06-01

In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization. We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions. The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation. Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both

Visual analysis of transcriptome data in the context of anatomical structures and biological networks

Directory of Open Access Journals (Sweden)

Astrid eJunker

2012-11-01

Full Text Available The complexity and temporal as well as spatial resolution of transcriptome datasets is constantly increasing due to extensive technological developments. Here we present methods for advanced visualization and intuitive exploration of transcriptomics data as necessary prerequisites in order to facilitate the gain of biological knowledge. Color-coding of structural images based on the expression level enables a fast visual data analysis in the background of the examined biological system. The network-based exploration of these visualizations allows for comparative analysis of genes with specific transcript patterns and supports the extraction of functional relationships even from large datasets. In order to illustrate the presented methods, the tool HIVE was applied for visualization and exploration of database-retrieved expression data for master regulators of Arabidopsis thaliana flower and seed development in the context of corresponding tissue-specific regulatory networks.

Phylogenetic analysis at deep timescales: unreliable gene trees, bypassed hidden support, and the coalescence/concatalescence conundrum.

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Gatesy, John; Springer, Mark S

2014-11-01

Large datasets are required to solve difficult phylogenetic problems that are deep in the Tree of Life. Currently, two divergent systematic methods are commonly applied to such datasets: the traditional supermatrix approach (= concatenation) and "shortcut" coalescence (= coalescence methods wherein gene trees and the species tree are not co-estimated). When applied to ancient clades, these contrasting frameworks often produce congruent results, but in recent phylogenetic analyses of Placentalia (placental mammals), this is not the case. A recent series of papers has alternatively disputed and defended the utility of shortcut coalescence methods at deep phylogenetic scales. Here, we examine this exchange in the context of published phylogenomic data from Mammalia; in particular we explore two critical issues - the delimitation of data partitions ("genes") in coalescence analysis and hidden support that emerges with the combination of such partitions in phylogenetic studies. Hidden support - increased support for a clade in combined analysis of all data partitions relative to the support evident in separate analyses of the various data partitions, is a hallmark of the supermatrix approach and a primary rationale for concatenating all characters into a single matrix. In the most extreme cases of hidden support, relationships that are contradicted by all gene trees are supported when all of the genes are analyzed together. A valid fear is that shortcut coalescence methods might bypass or distort character support that is hidden in individual loci because small gene fragments are analyzed in isolation. Given the extensive systematic database for Mammalia, the assumptions and applicability of shortcut coalescence methods can be assessed with rigor to complement a small but growing body of simulation work that has directly compared these methods to concatenation. We document several remarkable cases of hidden support in both supermatrix and coalescence paradigms and argue

Gene coexpression network analysis as a source of functional annotation for rice genes.

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Kevin L Childs

Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

Analysis of Characteristics of Power Consumption for Context-Aware Mobile Applications

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Meeyeon Lee

2014-11-01

Full Text Available In recent years, a large portion of smartphone applications (Apps has targeted context-aware services. They aim to perceive users’ real-time context like his/her location, actions, or even emotion, and to provide various customized services based on the inferred context. However, context-awareness in mobile environments has some challenging issues due to limitations of devices themselves. Limited power is regarded as the most critical problem in context-awareness on smartphones. Many studies have tried to develop low-power methods, but most of them have focused on the power consumption of H/W modules of smartphones such as CPU and LCD. Only a few research papers have recently started to present some S/W-based approaches to improve the power consumption. That is, previous works did not consider energy consumed by context-awareness of Apps. Therefore, in this paper, we focus on the power consumption of context-aware Apps. We analyze the characteristics of context-aware Apps in a perspective of the power consumption, and then define two main factors which significantly influence the power consumption: a sort of context that context-aware Apps require for their services and a type of ways that a user uses them. The experimental result shows the reasonability and the possibility to develop low-power methods based on our analysis. That is, our analysis presented in this paper will be a foundation for energy-efficient context-aware services in mobile environments.

AnGeLi: A Tool for the Analysis of Gene Lists from Fission Yeast

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Danny A Bitton

2015-11-01

Full Text Available Genome-wide assays and screens typically result in large lists of genes or proteins. Enrichments of functional or other biological properties within such lists can provide valuable insights and testable hypotheses. To systematically detect these enrichments can be challenging and time-consuming, because relevant data to compare against query gene lists are spread over many different sources. We have developed AnGeLi (Analysis of Gene Lists, an intuitive, integrated web-tool for comprehensive and customized interrogation of gene lists from the fission yeast, Schizosaccharomyces pombe. AnGeLi searches for significant enrichments among multiple qualitative and quantitative information sources, including gene and phenotype ontologies, genetic and protein interactions, numerous features of genes, transcripts, translation, and proteins such as copy numbers, chromosomal positions, genetic diversity, RNA polymerase II and ribosome occupancy, localization, conservation, half-lives, domains and molecular weight among others, as well as diverse sets of genes that are co-regulated or lead to the same phenotypes when mutated. AnGeLi uses robust statistics which can be tailored to specific needs. It also provides the option to upload user-defined gene sets to compare against the query list. Through an integrated data submission form, AnGeLi encourages the community to contribute additional curated gene lists to further increase the usefulness of this resource and to get the most from the ever increasing large-scale experiments. AnGeLi offers a rigorous yet flexible statistical analysis platform for rich insights into functional enrichments and biological context for query gene lists, thus providing a powerful exploratory tool through which S. pombe researchers can uncover fresh perspectives and unexpected connections from genomic data. AnGeLi is freely available at: www.bahlerlab.info/AnGeLi

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.

Digital gene expression analysis of gene expression differences within Brassica diploids and allopolyploids.

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Jiang, Jinjin; Wang, Yue; Zhu, Bao; Fang, Tingting; Fang, Yujie; Wang, Youping

2015-01-27

Brassica includes many successfully cultivated crop species of polyploid origin, either by ancestral genome triplication or by hybridization between two diploid progenitors, displaying complex repetitive sequences and transposons. The U's triangle, which consists of three diploids and three amphidiploids, is optimal for the analysis of complicated genomes after polyploidization. Next-generation sequencing enables the transcriptome profiling of polyploids on a global scale. We examined the gene expression patterns of three diploids (Brassica rapa, B. nigra, and B. oleracea) and three amphidiploids (B. napus, B. juncea, and B. carinata) via digital gene expression analysis. In total, the libraries generated between 5.7 and 6.1 million raw reads, and the clean tags of each library were mapped to 18547-21995 genes of B. rapa genome. The unambiguous tag-mapped genes in the libraries were compared. Moreover, the majority of differentially expressed genes (DEGs) were explored among diploids as well as between diploids and amphidiploids. Gene ontological analysis was performed to functionally categorize these DEGs into different classes. The Kyoto Encyclopedia of Genes and Genomes analysis was performed to assign these DEGs into approximately 120 pathways, among which the metabolic pathway, biosynthesis of secondary metabolites, and peroxisomal pathway were enriched. The non-additive genes in Brassica amphidiploids were analyzed, and the results indicated that orthologous genes in polyploids are frequently expressed in a non-additive pattern. Methyltransferase genes showed differential expression pattern in Brassica species. Our results provided an understanding of the transcriptome complexity of natural Brassica species. The gene expression changes in diploids and allopolyploids may help elucidate the morphological and physiological differences among Brassica species.

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

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Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Sensitivity analysis in a structural reliability context

International Nuclear Information System (INIS)

Lemaitre, Paul

2014-01-01

This thesis' subject is sensitivity analysis in a structural reliability context. The general framework is the study of a deterministic numerical model that allows to reproduce a complex physical phenomenon. The aim of a reliability study is to estimate the failure probability of the system from the numerical model and the uncertainties of the inputs. In this context, the quantification of the impact of the uncertainty of each input parameter on the output might be of interest. This step is called sensitivity analysis. Many scientific works deal with this topic but not in the reliability scope. This thesis' aim is to test existing sensitivity analysis methods, and to propose more efficient original methods. A bibliographical step on sensitivity analysis on one hand and on the estimation of small failure probabilities on the other hand is first proposed. This step raises the need to develop appropriate techniques. Two variables ranking methods are then explored. The first one proposes to make use of binary classifiers (random forests). The second one measures the departure, at each step of a subset method, between each input original density and the density given the subset reached. A more general and original methodology reflecting the impact of the input density modification on the failure probability is then explored. The proposed methods are then applied on the CWNR case, which motivates this thesis. (author)

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.

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Kwon, Minseok; Leem, Sangseob; Yoon, Joon; Park, Taesung

2018-03-19

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

Model-based gene set analysis for Bioconductor.

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Bauer, Sebastian; Robinson, Peter N; Gagneur, Julien

2011-07-01

Gene Ontology and other forms of gene-category analysis play a major role in the evaluation of high-throughput experiments in molecular biology. Single-category enrichment analysis procedures such as Fisher's exact test tend to flag large numbers of redundant categories as significant, which can complicate interpretation. We have recently developed an approach called model-based gene set analysis (MGSA), that substantially reduces the number of redundant categories returned by the gene-category analysis. In this work, we present the Bioconductor package mgsa, which makes the MGSA algorithm available to users of the R language. Our package provides a simple and flexible application programming interface for applying the approach. The mgsa package has been made available as part of Bioconductor 2.8. It is released under the conditions of the Artistic license 2.0. peter.robinson@charite.de; julien.gagneur@embl.de.

Comparative Genomic Analysis of Soybean Flowering Genes

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Jung, Chol-Hee; Wong, Chui E.; Singh, Mohan B.; Bhalla, Prem L.

2012-01-01

Flowering is an important agronomic trait that determines crop yield. Soybean is a major oilseed legume crop used for human and animal feed. Legumes have unique vegetative and floral complexities. Our understanding of the molecular basis of flower initiation and development in legumes is limited. Here, we address this by using a computational approach to examine flowering regulatory genes in the soybean genome in comparison to the most studied model plant, Arabidopsis. For this comparison, a genome-wide analysis of orthologue groups was performed, followed by an in silico gene expression analysis of the identified soybean flowering genes. Phylogenetic analyses of the gene families highlighted the evolutionary relationships among these candidates. Our study identified key flowering genes in soybean and indicates that the vernalisation and the ambient-temperature pathways seem to be the most variant in soybean. A comparison of the orthologue groups containing flowering genes indicated that, on average, each Arabidopsis flowering gene has 2-3 orthologous copies in soybean. Our analysis highlighted that the CDF3, VRN1, SVP, AP3 and PIF3 genes are paralogue-rich genes in soybean. Furthermore, the genome mapping of the soybean flowering genes showed that these genes are scattered randomly across the genome. A paralogue comparison indicated that the soybean genes comprising the largest orthologue group are clustered in a 1.4 Mb region on chromosome 16 of soybean. Furthermore, a comparison with the undomesticated soybean (Glycine soja) revealed that there are hundreds of SNPs that are associated with putative soybean flowering genes and that there are structural variants that may affect the genes of the light-signalling and ambient-temperature pathways in soybean. Our study provides a framework for the soybean flowering pathway and insights into the relationship and evolution of flowering genes between a short-day soybean and the long-day plant, Arabidopsis. PMID:22679494

Visualizing conserved gene location across microbe genomes

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Shaw, Chris D.

2009-01-01

This paper introduces an analysis-based zoomable visualization technique for displaying the location of genes across many related species of microbes. The purpose of this visualizatiuon is to enable a biologist to examine the layout of genes in the organism of interest with respect to the gene organization of related organisms. During the genomic annotation process, the ability to observe gene organization in common with previously annotated genomes can help a biologist better confirm the structure and function of newly analyzed microbe DNA sequences. We have developed a visualization and analysis tool that enables the biologist to observe and examine gene organization among genomes, in the context of the primary sequence of interest. This paper describes the visualization and analysis steps, and presents a case study using a number of Rickettsia genomes.

A Regulatory Network Analysis of Orphan Genes in Arabidopsis Thaliana

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Singh, Pramesh; Chen, Tianlong; Arendsee, Zebulun; Wurtele, Eve S.; Bassler, Kevin E.

Orphan genes, which are genes unique to each particular species, have recently drawn significant attention for their potential usefulness for organismal robustness. Their origin and regulatory interaction patterns remain largely undiscovered. Recently, methods that use the context likelihood of relatedness to infer a network followed by modularity maximizing community detection algorithms on the inferred network to find the functional structure of regulatory networks were shown to be effective. We apply improved versions of these methods to gene expression data from Arabidopsis thaliana, identify groups (clusters) of interacting genes with related patterns of expression and analyze the structure within those groups. Focusing on clusters that contain orphan genes, we compare the identified clusters to gene ontology (GO) terms, regulons, and pathway designations and analyze their hierarchical structure. We predict new regulatory interactions and unravel the structure of the regulatory interaction patterns of orphan genes. Work supported by the NSF through Grants DMR-1507371 and IOS-1546858.

Separate enrichment analysis of pathways for up- and downregulated genes.

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Hong, Guini; Zhang, Wenjing; Li, Hongdong; Shen, Xiaopei; Guo, Zheng

2014-03-06

Two strategies are often adopted for enrichment analysis of pathways: the analysis of all differentially expressed (DE) genes together or the analysis of up- and downregulated genes separately. However, few studies have examined the rationales of these enrichment analysis strategies. Using both microarray and RNA-seq data, we show that gene pairs with functional links in pathways tended to have positively correlated expression levels, which could result in an imbalance between the up- and downregulated genes in particular pathways. We then show that the imbalance could greatly reduce the statistical power for finding disease-associated pathways through the analysis of all-DE genes. Further, using gene expression profiles from five types of tumours, we illustrate that the separate analysis of up- and downregulated genes could identify more pathways that are really pertinent to phenotypic difference. In conclusion, analysing up- and downregulated genes separately is more powerful than analysing all of the DE genes together.

Value chain analysis in quality management context

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Popescu, M.

2011-01-01

Full Text Available Based on the description of value chain analysis, which is a strategic management tool attributed to Michel Porter, the paper aims to demonstrate that quality management applies this method, under specific forms. The paper's specific objectives are: to redefine the functions of value chain analysis in the context of quality management; to clarify the significance and the possibilities of measuring the value added; to present management tools and techniques needed to control and systematically improve performance. Research methodology is based on examples, previous studies and a case study that reveals the diversity of indicators for measuring the value added and analysis tools used in quality management.

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

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Tintle Nathan L

2012-08-01

Full Text Available Abstract Background Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO and Kyoto Encyclopedia of Genes and Genomes (KEGG are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed. Results We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix® gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size. Conclusions Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.

Meta-analysis of Arabidopsis KANADI1 direct target genes identifies basic growth-promoting module acting upstream of hormonal signaling pathways

DEFF Research Database (Denmark)

Xie, Yakun; Straub, Daniel; Eguen, Teinai Ebimienere

2015-01-01

An intricate network of antagonistically acting transcription factors mediates formation of a flat leaf lamina of Arabidopsis thaliana plants. In this context, members of the class III homeodomain leucine zipper (HD-ZIPIII) transcription factor family specify the adaxial domain (future upper side......) of the leaf, while antagonistically acting KANADI transcription factors determine the abaxial domain (future lower side). Here we used an mRNA-seq approach to identify genes regulated by KANADI1 (KAN1) and subsequently performed a meta-analysis approach combining our datasets with published genome......-wide datasets. Our analysis revealed that KAN1 acts upstream of several genes encoding auxin biosynthetic enzymes. When exposed to shade, we find three YUCCA genes, YUC2, YUC5 and YUC8 to be transcriptionally upregulated, which correlates with an increase in the levels of free auxin. When ectopically expressed...

Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks.

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Haibe-Kains, Benjamin; Olsen, Catharina; Djebbari, Amira; Bontempi, Gianluca; Correll, Mick; Bouton, Christopher; Quackenbush, John

2012-01-01

Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.

Gene Circuit Analysis of the Terminal Gap Gene huckebein

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Ashyraliyev, Maksat; Siggens, Ken; Janssens, Hilde; Blom, Joke; Akam, Michael; Jaeger, Johannes

2009-01-01

The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network. PMID:19876378

Decision theory, the context for risk and reliability analysis

International Nuclear Information System (INIS)

Kaplan, S.

1985-01-01

According to this model of the decision process then, the optimum decision is that option having the largest expected utility. This is the fundamental model of a decision situation. It is necessary to remark that in order for the model to represent a real-life decision situation, it must include all the options present in that situation, including, for example, the option of not deciding--which is itself a decision, although usually not the optimum one. Similarly, it should include the option of delaying the decision while the authors gather further information. Both of these options have probabilities, outcomes, impacts, and utilities like any option and should be included explicitly in the decision diagram. The reason for doing a quantitative risk or reliability analysis is always that, somewhere underlying there is a decision to be made. The decision analysis therefore always forms the context for the risk or reliability analysis, and this context shapes the form and language of that analysis. Therefore, they give in this section a brief review of the well-known decision theory diagram

DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis

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Baseler Michael W

2007-11-01

Full Text Available Abstract Background Due to the complex and distributed nature of biological research, our current biological knowledge is spread over many redundant annotation databases maintained by many independent groups. Analysts usually need to visit many of these bioinformatics databases in order to integrate comprehensive annotation information for their genes, which becomes one of the bottlenecks, particularly for the analytic task associated with a large gene list. Thus, a highly centralized and ready-to-use gene-annotation knowledgebase is in demand for high throughput gene functional analysis. Description The DAVID Knowledgebase is built around the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of gene/protein identifiers from a variety of public genomic resources into DAVID gene clusters. The grouping of such identifiers improves the cross-reference capability, particularly across NCBI and UniProt systems, enabling more than 40 publicly available functional annotation sources to be comprehensively integrated and centralized by the DAVID gene clusters. The simple, pair-wise, text format files which make up the DAVID Knowledgebase are freely downloadable for various data analysis uses. In addition, a well organized web interface allows users to query different types of heterogeneous annotations in a high-throughput manner. Conclusion The DAVID Knowledgebase is designed to facilitate high throughput gene functional analysis. For a given gene list, it not only provides the quick accessibility to a wide range of heterogeneous annotation data in a centralized location, but also enriches the level of biological information for an individual gene. Moreover, the entire DAVID Knowledgebase is freely downloadable or searchable at http://david.abcc.ncifcrf.gov/knowledgebase/.

Dynamic association rules for gene expression data analysis.

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Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed

Comparative genome analysis of PHB gene family reveals deep evolutionary origins and diverse gene function.

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Di, Chao; Xu, Wenying; Su, Zhen; Yuan, Joshua S

2010-10-07

PHB (Prohibitin) gene family is involved in a variety of functions important for different biological processes. PHB genes are ubiquitously present in divergent species from prokaryotes to eukaryotes. Human PHB genes have been found to be associated with various diseases. Recent studies by our group and others have shown diverse function of PHB genes in plants for development, senescence, defence, and others. Despite the importance of the PHB gene family, no comprehensive gene family analysis has been carried to evaluate the relatedness of PHB genes across different species. In order to better guide the gene function analysis and understand the evolution of the PHB gene family, we therefore carried out the comparative genome analysis of the PHB genes across different kingdoms. The relatedness, motif distribution, and intron/exon distribution all indicated that PHB genes is a relatively conserved gene family. The PHB genes can be classified into 5 classes and each class have a very deep evolutionary origin. The PHB genes within the class maintained the same motif patterns during the evolution. With Arabidopsis as the model species, we found that PHB gene intron/exon structure and domains are also conserved during the evolution. Despite being a conserved gene family, various gene duplication events led to the expansion of the PHB genes. Both segmental and tandem gene duplication were involved in Arabidopsis PHB gene family expansion. However, segmental duplication is predominant in Arabidopsis. Moreover, most of the duplicated genes experienced neofunctionalization. The results highlighted that PHB genes might be involved in important functions so that the duplicated genes are under the evolutionary pressure to derive new function. PHB gene family is a conserved gene family and accounts for diverse but important biological functions based on the similar molecular mechanisms. The highly diverse biological function indicated that more research needs to be carried out

Gene set analysis using variance component tests.

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Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

A flood-based information flow analysis and network minimization method for gene regulatory networks.

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Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

2013-04-24

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

Serial analysis of gene expression (SAGE)

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van Ruissen, Fred; Baas, Frank

2007-01-01

In 1995, serial analysis of gene expression (SAGE) was developed as a versatile tool for gene expression studies. SAGE technology does not require pre-existing knowledge of the genome that is being examined and therefore SAGE can be applied to many different model systems. In this chapter, the SAGE

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

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Spitznagel Edward

2003-11-01

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

Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

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Sungyoung Lee

2012-12-01

Full Text Available Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs. For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR is one of the powerful and efficient methods for detecting high-order gene-gene (GxG interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI. Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Network graph analysis of gene-gene interactions in genome-wide association study data.

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Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung

2012-12-01

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

SIGNATURE: A workbench for gene expression signature analysis

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Chang Jeffrey T

2011-11-01

Full Text Available Abstract Background The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise. Results We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access. Conclusions SIGNATURE is available for public use at http://genepattern.genome.duke.edu/signature/.

Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

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Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

2017-08-01

This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data

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Keita Mori

2013-01-01

Full Text Available Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.

ADAGE signature analysis: differential expression analysis with data-defined gene sets.

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Tan, Jie; Huyck, Matthew; Hu, Dongbo; Zelaya, René A; Hogan, Deborah A; Greene, Casey S

2017-11-22

Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. We designed

Selector genes display tumor cooperation and inhibition in Drosophila epithelium in a developmental context-dependent manner

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Ram Prakash Gupta

2017-11-01

Full Text Available During animal development, selector genes determine identities of body segments and those of individual organs. Selector genes are also misexpressed in cancers, although their contributions to tumor progression per se remain poorly understood. Using a model of cooperative tumorigenesis, we show that gain of selector genes results in tumor cooperation, but in only select developmental domains of the wing, haltere and eye-antennal imaginal discs of Drosophila larva. Thus, the field selector, Eyeless (Ey, and the segment selector, Ultrabithorax (Ubx, readily cooperate to bring about neoplastic transformation of cells displaying somatic loss of the tumor suppressor, Lgl, but in only those developmental domains that express the homeo-box protein, Homothorax (Hth, and/or the Zinc-finger protein, Teashirt (Tsh. In non-Hth/Tsh-expressing domains of these imaginal discs, however, gain of Ey in lgl− somatic clones induces neoplastic transformation in the distal wing disc and haltere, but not in the eye imaginal disc. Likewise, gain of Ubx in lgl− somatic clones induces transformation in the eye imaginal disc but not in its endogenous domain, namely, the haltere imaginal disc. Our results reveal that selector genes could behave as tumor drivers or inhibitors depending on the tissue contexts of their gains.

Expression Analysis of Multiple Genes May Involve in Antimony Resistance among Leishmania major Clinical Isolates from Fars Province, Central Iran

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Nafiseh GHOBAKHLOO

2016-10-01

Full Text Available Background: Treatment of Cutaneous Leishmaniasis (CL is being faced with serious difficulties in Fars Province, due to emerging of resistance against meglumine antimonite (Glucantime®. In this context, determining some biomarkers for drug sensitivity monitoring seems to be highly essential. Different studies have been carried out to decipher the genes might be involved in antimony resistant phenotype in Leishmania spp. Here, we selected three genes: AQP (as drug transporter, TDR-1-1(as drug activator, and γ-GCS (inducing reduction environment for comparative expression analysis on clinical resistant and sensitive isolates of L. major.Methods: The clinical isolates of L. major were collected from CL patients referred to Valfajr Health Center, Shiraz from Oct 2011 to Feb 2012. The susceptibility test was performed to confirm drug sensitivity of strains in vitro as well. Then, the gene expression analysis was performed by quantitative real-time PCR using SYBR® Green.Results: By comparison of expression level between strains, up regulation of γ-GCS gene and down regulation of AQP gene were observed in resistant strains compared to the sensitive isolates; however, down regulation of AQP was not statistically specific. Analysis of TDR-1-1 gene unexpectedly showed a high level of expression in the non-responsive cases.Conclusion: The γ-GCS, at least, can be considered as a suitable molecular marker for screening antimony sensitivity in clinical isolates, although AQP and TDR-1-1gene seem not to be reliable resistant markers. 

Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

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Ueki Masao

2012-05-01

Full Text Available Abstract Background Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. Results We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case–control Consortium data. Conclusions Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.

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

Understanding gene sequence variation in the context of transcription regulation in yeast.

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Irit Gat-Viks

2010-01-01

Full Text Available DNA sequence polymorphism in a regulatory protein can have a widespread transcriptional effect. Here we present a computational approach for analyzing modules of genes with a common regulation that are affected by specific DNA polymorphisms. We identify such regulatory-linkage modules by integrating genotypic and expression data for individuals in a segregating population with complementary expression data of strains mutated in a variety of regulatory proteins. Our procedure searches simultaneously for groups of co-expressed genes, for their common underlying linkage interval, and for their shared regulatory proteins. We applied the method to a cross between laboratory and wild strains of S. cerevisiae, demonstrating its ability to correctly suggest modules and to outperform extant approaches. Our results suggest that middle sporulation genes are under the control of polymorphism in the sporulation-specific tertiary complex Sum1p/Rfm1p/Hst1p. In another example, our analysis reveals novel inter-relations between Swi3 and two mitochondrial inner membrane proteins underlying variation in a module of aerobic cellular respiration genes. Overall, our findings demonstrate that this approach provides a useful framework for the systematic mapping of quantitative trait loci and their role in gene expression variation.

Discrimination of Deletion and Duplication Subtypes of the Deleted in Azoospermia Gene Family in the Context of Frequent Interloci Gene Conversion

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Vaszkó, Tibor; Papp, János; Krausz, Csilla; Casamonti, Elena; Géczi, Lajos; Olah, Edith

2016-01-01

Due to its palindromic setup, AZFc (Azoospermia Factor c) region of chromosome Y is one of the most unstable regions of the human genome. It contains eight gene families expressed mainly in the testes. Several types of rearrangement resulting in changes in the cumulative copy number of the gene families were reported to be associated with diseases such as male infertility and testicular germ cell tumors. The best studied AZFc rearrangement is gr/gr deletion. Its carriers show widespread phenotypic variation from azoospermia to normospermia. This phenomenon was initially attributed to different gr/gr subtypes that would eliminate distinct members of the affected gene families. However, studies conducted to confirm this hypothesis have brought controversial results, perhaps, in part, due to the shortcomings of the utilized subtyping methodology. This proof-of-concept paper is meant to introduce here a novel method aimed at subtyping AZFc rearrangements. It is able to differentiate the partial deletion and partial duplication subtypes of the Deleted in Azoospermia (DAZ) gene family. The keystone of the method is the determination of the copy number of the gene family member-specific variant(s) in a series of sequence family variant (SFV) positions. Most importantly, we present a novel approach for the correct interpretation of the variant copy number data to determine the copy number of the individual DAZ family members in the context of frequent interloci gene conversion.Besides DAZ1/DAZ2 and DAZ3/DAZ4 deletions, not yet described rearrangements such as DAZ2/DAZ4 deletion and three duplication subtypes were also found by the utilization of the novel approach. A striking feature is the extremely high concordance among the individual data pointing to a certain type of rearrangement. In addition to being able to identify DAZ deletion subtypes more reliably than the methods used previously, this approach is the first that can discriminate DAZ duplication subtypes as well

Longevity Genes Revealed by Integrative Analysis of Isoform-Specific daf-16/FoxO Mutants of Caenorhabditis elegans.

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Chen, Albert Tzong-Yang; Guo, Chunfang; Itani, Omar A; Budaitis, Breane G; Williams, Travis W; Hopkins, Christopher E; McEachin, Richard C; Pande, Manjusha; Grant, Ana R; Yoshina, Sawako; Mitani, Shohei; Hu, Patrick J

2015-10-01

FoxO transcription factors promote longevity across taxa. How they do so is poorly understood. In the nematode Caenorhabditis elegans, the A- and F-isoforms of the FoxO transcription factor DAF-16 extend life span in the context of reduced DAF-2 insulin-like growth factor receptor (IGFR) signaling. To elucidate the mechanistic basis for DAF-16/FoxO-dependent life span extension, we performed an integrative analysis of isoform-specific daf-16/FoxO mutants. In contrast to previous studies suggesting that DAF-16F plays a more prominent role in life span control than DAF-16A, isoform-specific daf-16/FoxO mutant phenotypes and whole transcriptome profiling revealed a predominant role for DAF-16A over DAF-16F in life span control, stress resistance, and target gene regulation. Integration of these datasets enabled the prioritization of a subset of 92 DAF-16/FoxO target genes for functional interrogation. Among 29 genes tested, two DAF-16A-specific target genes significantly influenced longevity. A loss-of-function mutation in the conserved gene gst-20, which is induced by DAF-16A, reduced life span extension in the context of daf-2/IGFR RNAi without influencing longevity in animals subjected to control RNAi. Therefore, gst-20 promotes DAF-16/FoxO-dependent longevity. Conversely, a loss-of-function mutation in srr-4, a gene encoding a seven-transmembrane-domain receptor family member that is repressed by DAF-16A, extended life span in control animals, indicating that DAF-16/FoxO may extend life span at least in part by reducing srr-4 expression. Our discovery of new longevity genes underscores the efficacy of our integrative strategy while providing a general framework for identifying specific downstream gene regulatory events that contribute substantially to transcription factor functions. As FoxO transcription factors have conserved functions in promoting longevity and may be dysregulated in aging-related diseases, these findings promise to illuminate fundamental

In silico analysis of miRNA-mediated gene regulation in OCA and OA genes.

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Kamaraj, Balu; Gopalakrishnan, Chandrasekhar; Purohit, Rituraj

2014-12-01

Albinism is an autosomal recessive genetic disorder due to low secretion of melanin. The oculocutaneous albinism (OCA) and ocular albinism (OA) genes are responsible for melanin production and also act as a potential targets for miRNAs. The role of miRNA is to inhibit the protein synthesis partially or completely by binding with the 3'UTR of the mRNA thus regulating gene expression. In this analysis, we predicted the genetic variation that occurred in 3'UTR of the transcript which can be a reason for low melanin production thus causing albinism. The single nucleotide polymorphisms (SNPs) in 3'UTR cause more new binding sites for miRNA which binds with mRNA which leads to inhibit the translation process either partially or completely. The SNPs in the mRNA of OCA and OA genes can create new binding sites for miRNA which may control the gene expression and lead to hypopigmentation. We have developed a computational procedure to determine the SNPs in the 3'UTR region of mRNA of OCA (TYR, OCA2, TYRP1 and SLC45A2) and OA (GPR143) genes which will be a potential cause for albinism. We identified 37 SNPs in five genes that are predicted to create 87 new binding sites on mRNA, which may lead to abrogation of the translation process. Expression analysis confirms that these genes are highly expressed in skin and eye regions. It is well supported by enrichment analysis that these genes are mainly involved in eye pigmentation and melanin biosynthesis process. The network analysis also shows how the genes are interacting and expressing in a complex network. This insight provides clue to wet-lab researches to understand the expression pattern of OCA and OA genes and binding phenomenon of mRNA and miRNA upon mutation, which is responsible for inhibition of translation process at genomic levels.

DAF-16: FOXO in the Context of C. elegans.

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Tissenbaum, Heidi A

2018-01-01

In Caenorhabditis elegans, there is a single FOXO transcription factor homolog, encoded by the gene, daf-16. As a central regulator for multiple signaling pathways, DAF-16 integrates these signals which results in modulation of several biological processes including longevity, development, fat storage, stress resistance, innate immunity, and reproduction. Using C. elegans allows for studies of FOXO in the context of the whole animal. Therefore, manipulating levels or the activity of daf-16 results in phenotypic changes. Genetic and molecular analysis revealed that similar to other systems, DAF-16 is the downstream target of the conserved insulin/IGF-1 signaling (IIS) pathway; a PI 3-kinase/AKT signaling cascade that ultimately controls the regulation of DAF-16 nuclear localization. In this chapter, I will focus on understanding how a single gene daf-16 can incorporate signals from multiple upstream pathways and in turn modulate different phenotypes as well as the study of FOXO in the context of a whole organism. © 2018 Elsevier Inc. All rights reserved.

Comprehensive analysis of gene expression patterns of hedgehog-related genes

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Baillie David

2006-10-01

Full Text Available Abstract Background The Caenorhabditis elegans genome encodes ten proteins that share sequence similarity with the Hedgehog signaling molecule through their C-terminal autoprocessing Hint/Hog domain. These proteins contain novel N-terminal domains, and C. elegans encodes dozens of additional proteins containing only these N-terminal domains. These gene families are called warthog, groundhog, ground-like and quahog, collectively called hedgehog (hh-related genes. Previously, the expression pattern of seventeen genes was examined, which showed that they are primarily expressed in the ectoderm. Results With the completion of the C. elegans genome sequence in November 2002, we reexamined and identified 61 hh-related ORFs. Further, we identified 49 hh-related ORFs in C. briggsae. ORF analysis revealed that 30% of the genes still had errors in their predictions and we improved these predictions here. We performed a comprehensive expression analysis using GFP fusions of the putative intergenic regulatory sequence with one or two transgenic lines for most genes. The hh-related genes are expressed in one or a few of the following tissues: hypodermis, seam cells, excretory duct and pore cells, vulval epithelial cells, rectal epithelial cells, pharyngeal muscle or marginal cells, arcade cells, support cells of sensory organs, and neuronal cells. Using time-lapse recordings, we discovered that some hh-related genes are expressed in a cyclical fashion in phase with molting during larval development. We also generated several translational GFP fusions, but they did not show any subcellular localization. In addition, we also studied the expression patterns of two genes with similarity to Drosophila frizzled, T23D8.1 and F27E11.3A, and the ortholog of the Drosophila gene dally-like, gpn-1, which is a heparan sulfate proteoglycan. The two frizzled homologs are expressed in a few neurons in the head, and gpn-1 is expressed in the pharynx. Finally, we compare the

Genome-wide analysis of the WRKY gene family in cotton.

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Dou, Lingling; Zhang, Xiaohong; Pang, Chaoyou; Song, Meizhen; Wei, Hengling; Fan, Shuli; Yu, Shuxun

2014-12-01

WRKY proteins are major transcription factors involved in regulating plant growth and development. Although many studies have focused on the functional identification of WRKY genes, our knowledge concerning many areas of WRKY gene biology is limited. For example, in cotton, the phylogenetic characteristics, global expression patterns, molecular mechanisms regulating expression, and target genes/pathways of WRKY genes are poorly characterized. Therefore, in this study, we present a genome-wide analysis of the WRKY gene family in cotton (Gossypium raimondii and Gossypium hirsutum). We identified 116 WRKY genes in G. raimondii from the completed genome sequence, and we cloned 102 WRKY genes in G. hirsutum. Chromosomal location analysis indicated that WRKY genes in G. raimondii evolved mainly from segmental duplication followed by tandem amplifications. Phylogenetic analysis of alga, bryophyte, lycophyta, monocot and eudicot WRKY domains revealed family member expansion with increasing complexity of the plant body. Microarray, expression profiling and qRT-PCR data revealed that WRKY genes in G. hirsutum may regulate the development of fibers, anthers, tissues (roots, stems, leaves and embryos), and are involved in the response to stresses. Expression analysis showed that most group II and III GhWRKY genes are highly expressed under diverse stresses. Group I members, representing the ancestral form, seem to be insensitive to abiotic stress, with low expression divergence. Our results indicate that cotton WRKY genes might have evolved by adaptive duplication, leading to sensitivity to diverse stresses. This study provides fundamental information to inform further analysis and understanding of WRKY gene functions in cotton species.

Context and Natural Language in Formal Concept Analysis

DEFF Research Database (Denmark)

Wray, Tim; Eklund, Peter

2017-01-01

perspectives that emphasise the importance of the human, social and cultural contexts that are associated with objects. This paper presents an application of these museological concepts as related to the principles of Formal Concept Analysis along with a description of how the CollectionWeb framework generates......CollectionWeb is a framework that uses Formal Concept Analysis (FCA) to link contextually related objects within museum collections. These connections are used to drive a number of user interactions that are intended to promote exploration and discovery. The idea is based on museological...

When Is Hub Gene Selection Better than Standard Meta-Analysis?

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Langfelder, Peter; Mischel, Paul S.; Horvath, Steve

2013-01-01

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to

When is hub gene selection better than standard meta-analysis?

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Peter Langfelder

Full Text Available Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data. Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA in three comprehensive and unbiased empirical studies: (1 Finding genes predictive of lung cancer survival, (2 finding methylation markers related to age, and (3 finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1. However, standard meta-analysis methods perform as good as (if not better than a consensus network approach in terms of validation success (criterion 2. The article also reports a comparison of meta-analysis techniques

When is hub gene selection better than standard meta-analysis?

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Langfelder, Peter; Mischel, Paul S; Horvath, Steve

2013-01-01

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to

A Historical Context Analysis of Changes in Content Management Ideology

National Research Council Canada - National Science Library

Lee, William, Jr

2005-01-01

.... This research will be a qualitative study using a combined approach of historical and context analysis of literary artifacts for drawing inferences to explore the evolutionary changes in content management ideology...

IGEMS: The Consortium on Interplay of Genes and Environment Across Multiple Studies

DEFF Research Database (Denmark)

Pedersen, Nancy L; Christensen, Kaare; Dahl, Anna K

2013-01-01

The Interplay of Genes and Environment across Multiple Studies (IGEMS) group is a consortium of eight longitudinal twin studies established to explore the nature of social context effects and gene-environment interplay in late-life functioning. The resulting analysis of the combined data from ove...

Gene Ontology-Based Analysis of Zebrafish Omics Data Using the Web Tool Comparative Gene Ontology.

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Ebrahimie, Esmaeil; Fruzangohar, Mario; Moussavi Nik, Seyyed Hani; Newman, Morgan

2017-10-01

Gene Ontology (GO) analysis is a powerful tool in systems biology, which uses a defined nomenclature to annotate genes/proteins within three categories: "Molecular Function," "Biological Process," and "Cellular Component." GO analysis can assist in revealing functional mechanisms underlying observed patterns in transcriptomic, genomic, and proteomic data. The already extensive and increasing use of zebrafish for modeling genetic and other diseases highlights the need to develop a GO analytical tool for this organism. The web tool Comparative GO was originally developed for GO analysis of bacterial data in 2013 ( www.comparativego.com ). We have now upgraded and elaborated this web tool for analysis of zebrafish genetic data using GOs and annotations from the Gene Ontology Consortium.

A comparative study of three different gene expression analysis methods.

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Choe, Jae Young; Han, Hyung Soo; Lee, Seon Duk; Lee, Hanna; Lee, Dong Eun; Ahn, Jae Yun; Ryoo, Hyun Wook; Seo, Kang Suk; Kim, Jong Kun

2017-12-04

TNF-α regulates immune cells and acts as an endogenous pyrogen. Reverse transcription polymerase chain reaction (RT-PCR) is one of the most commonly used methods for gene expression analysis. Among the alternatives to PCR, loop-mediated isothermal amplification (LAMP) shows good potential in terms of specificity and sensitivity. However, few studies have compared RT-PCR and LAMP for human gene expression analysis. Therefore, in the present study, we compared one-step RT-PCR, two-step RT-LAMP and one-step RT-LAMP for human gene expression analysis. We compared three gene expression analysis methods using the human TNF-α gene as a biomarker from peripheral blood cells. Total RNA from the three selected febrile patients were subjected to the three different methods of gene expression analysis. In the comparison of three gene expression analysis methods, the detection limit of both one-step RT-PCR and one-step RT-LAMP were the same, while that of two-step RT-LAMP was inferior. One-step RT-LAMP takes less time, and the experimental result is easy to determine. One-step RT-LAMP is a potentially useful and complementary tool that is fast and reasonably sensitive. In addition, one-step RT-LAMP could be useful in environments lacking specialized equipment or expertise.

Inferring gene expression dynamics via functional regression analysis

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Leng Xiaoyan

2008-01-01

Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

Identification of pathogenic genes related to rheumatoid arthritis through integrated analysis of DNA methylation and gene expression profiling.

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Zhang, Lei; Ma, Shiyun; Wang, Huailiang; Su, Hang; Su, Ke; Li, Longjie

2017-11-15

The purpose of our study was to identify new pathogenic genes used for exploring the pathogenesis of rheumatoid arthritis (RA). To screen pathogenic genes of RA, an integrated analysis was performed by using the microarray datasets in RA derived from the Gene Expression Omnibus (GEO) database. The functional annotation and potential pathways of differentially expressed genes (DEGs) were further discovered by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Afterwards, the integrated analysis of DNA methylation and gene expression profiling was used to screen crucial genes. In addition, we used RT-PCR and MSP to verify the expression levels and methylation status of these crucial genes in 20 synovial biopsy samples obtained from 10 RA model mice and 10 normal mice. BCL11B, CCDC88C, FCRLA and APOL6 were both up-regulated and hypomethylated in RA according to integrated analysis, RT-PCR and MSP verification. Four crucial genes (BCL11B, CCDC88C, FCRLA and APOL6) identified and analyzed in this study might be closely connected with the pathogenesis of RA. Copyright © 2017. Published by Elsevier B.V.

Context and Individual Characteristics Modulate the Association between Oxytocin Receptor Gene Polymorphism and Social Behavior in Border Collies

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Borbála Turcsán

2017-12-01

Full Text Available Recent studies suggest that the relationship between endogenous oxytocin and social affiliative behavior can be critically moderated by contextual and individual factors in humans. While oxytocin has been shown to influence human-directed affiliative behaviors in dogs, no study investigated yet how such factors moderate these effects. Our study aimed to investigate whether the context and the dogs’ individual characteristics moderate the associations between the social affiliative (greeting behavior and four single-nucleotide polymorphisms (SNPs of the oxytocin receptor (OXTR gene. We recorded the greeting behavior in three contexts: (1 when the dog first met an unfamiliar experimenter, (2 during a separation from the owner, and (3 after the experimenter approached the dog in a threatening manner. In the latter two contexts (during separation and after threatening, we categorized the dogs into stressed and non-stressed groups based on their behavior in the preceding situations. In line with previous studies, we found that polymorphisms in the OXTR gene are related to the greeting behavior of dogs. However, we also showed that the analyzed SNPs were associated with greeting in different contexts and in different individuals, suggesting that the four SNPs might be related to different functions of the oxytocin system. The -213A/G was associated with greeting only when the dog had no prior negative experience with the experimenter. The rs8679682 was found in association with greeting in all three contexts but these associations were significant only in non-stressed dogs. The -94T/C was associated with greeting only when the dog was stressed and had an interaction with the sex of the dog. The -74C/G SNP was associated with greeting only when the dog was stressed during separation and also had a sex interaction. Taken together, our results suggest that, similarly to humans, the effects of oxytocin on the dogs’ social behavior are not universal

Validation of suitable reference genes for quantitative gene expression analysis in Panax ginseng

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Meizhen eWang

2016-01-01

Full Text Available Reverse transcription-qPCR (RT-qPCR has become a popular method for gene expression studies. Its results require data normalization by housekeeping genes. No single gene is proved to be stably expressed under all experimental conditions. Therefore, systematic evaluation of reference genes is necessary. With the aim to identify optimum reference genes for RT-qPCR analysis of gene expression in different tissues of Panax ginseng and the seedlings grown under heat stress, we investigated the expression stability of eight candidate reference genes, including elongation factor 1-beta (EF1-β, elongation factor 1-gamma (EF1-γ, eukaryotic translation initiation factor 3G (IF3G, eukaryotic translation initiation factor 3B (IF3B, actin (ACT, actin11 (ACT11, glyceraldehyde-3-phosphate dehydrogenase (GAPDH and cyclophilin ABH-like protein (CYC, using four widely used computational programs: geNorm, Normfinder, BestKeeper, and the comparative ΔCt method. The results were then integrated using the web-based tool RefFinder. As a result, EF1-γ, IF3G and EF1-β were the three most stable genes in different tissues of P. ginseng, while IF3G, ACT11 and GAPDH were the top three-ranked genes in seedlings treated with heat. Using three better reference genes alone or in combination as internal control, we examined the expression profiles of MAR, a multiple function-associated mRNA-like non-coding RNA (mlncRNA in P. ginseng. Taken together, we recommended EF1-γ/IF3G and IF3G/ACT11 as the suitable pair of reference genes for RT-qPCR analysis of gene expression in different tissues of P. ginseng and the seedlings grown under heat stress, respectively. The results serve as a foundation for future studies on P. ginseng functional genomics.

Multiscale Embedded Gene Co-expression Network Analysis.

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Won-Min Song

2015-11-01

Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Multiscale Embedded Gene Co-expression Network Analysis.

Science.gov (United States)

Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Genome-Wide Detection and Analysis of Multifunctional Genes

Science.gov (United States)

Pritykin, Yuri; Ghersi, Dario; Singh, Mona

2015-01-01

Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular functioning, thereby leading to a better understanding of the functional landscape of the cell. However, to date, genome-wide analysis of multifunctional genes (and the proteins they encode) has been limited. Here we introduce a computational approach that uses known functional annotations to extract genes playing a role in at least two distinct biological processes. We leverage functional genomics data sets for three organisms—H. sapiens, D. melanogaster, and S. cerevisiae—and show that, as compared to other annotated genes, genes involved in multiple biological processes possess distinct physicochemical properties, are more broadly expressed, tend to be more central in protein interaction networks, tend to be more evolutionarily conserved, and are more likely to be essential. We also find that multifunctional genes are significantly more likely to be involved in human disorders. These same features also hold when multifunctionality is defined with respect to molecular functions instead of biological processes. Our analysis uncovers key features about multifunctional genes, and is a step towards a better genome-wide understanding of gene multifunctionality. PMID:26436655

Analysis of the clonal repertoire of gene-corrected cells in gene therapy.

Science.gov (United States)

Paruzynski, Anna; Glimm, Hanno; Schmidt, Manfred; Kalle, Christof von

2012-01-01

Gene therapy-based clinical phase I/II studies using integrating retroviral vectors could successfully treat different monogenetic inherited diseases. However, with increased efficiency of this therapy, severe side effects occurred in various gene therapy trials. In all cases, integration of the vector close to or within a proto-oncogene contributed substantially to the development of the malignancies. Thus, the in-depth analysis of integration site patterns is of high importance to uncover potential clonal outgrowth and to assess the safety of gene transfer vectors and gene therapy protocols. The standard and nonrestrictive linear amplification-mediated PCR (nrLAM-PCR) in combination with high-throughput sequencing exhibits technologies that allow to comprehensively analyze the clonal repertoire of gene-corrected cells and to assess the safety of the used vector system at an early stage on the molecular level. It enables clarifying the biological consequences of the vector system on the fate of the transduced cell. Furthermore, the downstream performance of real-time PCR allows a quantitative estimation of the clonality of individual cells and their clonal progeny. Here, we present a guideline that should allow researchers to perform comprehensive integration site analysis in preclinical and clinical studies. Copyright © 2012 Elsevier Inc. All rights reserved.

In Silico Analysis of FMR1 Gene Missense SNPs.

Science.gov (United States)

Tekcan, Akin

2016-06-01

The FMR1 gene, a member of the fragile X-related gene family, is responsible for fragile X syndrome (FXS). Missense single-nucleotide polymorphisms (SNPs) are responsible for many complex diseases. The effect of FMR1 gene missense SNPs is unknown. The aim of this study, using in silico techniques, was to analyze all known missense mutations that can affect the functionality of the FMR1 gene, leading to mental retardation (MR) and FXS. Data on the human FMR1 gene were collected from the Ensembl database (release 81), National Centre for Biological Information dbSNP Short Genetic Variations database, 1000 Genomes Browser, and NHLBI Exome Sequencing Project Exome Variant Server. In silico analysis was then performed. One hundred-twenty different missense SNPs of the FMR1 gene were determined. Of these, 11.66 % of the FMR1 gene missense SNPs were in highly conserved domains, and 83.33 % were in domains with high variety. The results of the in silico prediction analysis showed that 31.66 % of the FMR1 gene SNPs were disease related and that 50 % of SNPs had a pathogenic effect. The results of the structural and functional analysis revealed that although the R138Q mutation did not seem to have a damaging effect on the protein, the G266E and I304N SNPs appeared to disturb the interaction between the domains and affect the function of the protein. This is the first study to analyze all missense SNPs of the FMR1 gene. The results indicate the applicability of a bioinformatics approach to FXS and other FMR1-related diseases. I think that the analysis of FMR1 gene missense SNPs using bioinformatics methods would help diagnosis of FXS and other FMR1-related diseases.

Early community context, genes, and youth body mass index trajectories: an investigation of gene-community interplay over early life course.

Science.gov (United States)

Wickrama, Kandauda K A S; O'Neal, Catherine Walker; Lee, Tae Kyoung

2013-09-01

To investigate additive and interactive influences of community adversity and cumulative genetic sensitivity on youth body mass index (BMI) trajectories over adolescence and young adulthood. We used latent growth curve modeling to examine BMI trajectories over three waves (1995, 2001, and 2008) of the National Longitudinal Study of Adolescent Health (n = 14,563). We measured genetic sensitivity by a cumulative index of genes associated with serotonin and dopamine functions. Community adversity was positively associated with the initial level and rate of change in BMI trajectories over time. Adolescents experiencing community adversity had a higher BMI at Wave 1 and gained weight more quickly than those who did not live in adverse communities. Community adversity interacted with cumulative genetic sensitivity to explain variation in the rate of change in BMI trajectories. The influence of community adversity was greater for those with more sensitivity alleles than those with fewer sensitivity alleles. Gender, race/ethnicity, and family contexts were also associated with youth BMI trajectories. Community adversity in early adolescence, and its interaction with genes, has far-reaching consequences, including the rate of change in BMI trajectories extending into adulthood. This work has practical implications for future intervention/prevention programs. Published by Elsevier Inc.

Gene and MicroRNA transcriptome analysis of Parkinson's related LRRK2 mouse models.

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Véronique Dorval

Full Text Available Mutations in leucine-rich repeat kinase 2 (LRRK2 are the most frequent cause of genetic Parkinson's disease (PD. The biological function of LRRK2 and how mutations lead to disease remain poorly defined. It has been proposed that LRRK2 could function in gene transcription regulation; however, this issue remains controversial. Here, we investigated in parallel gene and microRNA (miRNA transcriptome profiles of three different LRRK2 mouse models. Striatal tissue was isolated from adult LRRK2 knockout (KO mice, as well as mice expressing human LRRK2 wildtype (hLRRK2-WT or the PD-associated R1441G mutation (hLRRK2-R1441G. We identified a total of 761 genes and 24 miRNAs that were misregulated in the absence of LRRK2 when a false discovery rate of 0.2 was applied. Notably, most changes in gene expression were modest (i.e., <2 fold. By real-time quantitative RT-PCR, we confirmed the variations of selected genes (e.g., adra2, syt2, opalin and miRNAs (e.g., miR-16, miR-25. Surprisingly, little or no changes in gene expression were observed in mice expressing hLRRK2-WT or hLRRK2-R1441G when compared to non-transgenic controls. Nevertheless, a number of miRNAs were misexpressed in these models. Bioinformatics analysis identified several miRNA-dependent and independent networks dysregulated in LRRK2-deficient mice, including PD-related pathways. These results suggest that brain LRRK2 plays an overall modest role in gene transcription regulation in mammals; however, these effects seem context and RNA type-dependent. Our data thus set the stage for future investigations regarding LRRK2 function in PD development.

Towards Context-Aware Search and Analysis on Social Media Data

DEFF Research Database (Denmark)

Derczynski, Leon; Yang, Bin; Jensen, Christian S.

2013-01-01

Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains......, from politics and business to social science and epidemiology. A notable proportion of social media data comes with explicit or implicit spatial annotations, and almost all social media data has temporal metadata. We view social media data as a constant stream of data points, each containing text...... with spatial and temporal contexts. We identify challenges relevant to each context, which we intend to subject to context aware querying and analysis, specifically including longitudinal analyses on social media archives, spatial keyword search, local intent search, and spatio-temporal intent search. Finally...

A gene network bioinformatics analysis for pemphigoid autoimmune blistering diseases.

Science.gov (United States)

Barone, Antonio; Toti, Paolo; Giuca, Maria Rita; Derchi, Giacomo; Covani, Ugo

2015-07-01

In this theoretical study, a text mining search and clustering analysis of data related to genes potentially involved in human pemphigoid autoimmune blistering diseases (PAIBD) was performed using web tools to create a gene/protein interaction network. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was employed to identify a final set of PAIBD-involved genes and to calculate the overall significant interactions among genes: for each gene, the weighted number of links, or WNL, was registered and a clustering procedure was performed using the WNL analysis. Genes were ranked in class (leader, B, C, D and so on, up to orphans). An ontological analysis was performed for the set of 'leader' genes. Using the above-mentioned data network, 115 genes represented the final set; leader genes numbered 7 (intercellular adhesion molecule 1 (ICAM-1), interferon gamma (IFNG), interleukin (IL)-2, IL-4, IL-6, IL-8 and tumour necrosis factor (TNF)), class B genes were 13, whereas the orphans were 24. The ontological analysis attested that the molecular action was focused on extracellular space and cell surface, whereas the activation and regulation of the immunity system was widely involved. Despite the limited knowledge of the present pathologic phenomenon, attested by the presence of 24 genes revealing no protein-protein direct or indirect interactions, the network showed significant pathways gathered in several subgroups: cellular components, molecular functions, biological processes and the pathologic phenomenon obtained from the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database. The molecular basis for PAIBD was summarised and expanded, which will perhaps give researchers promising directions for the identification of new therapeutic targets.

Analysis of functional importance of binding sites in the Drosophila gap gene network model.

Science.gov (United States)

Kozlov, Konstantin; Gursky, Vitaly V; Kulakovskiy, Ivan V; Dymova, Arina; Samsonova, Maria

2015-01-01

The statistical thermodynamics based approach provides a promising framework for construction of the genotype-phenotype map in many biological systems. Among important aspects of a good model connecting the DNA sequence information with that of a molecular phenotype (gene expression) is the selection of regulatory interactions and relevant transcription factor bindings sites. As the model may predict different levels of the functional importance of specific binding sites in different genomic and regulatory contexts, it is essential to formulate and study such models under different modeling assumptions. We elaborate a two-layer model for the Drosophila gap gene network and include in the model a combined set of transcription factor binding sites and concentration dependent regulatory interaction between gap genes hunchback and Kruppel. We show that the new variants of the model are more consistent in terms of gene expression predictions for various genetic constructs in comparison to previous work. We quantify the functional importance of binding sites by calculating their impact on gene expression in the model and calculate how these impacts correlate across all sites under different modeling assumptions. The assumption about the dual interaction between hb and Kr leads to the most consistent modeling results, but, on the other hand, may obscure existence of indirect interactions between binding sites in regulatory regions of distinct genes. The analysis confirms the previously formulated regulation concept of many weak binding sites working in concert. The model predicts a more or less uniform distribution of functionally important binding sites over the sets of experimentally characterized regulatory modules and other open chromatin domains.

Knowledge Enrichment Analysis for Human Tissue- Specific Genes Uncover New Biological Insights

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Gong Xiu-Jun

2012-06-01

Full Text Available The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS and housekeeping (HK genes may help identify their roles in biological process or diseases and gain new biological insights.In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA and Hypergeometric Analysis (HA against three biological ontologies: Gene Ontology (GO, KEGG pathways and Disease Ontology (DO respectively.The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissuegenes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson’s disease, Huntington’s disease, Alzheimer’s disease are intensively enriched.Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of “Hypertrophic cardiomyopathy (HCM” and “Arrhythmogenic right ventricular cardiomyopathy (ARVC” are heavily enriched, which are also considered as no relationship with skeletal functions.Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon.

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

Science.gov (United States)

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

Science.gov (United States)

Jia, Zhilong; Liu, Ying; Guan, Naiyang; Bo, Xiaochen; Luo, Zhigang; Barnes, Michael R

2016-05-27

Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and

A Serial Analysis of Gene Expression (SAGE) database analysis of chemosensitivity

DEFF Research Database (Denmark)

Stein, Wilfred D; Litman, Thomas; Fojo, Tito

2004-01-01

are their corresponding solid tumors. We used the Serial Analysis of Gene Expression (SAGE) database to identify differences between solid tumors and cell lines, hoping to detect genes that could potentially explain differences in drug sensitivity. SAGE libraries were available for both solid tumors and cell lines from...

Gene set analysis of purine and pyrimidine antimetabolites cancer therapies.

Science.gov (United States)

Fridley, Brooke L; Batzler, Anthony; Li, Liang; Li, Fang; Matimba, Alice; Jenkins, Gregory D; Ji, Yuan; Wang, Liewei; Weinshilboum, Richard M

2011-11-01

Responses to therapies, either with regard to toxicities or efficacy, are expected to involve complex relationships of gene products within the same molecular pathway or functional gene set. Therefore, pathways or gene sets, as opposed to single genes, may better reflect the true underlying biology and may be more appropriate units for analysis of pharmacogenomic studies. Application of such methods to pharmacogenomic studies may enable the detection of more subtle effects of multiple genes in the same pathway that may be missed by assessing each gene individually. A gene set analysis of 3821 gene sets is presented assessing the association between basal messenger RNA expression and drug cytotoxicity using ethnically defined human lymphoblastoid cell lines for two classes of drugs: pyrimidines [gemcitabine (dFdC) and arabinoside] and purines [6-thioguanine and 6-mercaptopurine]. The gene set nucleoside-diphosphatase activity was found to be significantly associated with both dFdC and arabinoside, whereas gene set γ-aminobutyric acid catabolic process was associated with dFdC and 6-thioguanine. These gene sets were significantly associated with the phenotype even after adjusting for multiple testing. In addition, five associated gene sets were found in common between the pyrimidines and two gene sets for the purines (3',5'-cyclic-AMP phosphodiesterase activity and γ-aminobutyric acid catabolic process) with a P value of less than 0.0001. Functional validation was attempted with four genes each in gene sets for thiopurine and pyrimidine antimetabolites. All four genes selected from the pyrimidine gene sets (PSME3, CANT1, ENTPD6, ADRM1) were validated, but only one (PDE4D) was validated for the thiopurine gene sets. In summary, results from the gene set analysis of pyrimidine and purine therapies, used often in the treatment of various cancers, provide novel insight into the relationship between genomic variation and drug response.

Context incorporation using context-aware language features

OpenAIRE

Vlachostergiou, Aggeliki; Marandianos, George; Kollias, Stefanos

2017-01-01

This paper investigates the problem of context incorporation into human language systems and particular in Sentiment Analysis (SA) systems. So far, the analysis of how different features, when incorporated into such systems, improve their performance, has been discussed in a number of studies. However, a complete picture of their effectiveness remains unexplored. With this work, we attempt to extend the pool of the context - aware language features at the sentence level and to provide the ...

Gene expression profiling of resting and activated vascular smooth muscle cells by serial analysis of gene expression and clustering analysis

NARCIS (Netherlands)

Beauchamp, Nicholas J.; van Achterberg, Tanja A. E.; Engelse, Marten A.; Pannekoek, Hans; de Vries, Carlie J. M.

2003-01-01

Migration and proliferation of vascular smooth muscle cells (SMCs) are key events in atherosclerosis. However, little is known about alterations in gene expression upon transition of the quiescent, contractile SMC to the proliferative SMC. We performed serial analysis of gene expression (SAGE) of

Exploring factors that influence work analysis data: A meta-analysis of design choices, purposes, and organizational context.

Science.gov (United States)

DuVernet, Amy M; Dierdorff, Erich C; Wilson, Mark A

2015-09-01

Work analysis is fundamental to designing effective human resource systems. The current investigation extends previous research by identifying the differential effects of common design decisions, purposes, and organizational contexts on the data generated by work analyses. The effects of 19 distinct factors that span choices of descriptor, collection method, rating scale, and data source, as well as project purpose and organizational features, are explored. Meta-analytic results cumulated from 205 articles indicate that many of these variables hold significant consequences for work analysis data. Factors pertaining to descriptor choice, collection method, rating scale, and the purpose for conducting the work analysis each showed strong associations with work analysis data. The source of the work analysis information and organizational context in which it was conducted displayed fewer relationships. Findings can be used to inform choices work analysts make about methodology and postcollection evaluations of work analysis information. (c) 2015 APA, all rights reserved).

Reference genes for gene expression analysis by real-time reverse transcription polymerase chain reaction of renal cell carcinoma.

Science.gov (United States)

Bjerregaard, Henriette; Pedersen, Shona; Kristensen, Søren Risom; Marcussen, Niels

2011-12-01

Differentiation between malignant renal cell carcinoma and benign oncocytoma is of great importance to choose the optimal treatment. Accurate preoperative diagnosis of renal tumor is therefore crucial; however, existing imaging techniques and histologic examinations are incapable of providing an optimal differentiation profile. Analysis of gene expression of molecular markers is a new possibility but relies on appropriate standardization to compare different samples. The aim of this study was to identify stably expressed reference genes suitable for the normalization of results extracted from gene expression analysis of renal tumors. Expression levels of 8 potential reference genes (ATP5J, HMBS, HPRT1, PPIA, TBP, 18S, GAPDH, and POLR2A) were examined by real-time reverse transcription polymerase chain reaction in tumor and normal tissue from removed kidneys from 13 patients with renal cell carcinoma and 5 patients with oncocytoma. The expression levels of genes were compared by gene stability value M, average gene stability M, pairwise variation V, and coefficient of variation CV. More candidates were not suitable for the purpose, but a combination of HMBS, PPIA, ATP5J, and TBP was found to be the best combination with an average gene stability value M of 0.9 and a CV of 0.4 in the 18 tumors and normal tissues. A combination of 4 genes, HMBS, PPIA, ATP5J, and TBP, is a possible reference in renal tumor gene expression analysis by reverse transcription polymerase chain reaction. A combination of four genes, HMBS, PPIA, ATP5J and TBP, being stably expressed in tissues from RCC is possible reference genes for gene expression analysis.

Comparative genomic analysis of Drosophila melanogaster and vector mosquito developmental genes.

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Susanta K Behura

Full Text Available Genome sequencing projects have presented the opportunity for analysis of developmental genes in three vector mosquito species: Aedes aegypti, Culex quinquefasciatus, and Anopheles gambiae. A comparative genomic analysis of developmental genes in Drosophila melanogaster and these three important vectors of human disease was performed in this investigation. While the study was comprehensive, special emphasis centered on genes that 1 are components of developmental signaling pathways, 2 regulate fundamental developmental processes, 3 are critical for the development of tissues of vector importance, 4 function in developmental processes known to have diverged within insects, and 5 encode microRNAs (miRNAs that regulate developmental transcripts in Drosophila. While most fruit fly developmental genes are conserved in the three vector mosquito species, several genes known to be critical for Drosophila development were not identified in one or more mosquito genomes. In other cases, mosquito lineage-specific gene gains with respect to D. melanogaster were noted. Sequence analyses also revealed that numerous repetitive sequences are a common structural feature of Drosophila and mosquito developmental genes. Finally, analysis of predicted miRNA binding sites in fruit fly and mosquito developmental genes suggests that the repertoire of developmental genes targeted by miRNAs is species-specific. The results of this study provide insight into the evolution of developmental genes and processes in dipterans and other arthropods, serve as a resource for those pursuing analysis of mosquito development, and will promote the design and refinement of functional analysis experiments.

Analysis of Msx1; Msx2 double mutants reveals multiple roles for Msx genes in limb development.

Science.gov (United States)

Lallemand, Yvan; Nicola, Marie-Anne; Ramos, Casto; Bach, Antoine; Cloment, Cécile Saint; Robert, Benoît

2005-07-01

The homeobox-containing genes Msx1 and Msx2 are highly expressed in the limb field from the earliest stages of limb formation and, subsequently, in both the apical ectodermal ridge and underlying mesenchyme. However, mice homozygous for a null mutation in either Msx1 or Msx2 do not display abnormalities in limb development. By contrast, Msx1; Msx2 double mutants exhibit a severe limb phenotype. Our analysis indicates that these genes play a role in crucial processes during limb morphogenesis along all three axes. Double mutant limbs are shorter and lack anterior skeletal elements (radius/tibia, thumb/hallux). Gene expression analysis confirms that there is no formation of regions with anterior identity. This correlates with the absence of dorsoventral boundary specification in the anterior ectoderm, which precludes apical ectodermal ridge formation anteriorly. As a result, anterior mesenchyme is not maintained, leading to oligodactyly. Paradoxically, polydactyly is also frequent and appears to be associated with extended Fgf activity in the apical ectodermal ridge, which is maintained up to 14.5 dpc. This results in a major outgrowth of the mesenchyme anteriorly, which nevertheless maintains a posterior identity, and leads to formation of extra digits. These defects are interpreted in the context of an impairment of Bmp signalling.

Characterization of the global profile of genes expressed in cervical epithelium by Serial Analysis of Gene Expression (SAGE

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Piña-Sanchez Patricia

2005-09-01

Full Text Available Abstract Background Serial Analysis of Gene Expression (SAGE is a new technique that allows a detailed and profound quantitative and qualitative knowledge of gene expression profile, without previous knowledge of sequence of analyzed genes. We carried out a modification of SAGE methodology (microSAGE, useful for the analysis of limited quantities of tissue samples, on normal human cervical tissue obtained from a donor without histopathological lesions. Cervical epithelium is constituted mainly by cervical keratinocytes which are the targets of human papilloma virus (HPV, where persistent HPV infection of cervical epithelium is associated with an increase risk for developing cervical carcinomas (CC. Results We report here a transcriptome analysis of cervical tissue by SAGE, derived from 30,418 sequenced tags that provide a wealth of information about the gene products involved in normal cervical epithelium physiology, as well as genes not previously found in uterine cervix tissue involved in the process of epidermal differentiation. Conclusion This first comprehensive and profound analysis of uterine cervix transcriptome, should be useful for the identification of genes involved in normal cervix uterine function, and candidate genes associated with cervical carcinoma.

Context based computational analysis and characterization of ARS consensus sequences (ACS of Saccharomyces cerevisiae genome

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Vinod Kumar Singh

2016-09-01

Full Text Available Genome-wide experimental studies in Saccharomyces cerevisiae reveal that autonomous replicating sequence (ARS requires an essential consensus sequence (ACS for replication activity. Computational studies identified thousands of ACS like patterns in the genome. However, only a few hundreds of these sites act as replicating sites and the rest are considered as dormant or evolving sites. In a bid to understand the sequence makeup of replication sites, a content and context-based analysis was performed on a set of replicating ACS sequences that binds to origin-recognition complex (ORC denoted as ORC-ACS and non-replicating ACS sequences (nrACS, that are not bound by ORC. In this study, DNA properties such as base composition, correlation, sequence dependent thermodynamic and DNA structural profiles, and their positions have been considered for characterizing ORC-ACS and nrACS. Analysis reveals that ORC-ACS depict marked differences in nucleotide composition and context features in its vicinity compared to nrACS. Interestingly, an A-rich motif was also discovered in ORC-ACS sequences within its nucleosome-free region. Profound changes in the conformational features, such as DNA helical twist, inclination angle and stacking energy between ORC-ACS and nrACS were observed. Distribution of ACS motifs in the non-coding segments points to the locations of ORC-ACS which are found far away from the adjacent gene start position compared to nrACS thereby enabling an accessible environment for ORC-proteins. Our attempt is novel in considering the contextual view of ACS and its flanking region along with nucleosome positioning in the S. cerevisiae genome and may be useful for any computational prediction scheme.

Ranking metrics in gene set enrichment analysis: do they matter?

Science.gov (United States)

Zyla, Joanna; Marczyk, Michal; Weiner, January; Polanska, Joanna

2017-05-12

There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results. In this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a group of four metrics with the highest performance in terms of overall sensitivity, overall false positive rate and computational load was established i.e. absolute value of Moderated Welch Test statistic, Minimum Significant Difference, absolute value of Signal-To-Noise ratio and Baumgartner-Weiss-Schindler test statistic. In case of false positive rate estimation, all selected ranking metrics were robust with respect to sample size. In case of sensitivity, the absolute value of Moderated Welch Test statistic and absolute value of Signal-To-Noise ratio gave stable results, while Baumgartner-Weiss-Schindler and Minimum Significant Difference showed better results for larger sample size. Finally, the Gene Set Enrichment Analysis method with all tested ranking metrics was parallelised and implemented in MATLAB, and is available at https://github.com/ZAEDPolSl/MrGSEA . Choosing a ranking metric in Gene Set Enrichment Analysis has critical impact on results of pathway enrichment analysis. The absolute value of Moderated Welch Test has the best overall sensitivity and Minimum Significant Difference has the best overall specificity of gene set analysis. When the number of non-normally distributed genes is high, using Baumgartner

Evolutionary Analysis of Minor Histocompatibility Genes In Hydra

KAUST Repository

Aalismail, Nojood

2016-05-01

Hydra is a simple freshwater solitary polyp used as a model system to study evolutionary aspects. The immune response of this organism has not been studied extensively and the immune response genes have not been identified and characterized. On the other hand, immune response has been investigated and genetic analysis has been initiated in other lower invertebrates. In the present study we took initiative to study the self/nonself recognition in hydra and its relation to the immune response. Moreover, performing phylogenetic analysis to look for annotated immune genes in hydra gave us a potential to analyze the expression of minor histocompatibility genes that have been shown to play a major role in grafting and transplantation in mammals. Here we obtained the cDNA library that shows expression of minor histocompatibility genes and confirmed that the annotated sequences in databases are actually present. In addition, grafting experiments suggested, although still preliminary, that homograft showed less rejection response than in heterograft. Involvement of possible minor histocompatibility gene orthologous in immune response was examined by qPCR.

Joint mapping of genes and conditions via multidimensional unfolding analysis

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Engelen Kristof

2007-06-01

Full Text Available Abstract Background Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. Results We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. Conclusion Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data.

Identification of Human HK Genes and Gene Expression Regulation Study in Cancer from Transcriptomics Data Analysis

Science.gov (United States)

Zhang, Zhang; Liu, Jingxing; Wu, Jiayan; Yu, Jun

2013-01-01

The regulation of gene expression is essential for eukaryotes, as it drives the processes of cellular differentiation and morphogenesis, leading to the creation of different cell types in multicellular organisms. RNA-Sequencing (RNA-Seq) provides researchers with a powerful toolbox for characterization and quantification of transcriptome. Many different human tissue/cell transcriptome datasets coming from RNA-Seq technology are available on public data resource. The fundamental issue here is how to develop an effective analysis method to estimate expression pattern similarities between different tumor tissues and their corresponding normal tissues. We define the gene expression pattern from three directions: 1) expression breadth, which reflects gene expression on/off status, and mainly concerns ubiquitously expressed genes; 2) low/high or constant/variable expression genes, based on gene expression level and variation; and 3) the regulation of gene expression at the gene structure level. The cluster analysis indicates that gene expression pattern is higher related to physiological condition rather than tissue spatial distance. Two sets of human housekeeping (HK) genes are defined according to cell/tissue types, respectively. To characterize the gene expression pattern in gene expression level and variation, we firstly apply improved K-means algorithm and a gene expression variance model. We find that cancer-associated HK genes (a HK gene is specific in cancer group, while not in normal group) are expressed higher and more variable in cancer condition than in normal condition. Cancer-associated HK genes prefer to AT-rich genes, and they are enriched in cell cycle regulation related functions and constitute some cancer signatures. The expression of large genes is also avoided in cancer group. These studies will help us understand which cell type-specific patterns of gene expression differ among different cell types, and particularly for cancer. PMID:23382867

Genome-wide analysis of the GRAS gene family in Prunus mume.

Science.gov (United States)

Lu, Jiuxing; Wang, Tao; Xu, Zongda; Sun, Lidan; Zhang, Qixiang

2015-02-01

Prunus mume is an ornamental flower and fruit tree in Rosaceae. We investigated the GRAS gene family to improve the breeding and cultivation of P. mume and other Rosaceae fruit trees. The GRAS gene family encodes transcriptional regulators that have diverse functions in plant growth and development, such as gibberellin and phytochrome A signal transduction, root radial patterning, and axillary meristem formation and gametogenesis in the P. mume genome. Despite the important roles of these genes in plant growth regulation, no findings on the GRAS genes of P. mume have been reported. In this study, we discerned phylogenetic relationships of P. mume GRAS genes, and their locations, structures in the genome and expression levels of different tissues. Out of 46 identified GRAS genes, 45 were located on the 8 P. mume chromosomes. Phylogenetic results showed that these genes could be classified into 11 groups. We found that Group X was P. mume-specific, and three genes of Group IX clustered with the rice-specific gene Os4. We speculated that these genes existed before the divergence of dicotyledons and monocotyledons and were lost in Arabidopsis. Tissue expression analysis indicated that 13 genes showed high expression levels in roots, stems, leaves, flowers and fruits, and were related to plant growth and development. Functional analysis of 24 GRAS genes and an orthologous relationship analysis indicated that many functioned during plant growth and flower and fruit development. Our bioinformatics analysis provides valuable information to improve the economic, agronomic and ecological benefits of P. mume and other Rosaceae fruit trees.

Interactive visualization of gene regulatory networks with associated gene expression time series data

NARCIS (Netherlands)

Westenberg, M.A.; Hijum, van S.A.F.T.; Lulko, A.T.; Kuipers, O.P.; Roerdink, J.B.T.M.; Linsen, L.; Hagen, H.; Hamann, B.

2008-01-01

We present GENeVis, an application to visualize gene expression time series data in a gene regulatory network context. This is a network of regulator proteins that regulate the expression of their respective target genes. The networks are represented as graphs, in which the nodes represent genes,

Gene set analysis: limitations in popular existing methods and proposed improvements.

Science.gov (United States)

Mishra, Pashupati; Törönen, Petri; Leino, Yrjö; Holm, Liisa

2014-10-01

Gene set analysis is the analysis of a set of genes that collectively contribute to a biological process. Most popular gene set analysis methods are based on empirical P-value that requires large number of permutations. Despite numerous gene set analysis methods developed in the past decade, the most popular methods still suffer from serious limitations. We present a gene set analysis method (mGSZ) based on Gene Set Z-scoring function (GSZ) and asymptotic P-values. Asymptotic P-value calculation requires fewer permutations, and thus speeds up the gene set analysis process. We compare the GSZ-scoring function with seven popular gene set scoring functions and show that GSZ stands out as the best scoring function. In addition, we show improved performance of the GSA method when the max-mean statistics is replaced by the GSZ scoring function. We demonstrate the importance of both gene and sample permutations by showing the consequences in the absence of one or the other. A comparison of asymptotic and empirical methods of P-value estimation demonstrates a clear advantage of asymptotic P-value over empirical P-value. We show that mGSZ outperforms the state-of-the-art methods based on two different evaluations. We compared mGSZ results with permutation and rotation tests and show that rotation does not improve our asymptotic P-values. We also propose well-known asymptotic distribution models for three of the compared methods. mGSZ is available as R package from cran.r-project.org. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Comprehensive Classification and Evolutionary Analysis of Plant Homeobox Genes

OpenAIRE

Mukherjee, Krishanu; Brocchieri, Luciano; B?rglin, Thomas R.

2009-01-01

The full complement of homeobox transcription factor sequences, including genes and pseudogenes, was determined from the analysis of 10 complete genomes from flowering plants, moss, Selaginella, unicellular green algae, and red algae. Our exhaustive genome-wide searches resulted in the discovery in each class of a greater number of homeobox genes than previously reported. All homeobox genes can be unambiguously classified by sequence evolutionary analysis into 14 distinct classes also charact...

Rice Transcriptome Analysis to Identify Possible Herbicide Quinclorac Detoxification Genes

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Wenying eXu

2015-09-01

Full Text Available Quinclorac is a highly selective auxin-type herbicide, and is widely used in the effective control of barnyard grass in paddy rice fields, improving the world’s rice yield. The herbicide mode of action of quinclorac has been proposed and hormone interactions affect quinclorac signaling. Because of widespread use, quinclorac may be transported outside rice fields with the drainage waters, leading to soil and water pollution and environmental health problems.In this study, we used 57K Affymetrix rice whole-genome array to identify quinclorac signaling response genes to study the molecular mechanisms of action and detoxification of quinclorac in rice plants. Overall, 637 probe sets were identified with differential expression levels under either 6 or 24 h of quinclorac treatment. Auxin-related genes such as GH3 and OsIAAs responded to quinclorac treatment. Gene Ontology analysis showed that genes of detoxification-related family genes were significantly enriched, including cytochrome P450, GST, UGT, and ABC and drug transporter genes. Moreover, real-time RT-PCR analysis showed that top candidate P450 families such as CYP81, CYP709C and CYP72A genes were universally induced by different herbicides. Some Arabidopsis genes for the same P450 family were up-regulated under quinclorac treatment.We conduct rice whole-genome GeneChip analysis and the first global identification of quinclorac response genes. This work may provide potential markers for detoxification of quinclorac and biomonitors of environmental chemical pollution.

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer.

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Verena Jabs

Full Text Available Non-small cell lung cancer (NSCLC represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190 and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes, high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%, including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05. Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.

The gene patent controversy on Twitter: a case study of Twitter users' responses to the CHEO lawsuit against Long QT gene patents.

Science.gov (United States)

Du, Li; Kamenova, Kalina; Caulfield, Timothy

2015-08-25

The recent Canadian lawsuit on patent infringement, filed by the Children's Hospital of Eastern Ontario (CHEO), has engendered a significant public debate on whether patenting genes should be legal in Canada. In part, this public debate has involved the use of social networking sites, such as Twitter. This case provides an opportunity to examine how Twitter was used in the context of this gene patent controversy. We collected 310 English-language tweets that contained the keyword "gene patents" by using TOPSY.com and Twitter's built-in search engine. A content analysis of the messages was conducted to establish the users' perspectives on both CHEO's court challenge and the broader controversy over the patenting of human DNA. More specifically, we analyzed the users' demographics, geographic locations, and attitudes toward the CHEO position on gene patents and the patentability of human genes in principle. Our analysis has shown that messages tweeted by news media and health care organizations were re-tweeted most frequently in Twitter discussions regarding both the CHEO patent infringement lawsuit and gene patents in general. 34.8% of tweets were supportive of CHEO, with 52.8% of the supportive tweets suggesting that gene patents contravene patients' rights to health care access. 17.6% of the supportive tweets cited ethical and social concerns against gene patents. Nearly 40% of tweets clearly expressed that human genes should not be patentable, and there were no tweets that presented perspectives favourable toward the patenting of human genes. Access to healthcare and the use of genetic testing were the most important concerns raised by Twitter users in the context of the CHEO case. Our analysis of tweets reveals an expectation that the CHEO lawsuit will provide an opportunity to clear the confusion on gene patents by establishing a legal precedent on the patentability of human genes in Canada. In general, there were no tweets arguing in favour of gene patents

Cloning and analysis of two Ceratopteris thalictroides MADS-box genes

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XU Daolan

2014-06-01

Full Text Available MADS-box transcription factors,as a large gene family,play an important role in plant growth and development,especially act as key regulators in controlling the identities of floral organs in flowering plants.They are also significant in the evolutionary revelation.In order to understand MADS-box genes,we need more information of MADS-box genes in non flowering plant.MADS-box genes of Ceratopteris thalictroides were selected to clone and analysis by using RACE method.Two MADS-box genes,designated CtMADS1 and CtMADS2 in C. thalictroides,were cloned.Analysis indicates that CtMADS1 is belonged to MIKC*-clade,while CtMADS2 is belonged to MIKCc-clade.Phylogeny suggests that these two MADS-box genes of C. thalictroides have a close relationship with flowering plants,the data indicates that at least two different MADS-box genes are homologous to floral homeotic genes existed in the last common ancestor of contemporary vascular plants.

DaGO-Fun: tool for Gene Ontology-based functional analysis using term information content measures.

Science.gov (United States)

Mazandu, Gaston K; Mulder, Nicola J

2013-09-25

The use of Gene Ontology (GO) data in protein analyses have largely contributed to the improved outcomes of these analyses. Several GO semantic similarity measures have been proposed in recent years and provide tools that allow the integration of biological knowledge embedded in the GO structure into different biological analyses. There is a need for a unified tool that provides the scientific community with the opportunity to explore these different GO similarity measure approaches and their biological applications. We have developed DaGO-Fun, an online tool available at http://web.cbio.uct.ac.za/ITGOM, which incorporates many different GO similarity measures for exploring, analyzing and comparing GO terms and proteins within the context of GO. It uses GO data and UniProt proteins with their GO annotations as provided by the Gene Ontology Annotation (GOA) project to precompute GO term information content (IC), enabling rapid response to user queries. The DaGO-Fun online tool presents the advantage of integrating all the relevant IC-based GO similarity measures, including topology- and annotation-based approaches to facilitate effective exploration of these measures, thus enabling users to choose the most relevant approach for their application. Furthermore, this tool includes several biological applications related to GO semantic similarity scores, including the retrieval of genes based on their GO annotations, the clustering of functionally related genes within a set, and term enrichment analysis.

A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis

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Wenyi Qin

2018-02-01

Full Text Available Abstract Motivation Detecting differentially expressed (DE genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Since most studies don’t have a lot of samples, researchers have used meta-analysis to group different datasets for the same disease. Even then, in many cases the statistical power is still not enough. Taking into account the fact that many diseases share the same disease genes, it is desirable to design a statistical framework that can identify diseases’ common and specific DE genes simultaneously to improve the identification power. Results We developed a novel empirical Bayes based mixture model to identify DE genes in specific study by leveraging the shared information across multiple different disease expression data sets. The effectiveness of joint analysis was demonstrated through comprehensive simulation studies and two real data applications. The simulation results showed that our method consistently outperformed single data set analysis and two other meta-analysis methods in identification power. In real data analysis, overall our method demonstrated better identification power in detecting DE genes and prioritized more disease related genes and disease related pathways than single data set analysis. Over 150% more disease related genes are identified by our method in application to Huntington’s disease. We expect that our method would provide researchers a new way of utilizing available data sets from different diseases when sample size of the focused disease is limited.

A gene-based linkage map for Bicyclus anynana butterflies allows for a comprehensive analysis of synteny with the lepidopteran reference genome.

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Patrícia Beldade

2009-02-01

Full Text Available Lepidopterans (butterflies and moths are a rich and diverse order of insects, which, despite their economic impact and unusual biological properties, are relatively underrepresented in terms of genomic resources. The genome of the silkworm Bombyx mori has been fully sequenced, but comparative lepidopteran genomics has been hampered by the scarcity of information for other species. This is especially striking for butterflies, even though they have diverse and derived phenotypes (such as color vision and wing color patterns and are considered prime models for the evolutionary and developmental analysis of ecologically relevant, complex traits. We focus on Bicyclus anynana butterflies, a laboratory system for studying the diversification of novelties and serially repeated traits. With a panel of 12 small families and a biphasic mapping approach, we first assigned 508 expressed genes to segregation groups and then ordered 297 of them within individual linkage groups. We also coarsely mapped seven color pattern loci. This is the richest gene-based map available for any butterfly species and allowed for a broad-coverage analysis of synteny with the lepidopteran reference genome. Based on 462 pairs of mapped orthologous markers in Bi. anynana and Bo. mori, we observed strong conservation of gene assignment to chromosomes, but also evidence for numerous large- and small-scale chromosomal rearrangements. With gene collections growing for a variety of target organisms, the ability to place those genes in their proper genomic context is paramount. Methods to map expressed genes and to compare maps with relevant model systems are crucial to extend genomic-level analysis outside classical model species. Maps with gene-based markers are useful for comparative genomics and to resolve mapped genomic regions to a tractable number of candidate genes, especially if there is synteny with related model species. This is discussed in relation to the identification of

Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

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Bing Jiang

2017-01-01

Full Text Available Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

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...

Serial Expression Analysis: a web tool for the analysis of serial gene expression data

Science.gov (United States)

Nueda, Maria José; Carbonell, José; Medina, Ignacio; Dopazo, Joaquín; Conesa, Ana

2010-01-01

Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es. PMID:20525784

A multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors for functional gene analysis.

Science.gov (United States)

Weber, Kristoffer; Bartsch, Udo; Stocking, Carol; Fehse, Boris

2008-04-01

Functional gene analysis requires the possibility of overexpression, as well as downregulation of one, or ideally several, potentially interacting genes. Lentiviral vectors are well suited for this purpose as they ensure stable expression of complementary DNAs (cDNAs), as well as short-hairpin RNAs (shRNAs), and can efficiently transduce a wide spectrum of cell targets when packaged within the coat proteins of other viruses. Here we introduce a multicolor panel of novel lentiviral "gene ontology" (LeGO) vectors designed according to the "building blocks" principle. Using a wide spectrum of different fluorescent markers, including drug-selectable enhanced green fluorescent protein (eGFP)- and dTomato-blasticidin-S resistance fusion proteins, LeGO vectors allow simultaneous analysis of multiple genes and shRNAs of interest within single, easily identifiable cells. Furthermore, each functional module is flanked by unique cloning sites, ensuring flexibility and individual optimization. The efficacy of these vectors for analyzing multiple genes in a single cell was demonstrated in several different cell types, including hematopoietic, endothelial, and neural stem and progenitor cells, as well as hepatocytes. LeGO vectors thus represent a valuable tool for investigating gene networks using conditional ectopic expression and knock-down approaches simultaneously.

Food recognition and recipe analysis: integrating visual content, context and external knowledge

OpenAIRE

Herranz, Luis; Min, Weiqing; Jiang, Shuqiang

2018-01-01

The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information. We review how visual content, context and external knowledge can be integrated effectively into food-oriented applications, with special focus on recipe analysis and retrieval, food recommendation, and the restaurant context as em...

Comparative genomic and transcriptomic analysis of selected fatty acid biosynthesis genes and CNL disease resistance genes in oil palm

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Rosli, Rozana; Amiruddin, Nadzirah; Ab Halim, Mohd Amin; Chan, Pek-Lan; Chan, Kuang-Lim; Azizi, Norazah; Morris, Priscilla E.; Leslie Low, Eng-Ti; Ong-Abdullah, Meilina; Sambanthamurthi, Ravigadevi; Singh, Rajinder

2018-01-01

Comparative genomics and transcriptomic analyses were performed on two agronomically important groups of genes from oil palm versus other major crop species and the model organism, Arabidopsis thaliana. The first analysis was of two gene families with key roles in regulation of oil quality and in particular the accumulation of oleic acid, namely stearoyl ACP desaturases (SAD) and acyl-acyl carrier protein (ACP) thioesterases (FAT). In both cases, these were found to be large gene families with complex expression profiles across a wide range of tissue types and developmental stages. The detailed classification of the oil palm SAD and FAT genes has enabled the updating of the latest version of the oil palm gene model. The second analysis focused on disease resistance (R) genes in order to elucidate possible candidates for breeding of pathogen tolerance/resistance. Ortholog analysis showed that 141 out of the 210 putative oil palm R genes had homologs in banana and rice. These genes formed 37 clusters with 634 orthologous genes. Classification of the 141 oil palm R genes showed that the genes belong to the Kinase (7), CNL (95), MLO-like (8), RLK (3) and Others (28) categories. The CNL R genes formed eight clusters. Expression data for selected R genes also identified potential candidates for breeding of disease resistance traits. Furthermore, these findings can provide information about the species evolution as well as the identification of agronomically important genes in oil palm and other major crops. PMID:29672525

Comparative genomic and transcriptomic analysis of selected fatty acid biosynthesis genes and CNL disease resistance genes in oil palm.

Science.gov (United States)

Rosli, Rozana; Amiruddin, Nadzirah; Ab Halim, Mohd Amin; Chan, Pek-Lan; Chan, Kuang-Lim; Azizi, Norazah; Morris, Priscilla E; Leslie Low, Eng-Ti; Ong-Abdullah, Meilina; Sambanthamurthi, Ravigadevi; Singh, Rajinder; Murphy, Denis J

2018-01-01

Comparative genomics and transcriptomic analyses were performed on two agronomically important groups of genes from oil palm versus other major crop species and the model organism, Arabidopsis thaliana. The first analysis was of two gene families with key roles in regulation of oil quality and in particular the accumulation of oleic acid, namely stearoyl ACP desaturases (SAD) and acyl-acyl carrier protein (ACP) thioesterases (FAT). In both cases, these were found to be large gene families with complex expression profiles across a wide range of tissue types and developmental stages. The detailed classification of the oil palm SAD and FAT genes has enabled the updating of the latest version of the oil palm gene model. The second analysis focused on disease resistance (R) genes in order to elucidate possible candidates for breeding of pathogen tolerance/resistance. Ortholog analysis showed that 141 out of the 210 putative oil palm R genes had homologs in banana and rice. These genes formed 37 clusters with 634 orthologous genes. Classification of the 141 oil palm R genes showed that the genes belong to the Kinase (7), CNL (95), MLO-like (8), RLK (3) and Others (28) categories. The CNL R genes formed eight clusters. Expression data for selected R genes also identified potential candidates for breeding of disease resistance traits. Furthermore, these findings can provide information about the species evolution as well as the identification of agronomically important genes in oil palm and other major crops.

Patenting human genes: Chinese academic articles' portrayal of gene patents.

Science.gov (United States)

Du, Li

2018-04-24

The patenting of human genes has been the subject of debate for decades. While China has gradually come to play an important role in the global genomics-based testing and treatment market, little is known about Chinese scholars' perspectives on patent protection for human genes. A content analysis of academic literature was conducted to identify Chinese scholars' concerns regarding gene patents, including benefits and risks of patenting human genes, attitudes that researchers hold towards gene patenting, and any legal and policy recommendations offered for the gene patent regime in China. 57.2% of articles were written by law professors, but scholars from health sciences, liberal arts, and ethics also participated in discussions on gene patent issues. While discussions of benefits and risks were relatively balanced in the articles, 63.5% of the articles favored gene patenting in general and, of the articles (n = 41) that explored gene patents in the Chinese context, 90.2% supported patent protections for human genes in China. The patentability of human genes was discussed in 33 articles, and 75.8% of these articles reached the conclusion that human genes are patentable. Chinese scholars view the patent regime as an important legal tool to protect the interests of inventors and inventions as well as the genetic resources of China. As such, many scholars support a gene patent system in China. These attitudes towards gene patents remain unchanged following the court ruling in the Myriad case in 2013, but arguments have been raised about the scope of gene patents, in particular that the increasing numbers of gene patents may negatively impact public health in China.

Transcriptome analysis reveals key differentially expressed genes involved in wheat grain development

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Yonglong Yu

2016-04-01

Full Text Available Wheat seed development is an important physiological process of seed maturation and directly affects wheat yield and quality. In this study, we performed dynamic transcriptome microarray analysis of an elite Chinese bread wheat cultivar (Jimai 20 during grain development using the GeneChip Wheat Genome Array. Grain morphology and scanning electron microscope observations showed that the period of 11–15 days post-anthesis (DPA was a key stage for the synthesis and accumulation of seed starch. Genome-wide transcriptional profiling and significance analysis of microarrays revealed that the period from 11 to 15 DPA was more important than the 15–20 DPA stage for the synthesis and accumulation of nutritive reserves. Series test of cluster analysis of differential genes revealed five statistically significant gene expression profiles. Gene ontology annotation and enrichment analysis gave further information about differentially expressed genes, and MapMan analysis revealed expression changes within functional groups during seed development. Metabolic pathway network analysis showed that major and minor metabolic pathways regulate one another to ensure regular seed development and nutritive reserve accumulation. We performed gene co-expression network analysis to identify genes that play vital roles in seed development and identified several key genes involved in important metabolic pathways. The transcriptional expression of eight key genes involved in starch and protein synthesis and stress defense was further validated by qRT-PCR. Our results provide new insight into the molecular mechanisms of wheat seed development and the determinants of yield and quality.

Bioinformatics Analysis of NBS-LRR Encoding Resistance Genes in Setaria italica.

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Zhao, Yan; Weng, Qiaoyun; Song, Jinhui; Ma, Hailian; Yuan, Jincheng; Dong, Zhiping; Liu, Yinghui

2016-06-01

In plants, resistance (R) genes are involved in pathogen recognition and subsequent activation of innate immune responses. The nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes family forms the largest R-gene family among plant genomes and play an important role in plant disease resistance. In this paper, comprehensive analysis of NBS-encoding genes is performed in the whole Setaria italica genome. A total of 96 NBS-LRR genes are identified, and comprehensive overview of the NBS-LRR genes is undertaken, including phylogenetic analysis, chromosome locations, conserved motifs of proteins, and gene expression. Based on the domain, these genes are divided into two groups and distributed in all Setaria italica chromosomes. Most NBS-LRR genes are located at the distal tip of the long arms of the chromosomes. Setaria italica NBS-LRR proteins share at least one nucleotide-biding domain and one leucine-rich repeat domain. Our results also show the duplication of NBS-LRR genes in Setaria italica is related to their gene structure.

Bioinformatics analysis and detection of gelatinase encoded gene in Lysinibacillussphaericus

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Repin, Rul Aisyah Mat; Mutalib, Sahilah Abdul; Shahimi, Safiyyah; Khalid, Rozida Mohd.; Ayob, Mohd. Khan; Bakar, Mohd. Faizal Abu; Isa, Mohd Noor Mat

2016-11-01

In this study, we performed bioinformatics analysis toward genome sequence of Lysinibacillussphaericus (L. sphaericus) to determine gene encoded for gelatinase. L. sphaericus was isolated from soil and gelatinase species-specific bacterium to porcine and bovine gelatin. This bacterium offers the possibility of enzymes production which is specific to both species of meat, respectively. The main focus of this research is to identify the gelatinase encoded gene within the bacteria of L. Sphaericus using bioinformatics analysis of partially sequence genome. From the research study, three candidate gene were identified which was, gelatinase candidate gene 1 (P1), NODE_71_length_93919_cov_158.931839_21 which containing 1563 base pair (bp) in size with 520 amino acids sequence; Secondly, gelatinase candidate gene 2 (P2), NODE_23_length_52851_cov_190.061386_17 which containing 1776 bp in size with 591 amino acids sequence; and Thirdly, gelatinase candidate gene 3 (P3), NODE_106_length_32943_cov_169.147919_8 containing 1701 bp in size with 566 amino acids sequence. Three pairs of oligonucleotide primers were designed and namely as, F1, R1, F2, R2, F3 and R3 were targeted short sequences of cDNA by PCR. The amplicons were reliably results in 1563 bp in size for candidate gene P1 and 1701 bp in size for candidate gene P3. Therefore, the results of bioinformatics analysis of L. Sphaericus resulting in gene encoded gelatinase were identified.

Comparative brain transcriptomic analyses of scouting across distinct behavioural and ecological contexts in honeybees

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Liang, Zhengzheng S.; Mattila, Heather R.; Rodriguez-Zas, Sandra L.; Southey, Bruce R.; Seeley, Thomas D.; Robinson, Gene E.

2014-01-01

Individual differences in behaviour are often consistent across time and contexts, but it is not clear whether such consistency is reflected at the molecular level. We explored this issue by studying scouting in honeybees in two different behavioural and ecological contexts: finding new sources of floral food resources and finding a new nest site. Brain gene expression profiles in food-source and nest-site scouts showed a significant overlap, despite large expression differences associated with the two different contexts. Class prediction and ‘leave-one-out’ cross-validation analyses revealed that a bee's role as a scout in either context could be predicted with 92.5% success using 89 genes at minimum. We also found that genes related to four neurotransmitter systems were part of a shared brain molecular signature in both types of scouts, and the two types of scouts were more similar for genes related to glutamate and GABA than catecholamine or acetylcholine signalling. These results indicate that consistent behavioural tendencies across different ecological contexts involve a mixture of similarities and differences in brain gene expression. PMID:25355476

Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis.

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Tejera, Eduardo; Cruz-Monteagudo, Maykel; Burgos, Germán; Sánchez, María-Eugenia; Sánchez-Rodríguez, Aminael; Pérez-Castillo, Yunierkis; Borges, Fernanda; Cordeiro, Maria Natália Dias Soeiro; Paz-Y-Miño, César; Rebelo, Irene

2017-08-08

Preeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis. We firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways. The consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism. Our results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further

Comparative Analysis of Context-Dependent Mutagenesis Using Human and Mouse Models

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Sofya A. Medvedeva

2013-01-01

Full Text Available Substitution rates strongly depend on their nucleotide context. One of the most studied examples is the excess of C > T mutations in the CG context in various groups of organisms, including vertebrates. Studies on the molecular mechanisms underlying this mutation regularity have provided insights into evolution, mutagenesis, and cancer development. Recently several other hypermutable motifs were identified in the human genome. There is an increased frequency of T > C mutations in the second position of the words ATTG and ATAG and an increased frequency of A > C mutations in the first position of the word ACAA. For a better understanding of evolution, it is of interest whether these mutation regularities are human specific or present in other vertebrates, as their presence might affect the validity of currently used substitution models and molecular clocks. A comprehensive analysis of mutagenesis in 4 bp mutation contexts requires a vast amount of mutation data. Such data may be derived from the comparisons of individual genomes or from single nucleotide polymorphism (SNP databases. Using this approach, we performed a systematical comparison of mutation regularities within 2–4 bp contexts in Mus musculus and Homo sapiens and uncovered that even closely related organisms may have notable differences in context-dependent mutation regularities.

Bioinformatic Analysis of Strawberry GSTF12 Gene

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Wang, Xiran; Jiang, Leiyu; Tang, Haoru

2018-01-01

GSTF12 has always been known as a key factor of proanthocyanins accumulate in plant testa. Through bioinformatics analysis of the nucleotide and encoded protein sequence of GSTF12, it is more advantageous to the study of genes related to anthocyanin biosynthesis accumulation pathway. Therefore, we chosen GSTF12 gene of 11 kinds species, downloaded their nucleotide and protein sequence from NCBI as the research object, found strawberry GSTF12 gene via bioinformation analyse, constructed phylogenetic tree. At the same time, we analysed the strawberry GSTF12 gene of physical and chemical properties and its protein structure and so on. The phylogenetic tree showed that Strawberry and petunia were closest relative. By the protein prediction, we found that the protein owed one proper signal peptide without obvious transmembrane regions.

An Integrative Analysis to Identify Driver Genes in Esophageal Squamous Cell Carcinoma.

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Genta Sawada

Full Text Available Few driver genes have been well established in esophageal squamous cell carcinoma (ESCC. Identification of the genomic aberrations that contribute to changes in gene expression profiles can be used to predict driver genes.We searched for driver genes in ESCC by integrative analysis of gene expression microarray profiles and copy number data. To narrow down candidate genes, we performed survival analysis on expression data and tested the genetic vulnerability of each genes using public RNAi screening data. We confirmed the results by performing RNAi experiments and evaluating the clinical relevance of candidate genes in an independent ESCC cohort.We found 10 significantly recurrent copy number alterations accompanying gene expression changes, including loci 11q13.2, 7p11.2, 3q26.33, and 17q12, which harbored CCND1, EGFR, SOX2, and ERBB2, respectively. Analysis of survival data and RNAi screening data suggested that GRB7, located on 17q12, was a driver gene in ESCC. In ESCC cell lines harboring 17q12 amplification, knockdown of GRB7 reduced the proliferation, migration, and invasion capacities of cells. Moreover, siRNA targeting GRB7 had a synergistic inhibitory effect when combined with trastuzumab, an anti-ERBB2 antibody. Survival analysis of the independent cohort also showed that high GRB7 expression was associated with poor prognosis in ESCC.Our integrative analysis provided important insights into ESCC pathogenesis. We identified GRB7 as a novel ESCC driver gene and potential new therapeutic target.

Ubiquitous Geo-Sensing for Context-Aware Analysis: Exploring Relationships between Environmental and Human Dynamics

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Euro Beinat

2012-07-01

Full Text Available Ubiquitous geo-sensing enables context-aware analyses of physical and social phenomena, i.e., analyzing one phenomenon in the context of another. Although such context-aware analysis can potentially enable a more holistic understanding of spatio-temporal processes, it is rarely documented in the scientific literature yet. In this paper we analyzed the collective human behavior in the context of the weather. We therefore explored the complex relationships between these two spatio-temporal phenomena to provide novel insights into the dynamics of urban systems. Aggregated mobile phone data, which served as a proxy for collective human behavior, was linked with the weather data from climate stations in the case study area, the city of Udine, Northern Italy. To identify and characterize potential patterns within the weather-human relationships, we developed a hybrid approach which integrates several spatio-temporal statistical analysis methods. Thereby we show that explanatory factor analysis, when applied to a number of meteorological variables, can be used to differentiate between normal and adverse weather conditions. Further, we measured the strength of the relationship between the ‘global’ adverse weather conditions and the spatially explicit effective variations in user-generated mobile network traffic for three distinct periods using the Maximal Information Coefficient (MIC. The analyses result in three spatially referenced maps of MICs which reveal interesting insights into collective human dynamics in the context of weather, but also initiate several new scientific challenges.

Molecular analysis of Rv0679c and Rv0180c genes of Mycobacterium tuberculosis from clinical isolates of pulmonary tuberculosis

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L Rupa

2016-01-01

Full Text Available Context: Two novel proteins/genes Rv0679c and Rv0180c of Mycobacterium tuberculosis (MTB H37Rv were classified as a hypothetical membrane and transmembrane proteins which might have a role in the invasion. Molecular analysis of these genes in human clinical isolates of pulmonary tuberculosis (PTB patients was not well characterised. Aims: To assess the molecular diversity of Rv0679c and Rv0180c genes of MTB from clinical isolates of PTB patients. Settings and Design: DNA from 97 clinical isolates was extracted and subjected to amplification using selective primers by polymerase chain reaction (PCR. The PCR product obtained was sequenced commercially. Patients and Methods: Clinical isolates obtained from tuberculosis patients were investigated for polymorphisms in the Rv0679c and Rv0180c genes by PCR and DNA sequencing. Genomic DNA isolated by cetyltrimethylammonium bromide method was used for amplification of genes. Results: Rv0679c gene was highly conserved in 61 out of 65 clinical isolates assessed for sequence homology with wild-type H37Rv gene and was identical using ClustalW. Fifty-five out of 78 (70.5% clinical isolates assessed for Rv0180c were positive for single nucleotide polymorphism (SNP at 258th position where the nucleotide G was replaced with T (G to T. In clinical isolates of untreated cases, the frequency was 54.5% for SNP at 258th position which is low compared to cases undergoing treatment where the frequency was 73.1%. Conclusions: Molecular analysis of Rv0180c in clinical isolates of PTB assessed in this study was the first report, where an SNP at 258th position G to T was identified within the gene. Rv0679c gene was highly conserved (94%, within Indian clinical isolates as compared to reports from other nations.

Context analysis : sky, water and motion

NARCIS (Netherlands)

Javanbakhti, S.; Zinger, S.; With, de P.H.N.

2011-01-01

Interpreting the events present in the video is a complex task, and the same gesture or motion can be understood in several ways depending on the context of the event and/or the scene. Therefore the context of the scene can contribute to the semantic understanding of the video. In this paper, we

Haplotyping, linkage mapping and expression analysis of barley genes regulated by terminal drought stress influencing seed quality

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Wobus Ulrich

2011-01-01

Full Text Available Abstract Background The increasingly narrow genetic background characteristic of modern crop germplasm presents a challenge for the breeding of cultivars that require adaptation to the anticipated change in climate. Thus, high priority research aims at the identification of relevant allelic variation present both in the crop itself as well as in its progenitors. This study is based on the characterization of genetic variation in barley, with a view to enhancing its response to terminal drought stress. Results The expression patterns of drought regulated genes were monitored during plant ontogeny, mapped and the location of these genes was incorporated into a comprehensive barley SNP linkage map. Haplotypes within a set of 17 starch biosynthesis/degradation genes were defined, and a particularly high level of haplotype variation was uncovered in the genes encoding sucrose synthase (types I and II and starch synthase. The ability of a panel of 50 barley accessions to maintain grain starch content under terminal drought conditions was explored. Conclusion The linkage/expression map is an informative resource in the context of characterizing the response of barley to drought stress. The high level of haplotype variation among starch biosynthesis/degradation genes in the progenitors of cultivated barley shows that domestication and breeding have greatly eroded their allelic diversity in current elite cultivars. Prospective association analysis based on core drought-regulated genes may simplify the process of identifying favourable alleles, and help to understand the genetic basis of the response to terminal drought.

Digital Gene Expression Profiling Analysis of Aged Mice under Moxibustion Treatment

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Nan Liu

2018-01-01

Full Text Available Aging is closely connected with death, progressive physiological decline, and increased risk of diseases, such as cancer, arteriosclerosis, heart disease, hypertension, and neurodegenerative diseases. It is reported that moxibustion can treat more than 300 kinds of diseases including aging related problems and can improve immune function and physiological functions. The digital gene expression profiling of aged mice with or without moxibustion treatment was investigated and the mechanisms of moxibustion in aged mice were speculated by gene ontology and pathway analysis in the study. Almost 145 million raw reads were obtained by digital gene expression analysis and about 140 million (96.55% were clean reads. Five differentially expressed genes with an adjusted P value 1 were identified between the control and moxibustion groups. They were Gm6563, Gm8116, Rps26-ps1, Nat8f4, and Igkv3-12. Gene ontology analysis was carried out by the GOseq R package and functional annotations of the differentially expressed genes related to translation, mRNA export from nucleus, mRNA transport, nuclear body, acetyltransferase activity, and so on. Kyoto Encyclopedia of Genes and Genomes database was used for pathway analysis and ribosome was the most significantly enriched pathway term.

Identification of distinct genes associated with seawater aspiration-induced acute lung injury by gene expression profile analysis

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Liu, Wei; Pan, Lei; Zhang, Minlong; Bo, Liyan; Li, Congcong; Liu, Qingqing; Wang, Li; Jin, Faguang

2016-01-01

Seawater aspiration-induced acute lung injury (ALI) is a syndrome associated with a high mortality rate, which is characterized by severe hypoxemia, pulmonary edema and inflammation. The present study is the first, to the best of our knowledge, to analyze gene expression profiles from a rat model of seawater aspiration-induced ALI. Adult male Sprague-Dawley rats were instilled with seawater (4 ml/kg) in the seawater aspiration-induced ALI group (S group) or with distilled water (4 ml/kg) in the distilled water negative control group (D group). In the blank control group (C group) the rats' tracheae were exposed without instillation. Subsequently, lung samples were examined by histopathology; total protein concentration was detected in bronchoalveolar lavage fluid (BALF); lung wet/dry weight ratios were determined; and transcript expression was detected by gene sequencing analysis. The results demonstrated that histopathological alterations, pulmonary edema and total protein concentrations in BALF were increased in the S group compared with in the D group. Analysis of differential gene expression identified up and downregulated genes in the S group compared with in the D and C groups. A gene ontology analysis of the differential gene expression revealed enrichment of genes in the functional pathways associated with neutrophil chemotaxis, immune and defense responses, and cytokine activity. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the cytokine-cytokine receptor interaction pathway was one of the most important pathways involved in seawater aspiration-induced ALI. In conclusion, activation of the cytokine-cytokine receptor interaction pathway may have an essential role in the progression of seawater aspiration-induced ALI, and the downregulation of tumor necrosis factor superfamily member 10 may enhance inflammation. Furthermore, IL-6 may be considered a biomarker in seawater aspiration-induced ALI. PMID:27509884

Qualitative uncertainty analysis in probabilistic safety assessment context

International Nuclear Information System (INIS)

Apostol, M.; Constantin, M; Turcu, I.

2007-01-01

In Probabilistic Safety Assessment (PSA) context, an uncertainty analysis is performed either to estimate the uncertainty in the final results (the risk to public health and safety) or to estimate the uncertainty in some intermediate quantities (the core damage frequency, the radionuclide release frequency or fatality frequency). The identification and evaluation of uncertainty are important tasks because they afford credit to the results and help in the decision-making process. Uncertainty analysis can be performed qualitatively or quantitatively. This paper performs a preliminary qualitative uncertainty analysis, by identification of major uncertainty in PSA level 1- level 2 interface and in the other two major procedural steps of a level 2 PSA i.e. the analysis of accident progression and of the containment and analysis of source term for severe accidents. One should mention that a level 2 PSA for a Nuclear Power Plant (NPP) involves the evaluation and quantification of the mechanisms, amount and probabilities of subsequent radioactive material releases from the containment. According to NUREG 1150, an important task in source term analysis is fission products transport analysis. The uncertainties related to the isotopes distribution in CANDU NPP primary circuit and isotopes' masses transferred in the containment, using SOPHAEROS module from ASTEC computer code will be also presented. (authors)

Mercury-induced hepatotoxicity in zebrafish: in vivo mechanistic insights from transcriptome analysis, phenotype anchoring and targeted gene expression validation

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Mathavan Sinnakaruppan

2010-03-01

Full Text Available Abstract Background Mercury is a prominent environmental contaminant that causes detrimental effects to human health. Although the liver has been known to be a main target organ, there is limited information on in vivo molecular mechanism of mercury-induced toxicity in the liver. By using transcriptome analysis, phenotypic anchoring and validation of targeted gene expression in zebrafish, mercury-induced hepatotoxicity was investigated and a number of perturbed cellular processes were identified and compared with those captured in the in vitro human cell line studies. Results Hepato-transcriptome analysis of mercury-exposed zebrafish revealed that the earliest deregulated genes were associated with electron transport chain, mitochondrial fatty acid beta-oxidation, nuclear receptor signaling and apoptotic pathway, followed by complement system and proteasome pathway, and thereafter DNA damage, hypoxia, Wnt signaling, fatty acid synthesis, gluconeogenesis, cell cycle and motility. Comparative meta-analysis of microarray data between zebrafish liver and human HepG2 cells exposed to mercury identified some common toxicological effects of mercury-induced hepatotoxicity in both models. Histological analyses of liver from mercury-exposed fish revealed morphological changes of liver parenchyma, decreased nucleated cell count, increased lipid vesicles, glycogen and apoptotic bodies, thus providing phenotypic evidence for anchoring of the transcriptome analysis. Validation of targeted gene expression confirmed deregulated gene-pathways from enrichment analysis. Some of these genes responding to low concentrations of mercury may serve as toxicogenomic-based markers for detection and health risk assessment of environmental mercury contaminations. Conclusion Mercury-induced hepatotoxicity was triggered by oxidative stresses, intrinsic apoptotic pathway, deregulation of nuclear receptor and kinase activities including Gsk3 that deregulates Wnt signaling

The CanOE strategy: integrating genomic and metabolic contexts across multiple prokaryote genomes to find candidate genes for orphan enzymes.

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Adam Alexander Thil Smith

2012-05-01

Full Text Available Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes, a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short. The first step locates "genomic metabolons", i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12.

BGDB: a database of bivalent genes.

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Li, Qingyan; Lian, Shuabin; Dai, Zhiming; Xiang, Qian; Dai, Xianhua

2013-01-01

Bivalent gene is a gene marked with both H3K4me3 and H3K27me3 epigenetic modification in the same area, and is proposed to play a pivotal role related to pluripotency in embryonic stem (ES) cells. Identification of these bivalent genes and understanding their functions are important for further research of lineage specification and embryo development. So far, lots of genome-wide histone modification data were generated in mouse and human ES cells. These valuable data make it possible to identify bivalent genes, but no comprehensive data repositories or analysis tools are available for bivalent genes currently. In this work, we develop BGDB, the database of bivalent genes. The database contains 6897 bivalent genes in human and mouse ES cells, which are manually collected from scientific literature. Each entry contains curated information, including genomic context, sequences, gene ontology and other relevant information. The web services of BGDB database were implemented with PHP + MySQL + JavaScript, and provide diverse query functions. Database URL: http://dailab.sysu.edu.cn/bgdb/

Identification of novel risk genes associated with type 1 diabetes mellitus using a genome-wide gene-based association analysis.

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Qiu, Ying-Hua; Deng, Fei-Yan; Li, Min-Jing; Lei, Shu-Feng

2014-11-01

Type 1 diabetes mellitus is a serious disorder characterized by destruction of pancreatic β-cells, culminating in absolute insulin deficiency. Genetic factors contribute to the susceptibility of type 1 diabetes mellitus. The aim of the present study was to identify more susceptibility genes of type 1 diabetes mellitus. We carried out an initial gene-based genome-wide association study in a total of 4,075 type 1 diabetes mellitus cases and 2,604 controls by using the Gene-based Association Test using Extended Simes procedure. Furthermore, we carried out replication studies, differential expression analysis and functional annotation clustering analysis to support the significance of the identified susceptibility genes. We identified 452 genes associated with type 1 diabetes mellitus, even after adapting the genome-wide threshold for significance (P diabetes mellitus, which were ignored in single-nucleotide polymorphism-based association analysis and were not previously reported. We found that 53 genes have supportive evidence from replication studies and/or differential expression studies. In particular, seven genes including four non-human leukocyte antigen (HLA) genes (RASIP1, STRN4, BCAR1 and MYL2) are replicated in at least one independent population and also differentially expressed in peripheral blood mononuclear cells or monocytes. Furthermore, the associated genes tend to enrich in immune-related pathways or Gene Ontology project terms. The present results suggest the high power of gene-based association analysis in detecting disease-susceptibility genes. Our findings provide more insights into the genetic basis of type 1 diabetes mellitus.

Serial analysis of gene expression (SAGE) in rat liver regeneration

International Nuclear Information System (INIS)

Cimica, Velasco; Batusic, Danko; Haralanova-Ilieva, Borislava; Chen, Yonglong; Hollemann, Thomas; Pieler, Tomas; Ramadori, Giuliano

2007-01-01

We have applied serial analysis of gene expression for studying the molecular mechanism of the rat liver regeneration in the model of 70% partial hepatectomy. We generated three SAGE libraries from a normal control liver (NL library: 52,343 tags), from a sham control operated liver (Sham library: 51,028 tags), and from a regenerating liver (PH library: 53,061 tags). By SAGE bioinformatics analysis we identified 40 induced genes and 20 repressed genes during the liver regeneration. We verified temporal expression of such genes by real time PCR during the regeneration process and we characterized 13 induced genes and 3 repressed genes. We found connective tissue growth factor transcript and protein induced very early at 4 h after PH operation before hepatocytes proliferation is triggered. Our study suggests CTGF as a growth factor signaling mediator that could be involved directly in the mechanism of liver regeneration induction

Communicating in context: a priority for gene therapy researchers.

Science.gov (United States)

Robillard, Julie M

2015-03-01

History shows that public opinion of emerging biotechnologies has the potential to impact the research process through mechanisms such as funding and advocacy. It is critical, therefore, to consider public attitudes towards modern biotechnology such as gene therapy and more specifically towards the ethics of gene therapy, alongside advances in basic and clinical research. Research conducted through social media recently assessed how online users view the ethics of gene therapy and showed that while acceptability is high, significant ethical concerns remain. To address these concerns, the development of effective and evidence-based communication strategies that engage a wide range of stakeholders should be a priority for researchers.

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

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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.

Microarray Data Analysis of Space Grown Arabidopsis Leaves for Genes Important in Vascular Patterning. Analysis of Space Grown Arabidopsis with Microarray Data from GeneLab: Identification of Genes Important in Vascular Patterning

Science.gov (United States)

Weitzel, A. J.; Wyatt, S. E.; Parsons-Wingerter, P.

2016-01-01

Venation patterning in leaves is a major determinant of photosynthesis efficiency because of its dependency on vascular transport of photo-assimilates, water, and minerals. Arabidopsis thaliana grown in microgravity show delayed growth and leaf maturation. Gene expression data from the roots, hypocotyl, and leaves of A. thaliana grown during spaceflight vs. ground control analyzed by Affymetrix microarray are available through NASA's GeneLab (GLDS-7). We analyzed the data for differential expression of genes in leaves resulting from the effects of spaceflight on vascular patterning. Two genes were found by preliminary analysis to be up-regulated during spaceflight that may be related to vascular formation. The genes are responsible for coding an ARGOS (Auxin-Regulated Gene Involved in Organ Size)-like protein (potentially affecting cell elongation in the leaves), and an F-box/kelch-repeat protein (possibly contributing to protoxylem specification). Further analysis that will focus on raw data quality assessment and a moderated t-test may further confirm up-regulation of the two genes and/or identify other gene candidates. Plants defective in these genes will then be assessed for phenotype by the mapping and quantification of leaf vascular patterning by NASA's VESsel GENeration (VESGEN) software to model specific vascular differences of plants grown in spaceflight.

The Reconstruction and Analysis of Gene Regulatory Networks.

Science.gov (United States)

Zheng, Guangyong; Huang, Tao

2018-01-01

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

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

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Marie Mooney

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

Digital gene expression analysis of Microsporum canis exposed to berberine chloride.

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Chen-Wen Xiao

Full Text Available Berberine, a natural isoquinoline alkaloid of many medicinal herbs, has an active function against a variety of microbial infections including Microsporum canis (M. canis. However, the underlying mechanisms are poorly understood. To study the effect of berberine chloride on M. canis infection, a Digital Gene Expression (DGE tag profiling was constructed and a transcriptome analysis of the M. canis cellular responses upon berberine treatment was performed. Illumina/Hisseq sequencing technique was used to generate the data of gene expression profile, and the following enrichment analysis of Gene Ontology (GO and Pathway function were conducted based on the data of transcriptome. The results of DGE showed that there were 8476945, 14256722, 7708575, 5669955, 6565513 and 9303468 tags respectively, which was obtained from M. canis incubated with berberine or control DMSO. 8,783 genes were totally mapped, and 1,890 genes have shown significant changes between the two groups. 1,030 genes were up-regulated and 860 genes were down-regulated (P<0.05 in berberine treated group compared to the control group. Besides, twenty-three GO terms were identified by Gene Ontology functional enrichment analysis, such as calcium-transporting ATPase activity, 2-oxoglutarate metabolic process, valine catabolic process, peroxisome and unfolded protein binding. Pathway significant enrichment analysis indicated 6 signaling pathways that are significant, including steroid biosynthesis, steroid hormone biosynthesis, Parkinson's disease, 2,4-Dichlorobenzoate degradation, and tropane, piperidine and Isoquinoline alkaloid biosynthesis. Among these, eleven selected genes were further verified by qRT-PCR. Our findings provide a comprehensive view on the gene expression profile of M. canis upon berberine treatment, and shed light on its complicated effects on M. canis.

Global gene expression analysis of the zoonotic parasite Trichinella spiralis revealed novel genes in host parasite interaction.

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Xiaolei Liu

Full Text Available BACKGROUND: Trichinellosis is a typical food-borne zoonotic disease which is epidemic worldwide and the nematode Trichinella spiralis is the main pathogen. The life cycle of T. spiralis contains three developmental stages, i.e. adult worms, new borne larva (new borne L1 larva and muscular larva (infective L1 larva. Stage-specific gene expression in the parasites has been investigated with various immunological and cDNA cloning approaches, whereas the genome-wide transcriptome and expression features of the parasite have been largely unknown. The availability of the genome sequence information of T. spiralis has made it possible to deeply dissect parasite biology in association with global gene expression and pathogenesis. METHODOLOGY AND PRINCIPAL FINDINGS: In this study, we analyzed the global gene expression patterns in the three developmental stages of T. spiralis using digital gene expression (DGE analysis. Almost 15 million sequence tags were generated with the Illumina RNA-seq technology, producing expression data for more than 9,000 genes, covering 65% of the genome. The transcriptome analysis revealed thousands of differentially expressed genes within the genome, and importantly, a panel of genes encoding functional proteins associated with parasite invasion and immuno-modulation were identified. More than 45% of the genes were found to be transcribed from both strands, indicating the importance of RNA-mediated gene regulation in the development of the parasite. Further, based on gene ontological analysis, over 3000 genes were functionally categorized and biological pathways in the three life cycle stage were elucidated. CONCLUSIONS AND SIGNIFICANCE: The global transcriptome of T. spiralis in three developmental stages has been profiled, and most gene activity in the genome was found to be developmentally regulated. Many metabolic and biological pathways have been revealed. The findings of the differential expression of several protein

Genome-wide analysis of immune system genes by EST profiling

Science.gov (United States)

Giallourakis, Cosmas; Benita, Yair; Molinie, Benoit; Cao, Zhifang; Despo, Orion; Pratt, Henry E.; Zukerberg, Lawrence R.; Daly, Mark J.; Rioux, John D.; Xavier, Ramnik J.

2013-01-01

Profiling studies of mRNA and miRNA, particularly microarray-based studies, have been extensively used to create compendia of genes that are preferentially expressed in the immune system. In some instances, functional studies have been subsequently pursued. Recent efforts such as ENCODE have demonstrated the benefit of coupling RNA-Seq analysis with information from expressed sequence tags (ESTs) for transcriptomic analysis. However, the full characterization and identification of transcripts that function as modulators of human immune responses remains incomplete. In this study, we demonstrate that an integrated analysis of human ESTs provides a robust platform to identify the immune transcriptome. Beyond recovering a reference set of immune-enriched genes and providing large-scale cross-validation of previous microarray studies, we discovered hundreds of novel genes preferentially expressed in the immune system, including non-coding RNAs. As a result, we have established the Immunogene database, representing an integrated EST “road map” of gene expression in human immune cells, which can be used to further investigate the function of coding and non-coding genes in the immune system. Using this approach, we have uncovered a unique metabolic gene signature of human macrophages and identified PRDM15 as a novel overexpressed gene in human lymphomas. Thus we demonstrate the utility of EST profiling as a basis for further deconstruction of physiologic and pathologic immune processes. PMID:23616578

Genome-Wide Analysis of the Aquaporin Gene Family in Chickpea (Cicer arietinum L.).

Science.gov (United States)

Deokar, Amit A; Tar'an, Bunyamin

2016-01-01

Aquaporins (AQPs) are essential membrane proteins that play critical role in the transport of water and many other solutes across cell membranes. In this study, a comprehensive genome-wide analysis identified 40 AQP genes in chickpea ( Cicer arietinum L.). A complete overview of the chickpea AQP (CaAQP) gene family is presented, including their chromosomal locations, gene structure, phylogeny, gene duplication, conserved functional motifs, gene expression, and conserved promoter motifs. To understand AQP's evolution, a comparative analysis of chickpea AQPs with AQP orthologs from soybean, Medicago, common bean, and Arabidopsis was performed. The chickpea AQP genes were found on all of the chickpea chromosomes, except chromosome 7, with a maximum of six genes on chromosome 6, and a minimum of one gene on chromosome 5. Gene duplication analysis indicated that the expansion of chickpea AQP gene family might have been due to segmental and tandem duplications. CaAQPs were grouped into four subfamilies including 15 NOD26-like intrinsic proteins (NIPs), 13 tonoplast intrinsic proteins (TIPs), eight plasma membrane intrinsic proteins (PIPs), and four small basic intrinsic proteins (SIPs) based on sequence similarities and phylogenetic position. Gene structure analysis revealed a highly conserved exon-intron pattern within CaAQP subfamilies supporting the CaAQP family classification. Functional prediction based on conserved Ar/R selectivity filters, Froger's residues, and specificity-determining positions suggested wide differences in substrate specificity among the subfamilies of CaAQPs. Expression analysis of the AQP genes indicated that some of the genes are tissue-specific, whereas few other AQP genes showed differential expression in response to biotic and abiotic stresses. Promoter profiling of CaAQP genes for conserved cis -acting regulatory elements revealed enrichment of cis -elements involved in circadian control, light response, defense and stress responsiveness

Spectral map-analysis: a method to analyze gene expression data

OpenAIRE

Bijnens, Luc J.M.; Lewi, Paul J.; Göhlmann, Hinrich W.; Molenberghs, Geert; Wouters, Luc

2004-01-01

bioinformatics; biplot; correspondence factor analysis; data mining; data visualization; gene expression data; microarray data; multivariate exploratory data analysis; principal component analysis; Spectral map analysis

A systematic study of genome context methods: calibration, normalization and combination

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Dale Joseph M

2010-10-01

Full Text Available Abstract Background Genome context methods have been introduced in the last decade as automatic methods to predict functional relatedness between genes in a target genome using the patterns of existence and relative locations of the homologs of those genes in a set of reference genomes. Much work has been done in the application of these methods to different bioinformatics tasks, but few papers present a systematic study of the methods and their combination necessary for their optimal use. Results We present a thorough study of the four main families of genome context methods found in the literature: phylogenetic profile, gene fusion, gene cluster, and gene neighbor. We find that for most organisms the gene neighbor method outperforms the phylogenetic profile method by as much as 40% in sensitivity, being competitive with the gene cluster method at low sensitivities. Gene fusion is generally the worst performing of the four methods. A thorough exploration of the parameter space for each method is performed and results across different target organisms are presented. We propose the use of normalization procedures as those used on microarray data for the genome context scores. We show that substantial gains can be achieved from the use of a simple normalization technique. In particular, the sensitivity of the phylogenetic profile method is improved by around 25% after normalization, resulting, to our knowledge, on the best-performing phylogenetic profile system in the literature. Finally, we show results from combining the various genome context methods into a single score. When using a cross-validation procedure to train the combiners, with both original and normalized scores as input, a decision tree combiner results in gains of up to 20% with respect to the gene neighbor method. Overall, this represents a gain of around 15% over what can be considered the state of the art in this area: the four original genome context methods combined using a

Comprehensive analysis of the flowering genes in Chinese cabbage and examination of evolutionary pattern of CO-like genes in plant kingdom

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Song, Xiaoming; Duan, Weike; Huang, Zhinan; Liu, Gaofeng; Wu, Peng; Liu, Tongkun; Li, Ying; Hou, Xilin

2015-09-01

In plants, flowering is the most important transition from vegetative to reproductive growth. The flowering patterns of monocots and eudicots are distinctly different, but few studies have described the evolutionary patterns of the flowering genes in them. In this study, we analysed the evolutionary pattern, duplication and expression level of these genes. The main results were as follows: (i) characterization of flowering genes in monocots and eudicots, including the identification of family-specific, orthologous and collinear genes; (ii) full characterization of CONSTANS-like genes in Brassica rapa (BraCOL genes), the key flowering genes; (iii) exploration of the evolution of COL genes in plant kingdom and construction of the evolutionary pattern of COL genes; (iv) comparative analysis of CO and FT genes between Brassicaceae and Grass, which identified several family-specific amino acids, and revealed that CO and FT protein structures were similar in B. rapa and Arabidopsis but different in rice; and (v) expression analysis of photoperiod pathway-related genes in B. rapa under different photoperiod treatments by RT-qPCR. This analysis will provide resources for understanding the flowering mechanisms and evolutionary pattern of COL genes. In addition, this genome-wide comparative study of COL genes may also provide clues for evolution of other flowering genes.

Gene function analysis by artificial microRNAs in Physcomitrella patens.

KAUST Repository

Khraiwesh, Basel

2011-01-01

MicroRNAs (miRNAs) are ~21 nt long small RNAs transcribed from endogenous MIR genes which form precursor RNAs with a characteristic hairpin structure. miRNAs control the expression of cognate target genes by binding to reverse complementary sequences resulting in cleavage or translational inhibition of the target RNA. Artificial miRNAs (amiRNAs) can be generated by exchanging the miRNA/miRNA sequence of endogenous MIR precursor genes, while maintaining the general pattern of matches and mismatches in the foldback. Thus, for functional gene analysis amiRNAs can be designed to target any gene of interest. During the last decade the moss Physcomitrella patens emerged as a model plant for functional gene analysis based on its unique ability to integrate DNA into the nuclear genome by homologous recombination which allows for the generation of targeted gene knockout mutants. In addition to this, we developed a protocol to express amiRNAs in P. patens that has particular advantages over the generation of knockout mutants and might be used to speed up reverse genetics approaches in this model species.

Screening Key Genes Associated with the Development and Progression of Non-small Cell Lung Cancer Based on Gene-enrichment Analysis and Meta-analysis

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Wenwu HE

2012-07-01

Full Text Available Background and objective Non-small cell lung cancer (NSCLC is one of the most common malignant tumors; however, its causes are still not completely understood. This study was designed to screen the key genes and pathways related to NSCLC occurrence and development and to establish the scientific foundation for the genetic mechanisms and targeted therapy of NSCLC. Methods Both gene set-enrichment analysis (GSEA and meta-analysis (meta were used to screen the critical pathways and genes that might be corretacted with the development and progression of lung cancer at the transcription level. Results Using the GSEA and meta methods, focal adhesion and regulation of actin cytoskeleton were determined to be the more prominent overlapping significant pathways. In the focal adhesion pathway, 31 genes were statistically significant (P<0.05, whereas in the regulation of actin cytoskeleton pathway, 32 genes were statistically significant (P<0.05. Conclusion The focal adhesion and the regulation of actin cytoskeleton pathways might play important roles in the occurrence and development of NSCLC. Further studies are needed to determine the biological function for the positiue genes.

Gene set analysis of the EADGENE chicken data-set

DEFF Research Database (Denmark)

Skarman, Axel; Jiang, Li; Hornshøj, Henrik

2009-01-01

 Abstract Background: Gene set analysis is considered to be a way of improving our biological interpretation of the observed expression patterns. This paper describes different methods applied to analyse expression data from a chicken DNA microarray dataset. Results: Applying different gene set...... analyses to the chicken expression data led to different ranking of the Gene Ontology terms tested. A method for prediction of possible annotations was applied. Conclusion: Biological interpretation based on gene set analyses dependent on the statistical method used. Methods for predicting the possible...

GECKO: a complete large-scale gene expression analysis platform

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Heuer Michael

2004-12-01

Full Text Available Abstract Background Gecko (Gene Expression: Computation and Knowledge Organization is a complete, high-capacity centralized gene expression analysis system, developed in response to the needs of a distributed user community. Results Based on a client-server architecture, with a centralized repository of typically many tens of thousands of Affymetrix scans, Gecko includes automatic processing pipelines for uploading data from remote sites, a data base, a computational engine implementing ~ 50 different analysis tools, and a client application. Among available analysis tools are clustering methods, principal component analysis, supervised classification including feature selection and cross-validation, multi-factorial ANOVA, statistical contrast calculations, and various post-processing tools for extracting data at given error rates or significance levels. On account of its open architecture, Gecko also allows for the integration of new algorithms. The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. A unique feature of the Gecko architecture is the concept of the Analysis Tree (actually, a directed acyclic graph, in which all successive results in ongoing analyses are saved. This approach has proven invaluable in allowing a large (~ 100 users and distributed community to share results, and to repeatedly return over a span of years to older and potentially very complex analyses of gene expression data. Conclusions The Gecko system is being made publicly available as free software http://sourceforge.net/projects/geckoe. In totality or in parts, the Gecko framework should prove useful to users and system developers with a broad range of analysis needs.

Operation Context

DEFF Research Database (Denmark)

Stüben, Henning; Tietjen, Anne

2006-01-01

Abstract: This paper seeks to challenge the notion of context from an operational perspective. Can we grasp the forces that shape the complex conditions for an architectural or urban design within the notion of context? By shifting the gaze towards the agency of architecture, contextual analysis...

Gene profile analysis of osteoblast genes differentially regulated by histone deacetylase inhibitors

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Lamblin Anne-Francoise

2007-10-01

Full Text Available Abstract Background Osteoblast differentiation requires the coordinated stepwise expression of multiple genes. Histone deacetylase inhibitors (HDIs accelerate the osteoblast differentiation process by blocking the activity of histone deacetylases (HDACs, which alter gene expression by modifying chromatin structure. We previously demonstrated that HDIs and HDAC3 shRNAs accelerate matrix mineralization and the expression of osteoblast maturation genes (e.g. alkaline phosphatase, osteocalcin. Identifying other genes that are differentially regulated by HDIs might identify new pathways that contribute to osteoblast differentiation. Results To identify other osteoblast genes that are altered early by HDIs, we incubated MC3T3-E1 preosteoblasts with HDIs (trichostatin A, MS-275, or valproic acid for 18 hours in osteogenic conditions. The promotion of osteoblast differentiation by HDIs in this experiment was confirmed by osteogenic assays. Gene expression profiles relative to vehicle-treated cells were assessed by microarray analysis with Affymetrix GeneChip 430 2.0 arrays. The regulation of several genes by HDIs in MC3T3-E1 cells and primary osteoblasts was verified by quantitative real-time PCR. Nine genes were differentially regulated by at least two-fold after exposure to each of the three HDIs and six were verified by PCR in osteoblasts. Four of the verified genes (solute carrier family 9 isoform 3 regulator 1 (Slc9a3r1, sorbitol dehydrogenase 1, a kinase anchor protein, and glutathione S-transferase alpha 4 were induced. Two genes (proteasome subunit, beta type 10 and adaptor-related protein complex AP-4 sigma 1 were suppressed. We also identified eight growth factors and growth factor receptor genes that are significantly altered by each of the HDIs, including Frizzled related proteins 1 and 4, which modulate the Wnt signaling pathway. Conclusion This study identifies osteoblast genes that are regulated early by HDIs and indicates pathways that

Genome-scale analysis of positional clustering of mouse testis-specific genes

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Lee Bernett TK

2005-01-01

Full Text Available Abstract Background Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes. Results Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats. Conclusion Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

Evolutionary Analysis of Minor Histocompatibility Genes In Hydra

KAUST Repository

Aalismail, Nojood

2016-01-01

In the present study we took initiative to study the self/nonself recognition in hydra and its relation to the immune response. Moreover, performing phylogenetic analysis to look for annotated immune genes in hydra gave us a potential to analyze the expression of minor histocompatibility genes that have been shown to play a major role in grafting and transplantation in mammals. Here we obtained the cDNA library that shows expression of minor histocompatibility genes and confirmed that the annotated sequences in databases are actually present. In addition, grafting experiments suggested, although still preliminary, that homograft showed less rejection response than in heterograft. Involvement of possible minor histocompatibility gene orthologous in immune response was examined by qPCR.

Analysis of gene expression profile microarray data in complex regional pain syndrome.

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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.

Gene expression analysis of flax seed development

Science.gov (United States)

2011-01-01

Background Flax, Linum usitatissimum L., is an important crop whose seed oil and stem fiber have multiple industrial applications. Flax seeds are also well-known for their nutritional attributes, viz., omega-3 fatty acids in the oil and lignans and mucilage from the seed coat. In spite of the importance of this crop, there are few molecular resources that can be utilized toward improving seed traits. Here, we describe flax embryo and seed development and generation of comprehensive genomic resources for the flax seed. Results We describe a large-scale generation and analysis of expressed sequences in various tissues. Collectively, the 13 libraries we have used provide a broad representation of genes active in developing embryos (globular, heart, torpedo, cotyledon and mature stages) seed coats (globular and torpedo stages) and endosperm (pooled globular to torpedo stages) and genes expressed in flowers, etiolated seedlings, leaves, and stem tissue. A total of 261,272 expressed sequence tags (EST) (GenBank accessions LIBEST_026995 to LIBEST_027011) were generated. These EST libraries included transcription factor genes that are typically expressed at low levels, indicating that the depth is adequate for in silico expression analysis. Assembly of the ESTs resulted in 30,640 unigenes and 82% of these could be identified on the basis of homology to known and hypothetical genes from other plants. When compared with fully sequenced plant genomes, the flax unigenes resembled poplar and castor bean more than grape, sorghum, rice or Arabidopsis. Nearly one-fifth of these (5,152) had no homologs in sequences reported for any organism, suggesting that this category represents genes that are likely unique to flax. Digital analyses revealed gene expression dynamics for the biosynthesis of a number of important seed constituents during seed development. Conclusions We have developed a foundational database of expressed sequences and collection of plasmid clones that comprise

Gene expression analysis of flax seed development

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Sharpe Andrew

2011-04-01

Full Text Available Abstract Background Flax, Linum usitatissimum L., is an important crop whose seed oil and stem fiber have multiple industrial applications. Flax seeds are also well-known for their nutritional attributes, viz., omega-3 fatty acids in the oil and lignans and mucilage from the seed coat. In spite of the importance of this crop, there are few molecular resources that can be utilized toward improving seed traits. Here, we describe flax embryo and seed development and generation of comprehensive genomic resources for the flax seed. Results We describe a large-scale generation and analysis of expressed sequences in various tissues. Collectively, the 13 libraries we have used provide a broad representation of genes active in developing embryos (globular, heart, torpedo, cotyledon and mature stages seed coats (globular and torpedo stages and endosperm (pooled globular to torpedo stages and genes expressed in flowers, etiolated seedlings, leaves, and stem tissue. A total of 261,272 expressed sequence tags (EST (GenBank accessions LIBEST_026995 to LIBEST_027011 were generated. These EST libraries included transcription factor genes that are typically expressed at low levels, indicating that the depth is adequate for in silico expression analysis. Assembly of the ESTs resulted in 30,640 unigenes and 82% of these could be identified on the basis of homology to known and hypothetical genes from other plants. When compared with fully sequenced plant genomes, the flax unigenes resembled poplar and castor bean more than grape, sorghum, rice or Arabidopsis. Nearly one-fifth of these (5,152 had no homologs in sequences reported for any organism, suggesting that this category represents genes that are likely unique to flax. Digital analyses revealed gene expression dynamics for the biosynthesis of a number of important seed constituents during seed development. Conclusions We have developed a foundational database of expressed sequences and collection of plasmid

Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean) Cattle.

Science.gov (United States)

Lim, Dajeong; Lee, Seung-Hwan; Kim, Nam-Kuk; Cho, Yong-Min; Chai, Han-Ha; Seong, Hwan-Hoo; Kim, Heebal

2013-01-01

Marbling (intramuscular fat) is an important trait that affects meat quality and is a casual factor determining the price of beef in the Korean beef market. It is a complex trait and has many biological pathways related to muscle and fat. There is a need to identify functional modules or genes related to marbling traits and investigate their relationships through a weighted gene co-expression network analysis based on the system level. Therefore, we investigated the co-expression relationships of genes related to the 'marbling score' trait and systemically analyzed the network topology in Hanwoo (Korean cattle). As a result, we determined 3 modules (gene groups) that showed statistically significant results for marbling score. In particular, one module (denoted as red) has a statistically significant result for marbling score (p = 0.008) and intramuscular fat (p = 0.02) and water capacity (p = 0.006). From functional enrichment and relationship analysis of the red module, the pathway hub genes (IL6, CHRNE, RB1, INHBA and NPPA) have a direct interaction relationship and share the biological functions related to fat or muscle, such as adipogenesis or muscle growth. This is the first gene network study with m.logissimus in Hanwoo to observe co-expression patterns in divergent marbling phenotypes. It may provide insights into the functional mechanisms of the marbling trait.

Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean Cattle

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Dajeong Lim

2013-01-01

Full Text Available Marbling (intramuscular fat is an important trait that affects meat quality and is a casual factor determining the price of beef in the Korean beef market. It is a complex trait and has many biological pathways related to muscle and fat. There is a need to identify functional modules or genes related to marbling traits and investigate their relationships through a weighted gene co-expression network analysis based on the system level. Therefore, we investigated the co-expression relationships of genes related to the ‘marbling score’ trait and systemically analyzed the network topology in Hanwoo (Korean cattle. As a result, we determined 3 modules (gene groups that showed statistically significant results for marbling score. In particular, one module (denoted as red has a statistically significant result for marbling score (p = 0.008 and intramuscular fat (p = 0.02 and water capacity (p = 0.006. From functional enrichment and relationship analysis of the red module, the pathway hub genes (IL6, CHRNE, RB1, INHBA and NPPA have a direct interaction relationship and share the biological functions related to fat or muscle, such as adipogenesis or muscle growth. This is the first gene network study with m.logissimus in Hanwoo to observe co-expression patterns in divergent marbling phenotypes. It may provide insights into the functional mechanisms of the marbling trait.

Gene organization in rice revealed by full-length cDNA mapping and gene expression analysis through microarray.

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Kouji Satoh

Full Text Available Rice (Oryza sativa L. is a model organism for the functional genomics of monocotyledonous plants since the genome size is considerably smaller than those of other monocotyledonous plants. Although highly accurate genome sequences of indica and japonica rice are available, additional resources such as full-length complementary DNA (FL-cDNA sequences are also indispensable for comprehensive analyses of gene structure and function. We cross-referenced 28.5K individual loci in the rice genome defined by mapping of 578K FL-cDNA clones with the 56K loci predicted in the TIGR genome assembly. Based on the annotation status and the presence of corresponding cDNA clones, genes were classified into 23K annotated expressed (AE genes, 33K annotated non-expressed (ANE genes, and 5.5K non-annotated expressed (NAE genes. We developed a 60mer oligo-array for analysis of gene expression from each locus. Analysis of gene structures and expression levels revealed that the general features of gene structure and expression of NAE and ANE genes were considerably different from those of AE genes. The results also suggested that the cloning efficiency of rice FL-cDNA is associated with the transcription activity of the corresponding genetic locus, although other factors may also have an effect. Comparison of the coverage of FL-cDNA among gene families suggested that FL-cDNA from genes encoding rice- or eukaryote-specific domains, and those involved in regulatory functions were difficult to produce in bacterial cells. Collectively, these results indicate that rice genes can be divided into distinct groups based on transcription activity and gene structure, and that the coverage bias of FL-cDNA clones exists due to the incompatibility of certain eukaryotic genes in bacteria.

Protease of Stenotrophomonas sp. from Indonesian fermented food: gene cloning and analysis

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Frans Kurnia

2018-02-01

Full Text Available Screening of proteolytic and fibrinolytic bacteria from Indonesian soy bean based fermented food Oncom revealed several potential isolates. Based on 16s rDNA gene analysis, one particular isolate with the highest proteolytic and fibrinolytic activity was identified as Stenotrophomonas sp. The protease gene was amplified to generate a 1749 bp Polymerase Chain Reaction product and BLAST analysis, revealed 90% homology with gene encoding protease enzyme from Stenotrophomonas maltophilia. The putative amino acid sequence indicated a serine protease enzyme with typical amino acid aspartate, histidine and serine in the catalytic triad. The gene was translated into a pre-pro-protein consisted of cleavage site on its N terminal and Pre-Peptidase Cterminal domain. Cloning of the protease gene in pET22b with Escherichia coli BL21 DE3 as the host showed that the gene was expressed as insoluble protein fraction. This is the first report for analysis of protease gene from food origin Stenotrophomonas sp.

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.

Ensemble coding of context-dependent fear memory in the amygdala.

Science.gov (United States)

Orsini, Caitlin A; Yan, Chen; Maren, Stephen

2013-01-01

After fear conditioning, presenting the conditioned stimulus (CS) alone yields a context-specific extinction memory; fear is suppressed in the extinction context, but renews in any other context. The context-dependence of extinction is mediated by a brain circuit consisting of the hippocampus, prefrontal cortex (PFC) and amygdala. In the present work, we sought to determine at what level of this circuit context-dependent representations of the CS emerge. To explore this question, we used cellular compartment analysis of temporal activity by fluorescent in situ hybridization (catFISH). This method exploits the intracellular expression profile of the immediate early gene (IEG), Arc, to visualize neuronal activation patterns to two different behavioral experiences. Rats were fear conditioned in one context and extinguished in another; 24 h later, they were sequentially exposed to the CS in the extinction context and another context. Control rats were also tested in each context, but were never extinguished. We assessed Arc mRNA expression within the basal amygdala (BA), lateral amygdala (LA), ventral hippocampus (VH), prelimbic cortex (PL) and infralimbic cortex (IL). We observed that the sequential retention tests induced context-dependent patterns of Arc expression in the BA, LA, and IL of extinguished rats; this was not observed in non-extinguished controls. In general, non-extinguished animals had proportionately greater numbers of non-selective (double-labeled) neurons than extinguished animals. Collectively, these findings suggest that extinction learning results in pattern separation, particularly within the BA, in which unique neuronal ensembles represent fear memories after extinction.

Ensemble coding of context-dependent fear memory in the amygdala

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Caitlin A Orsini

2013-12-01

Full Text Available After fear conditioning, presenting the conditioned stimulus (CS alone yields a context-specific extinction memory; fear is suppressed in the extinction context, but renews in any other context. The context-dependence of extinction is mediated by a brain circuit consisting of the hippocampus, prefrontal cortex and amygdala. In the present work, we sought to determine at what level of this circuit context-dependent representations of the CS emerge. To explore this question, we used cellular compartment analysis of temporal activity by fluorescent in situ hybridization (catFISH. This method exploits the intracellular expression profile of the immediate early gene, Arc, to visualize neuronal activation patterns to two different behavioral experiences. Rats were fear conditioned in one context and extinguished in another; twenty-four hours later, they were sequentially exposed to the CS in the extinction context and another context. Control rats were also tested in each context, but were never extinguished. We assessed Arc mRNA expression within the basal amygdala (BA, lateral amygdala (LA, ventral hippocampus (VH, prelimbic cortex (PL and infralimbic cortex (IL. We observed that the sequential retention tests induced context-dependent patterns of Arc expression in the BA, LA, and IL of extinguished rats; this was not observed in non-extinguished controls. In general, non-extinguished animals had proportionately greater numbers of non-selective (double-labeled neurons than extinguished animals. Collectively, these findings suggest that extinction learning results in pattern separation, particularly within the BA, in which unique neuronal ensembles represent fear memories after extinction.

The identification of functional motifs in temporal gene expression analysis

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Michael G. Surette

2005-01-01

Full Text Available The identification of transcription factor binding sites is essential to the understanding of the regulation of gene expression and the reconstruction of genetic regulatory networks. The in silico identification of cis-regulatory motifs is challenging due to sequence variability and lack of sufficient data to generate consensus motifs that are of quantitative or even qualitative predictive value. To determine functional motifs in gene expression, we propose a strategy to adopt false discovery rate (FDR and estimate motif effects to evaluate combinatorial analysis of motif candidates and temporal gene expression data. The method decreases the number of predicted motifs, which can then be confirmed by genetic analysis. To assess the method we used simulated motif/expression data to evaluate parameters. We applied this approach to experimental data for a group of iron responsive genes in Salmonella typhimurium 14028S. The method identified known and potentially new ferric-uptake regulator (Fur binding sites. In addition, we identified uncharacterized functional motif candidates that correlated with specific patterns of expression. A SAS code for the simulation and analysis gene expression data is available from the first author upon request.

Codon usage vis-a-vis start and stop codon context analysis of three ...

Indian Academy of Sciences (India)

To understand the variation in genomic composition and its effect on codon usage, we performed the comparative analysis of codon usage and nucleotide usage in the genes of three dicots, Glycine max, Arabidopsis thaliana and Medicago truncatula. The dicot genes were found to be A/T rich and have predominantly ...

Gene dosage, expression, and ontology analysis identifies driver genes in the carcinogenesis and chemoradioresistance of cervical cancer.

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Malin Lando

2009-11-01

Full Text Available Integrative analysis of gene dosage, expression, and ontology (GO data was performed to discover driver genes in the carcinogenesis and chemoradioresistance of cervical cancers. Gene dosage and expression profiles of 102 locally advanced cervical cancers were generated by microarray techniques. Fifty-two of these patients were also analyzed with the Illumina expression method to confirm the gene expression results. An independent cohort of 41 patients was used for validation of gene expressions associated with clinical outcome. Statistical analysis identified 29 recurrent gains and losses and 3 losses (on 3p, 13q, 21q associated with poor outcome after chemoradiotherapy. The intratumor heterogeneity, assessed from the gene dosage profiles, was low for these alterations, showing that they had emerged prior to many other alterations and probably were early events in carcinogenesis. Integration of the alterations with gene expression and GO data identified genes that were regulated by the alterations and revealed five biological processes that were significantly overrepresented among the affected genes: apoptosis, metabolism, macromolecule localization, translation, and transcription. Four genes on 3p (RYBP, GBE1 and 13q (FAM48A, MED4 correlated with outcome at both the gene dosage and expression level and were satisfactorily validated in the independent cohort. These integrated analyses yielded 57 candidate drivers of 24 genetic events, including novel loci responsible for chemoradioresistance. Further mapping of the connections among genetic events, drivers, and biological processes suggested that each individual event stimulates specific processes in carcinogenesis through the coordinated control of multiple genes. The present results may provide novel therapeutic opportunities of both early and advanced stage cervical cancers.

Prioritization of epilepsy associated candidate genes by convergent analysis.

Science.gov (United States)

Jia, Peilin; Ewers, Jeffrey M; Zhao, Zhongming

2011-02-24

Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research. In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways. The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be

Prioritization of epilepsy associated candidate genes by convergent analysis.

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Peilin Jia

2011-02-01

Full Text Available Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs that are more likely to be associated with epilepsy. The responsible gene(s within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research.In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways.The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The

Candidate Gene Identification of Flowering Time Genes in Cotton

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Corrinne E. Grover

2015-07-01

Full Text Available Flowering time control is critically important to all sexually reproducing angiosperms in both natural ecological and agronomic settings. Accordingly, there is much interest in defining the genes involved in the complex flowering-time network and how these respond to natural and artificial selection, the latter often entailing transitions in day-length responses. Here we describe a candidate gene analysis in the cotton genus , which uses homologs from the well-described flowering network to bioinformatically and phylogenetically identify orthologs in the published genome sequence from Ulbr., one of the two model diploid progenitors of the commercially important allopolyploid cottons, L. and L. Presence and patterns of expression were evaluated from 13 aboveground tissues related to flowering for each of the candidate genes using allopolyploid as a model. Furthermore, we use a comparative context to determine copy number variability of each key gene family across 10 published angiosperm genomes. Data suggest a pattern of repeated loss of duplicates following ancient whole-genome doubling events in diverse lineages. The data presented here provide a foundation for understanding both the parallel evolution of day-length neutrality in domesticated cottons and the flowering-time network, in general, in this important crop plant.

Screening key candidate genes and pathways involved in insulinoma by microarray analysis.

Science.gov (United States)

Zhou, Wuhua; Gong, Li; Li, Xuefeng; Wan, Yunyan; Wang, Xiangfei; Li, Huili; Jiang, Bin

2018-06-01

Insulinoma is a rare type tumor and its genetic features remain largely unknown. This study aimed to search for potential key genes and relevant enriched pathways of insulinoma.The gene expression data from GSE73338 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified between insulinoma tissues and normal pancreas tissues, followed by pathway enrichment analysis, protein-protein interaction (PPI) network construction, and module analysis. The expressions of candidate key genes were validated by quantitative real-time polymerase chain reaction (RT-PCR) in insulinoma tissues.A total of 1632 DEGs were obtained, including 1117 upregulated genes and 514 downregulated genes. Pathway enrichment results showed that upregulated DEGs were significantly implicated in insulin secretion, and downregulated DEGs were mainly enriched in pancreatic secretion. PPI network analysis revealed 7 hub genes with degrees more than 10, including GCG (glucagon), GCGR (glucagon receptor), PLCB1 (phospholipase C, beta 1), CASR (calcium sensing receptor), F2R (coagulation factor II thrombin receptor), GRM1 (glutamate metabotropic receptor 1), and GRM5 (glutamate metabotropic receptor 5). DEGs involved in the significant modules were enriched in calcium signaling pathway, protein ubiquitination, and platelet degranulation. Quantitative RT-PCR data confirmed that the expression trends of these hub genes were similar to the results of bioinformatic analysis.The present study demonstrated that candidate DEGs and enriched pathways were the potential critical molecule events involved in the development of insulinoma, and these findings were useful for better understanding of insulinoma genesis.

Analysis of the structural genes encoding M-factor in the fission yeast Schizosaccharomyces pombe: identification of a third gene, mfm3

DEFF Research Database (Denmark)

Kjaerulff, S; Davey, William John; Nielsen, O

1994-01-01

We previously identified two genes, mfm1 and mfm2, with the potential to encode the M-factor mating pheromone of the fission yeast Schizosaccharomyces pombe (J. Davey, EMBO J. 11:951-960, 1992), but further analysis revealed that a mutant strain lacking both genes still produced active M-factor. ......We previously identified two genes, mfm1 and mfm2, with the potential to encode the M-factor mating pheromone of the fission yeast Schizosaccharomyces pombe (J. Davey, EMBO J. 11:951-960, 1992), but further analysis revealed that a mutant strain lacking both genes still produced active M...... that is not rescued by addition of exogenous M-factor. A mutational analysis reveals that all three mfm genes contribute to the production of M-factor. Their transcription is limited to M cells and requires the mat1-Mc and ste11 gene products. Each gene is induced when the cells are starved of nitrogen and further...

Time-Course Analysis of Gene Expression During the Saccharomyces cerevisiae Hypoxic Response

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Nasrine Bendjilali

2017-01-01

Full Text Available Many cells experience hypoxia, or low oxygen, and respond by dramatically altering gene expression. In the yeast Saccharomyces cerevisiae, genes that respond are required for many oxygen-dependent cellular processes, such as respiration, biosynthesis, and redox regulation. To more fully characterize the global response to hypoxia, we exposed yeast to hypoxic conditions, extracted RNA at different times, and performed RNA sequencing (RNA-seq analysis. Time-course statistical analysis revealed hundreds of genes that changed expression by up to 550-fold. The genes responded with varying kinetics suggesting that multiple regulatory pathways are involved. We identified most known oxygen-regulated genes and also uncovered new regulated genes. Reverse transcription-quantitative PCR (RT-qPCR analysis confirmed that the lysine methyltransferase EFM6 and the recombinase DMC1, both conserved in humans, are indeed oxygen-responsive. Looking more broadly, oxygen-regulated genes participate in expected processes like respiration and lipid metabolism, but also in unexpected processes like amino acid and vitamin metabolism. Using principle component analysis, we discovered that the hypoxic response largely occurs during the first 2 hr and then a new steady-state expression state is achieved. Moreover, we show that the oxygen-dependent genes are not part of the previously described environmental stress response (ESR consisting of genes that respond to diverse types of stress. While hypoxia appears to cause a transient stress, the hypoxic response is mostly characterized by a transition to a new state of gene expression. In summary, our results reveal that hypoxia causes widespread and complex changes in gene expression to prepare the cell to function with little or no oxygen.

Time-Course Analysis of Gene Expression During the Saccharomyces cerevisiae Hypoxic Response.

Science.gov (United States)

Bendjilali, Nasrine; MacLeon, Samuel; Kalra, Gurmannat; Willis, Stephen D; Hossian, A K M Nawshad; Avery, Erica; Wojtowicz, Olivia; Hickman, Mark J

2017-01-05

Many cells experience hypoxia, or low oxygen, and respond by dramatically altering gene expression. In the yeast Saccharomyces cerevisiae, genes that respond are required for many oxygen-dependent cellular processes, such as respiration, biosynthesis, and redox regulation. To more fully characterize the global response to hypoxia, we exposed yeast to hypoxic conditions, extracted RNA at different times, and performed RNA sequencing (RNA-seq) analysis. Time-course statistical analysis revealed hundreds of genes that changed expression by up to 550-fold. The genes responded with varying kinetics suggesting that multiple regulatory pathways are involved. We identified most known oxygen-regulated genes and also uncovered new regulated genes. Reverse transcription-quantitative PCR (RT-qPCR) analysis confirmed that the lysine methyltransferase EFM6 and the recombinase DMC1, both conserved in humans, are indeed oxygen-responsive. Looking more broadly, oxygen-regulated genes participate in expected processes like respiration and lipid metabolism, but also in unexpected processes like amino acid and vitamin metabolism. Using principle component analysis, we discovered that the hypoxic response largely occurs during the first 2 hr and then a new steady-state expression state is achieved. Moreover, we show that the oxygen-dependent genes are not part of the previously described environmental stress response (ESR) consisting of genes that respond to diverse types of stress. While hypoxia appears to cause a transient stress, the hypoxic response is mostly characterized by a transition to a new state of gene expression. In summary, our results reveal that hypoxia causes widespread and complex changes in gene expression to prepare the cell to function with little or no oxygen. Copyright © 2017 Bendjilali et al.

Genomewide identification and expression analysis of the ARF gene ...

Indian Academy of Sciences (India)

Figure 1. Phylogenetic relation of apple ARF genes. The phylogenetic tree was constructed based on a complete protein sequence align- ment of MdARFs by the neighbour-joining method with bootstrapping analysis (1000 replicates). The scale bar represents 0.05 amino acid substitutions per site. Paralogous gene pairs ...

Protein functional links in Trypanosoma brucei, identified by gene fusion analysis

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Trimpalis Philip

2011-07-01

Full Text Available Abstract Background Domain or gene fusion analysis is a bioinformatics method for detecting gene fusions in one organism by comparing its genome to that of other organisms. The occurrence of gene fusions suggests that the two original genes that participated in the fusion are functionally linked, i.e. their gene products interact either as part of a multi-subunit protein complex, or in a metabolic pathway. Gene fusion analysis has been used to identify protein functional links in prokaryotes as well as in eukaryotic model organisms, such as yeast and Drosophila. Results In this study we have extended this approach to include a number of recently sequenced protists, four of which are pathogenic, to identify fusion linked proteins in Trypanosoma brucei, the causative agent of African sleeping sickness. We have also examined the evolution of the gene fusion events identified, to determine whether they can be attributed to fusion or fission, by looking at the conservation of the fused genes and of the individual component genes across the major eukaryotic and prokaryotic lineages. We find relatively limited occurrence of gene fusions/fissions within the protist lineages examined. Our results point to two trypanosome-specific gene fissions, which have recently been experimentally confirmed, one fusion involving proteins involved in the same metabolic pathway, as well as two novel putative functional links between fusion-linked protein pairs. Conclusions This is the first study of protein functional links in T. brucei identified by gene fusion analysis. We have used strict thresholds and only discuss results which are highly likely to be genuine and which either have already been or can be experimentally verified. We discuss the possible impact of the identification of these novel putative protein-protein interactions, to the development of new trypanosome therapeutic drugs.

Transcriptomic network analysis of micronuclei-related genes: a case study

DEFF Research Database (Denmark)

van Leeuwen, D. M.; Pedersen, Marie; Knudsen, Lisbeth E.

2011-01-01

checkpoint and aneuploidy. The MN-related gene network was tested against a transcriptomics case study associated with MN measurements. In this case study, transcriptomic data from children and adults differentially exposed to ambient air pollution in the Czech Republic were analysed and visualised......Mechanistically relevant information on responses of humans to xenobiotic exposure in relation to chemically induced biological effects, such as micronuclei (MN) formation can be obtained through large-scale transcriptomics studies. Network analysis may enhance the analysis and visualisation...... of such data. Therefore, this study aimed to develop a 'MN formation' network based on a priori knowledge, by using the pathway tool MetaCore. The gene network contained 27 genes and three gene complexes that are related to processes involved in MN formation, e.g. spindle assembly checkpoint, cell cycle...

SERVICES MARKETING WITHIN BUSINESS-TO-BUSINESS CONTEXT: A CONTENT ANALYSIS OF 1996 – 2014 PERIOD

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Ceren Akman Biyik

2017-06-01

Full Text Available The aim of the study is to conduct a content analysis of services marketing within business-tobusiness context that were published between years 1996-2014. A qualitative approach was used and content analysis was conducted on 71 articles from 24 journals in this study. Firstly, thematic investigation was conducted, and then coding process was completed. According to the results of content analysis, top research topics are determined based on services marketing and business-to-business context. The findings of the study also showed the least studied topics and shed light on new research areas to the researchers in the field of services marketing and business-to-business.

The TyrA family of aromatic-pathway dehydrogenases in phylogenetic context

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Wolinsky Murray

2005-05-01

Full Text Available Abstract Background The TyrA protein family includes members that catalyze two dehydrogenase reactions in distinct pathways leading to L-tyrosine and a third reaction that is not part of tyrosine biosynthesis. Family members share a catalytic core region of about 30 kDa, where inhibitors operate competitively by acting as substrate mimics. This protein family typifies many that are challenging for bioinformatic analysis because of relatively modest sequence conservation and small size. Results Phylogenetic relationships of TyrA domains were evaluated in the context of combinatorial patterns of specificity for the two substrates, as well as the presence or absence of a variety of fusions. An interactive tool is provided for prediction of substrate specificity. Interactive alignments for a suite of catalytic-core TyrA domains of differing specificity are also provided to facilitate phylogenetic analysis. tyrA membership in apparent operons (or supraoperons was examined, and patterns of conserved synteny in relationship to organismal positions on the 16S rRNA tree were ascertained for members of the domain Bacteria. A number of aromatic-pathway genes (hisHb, aroF, aroQ have fused with tyrA, and it must be more than coincidental that the free-standing counterparts of all of the latter fused genes exhibit a distinct trace of syntenic association. Conclusion We propose that the ancestral TyrA dehydrogenase had broad specificity for both the cyclohexadienyl and pyridine nucleotide substrates. Indeed, TyrA proteins of this type persist today, but it is also common to find instances of narrowed substrate specificities, as well as of acquisition via gene fusion of additional catalytic domains or regulatory domains. In some clades a qualitative change associated with either narrowed substrate specificity or gene fusion has produced an evolutionary "jump" in the vertical genealogy of TyrA homologs. The evolutionary history of gene organizations that include

Gene by Social-Context Interactions for Number of Sexual Partners Among White Male Youths: Genetics-informed Sociology

Science.gov (United States)

Guo, Guang; Tong, Yuying; Cai, Tianji

2010-01-01

In this study, we set out to investigate whether introducing molecular genetic measures into an analysis of sexual partner variety will yield novel sociological insights. The data source is the white male DNA sample in the National Longitudinal Study of Adolescent Health. Our empirical analysis has produced a robust protective effect of the 9R/9R genotype relative to the Any10R genotype in the dopamine transporter gene (DAT1). The gene-environment interaction analysis demonstrates that the protective effect of 9R/9R tends to be lost in schools in which higher proportions of students start having sex early or among those with relatively low levels of cognitive ability. Our genetics-informed sociological analysis suggests that the “one size” of a single social theory may not fit all. Explaining a human trait or behavior may require a theory that accommodates the complex interplay between social contextual and individual influences and genetic predispositions. PMID:19569400

System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

Science.gov (United States)

Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

2018-01-01

Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

Molecular characterization, sequence analysis and tissue expression of a porcine gene – MOSPD2

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Yang Jie

2017-01-01

Full Text Available The full-length cDNA sequence of a porcine gene, MOSPD2, was amplified using the rapid amplification of cDNA ends method based on a pig expressed sequence tag sequence which was highly homologous to the coding sequence of the human MOSPD2 gene. Sequence prediction analysis revealed that the open reading frame of this gene encodes a protein of 491 amino acids that has high homology with the motile sperm domain-containing protein 2 (MOSPD2 of five species: horse (89%, human (90%, chimpanzee (89%, rhesus monkey (89% and mouse (85%; thus, it could be defined as a porcine MOSPD2 gene. This novel porcine gene was assigned GeneID: 100153601. This gene is structured in 15 exons and 14 introns as revealed by computer-assisted analysis. The phylogenetic analysis revealed that the porcine MOSPD2 gene has a closer genetic relationship with the MOSPD2 gene of horse. Tissue expression analysis indicated that the porcine MOSPD2 gene is generally and differentially expressed in the spleen, muscle, skin, kidney, lung, liver, fat and heart. Our experiment is the first to establish the primary foundation for further research on the porcine MOSPD2 gene.

Insect-resistant transgenic plants in a multi-trophic context

NARCIS (Netherlands)

Groot, A.T.; Dicke, M.

2002-01-01

So far, genetic engineering of plants in the context of insect pest control has involved insertion of genes that code for toxins, and may be characterized as the incorporation of biopesticides into classical plant breeding. In the context of pesticide usage in pest control, natural enemies of

Capturing heterogeneity in gene expression studies by surrogate variable analysis.

Directory of Open Access Journals (Sweden)

Jeffrey T Leek

2007-09-01

Full Text Available It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce "surrogate variable analysis" (SVA to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.

DNA methylation analysis of the angiotensin converting enzyme (ACE gene in major depression.

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Peter Zill

Full Text Available BACKGROUND: The angiotensin converting enzyme (ACE has been repeatedly discussed as susceptibility factor for major depression (MD and the bi-directional relation between MD and cardiovascular disorders (CVD. In this context, functional polymorphisms of the ACE gene have been linked to depression, to antidepressant treatment response, to ACE serum concentrations, as well as to hypertension, myocardial infarction and CVD risk markers. The mostly investigated ACE Ins/Del polymorphism accounts for ~40%-50% of the ACE serum concentration variance, the remaining half is probably determined by other genetic, environmental or epigenetic factors, but these are poorly understood. MATERIALS AND METHODS: The main aim of the present study was the analysis of the DNA methylation pattern in the regulatory region of the ACE gene in peripheral leukocytes of 81 MD patients and 81 healthy controls. RESULTS: We detected intensive DNA methylation within a recently described, functional important region of the ACE gene promoter including hypermethylation in depressed patients (p = 0.008 and a significant inverse correlation between the ACE serum concentration and ACE promoter methylation frequency in the total sample (p = 0.02. Furthermore, a significant inverse correlation between the concentrations of the inflammatory CVD risk markers ICAM-1, E-selectin and P-selectin and the degree of ACE promoter methylation in MD patients could be demonstrated (p = 0.01 - 0.04. CONCLUSION: The results of the present study suggest that aberrations in ACE promoter DNA methylation may be an underlying cause of MD and probably a common pathogenic factor for the bi-directional relationship between MD and cardiovascular disorders.

Inter-genomic displacement via lateral gene transfer of bacterial trp operons in an overall context of vertical genealogy

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Keyhani Nemat O

2004-06-01

Full Text Available Abstract Background The growing conviction that lateral gene transfer plays a significant role in prokaryote genealogy opens up a need for comprehensive evaluations of gene-enzyme systems on a case-by-case basis. Genes of tryptophan biosynthesis are frequently organized as whole-pathway operons, an attribute that is expected to facilitate multi-gene transfer in a single step. We have asked whether events of lateral gene transfer are sufficient to have obscured our ability to track the vertical genealogy that underpins tryptophan biosynthesis. Results In 47 complete-genome Bacteria, the genes encoding the seven catalytic domains that participate in primary tryptophan biosynthesis were distinguished from any paralogs or xenologs engaged in other specialized functions. A reliable list of orthologs with carefully ascertained functional roles has thus been assembled and should be valuable as an annotation resource. The protein domains associated with primary tryptophan biosynthesis were then concatenated, yielding single amino-acid sequence strings that represent the entire tryptophan pathway. Lateral gene transfer of several whole-pathway trp operons was demonstrated by use of phylogenetic analysis. Lateral gene transfer of partial-pathway trp operons was also shown, with newly recruited genes functioning either in primary biosynthesis (rarely or specialized metabolism (more frequently. Conclusions (i Concatenated tryptophan protein trees are congruent with 16S rRNA subtrees provided that the genomes represented are of sufficiently close phylogenetic spacing. There are currently seven tryptophan congruency groups in the Bacteria. Recognition of a succession of others can be expected in the near future, but ultimately these should coalesce to a single grouping that parallels the 16S rRNA tree (except for cases of lateral gene transfer. (ii The vertical trace of evolution for tryptophan biosynthesis can be deduced. The daunting complexities engendered

Genome-Wide Identification and Analysis of Drought-Responsive Genes and MicroRNAs in Tobacco

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Fuqiang Yin

2015-03-01

Full Text Available Drought stress response is a complex trait regulated at transcriptional and post-transcriptional levels in tobacco. Since the 1990s, many studies have shown that miRNAs act in many ways to regulate target expression in plant growth, development and stress response. The recent draft genome sequence of Nicotiana benthamiana has provided a framework for Digital Gene Expression (DGE and small RNA sequencing to understand patterns of transcription in the context of plant response to environmental stress. We sequenced and analyzed three Digital Gene Expression (DGE libraries from roots of normal and drought-stressed tobacco plants, and four small RNA populations from roots, stems and leaves of control or drought-treated tobacco plants, respectively. We identified 276 candidate drought responsive genes (DRGs with sequence similarities to 64 known DRGs from other model plant crops, 82 were transcription factors (TFs including WRKY, NAC, ERF and bZIP families. Of these tobacco DRGs, 54 differentially expressed DRGs included 21 TFs, which belonged to 4 TF families such as NAC (6, MYB (4, ERF (10, and bZIP (1. Additionally, we confirmed expression of 39 known miRNA families (122 members and five conserved miRNA families, which showed differential regulation under drought stress. Targets of miRNAs were further surveyed based on a recently published study, of which ten targets were DRGs. An integrated gene regulatory network is proposed for the molecular mechanisms of tobacco root response to drought stress using differentially expressed DRGs, the changed expression profiles of miRNAs and their target transcripts. This network analysis serves as a reference for future studies on tobacco response stresses such as drought, cold and heavy metals.

Life cycle analysis of kidney gene expression in male F344 rats.

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Joshua C Kwekel

Full Text Available Age is a predisposing condition for susceptibility to chronic kidney disease and progression as well as acute kidney injury that may arise due to the adverse effects of some drugs. Age-related differences in kidney biology, therefore, are a key concern in understanding drug safety and disease progression. We hypothesize that the underlying suite of genes expressed in the kidney at various life cycle stages will impact susceptibility to adverse drug reactions. Therefore, establishing changes in baseline expression data between these life stages is the first and necessary step in evaluating this hypothesis. Untreated male F344 rats were sacrificed at 2, 5, 6, 8, 15, 21, 78, and 104 weeks of age. Kidneys were collected for histology and gene expression analysis. Agilent whole-genome rat microarrays were used to query global expression profiles. An ANOVA (p1.5 in relative mRNA expression, was used to identify 3,724 unique differentially expressed genes (DEGs. Principal component analyses of these DEGs revealed three major divisions in life-cycle renal gene expression. K-means cluster analysis identified several groups of genes that shared age-specific patterns of expression. Pathway analysis of these gene groups revealed age-specific gene networks and functions related to renal function and aging, including extracellular matrix turnover, immune cell response, and renal tubular injury. Large age-related changes in expression were also demonstrated for the genes that code for qualified renal injury biomarkers KIM-1, Clu, and Tff3. These results suggest specific groups of genes that may underlie age-specific susceptibilities to adverse drug reactions and disease. This analysis of the basal gene expression patterns of renal genes throughout the life cycle of the rat will improve the use of current and future renal biomarkers and inform our assessments of kidney injury and disease.

Genome-Wide Analysis of the RNA Helicase Gene Family in Gossypium raimondii

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Jie Chen

2014-03-01

Full Text Available The RNA helicases, which help to unwind stable RNA duplexes, and have important roles in RNA metabolism, belong to a class of motor proteins that play important roles in plant development and responses to stress. Although this family of genes has been the subject of systematic investigation in Arabidopsis, rice, and tomato, it has not yet been characterized in cotton. In this study, we identified 161 putative RNA helicase genes in the genome of the diploid cotton species Gossypium raimondii. We classified these genes into three subfamilies, based on the presence of either a DEAD-box (51 genes, DEAH-box (52 genes, or DExD/H-box (58 genes in their coding regions. Chromosome location analysis showed that the genes that encode RNA helicases are distributed across all 13 chromosomes of G. raimondii. Syntenic analysis revealed that 62 of the 161 G. raimondii helicase genes (38.5% are within the identified syntenic blocks. Sixty-six (40.99% helicase genes from G. raimondii have one or several putative orthologs in tomato. Additionally, GrDEADs have more conserved gene structures and more simple domains than GrDEAHs and GrDExD/Hs. Transcriptome sequencing data demonstrated that many of these helicases, especially GrDEADs, are highly expressed at the fiber initiation stage and in mature leaves. To our knowledge, this is the first report of a genome-wide analysis of the RNA helicase gene family in cotton.

GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression Data

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Chris Cheadle

2007-01-01

Full Text Available Background: Microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The assignment of functional information to these complex patterns remains a challenging task in effectively interpreting data and correlating results from across experiments, projects and laboratories. Methods which allow the rapid and robust evaluation of multiple functional hypotheses increase the power of individual researchers to data mine gene expression data more efficiently.Results: We have developed (gene set matrix analysis GSMA as a useful method for the rapid testing of group-wise up- or downregulation of gene expression simultaneously for multiple lists of genes (gene sets against entire distributions of gene expression changes (datasets for single or multiple experiments. The utility of GSMA lies in its flexibility to rapidly poll gene sets related by known biological function or as designated solely by the end-user against large numbers of datasets simultaneously.Conclusions: GSMA provides a simple and straightforward method for hypothesis testing in which genes are tested by groups across multiple datasets for patterns of expression enrichment.

Testing-Context Analysis: Assessment Is Just Another Part of Language Curriculum Development

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Brown, James Dean

2008-01-01

In keeping with the theme of the International Language Testing Association/Language Testing Research Colloquium Conference in 2008, "Focusing on the Core: Justifying the Use of Language Assessments to Stakeholders," I define "stakeholder-friendly tests," "defensible testing," and "testing-context analysis."…

Psychometric analysis of export market orientation measurement scale in Croatian SME exporters’ context

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Dario Miočević

2009-07-01

Full Text Available Market orientation is a vital construct of the marketing concept. Although different conceptualization approaches to market orientation have been discussed by literature so far, a common denominator is its interdependence with business performance. Increasing globalization trends affect both the markets’ convergence and competition. Consequently, focusing on market orientation within an international context is of utmost importance. Export market orientation (EMO is relatively new concept, which puts market orientation into the international context. Since export is a dominant international entry strategy in the Croatian economy which comprises mostly SMEs, it is crucial to investigate the importance of the EMO in the Croatian SME context. Determining an appropriate measurement scale of the EMO to be applied in various national research contexts leading to generalization represents a challenge for marketing academicians. The paper aims to find out whether the EMO construct and measurement scale can be applied within the Croatian SME context. The authors have used the exploratory and the confirmatory factor analysis to determine the psychometric properties of the EMO scale. The results of psychometric assessment of the EMO scale confirm its dimensionability, reliability, validity and applicability in the Croatian SME context. Results clearly indicate the necessity of pursuing EMO activities in order to achieve a high level of export performance.

[FANCA gene mutation analysis in Fanconi anemia patients].

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Chen, Fei; Peng, Guang-Jie; Zhang, Kejian; Hu, Qun; Zhang, Liu-Qing; Liu, Ai-Guo

2005-10-01

To screen the FANCA gene mutation and explore the FANCA protein function in Fanconi anemia (FA) patients. FANCA protein expression and its interaction with FANCF were analyzed using Western blot and immunoprecipitation in 3 cases of FA-A. Genomic DNA was used for MLPA analysis followed by sequencing. FANCA protein was undetectable and FANCA and FANCF protein interaction was impaired in these 3 cases of FA-A. Each case of FA-A contained biallelic pathogenic mutations in FANCA gene. No functional FANCA protein was found in these 3 cases of FA-A, and intragenic deletion, frame shift and splice site mutation were the major pathogenic mutations found in FANCA gene.

Gene set analysis for interpreting genetic studies

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Pers, Tune H

2016-01-01

Interpretation of genome-wide association study (GWAS) results is lacking behind the discovery of new genetic associations. Consequently, there is an urgent need for data-driven methods for interpreting genetic association studies. Gene set analysis (GSA) can identify aetiologic pathways...

Suitable Reference Genes for Accurate Gene Expression Analysis in Parsley (Petroselinum crispum) for Abiotic Stresses and Hormone Stimuli.

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Li, Meng-Yao; Song, Xiong; Wang, Feng; Xiong, Ai-Sheng

2016-01-01

Parsley, one of the most important vegetables in the Apiaceae family, is widely used in the food, medicinal, and cosmetic industries. Recent studies on parsley mainly focus on its chemical composition, and further research involving the analysis of the plant's gene functions and expressions is required. qPCR is a powerful method for detecting very low quantities of target transcript levels and is widely used to study gene expression. To ensure the accuracy of results, a suitable reference gene is necessary for expression normalization. In this study, four software, namely geNorm, NormFinder, BestKeeper, and RefFinder were used to evaluate the expression stabilities of eight candidate reference genes of parsley ( GAPDH, ACTIN, eIF-4 α, SAND, UBC, TIP41, EF-1 α, and TUB ) under various conditions, including abiotic stresses (heat, cold, salt, and drought) and hormone stimuli treatments (GA, SA, MeJA, and ABA). Results showed that EF-1 α and TUB were the most stable genes for abiotic stresses, whereas EF-1 α, GAPDH , and TUB were the top three choices for hormone stimuli treatments. Moreover, EF-1 α and TUB were the most stable reference genes among all tested samples, and UBC was the least stable one. Expression analysis of PcDREB1 and PcDREB2 further verified that the selected stable reference genes were suitable for gene expression normalization. This study can guide the selection of suitable reference genes in gene expression in parsley.

Suitable reference genes for accurate gene expression analysis in parsley (Petroselinum crispum for abiotic stresses and hormone stimuli

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Meng-Yao Li

2016-09-01

Full Text Available Parsley is one of the most important vegetable in Apiaceae family and widely used in food industry, medicinal and cosmetic. The recent studies in parsley are mainly focus on chemical composition, further research involving the analysis of the gene functions and expressions will be required. qPCR is a powerful method for detecting very low quantities of target transcript levels and widely used for gene expression studies. To ensure the accuracy of results, a suitable reference gene is necessary for expression normalization. In this study, three software geNorm, NormFinder, and BestKeeper were used to evaluate the expression stabilities of eight candidate reference genes (GAPDH, ACTIN, eIF-4α, SAND, UBC, TIP41, EF-1α, and TUB under various conditions including abiotic stresses (heat, cold, salt, and drought and hormone stimuli treatments (GA, SA, MeJA, and ABA. The results showed that EF-1α and TUB were identified as the most stable genes for abiotic stresses, while EF-1α, GAPDH, and TUB were the top three choices for hormone stimuli treatments. Moreover, EF-1α and TUB were the most stable reference genes across all the tested samples, while UBC was the least stable one. The expression analysis of PcDREB1 and PcDREB2 further verified that the selected stable reference genes were suitable for gene expression normalization. This study provides a guideline for selection the suitable reference genes in gene expression in parsley.

Human Behavior Analysis by Means of Multimodal Context Mining

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Oresti Banos

2016-08-01

Full Text Available There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.

Clinical Omics Analysis of Colorectal Cancer Incorporating Copy Number Aberrations and Gene Expression Data

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Tsuyoshi Yoshida

2010-07-01

Full Text Available Background: Colorectal cancer (CRC is one of the most frequently occurring cancers in Japan, and thus a wide range of methods have been deployed to study the molecular mechanisms of CRC. In this study, we performed a comprehensive analysis of CRC, incorporating copy number aberration (CRC and gene expression data. For the last four years, we have been collecting data from CRC cases and organizing the information as an “omics” study by integrating many kinds of analysis into a single comprehensive investigation. In our previous studies, we had experienced difficulty in finding genes related to CRC, as we observed higher noise levels in the expression data than in the data for other cancers. Because chromosomal aberrations are often observed in CRC, here, we have performed a combination of CNA analysis and expression analysis in order to identify some new genes responsible for CRC. This study was performed as part of the Clinical Omics Database Project at Tokyo Medical and Dental University. The purpose of this study was to investigate the mechanism of genetic instability in CRC by this combination of expression analysis and CNA, and to establish a new method for the diagnosis and treatment of CRC. Materials and methods: Comprehensive gene expression analysis was performed on 79 CRC cases using an Affymetrix Gene Chip, and comprehensive CNA analysis was performed using an Affymetrix DNA Sty array. To avoid the contamination of cancer tissue with normal cells, laser micro-dissection was performed before DNA/RNA extraction. Data analysis was performed using original software written in the R language. Result: We observed a high percentage of CNA in colorectal cancer, including copy number gains at 7, 8q, 13 and 20q, and copy number losses at 8p, 17p and 18. Gene expression analysis provided many candidates for CRC-related genes, but their association with CRC did not reach the level of statistical significance. The combination of CNA and gene

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

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Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju

2017-04-27

Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By

Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans

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Benjamin Mayne

2016-10-01

Full Text Available The severity and prevalence of many diseases are known to differ between the sexes. Organ specific sex-biased gene expression may underpin these and other sexually dimorphic traits. To further our understanding of sex differences in transcriptional regulation, we performed meta-analyses of sex biased gene expression in multiple human tissues. We analysed 22 publicly available human gene expression microarray data sets including over 2500 samples from 15 different tissues and 9 different organs. Briefly, by using an inverse-variance method we determined the effect size difference of gene expression between males and females. We found the greatest sex differences in gene expression in the brain, specifically in the anterior cingulate cortex, (1818 genes, followed by the heart (375 genes, kidney (224 genes, colon (218 genes and thyroid (163 genes. More interestingly, we found different parts of the brain with varying numbers and identity of sex-biased genes, indicating that specific cortical regions may influence sexually dimorphic traits. The majority of sex-biased genes in other tissues such as the bladder, liver, lungs and pancreas were on the sex chromosomes or involved in sex hormone production. On average in each tissue, 32% of autosomal genes that were expressed in a sex-biased fashion contained androgen or estrogen hormone response elements. Interestingly, across all tissues, we found approximately two-thirds of autosomal genes that were sex-biased were not under direct influence of sex hormones. To our knowledge this is the largest analysis of sex-biased gene expression in human tissues to date. We identified many sex-biased genes that were not under the direct influence of sex chromosome genes or sex hormones. These may provide targets for future development of sex-specific treatments for diseases.

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

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Mary Qu Yang

Full Text Available Clear cell renal cell carcinoma (ccRCC is the most common and most aggressive form of renal cell cancer (RCC. The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways. Keywords: ccRCC, Causative mutation, Pathways, Protein-protein interaction, Gene module, eQTL

Length bias correction in gene ontology enrichment analysis using logistic regression.

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Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

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Andrew Williams

2015-12-01

Full Text Available Background: The presence of diverse types of nanomaterials (NMs in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2, carbon black (CB or carbon nanotubes (CNTs to determine the disease significance of these data-driven gene sets.Results: Biclusters representing inflammation (chemokine activity, DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032. The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles.Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several

Cartilage-selective genes identified in genome-scale analysis of non-cartilage and cartilage gene expression

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Cohn Zachary A

2007-06-01

Full Text Available Abstract Background Cartilage plays a fundamental role in the development of the human skeleton. Early in embryogenesis, mesenchymal cells condense and differentiate into chondrocytes to shape the early skeleton. Subsequently, the cartilage anlagen differentiate to form the growth plates, which are responsible for linear bone growth, and the articular chondrocytes, which facilitate joint function. However, despite the multiplicity of roles of cartilage during human fetal life, surprisingly little is known about its transcriptome. To address this, a whole genome microarray expression profile was generated using RNA isolated from 18–22 week human distal femur fetal cartilage and compared with a database of control normal human tissues aggregated at UCLA, termed Celsius. Results 161 cartilage-selective genes were identified, defined as genes significantly expressed in cartilage with low expression and little variation across a panel of 34 non-cartilage tissues. Among these 161 genes were cartilage-specific genes such as cartilage collagen genes and 25 genes which have been associated with skeletal phenotypes in humans and/or mice. Many of the other cartilage-selective genes do not have established roles in cartilage or are novel, unannotated genes. Quantitative RT-PCR confirmed the unique pattern of gene expression observed by microarray analysis. Conclusion Defining the gene expression pattern for cartilage has identified new genes that may contribute to human skeletogenesis as well as provided further candidate genes for skeletal dysplasias. The data suggest that fetal cartilage is a complex and transcriptionally active tissue and demonstrate that the set of genes selectively expressed in the tissue has been greatly underestimated.

Analysis of phylogeny and codon usage bias and relationship of GC content, amino acid composition with expression of the structural nif genes.

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Mondal, Sunil Kanti; Kundu, Sudip; Das, Rabindranath; Roy, Sujit

2016-08-01

Bacteria and archaea have evolved with the ability to fix atmospheric dinitrogen in the form of ammonia, catalyzed by the nitrogenase enzyme complex which comprises three structural genes nifK, nifD and nifH. The nifK and nifD encodes for the beta and alpha subunits, respectively, of component 1, while nifH encodes for component 2 of nitrogenase. Phylogeny based on nifDHK have indicated that Cyanobacteria is closer to Proteobacteria alpha and gamma but not supported by the tree based on 16SrRNA. The evolutionary ancestor for the different trees was also different. The GC1 and GC2% analysis showed more consistency than GC3% which appeared to below for Firmicutes, Cyanobacteria and Euarchaeota while highest in Proteobacteria beta and clearly showed the proportional effect on the codon usage with a few exceptions. Few genes from Firmicutes, Euryarchaeota, Proteobacteria alpha and delta were found under mutational pressure. These nif genes with low and high GC3% from different classes of organisms showed similar expected number of codons. Distribution of the genes and codons, based on codon usage demonstrated opposite pattern for different orientation of mirror plane when compared with each other. Overall our results provide a comprehensive analysis on the evolutionary relationship of the three structural nif genes, nifK, nifD and nifH, respectively, in the context of codon usage bias, GC content relationship and amino acid composition of the encoded proteins and exploration of crucial statistical method for the analysis of positive data with non-constant variance to identify the shape factors of codon adaptation index.

Comparative genomic analysis of the PKS genes in five species and expression analysis in upland cotton

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Xueqiang Su

2017-10-01

Full Text Available Plant type III polyketide synthase (PKS can catalyse the formation of a series of secondary metabolites with different structures and different biological functions; the enzyme plays an important role in plant growth, development and resistance to stress. At present, the PKS gene has been identified and studied in a variety of plants. Here, we identified 11 PKS genes from upland cotton (Gossypium hirsutum and compared them with 41 PKS genes in Populus tremula, Vitis vinifera, Malus domestica and Arabidopsis thaliana. According to the phylogenetic tree, a total of 52 PKS genes can be divided into four subfamilies (I–IV. The analysis of gene structures and conserved motifs revealed that most of the PKS genes were composed of two exons and one intron and there are two characteristic conserved domains (Chal_sti_synt_N and Chal_sti_synt_C of the PKS gene family. In our study of the five species, gene duplication was found in addition to Arabidopsis thaliana and we determined that purifying selection has been of great significance in maintaining the function of PKS gene family. From qRT-PCR analysis and a combination of the role of the accumulation of proanthocyanidins (PAs in brown cotton fibers, we concluded that five PKS genes are candidate genes involved in brown cotton fiber pigment synthesis. These results are important for the further study of brown cotton PKS genes. It not only reveals the relationship between PKS gene family and pigment in brown cotton, but also creates conditions for improving the quality of brown cotton fiber.

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.

Analysis of gene evolution and metabolic pathways using the Candida Gene Order Browser

LENUS (Irish Health Repository)

Fitzpatrick, David A

2010-05-10

Abstract Background Candida species are the most common cause of opportunistic fungal infection worldwide. Recent sequencing efforts have provided a wealth of Candida genomic data. We have developed the Candida Gene Order Browser (CGOB), an online tool that aids comparative syntenic analyses of Candida species. CGOB incorporates all available Candida clade genome sequences including two Candida albicans isolates (SC5314 and WO-1) and 8 closely related species (Candida dubliniensis, Candida tropicalis, Candida parapsilosis, Lodderomyces elongisporus, Debaryomyces hansenii, Pichia stipitis, Candida guilliermondii and Candida lusitaniae). Saccharomyces cerevisiae is also included as a reference genome. Results CGOB assignments of homology were manually curated based on sequence similarity and synteny. In total CGOB includes 65617 genes arranged into 13625 homology columns. We have also generated improved Candida gene sets by merging\\/removing partial genes in each genome. Interrogation of CGOB revealed that the majority of tandemly duplicated genes are under strong purifying selection in all Candida species. We identified clusters of adjacent genes involved in the same metabolic pathways (such as catabolism of biotin, galactose and N-acetyl glucosamine) and we showed that some clusters are species or lineage-specific. We also identified one example of intron gain in C. albicans. Conclusions Our analysis provides an important resource that is now available for the Candida community. CGOB is available at http:\\/\\/cgob.ucd.ie.

Singular Perturbation Analysis and Gene Regulatory Networks with Delay

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Shlykova, Irina; Ponosov, Arcady

2009-09-01

There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

Xylella fastidiosa gene expression analysis by DNA microarrays

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Regiane F. Travensolo

2009-01-01

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

Recent adaptive events in human brain revealed by meta-analysis of positively selected genes.

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Yue Huang

Full Text Available BACKGROUND AND OBJECTIVES: Analysis of positively-selected genes can help us understand how human evolved, especially the evolution of highly developed cognitive functions. However, previous works have reached conflicting conclusions regarding whether human neuronal genes are over-represented among genes under positive selection. METHODS AND RESULTS: We divided positively-selected genes into four groups according to the identification approaches, compiling a comprehensive list from 27 previous studies. We showed that genes that are highly expressed in the central nervous system are enriched in recent positive selection events in human history identified by intra-species genomic scan, especially in brain regions related to cognitive functions. This pattern holds when different datasets, parameters and analysis pipelines were used. Functional category enrichment analysis supported these findings, showing that synapse-related functions are enriched in genes under recent positive selection. In contrast, immune-related functions, for instance, are enriched in genes under ancient positive selection revealed by inter-species coding region comparison. We further demonstrated that most of these patterns still hold even after controlling for genomic characteristics that might bias genome-wide identification of positively-selected genes including gene length, gene density, GC composition, and intensity of negative selection. CONCLUSION: Our rigorous analysis resolved previous conflicting conclusions and revealed recent adaptation of human brain functions.

GWATCH: a web platform for automated gene association discovery analysis

Science.gov (United States)

2014-01-01

Background As genome-wide sequence analyses for complex human disease determinants are expanding, it is increasingly necessary to develop strategies to promote discovery and validation of potential disease-gene associations. Findings Here we present a dynamic web-based platform – GWATCH – that automates and facilitates four steps in genetic epidemiological discovery: 1) Rapid gene association search and discovery analysis of large genome-wide datasets; 2) Expanded visual display of gene associations for genome-wide variants (SNPs, indels, CNVs), including Manhattan plots, 2D and 3D snapshots of any gene region, and a dynamic genome browser illustrating gene association chromosomal regions; 3) Real-time validation/replication of candidate or putative genes suggested from other sources, limiting Bonferroni genome-wide association study (GWAS) penalties; 4) Open data release and sharing by eliminating privacy constraints (The National Human Genome Research Institute (NHGRI) Institutional Review Board (IRB), informed consent, The Health Insurance Portability and Accountability Act (HIPAA) of 1996 etc.) on unabridged results, which allows for open access comparative and meta-analysis. Conclusions GWATCH is suitable for both GWAS and whole genome sequence association datasets. We illustrate the utility of GWATCH with three large genome-wide association studies for HIV-AIDS resistance genes screened in large multicenter cohorts; however, association datasets from any study can be uploaded and analyzed by GWATCH. PMID:25374661

Uniform approximation is more appropriate for Wilcoxon Rank-Sum Test in gene set analysis.

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Zhide Fang

Full Text Available Gene set analysis is widely used to facilitate biological interpretations in the analyses of differential expression from high throughput profiling data. Wilcoxon Rank-Sum (WRS test is one of the commonly used methods in gene set enrichment analysis. It compares the ranks of genes in a gene set against those of genes outside the gene set. This method is easy to implement and it eliminates the dichotomization of genes into significant and non-significant in a competitive hypothesis testing. Due to the large number of genes being examined, it is impractical to calculate the exact null distribution for the WRS test. Therefore, the normal distribution is commonly used as an approximation. However, as we demonstrate in this paper, the normal approximation is problematic when a gene set with relative small number of genes is tested against the large number of genes in the complementary set. In this situation, a uniform approximation is substantially more powerful, more accurate, and less intensive in computation. We demonstrate the advantage of the uniform approximations in Gene Ontology (GO term analysis using simulations and real data sets.

Pathway-based analysis of a melanoma genome-wide association study: analysis of genes related to tumour-immunosuppression.

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Nils Schoof

Full Text Available Systemic immunosuppression is a risk factor for melanoma, and sunburn-induced immunosuppression is thought to be causal. Genes in immunosuppression pathways are therefore candidate melanoma-susceptibility genes. If variants within these genes individually have a small effect on disease risk, the association may be undetected in genome-wide association (GWA studies due to low power to reach a high significance level. Pathway-based approaches have been suggested as a method of incorporating a priori knowledge into the analysis of GWA studies. In this study, the association of 1113 single nucleotide polymorphisms (SNPs in 43 genes (39 genomic regions related to immunosuppression have been analysed using a gene-set approach in 1539 melanoma cases and 3917 controls from the GenoMEL consortium GWA study. The association between melanoma susceptibility and the whole set of tumour-immunosuppression genes, and also predefined functional subgroups of genes, was considered. The analysis was based on a measure formed by summing the evidence from the most significant SNP in each gene, and significance was evaluated empirically by case-control label permutation. An association was found between melanoma and the complete set of genes (p(emp=0.002, as well as the subgroups related to the generation of tolerogenic dendritic cells (p(emp=0.006 and secretion of suppressive factors (p(emp=0.0004, thus providing preliminary evidence of involvement of tumour-immunosuppression gene polymorphisms in melanoma susceptibility. The analysis was repeated on a second phase of the GenoMEL study, which showed no evidence of an association. As one of the first attempts to replicate a pathway-level association, our results suggest that low power and heterogeneity may present challenges.

Comparative study on gene set and pathway topology-based enrichment methods.

Science.gov (United States)

Bayerlová, Michaela; Jung, Klaus; Kramer, Frank; Klemm, Florian; Bleckmann, Annalen; Beißbarth, Tim

2015-10-22

Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis. We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods. In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower. We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both

Identification of Genetic Susceptibility to Childhood Cancer through Analysis of Genes in Parallel

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Plon, Sharon E.; Wheeler, David A.; Strong, Louise C.; Tomlinson, Gail E.; Pirics, Michael; Meng, Qingchang; Cheung, Hannah C.; Begin, Phyllis R.; Muzny, Donna M.; Lewis, Lora; Biegel, Jaclyn A.; Gibbs, Richard A.

2011-01-01

Clinical cancer genetic susceptibility analysis typically proceeds sequentially beginning with the most likely causative gene. The process is time consuming and the yield is low particularly for families with unusual patterns of cancer. We determined the results of in parallel mutation analysis of a large cancer-associated gene panel. We performed deletion analysis and sequenced the coding regions of 45 genes (8 oncogenes and 37 tumor suppressor or DNA repair genes) in 48 childhood cancer patients who also (1) were diagnosed with a second malignancy under age 30, (2) have a sibling diagnosed with cancer under age 30 and/or (3) have a major congenital anomaly or developmental delay. Deleterious mutations were identified in 6 of 48 (13%) families, 4 of which met the sibling criteria. Mutations were identified in genes previously implicated in both dominant and recessive childhood syndromes including SMARCB1, PMS2, and TP53. No pathogenic deletions were identified. This approach has provided efficient identification of childhood cancer susceptibility mutations and will have greater utility as additional cancer susceptibility genes are identified. Integrating parallel analysis of large gene panels into clinical testing will speed results and increase diagnostic yield. The failure to detect mutations in 87% of families highlights that a number of childhood cancer susceptibility genes remain to be discovered. PMID:21356188

Comparative genomic analysis of the WRKY III gene family in populus, grape, arabidopsis and rice.

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Wang, Yiyi; Feng, Lin; Zhu, Yuxin; Li, Yuan; Yan, Hanwei; Xiang, Yan

2015-09-08

WRKY III genes have significant functions in regulating plant development and resistance. In plant, WRKY gene family has been studied in many species, however, there still lack a comprehensive analysis of WRKY III genes in the woody plant species poplar, three representative lineages of flowering plant species are incorporated in most analyses: Arabidopsis (a model plant for annual herbaceous dicots), grape (one model plant for perennial dicots) and Oryza sativa (a model plant for monocots). In this study, we identified 10, 6, 13 and 28 WRKY III genes in the genomes of Populus trichocarpa, grape (Vitis vinifera), Arabidopsis thaliana and rice (Oryza sativa), respectively. Phylogenetic analysis revealed that the WRKY III proteins could be divided into four clades. By microsynteny analysis, we found that the duplicated regions were more conserved between poplar and grape than Arabidopsis or rice. We dated their duplications by Ks analysis of Populus WRKY III genes and demonstrated that all the blocks were formed after the divergence of monocots and dicots. Strong purifying selection has played a key role in the maintenance of WRKY III genes in Populus. Tissue expression analysis of the WRKY III genes in Populus revealed that five were most highly expressed in the xylem. We also performed quantitative real-time reverse transcription PCR analysis of WRKY III genes in Populus treated with salicylic acid, abscisic acid and polyethylene glycol to explore their stress-related expression patterns. This study highlighted the duplication and diversification of the WRKY III gene family in Populus and provided a comprehensive analysis of this gene family in the Populus genome. Our results indicated that the majority of WRKY III genes of Populus was expanded by large-scale gene duplication. The expression pattern of PtrWRKYIII gene identified that these genes play important roles in the xylem during poplar growth and development, and may play crucial role in defense to drought

Comparative analysis of clustering methods for gene expression time course data

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Ivan G. Costa

2004-01-01

Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

Microarray analysis reveals key genes and pathways in Tetralogy of Fallot

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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

Disease gene characterization through large-scale co-expression analysis.

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Allen Day

2009-12-01

Full Text Available In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET.Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2 and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders.UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis.

Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

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Uddin, Raihan; Singh, Shiva M

2017-01-01

As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

Stochastic biological response to radiation. Comprehensive analysis of gene expression

International Nuclear Information System (INIS)

Inoue, Tohru; Hirabayashi, Yoko

2012-01-01

Authors explain that the radiation effect on biological system is stochastic along the law of physics, differing from chemical effect, using instances of Cs-137 gamma-ray (GR) and benzene (BZ) exposures to mice and of resultant comprehensive analyses of gene expression. Single GR irradiation is done with Gamma Cell 40 (CSR) to C57BL/6 or C3H/He mouse at 0, 0.6 and 3 Gy. BE is given orally at 150 mg/kg/day for 5 days x 2 weeks. Bone marrow cells are sampled 1 month after the exposure. Comprehensive gene expression is analyzed by Gene Chip Mouse Genome 430 2.0 Array (Affymetrix) and data are processed by programs like case normalization, statistics, network generation, functional analysis etc. GR irradiation brings about changes of gene expression, which are classifiable in common genes variable commonly on the dose change and stochastic genes variable stochastically within each dose: e.g., with Welch-t-test, significant differences are between 0/3 Gy (dose-specific difference, 455 pbs (probe set), in stochastic 2113 pbs), 0/0.6 Gy (267 in 1284 pbs) and 0.6/3 Gy (532 pbs); and with one-way analysis of variation (ANOVA) and hierarchial/dendrographic analyses, 520 pbs are shown to involve the dose-dependent 226 and dose-specific 294 pbs. It is also shown that at 3 Gy, expression of common genes are rather suppressed, including those related to the proliferation/apoptosis of B/T cells, and of stochastic genes, related to cell division/signaling. Ven diagram of the common genes of above 520 pbs, stochastic 2113 pbs at 3 Gy and 1284 pbs at 0.6 Gy shows the overlapping genes 29, 2 and 4, respectively, indicating only 35 pbs are overlapping in total. Network analysis of changes by GR shows the rather high expression of genes around hub of cAMP response element binding protein (CREB) at 0.6 Gy, and rather variable expression around CREB hub/suppressed expression of kinesin hub at 3 Gy; in the network by BZ exposure, unchanged or low expression around p53 hub and suppression

Bioinformatics analysis of RNA-seq data revealed critical genes in colon adenocarcinoma.

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Xi, W-D; Liu, Y-J; Sun, X-B; Shan, J; Yi, L; Zhang, T-T

2017-07-01

RNA-seq data of colon adenocarcinoma (COAD) were analyzed with bioinformatics tools to discover critical genes in the disease. Relevant small molecule drugs, transcription factors (TFs) and microRNAs (miRNAs) were also investigated. RNA-seq data of COAD were downloaded from The Cancer Genome Atlas (TCGA). Differential analysis was performed with package edgeR. False positive discovery (FDR) 1 were set as the cut-offs to screen out differentially expressed genes (DEGs). Gene coexpression network was constructed with package Ebcoexpress. GO enrichment analysis was performed for the DEGs in the gene coexpression network with DAVID. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was also performed for the genes with KOBASS 2.0. Modules were identified with MCODE of Cytoscape. Relevant small molecules drugs were predicted by Connectivity map. Relevant miRNAs and TFs were searched by WebGestalt. A total of 457 DEGs, including 255 up-regulated and 202 down-regulated genes, were identified from 437 COAD and 39 control samples. A gene coexpression network was constructed containing 40 DEGs and 101 edges. The genes were mainly associated with collagen fibril organization, extracellular matrix organization and translation. Two modules were identified from the gene coexpression network, which were implicated in muscle contraction and extracellular matrix organization, respectively. Several critical genes were disclosed, such as MYH11, COL5A2 and ribosomal proteins. Nine relevant small molecule drugs were identified, such as scriptaid and STOCK1N-35874. Accordingly, a total of 17 TFs and 10 miRNAs related to COAD were acquired, such as ETS2, NFAT, AP4, miR-124A, MiR-9, miR-96 and let-7. Several critical genes and relevant drugs, TFs and miRNAs were revealed in COAD. These findings could advance the understanding of the disease and benefit therapy development.

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

NARCIS (Netherlands)

Chiu, Hua Sheng; Somvanshi, Sonal; Patel, Ektaben; Chen, Ting Wen; Singh, Vivek P.; Zorman, Barry; Patil, Sagar L.; Pan, Yinghong; Chatterjee, Sujash S.; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Gonzalez, Ana Maria Angulo; Behrens, Carmen; Bondaruk, Jolanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Pinero, Edna M.Mora; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz; Sood, Anil K.; Gunaratne, Preethi H.; Sumazin, Pavel

2018-01-01

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription

Global Gene Expression Analysis of Yeast Cells during Sake Brewing▿ †

Science.gov (United States)

Wu, Hong; Zheng, Xiaohong; Araki, Yoshio; Sahara, Hiroshi; Takagi, Hiroshi; Shimoi, Hitoshi

2006-01-01

During the brewing of Japanese sake, Saccharomyces cerevisiae cells produce a high concentration of ethanol compared with other ethanol fermentation methods. We analyzed the gene expression profiles of yeast cells during sake brewing using DNA microarray analysis. This analysis revealed some characteristics of yeast gene expression during sake brewing and provided a scaffold for a molecular level understanding of the sake brewing process. PMID:16997994

Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis

Directory of Open Access Journals (Sweden)

Mario Flores

2013-01-01

Full Text Available Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA, into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.

In Silico Identification, Phylogenetic and Bioinformatic Analysis of Argonaute Genes in Plants

Directory of Open Access Journals (Sweden)

Khaled Mirzaei

2014-01-01

Full Text Available Argonaute protein family is the key players in pathways of gene silencing and small regulatory RNAs in different organisms. Argonaute proteins can bind small noncoding RNAs and control protein synthesis, affect messenger RNA stability, and even participate in the production of new forms of small RNAs. The aim of this study was to characterize and perform bioinformatic analysis of Argonaute proteins in 32 plant species that their genome was sequenced. A total of 437 Argonaute genes were identified and were analyzed based on lengths, gene structure, and protein structure. Results showed that Argonaute proteins were highly conserved across plant kingdom. Phylogenic analysis divided plant Argonautes into three classes. Argonaute proteins have three conserved domains PAZ, MID and PIWI. In addition to three conserved domains namely, PAZ, MID, and PIWI, we identified few more domains in AGO of some plant species. Expression profile analysis of Argonaute proteins showed that expression of these genes varies in most of tissues, which means that these proteins are involved in regulation of most pathways of the plant system. Numbers of alternative transcripts of Argonaute genes were highly variable among the plants. A thorough analysis of large number of putative Argonaute genes revealed several interesting aspects associated with this protein and brought novel information with promising usefulness for both basic and biotechnological applications.

Genome-wide comparative analysis of NBS-encoding genes between Brassica species and Arabidopsis thaliana.

Science.gov (United States)

Yu, Jingyin; Tehrim, Sadia; Zhang, Fengqi; Tong, Chaobo; Huang, Junyan; Cheng, Xiaohui; Dong, Caihua; Zhou, Yanqiu; Qin, Rui; Hua, Wei; Liu, Shengyi

2014-01-03

Plant disease resistance (R) genes with the nucleotide binding site (NBS) play an important role in offering resistance to pathogens. The availability of complete genome sequences of Brassica oleracea and Brassica rapa provides an important opportunity for researchers to identify and characterize NBS-encoding R genes in Brassica species and to compare with analogues in Arabidopsis thaliana based on a comparative genomics approach. However, little is known about the evolutionary fate of NBS-encoding genes in the Brassica lineage after split from A. thaliana. Here we present genome-wide analysis of NBS-encoding genes in B. oleracea, B. rapa and A. thaliana. Through the employment of HMM search and manual curation, we identified 157, 206 and 167 NBS-encoding genes in B. oleracea, B. rapa and A. thaliana genomes, respectively. Phylogenetic analysis among 3 species classified NBS-encoding genes into 6 subgroups. Tandem duplication and whole genome triplication (WGT) analyses revealed that after WGT of the Brassica ancestor, NBS-encoding homologous gene pairs on triplicated regions in Brassica ancestor were deleted or lost quickly, but NBS-encoding genes in Brassica species experienced species-specific gene amplification by tandem duplication after divergence of B. rapa and B. oleracea. Expression profiling of NBS-encoding orthologous gene pairs indicated the differential expression pattern of retained orthologous gene copies in B. oleracea and B. rapa. Furthermore, evolutionary analysis of CNL type NBS-encoding orthologous gene pairs among 3 species suggested that orthologous genes in B. rapa species have undergone stronger negative selection than those in B .oleracea species. But for TNL type, there are no significant differences in the orthologous gene pairs between the two species. This study is first identification and characterization of NBS-encoding genes in B. rapa and B. oleracea based on whole genome sequences. Through tandem duplication and whole genome

DNA Methylation Analysis of the Angiotensin Converting Enzyme (ACE) Gene in Major Depression

Science.gov (United States)

Zill, Peter; Baghai, Thomas C.; Schüle, Cornelius; Born, Christoph; Früstück, Clemens; Büttner, Andreas; Eisenmenger, Wolfgang; Varallo-Bedarida, Gabriella; Rupprecht, Rainer; Möller, Hans-Jürgen; Bondy, Brigitta

2012-01-01

Background The angiotensin converting enzyme (ACE) has been repeatedly discussed as susceptibility factor for major depression (MD) and the bi-directional relation between MD and cardiovascular disorders (CVD). In this context, functional polymorphisms of the ACE gene have been linked to depression, to antidepressant treatment response, to ACE serum concentrations, as well as to hypertension, myocardial infarction and CVD risk markers. The mostly investigated ACE Ins/Del polymorphism accounts for ∼40%–50% of the ACE serum concentration variance, the remaining half is probably determined by other genetic, environmental or epigenetic factors, but these are poorly understood. Materials and Methods The main aim of the present study was the analysis of the DNA methylation pattern in the regulatory region of the ACE gene in peripheral leukocytes of 81 MD patients and 81 healthy controls. Results We detected intensive DNA methylation within a recently described, functional important region of the ACE gene promoter including hypermethylation in depressed patients (p = 0.008) and a significant inverse correlation between the ACE serum concentration and ACE promoter methylation frequency in the total sample (p = 0.02). Furthermore, a significant inverse correlation between the concentrations of the inflammatory CVD risk markers ICAM-1, E-selectin and P-selectin and the degree of ACE promoter methylation in MD patients could be demonstrated (p = 0.01 - 0.04). Conclusion The results of the present study suggest that aberrations in ACE promoter DNA methylation may be an underlying cause of MD and probably a common pathogenic factor for the bi-directional relationship between MD and cardiovascular disorders. PMID:22808171

Meta-analysis of differentiating mouse embryonic stem cell gene expression kinetics reveals early change of a small gene set.

Directory of Open Access Journals (Sweden)

Clive H Glover

2006-11-01

Full Text Available Stem cell differentiation involves critical changes in gene expression. Identification of these should provide endpoints useful for optimizing stem cell propagation as well as potential clues about mechanisms governing stem cell maintenance. Here we describe the results of a new meta-analysis methodology applied to multiple gene expression datasets from three mouse embryonic stem cell (ESC lines obtained at specific time points during the course of their differentiation into various lineages. We developed methods to identify genes with expression changes that correlated with the altered frequency of functionally defined, undifferentiated ESC in culture. In each dataset, we computed a novel statistical confidence measure for every gene which captured the certainty that a particular gene exhibited an expression pattern of interest within that dataset. This permitted a joint analysis of the datasets, despite the different experimental designs. Using a ranking scheme that favored genes exhibiting patterns of interest, we focused on the top 88 genes whose expression was consistently changed when ESC were induced to differentiate. Seven of these (103728_at, 8430410A17Rik, Klf2, Nr0b1, Sox2, Tcl1, and Zfp42 showed a rapid decrease in expression concurrent with a decrease in frequency of undifferentiated cells and remained predictive when evaluated in additional maintenance and differentiating protocols. Through a novel meta-analysis, this study identifies a small set of genes whose expression is useful for identifying changes in stem cell frequencies in cultures of mouse ESC. The methods and findings have broader applicability to understanding the regulation of self-renewal of other stem cell types.

MicroScope-an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data.

Science.gov (United States)

Médigue, Claudine; Calteau, Alexandra; Cruveiller, Stéphane; Gachet, Mathieu; Gautreau, Guillaume; Josso, Adrien; Lajus, Aurélie; Langlois, Jordan; Pereira, Hugo; Planel, Rémi; Roche, David; Rollin, Johan; Rouy, Zoe; Vallenet, David

2017-09-12

The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources. © The Author 2017. Published by Oxford University Press.

Genomic analysis of primordial dwarfism reveals novel disease genes.

Science.gov (United States)

Shaheen, Ranad; Faqeih, Eissa; Ansari, Shinu; Abdel-Salam, Ghada; Al-Hassnan, Zuhair N; Al-Shidi, Tarfa; Alomar, Rana; Sogaty, Sameera; Alkuraya, Fowzan S

2014-02-01

Primordial dwarfism (PD) is a disease in which severely impaired fetal growth persists throughout postnatal development and results in stunted adult size. The condition is highly heterogeneous clinically, but the use of certain phenotypic aspects such as head circumference and facial appearance has proven helpful in defining clinical subgroups. In this study, we present the results of clinical and genomic characterization of 16 new patients in whom a broad definition of PD was used (e.g., 3M syndrome was included). We report a novel PD syndrome with distinct facies in two unrelated patients, each with a different homozygous truncating mutation in CRIPT. Our analysis also reveals, in addition to mutations in known PD disease genes, the first instance of biallelic truncating BRCA2 mutation causing PD with normal bone marrow analysis. In addition, we have identified a novel locus for Seckel syndrome based on a consanguineous multiplex family and identified a homozygous truncating mutation in DNA2 as the likely cause. An additional novel PD disease candidate gene XRCC4 was identified by autozygome/exome analysis, and the knockout mouse phenotype is highly compatible with PD. Thus, we add a number of novel genes to the growing list of PD-linked genes, including one which we show to be linked to a novel PD syndrome with a distinct facial appearance. PD is extremely heterogeneous genetically and clinically, and genomic tools are often required to reach a molecular diagnosis.

In silico identification and analysis of phytoene synthase genes in plants.

Science.gov (United States)

Han, Y; Zheng, Q S; Wei, Y P; Chen, J; Liu, R; Wan, H J

2015-08-14

In this study, we examined phytoene synthetase (PSY), the first key limiting enzyme in the synthesis of carotenoids and catalyzing the formation of geranylgeranyl pyrophosphate in terpenoid biosynthesis. We used known amino acid sequences of the PSY gene in tomato plants to conduct a genome-wide search and identify putative candidates in 34 sequenced plants. A total of 101 homologous genes were identified. Phylogenetic analysis revealed that PSY evolved independently in algae as well as monocotyledonous and dicotyledonous plants. Our results showed that the amino acid structures exhibited 5 motifs (motifs 1 to 5) in algae and those in higher plants were highly conserved. The PSY gene structures showed that the number of intron in algae varied widely, while the number of introns in higher plants was 4 to 5. Identification of PSY genes in plants and the analysis of the gene structure may provide a theoretical basis for studying evolutionary relationships in future analyses.

Performance of PCR-restriction fragment length polymorphism analysis of the Helicobacter pylori ureB gene in differentiating gene variants

DEFF Research Database (Denmark)

Colding, H; Hartzen, S H; Mohammadi, M

2003-01-01

Recently, PCR-restriction fragment length polymorphism (PCR-RFLP) of the urease genes of Helicobacter pylori was evaluated in a meta-analysis; acceptable discriminatory indices of the ureAB and C genes were found. In the present investigation, we found a discriminatory index of 0.95 for 191...... is comparable to typing of other H. pylori urease genes....

Development of gene diagnosis for diabetes and cholecystitis based on gene analysis of CCK-A receptor

International Nuclear Information System (INIS)

Kono, Akira

1999-01-01

Base sequence analysis of CCKAR gene (a gene of A-type receptor for cholecystokinin) from OLETF rat, a model rat for insulin-independent diabetes was made based on the base sequence of wild CCKAR gene, which had been clarified in the previous year. From the pancreas of OLETF rat, DNA was extracted and transduced into λphage after fragmentation to construct the gene library of OLETF. Then, λphage DNA clone bound with labelled cDNA of CCKAR gene was analyzed and the gene structure was compared with that of the wild gene. It was demonstrated that CCKAR gene of OLETF had a deletion (6800 b.p.) ranging from the promoter region to the Exon 2, suggesting that CCKAR gene is not functional in OLETF rat. The whole sequence of this mutant gene was registered into Japan DNA Bank (D 50610). Then, F 2 offspring rats were obtained through crossing OLETF (female) and F344 (male) and the time course-changes in the blood glucose level after glucose loading were compared among them. The blood glucose level after glucose loading was significantly higher in the homo-mutant F 2 (CCKAR,-/-) as well as the parent OLETF rat than hetero-mutant F 2 (CCKARm-/+) or the wild rat (CCKAR,+/+). This suggests that CCKAR gene might be involved in the control of blood glucose level and an alteration of the expression level or the functions of CCKAR gene might affect the blood glucose level. (M.N.)

Serial analysis of gene expression in the silkworm, Bombyx mori.

Science.gov (United States)

Huang, Jianhua; Miao, Xuexia; Jin, Weirong; Couble, Pierre; Mita, Kasuei; Zhang, Yong; Liu, Wenbin; Zhuang, Leijun; Shen, Yan; Keime, Celine; Gandrillon, Olivier; Brouilly, Patrick; Briolay, Jerome; Zhao, Guoping; Huang, Yongping

2005-08-01

The silkworm Bombyx mori is one of the most economically important insects and serves as a model for Lepidoptera insects. We used serial analysis of gene expression (SAGE) to derive profiles of expressed genes during the developmental life cycle of the silkworm and to create a reference for understanding silkworm metamorphosis. We generated four SAGE libraries, one from each of the four developmental stages of the silkworm. In total we obtained 257,964 SAGE tags, of which 39,485 were unique tags. Sorted by copy number, 14.1% of the unique tags were detected at a median to high level (five or more copies), 24.2% at lower levels (two to four copies), and 61.7% as single copies. Using a basic local alignment search tool on the EST database, 35% of the tags matched known silkworm expressed sequence tags. SAGE demonstrated that a number of the genes were up- or down-regulated during the four developmental phases of the egg, larva, pupa, and adult. Furthermore, we found that the generation of longer cDNA fragments from SAGE tags constituted the most efficient method of gene identification, which facilitated the analysis of a large number of unknown genes.

Weighted gene co-expression network analysis reveals potential genes involved in early metamorphosis process in sea cucumber Apostichopus japonicus.

Science.gov (United States)

Li, Yongxin; Kikuchi, Mani; Li, Xueyan; Gao, Qionghua; Xiong, Zijun; Ren, Yandong; Zhao, Ruoping; Mao, Bingyu; Kondo, Mariko; Irie, Naoki; Wang, Wen

2018-01-01

Sea cucumbers, one main class of Echinoderms, have a very fast and drastic metamorphosis process during their development. However, the molecular basis under this process remains largely unknown. Here we systematically examined the gene expression profiles of Japanese common sea cucumber (Apostichopus japonicus) for the first time by RNA sequencing across 16 developmental time points from fertilized egg to juvenile stage. Based on the weighted gene co-expression network analysis (WGCNA), we identified 21 modules. Among them, MEdarkmagenta was highly expressed and correlated with the early metamorphosis process from late auricularia to doliolaria larva. Furthermore, gene enrichment and differentially expressed gene analysis identified several genes in the module that may play key roles in the metamorphosis process. Our results not only provide a molecular basis for experimentally studying the development and morphological complexity of sea cucumber, but also lay a foundation for improving its emergence rate. Copyright © 2017 Elsevier Inc. All rights reserved.

Sequence comparison and phylogenetic analysis of core gene of ...

African Journals Online (AJOL)

Phylogenetic analysis suggests that our sequences are clustered with sequences reported from Japan. This is the first phylogenetic analysis of HCV core gene from Pakistani population. Our sequences and sequences from Japan are grouped into same cluster in the phylogenetic tree. Sequence comparison and ...

Analysis of gene and protein name synonyms in Entrez Gene and UniProtKB resources

KAUST Repository

Arkasosy, Basil

2013-05-11

Ambiguity in texts is a well-known problem: words can carry several meanings, and hence, can be read and interpreted differently. This is also true in the biological literature; names of biological concepts, such as genes and proteins, might be ambiguous, referring in some cases to more than one gene or one protein, or in others, to both genes and proteins at the same time. Public biological databases give a very useful insight about genes and proteins information, including their names. In this study, we made a thorough analysis of the nomenclatures of genes and proteins in two data sources and for six different species. We developed an automated process that parses, extracts, processes and stores information available in two major biological databases: Entrez Gene and UniProtKB. We analysed gene and protein synonyms, their types, frequencies, and the ambiguities within a species, in between data sources and cross-species. We found that at least 40% of the cross-species ambiguities are caused by names that are already ambiguous within the species. Our study shows that from the six species we analysed (Homo Sapiens, Mus Musculus, Arabidopsis Thaliana, Oryza Sativa, Bacillus Subtilis and Pseudomonas Fluorescens), rice (Oriza Sativa) has the best naming model in Entrez Gene database, with low ambiguities between data sources and cross-species.

Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice

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Shuchi eSmita

2015-12-01

Full Text Available MYB transcription factor (TF is one of the largest TF families and regulates defense responses to various stresses, hormone signaling as well as many metabolic and developmental processes in plants. Understanding these regulatory hierarchies of gene expression networks in response to developmental and environmental cues is a major challenge due to the complex interactions between the genetic elements. Correlation analyses are useful to unravel co-regulated gene pairs governing biological process as well as identification of new candidate hub genes in response to these complex processes. High throughput expression profiling data are highly useful for construction of co-expression networks. In the present study, we utilized transcriptome data for comprehensive regulatory network studies of MYB TFs by top down and guide gene approaches. More than 50% of OsMYBs were strongly correlated under fifty experimental conditions with 51 hub genes via top down approach. Further, clusters were identified using Markov Clustering (MCL. To maximize the clustering performance, parameter evaluation of the MCL inflation score (I was performed in terms of enriched GO categories by measuring F-score. Comparison of co-expressed cluster and clads analyzed from phylogenetic analysis signifies their evolutionarily conserved co-regulatory role. We utilized compendium of known interaction and biological role with Gene Ontology enrichment analysis to hypothesize function of coexpressed OsMYBs. In the other part, the transcriptional regulatory network analysis by guide gene approach revealed 40 putative targets of 26 OsMYB TF hubs with high correlation value utilizing 815 microarray data. The putative targets with MYB-binding cis-elements enrichment in their promoter region, functional co-occurrence as well as nuclear localization supports our finding. Specially, enrichment of MYB binding regions involved in drought-inducibility implying their regulatory role in drought

Analysis of multiplex gene expression maps obtained by voxelation

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Smith Desmond J

2009-04-01

Full Text Available Abstract Background Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. Results To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in

Analysis of multiplex gene expression maps obtained by voxelation.

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An, Li; Xie, Hongbo; Chin, Mark H; Obradovic, Zoran; Smith, Desmond J; Megalooikonomou, Vasileios

2009-04-29

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions. To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum. The experimental

Gene-level association analysis of systemic sclerosis: A comparison of African-Americans and White populations.

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Gorlova, Olga Y; Li, Yafang; Gorlov, Ivan; Ying, Jun; Chen, Wei V; Assassi, Shervin; Reveille, John D; Arnett, Frank C; Zhou, Xiaodong; Bossini-Castillo, Lara; Lopez-Isac, Elena; Acosta-Herrera, Marialbert; Gregersen, Peter K; Lee, Annette T; Steen, Virginia D; Fessler, Barri J; Khanna, Dinesh; Schiopu, Elena; Silver, Richard M; Molitor, Jerry A; Furst, Daniel E; Kafaja, Suzanne; Simms, Robert W; Lafyatis, Robert A; Carreira, Patricia; Simeon, Carmen Pilar; Castellvi, Ivan; Beltran, Emma; Ortego, Norberto; Amos, Christopher I; Martin, Javier; Mayes, Maureen D

2018-01-01

Gene-level analysis of ImmunoChip or genome-wide association studies (GWAS) data has not been previously reported for systemic sclerosis (SSc, scleroderma). The objective of this study was to analyze genetic susceptibility loci in SSc at the gene level and to determine if the detected associations were shared in African-American and White populations, using data from ImmunoChip and GWAS genotyping studies. The White sample included 1833 cases and 3466 controls (956 cases and 2741 controls from the US and 877 cases and 725 controls from Spain) and the African American sample, 291 cases and 260 controls. In both Whites and African Americans, we performed a gene-level analysis that integrates association statistics in a gene possibly harboring multiple SNPs with weak effect on disease risk, using Versatile Gene-based Association Study (VEGAS) software. The SNP-level analysis was performed using PLINK v.1.07. We identified 4 novel candidate genes (STAT1, FCGR2C, NIPSNAP3B, and SCT) significantly associated and 4 genes (SERBP1, PINX1, TMEM175 and EXOC2) suggestively associated with SSc in the gene level analysis in White patients. As an exploratory analysis we compared the results on Whites with those from African Americans. Of previously established susceptibility genes identified in Whites, only TNFAIP3 was significant at the nominal level (p = 6.13x10-3) in African Americans in the gene-level analysis of the ImmunoChip data. Among the top suggestive novel genes identified in Whites based on the ImmunoChip data, FCGR2C and PINX1 were only nominally significant in African Americans (p = 0.016 and p = 0.028, respectively), while among the top novel genes identified in the gene-level analysis in African Americans, UNC5C (p = 5.57x10-4) and CLEC16A (p = 0.0463) were also nominally significant in Whites. We also present the gene-level analysis of SSc clinical and autoantibody phenotypes among Whites. Our findings need to be validated by independent studies, particularly

TARGETED ANALYSIS OF JAK-STAT-SOCS GENES IN DAIRY CATTLE

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Arun Sondur Jayappa

2015-12-01

Full Text Available The Janus kinase and signal transducer and activator of transcription (JAK-STAT pathway genes along with suppressors of cytokine signalling (SOCS family genes play a crucial role in controlling cytokine signals in the mammary gland and thus mammary gland development. Mammary gene expression studies showed differential expression patterns for all the JAK-STAT pathway genes. Gene expression studies using qRT-PCR revealed differential expression of SOCS2, SOCS4 and SOCS5 genes across the lactation cycle in dairy cows. Using genotypes from 1,546 Australian Holstein- Friesian bulls, a statistical model based on SNPs within 500kb of JAK-STAT pathway genes, and SOCS genes alone was carried out. The analysis suggested that these genes and pathways make a significant contribution to the Australian milk production traits. Selection of 24 SNPs close to SOCS1, SOCS3, SOCS5, SOCS7 and CISH genes were significantly associated with, Australian Profit Ranking (APR, Australian Selection Index (ASI and protein yield (PY. This study supports the view that there may be some merit in choosing SNPs around functionally relevant genes for the selection and genetic improvement schemes for dairy production traits.

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.

Birth and death of gene overlaps in vertebrates

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Makałowska Izabela

2007-10-01

Full Text Available Abstract Background Between five and fourteen per cent of genes in the vertebrate genomes do overlap sharing some intronic and/or exonic sequence. It was observed that majority of these overlaps are not conserved among vertebrate lineages. Although several mechanisms have been proposed to explain gene overlap origination the evolutionary basis of these phenomenon are still not well understood. Here, we present results of the comparative analysis of several vertebrate genomes. The purpose of this study was to examine overlapping genes in the context of their evolution and mechanisms leading to their origin. Results Based on the presence and arrangement of human overlapping genes orthologs in rodent and fish genomes we developed 15 theoretical scenarios of overlapping genes evolution. Analysis of these theoretical scenarios and close examination of genomic sequences revealed new mechanisms leading to the overlaps evolution and confirmed that many of the vertebrate gene overlaps are not conserved. This study also demonstrates that repetitive elements contribute to the overlapping genes origination and, for the first time, that evolutionary events could lead to the loss of an ancient overlap. Conclusion Birth as well as most probably death of gene overlaps occurred over the entire time of vertebrate evolution and there wasn't any rapid origin or 'big bang' in the course of overlapping genes evolution. The major forces in the gene overlaps origination are transposition and exaptation. Our results also imply that origin of overlapping genes is not an issue of saving space and contracting genomes size.

Effects of in ovo electroporation on endogenous gene expression: genome-wide analysis

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Chambers David

2011-04-01

Full Text Available Abstract Background In ovo electroporation is a widely used technique to study gene function in developmental biology. Despite the widespread acceptance of this technique, no genome-wide analysis of the effects of in ovo electroporation, principally the current applied across the tissue and exogenous vector DNA introduced, on endogenous gene expression has been undertaken. Here, the effects of electric current and expression of a GFP-containing construct, via electroporation into the midbrain of Hamburger-Hamilton stage 10 chicken embryos, are analysed by microarray. Results Both current alone and in combination with exogenous DNA expression have a small but reproducible effect on endogenous gene expression, changing the expression of the genes represented on the array by less than 0.1% (current and less than 0.5% (current + DNA, respectively. The subset of genes regulated by electric current and exogenous DNA span a disparate set of cellular functions. However, no genes involved in the regional identity were affected. In sharp contrast to this, electroporation of a known transcription factor, Dmrt5, caused a much greater change in gene expression. Conclusions These findings represent the first systematic genome-wide analysis of the effects of in ovo electroporation on gene expression during embryonic development. The analysis reveals that this process has minimal impact on the genetic basis of cell fate specification. Thus, the study demonstrates the validity of the in ovo electroporation technique to study gene function and expression during development. Furthermore, the data presented here can be used as a resource to refine the set of transcriptional responders in future in ovo electroporation studies of specific gene function.

A comparative analysis of soft computing techniques for gene prediction.

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Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

Encyclopedia of bacterial gene circuits whose presence or absence correlate with pathogenicity--a large-scale system analysis of decoded bacterial genomes.

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Shestov, Maksim; Ontañón, Santiago; Tozeren, Aydin

2015-10-13

Bacterial infections comprise a global health challenge as the incidences of antibiotic resistance increase. Pathogenic potential of bacteria has been shown to be context dependent, varying in response to environment and even within the strains of the same genus. We used the KEGG repository and extensive literature searches to identify among the 2527 bacterial genomes in the literature those implicated as pathogenic to the host, including those which show pathogenicity in a context dependent manner. Using data on the gene contents of these genomes, we identified sets of genes highly abundant in pathogenic but relatively absent in commensal strains and vice versa. In addition, we carried out genome comparison within a genus for the seventeen largest genera in our genome collection. We projected the resultant lists of ortholog genes onto KEGG bacterial pathways to identify clusters and circuits, which can be linked to either pathogenicity or synergy. Gene circuits relatively abundant in nonpathogenic bacteria often mediated biosynthesis of antibiotics. Other synergy-linked circuits reduced drug-induced toxicity. Pathogen-abundant gene circuits included modules in one-carbon folate, two-component system, type-3 secretion system, and peptidoglycan biosynthesis. Antibiotics-resistant bacterial strains possessed genes modulating phagocytosis, vesicle trafficking, cytoskeletal reorganization, and regulation of the inflammatory response. Our study also identified bacterial genera containing a circuit, elements of which were previously linked to Alzheimer's disease. Present study produces for the first time, a signature, in the form of a robust list of gene circuitry whose presence or absence could potentially define the pathogenicity of a microbiome. Extensive literature search substantiated a bulk majority of the commensal and pathogenic circuitry in our predicted list. Scanning microbiome libraries for these circuitry motifs will provide further insights into the complex

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

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Ma'ayan Avi

2007-10-01

Full Text Available Abstract Background In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP, generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Results Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Conclusion Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence.

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Andrea Vereczkei

Full Text Available BACKGROUND: Heroin dependence is a debilitating psychiatric disorder with complex inheritance. Since the dopaminergic system has a key role in rewarding mechanism of the brain, which is directly or indirectly targeted by most drugs of abuse, we focus on the effects and interactions among dopaminergic gene variants. OBJECTIVE: To study the potential association between allelic variants of dopamine D2 receptor (DRD2, ANKK1 (ankyrin repeat and kinase domain containing 1, dopamine D4 receptor (DRD4, catechol-O-methyl transferase (COMT and dopamine transporter (SLC6A3 genes and heroin dependence in Hungarian patients. METHODS: 303 heroin dependent subjects and 555 healthy controls were genotyped for 7 single nucleotide polymorphisms (SNPs rs4680 of the COMT gene; rs1079597 and rs1800498 of the DRD2 gene; rs1800497 of the ANKK1 gene; rs1800955, rs936462 and rs747302 of the DRD4 gene. Four variable number of tandem repeats (VNTRs were also genotyped: 120 bp duplication and 48 bp VNTR in exon 3 of DRD4 and 40 bp VNTR and intron 8 VNTR of SLC6A3. We also perform a multivariate analysis of associations using Bayesian networks in Bayesian multilevel analysis (BN-BMLA. FINDINGS AND CONCLUSIONS: In single marker analysis the TaqIA (rs1800497 and TaqIB (rs1079597 variants were associated with heroin dependence. Moreover, -521 C/T SNP (rs1800955 of the DRD4 gene showed nominal association with a possible protective effect of the C allele. After applying the Bonferroni correction TaqIB was still significant suggesting that the minor (A allele of the TaqIB SNP is a risk component in the genetic background of heroin dependence. The findings of the additional multiple marker analysis are consistent with the results of the single marker analysis, but this method was able to reveal an indirect effect of a promoter polymorphism (rs936462 of the DRD4 gene and this effect is mediated through the -521 C/T (rs1800955 polymorphism in the promoter.

Expression analysis of the mouse S100A7/psoriasin gene in skin inflammation and mammary tumorigenesis

International Nuclear Information System (INIS)

Webb, Meghan; Myal, Yvonne; Shiu, Robert; Murphy, Leigh C; Watson, Peter H; Emberley, Ethan D; Lizardo, Michael; Alowami, Salem; Qing, Gefei; Alfia'ar, Abdullah; Snell-Curtis, Linda J; Niu, Yulian; Civetta, Alberto

2005-01-01

The human psoriasin (S100A7) gene has been implicated in inflammation and tumor progression. Implementation of a mouse model would facilitate further investigation of its function, however little is known of the murine psoriasin gene. In this study we have cloned the cDNA and characterized the expression of the potential murine ortholog of human S100A7/psoriasin in skin inflammation and mammary tumorigenesis. On the basis of chromosomal location, phylogenetic analysis, amino acid sequence similarity, conservation of a putative Jab1-binding motif, and similarities of the patterns of mouse S100A7/psoriasin gene expression (measured by RT-PCR and in-situ hybridization) with those of human S100A7/psoriasin, we propose that mouse S100A7/psoriasin is the murine ortholog of human psoriasin/S100A7. Although mouse S100A7/psoriasin is poorly conserved relative to other S100 family members, its pattern of expression parallels that of the human psoriasin gene. In murine skin S100A7/psoriasin was significantly upregulated in relation to inflammation. In murine mammary gland expression is also upregulated in mammary tumors, where it is localized to areas of squamous differentiation. This mirrors the context of expression in human tumor types where both squamous and glandular differentiation occur, including cervical and lung carcinomas. Additionally, mouse S100A7/psoriasin possesses a putative Jab1 binding motif that mediates many downstream functions of the human S100A7 gene. These observations and results support the hypothesis that the mouse S100A7 gene is structurally and functionally similar to human S100A7 and may offer a relevant model system for studying its normal biological function and putative role in tumor progression

On Special Functions in the Context of Clifford Analysis

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Malonek, H. R.; Falcão, M. I.

2010-09-01

Considering the foundation of Quaternionic Analysis by R. Fueter and his collaborators in the beginning of the 1930s as starting point of Clifford Analysis, we can look back to 80 years of work in this field. However the interest in multivariate analysis using Clifford algebras only started to grow significantly in the 70s. Since then a great amount of papers on Clifford Analysis referring different classes of Special Functions have appeared. This situation may have been triggered by a more systematic treatment of monogenic functions by their multiple series development derived from Gegenbauer or associated Legendre polynomials (and not only by their integral representation). Also approaches to Special Functions by means of algebraic methods, either Lie algebras or through Lie groups and symmetric spaces gained by that time importance and influenced their treatment in Clifford Analysis. In our talk we will rely on the generalization of the classical approach to Special Functions through differential equations with respect to the hypercomplex derivative, which is a more recently developed tool in Clifford Analysis. In this context special attention will be payed to the role of Special Functions as intermediator between continuous and discrete mathematics. This corresponds to a more recent trend in combinatorics, since it has been revealed that many algebraic structures have hidden combinatorial underpinnings.

Validation of reference genes for gene expression analysis in olive (Olea europaea) mesocarp tissue by quantitative real-time RT-PCR

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2014-01-01

Background Gene expression analysis using quantitative reverse transcription PCR (qRT-PCR) is a robust method wherein the expression levels of target genes are normalised using internal control genes, known as reference genes, to derive changes in gene expression levels. Although reference genes have recently been suggested for olive tissues, combined/independent analysis on different cultivars has not yet been tested. Therefore, an assessment of reference genes was required to validate the recent findings and select stably expressed genes across different olive cultivars. Results A total of eight candidate reference genes [glyceraldehyde 3-phosphate dehydrogenase (GAPDH), serine/threonine-protein phosphatase catalytic subunit (PP2A), elongation factor 1 alpha (EF1-alpha), polyubiquitin (OUB2), aquaporin tonoplast intrinsic protein (TIP2), tubulin alpha (TUBA), 60S ribosomal protein L18-3 (60S RBP L18-3) and polypyrimidine tract-binding protein homolog 3 (PTB)] were chosen based on their stability in olive tissues as well as in other plants. Expression stability was examined by qRT-PCR across 12 biological samples, representing mesocarp tissues at various developmental stages in three different olive cultivars, Barnea, Frantoio and Picual, independently and together during the 2009 season with two software programs, GeNorm and BestKeeper. Both software packages identified GAPDH, EF1-alpha and PP2A as the three most stable reference genes across the three cultivars and in the cultivar, Barnea. GAPDH, EF1-alpha and 60S RBP L18-3 were found to be most stable reference genes in the cultivar Frantoio while 60S RBP L18-3, OUB2 and PP2A were found to be most stable reference genes in the cultivar Picual. Conclusions The analyses of expression stability of reference genes using qRT-PCR revealed that GAPDH, EF1-alpha, PP2A, 60S RBP L18-3 and OUB2 are suitable reference genes for expression analysis in developing Olea europaea mesocarp tissues, displaying the highest level

Cloning and sequence analysis of hyaluronoglucosaminidase (nagH gene of Clostridium chauvoei

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Saroj K. Dangi

2017-09-01

Full Text Available Aim: Blackleg disease is caused by Clostridium chauvoei in ruminants. Although virulence factors such as C. chauvoei toxin A, sialidase, and flagellin are well characterized, hyaluronidases of C. chauvoei are not characterized. The present study was aimed at cloning and sequence analysis of hyaluronoglucosaminidase (nagH gene of C. chauvoei. Materials and Methods: C. chauvoei strain ATCC 10092 was grown in ATCC 2107 media and confirmed by polymerase chain reaction (PCR using the primers specific for 16-23S rDNA spacer region. nagH gene of C. chauvoei was amplified and cloned into pRham-SUMO vector and transformed into Escherichia cloni 10G cells. The construct was then transformed into E. cloni cells. Colony PCR was carried out to screen the colonies followed by sequencing of nagH gene in the construct. Results: PCR amplification yielded nagH gene of 1143 bp product, which was cloned in prokaryotic expression system. Colony PCR, as well as sequencing of nagH gene, confirmed the presence of insert. Sequence was then subjected to BLAST analysis of NCBI, which confirmed that the sequence was indeed of nagH gene of C. chauvoei. Phylogenetic analysis of the sequence showed that it is closely related to Clostridium perfringens and Clostridium paraputrificum. Conclusion: The gene for virulence factor nagH was cloned into a prokaryotic expression vector and confirmed by sequencing.

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

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Cumbie, Jason S; Kimbrel, Jeffrey A; Di, Yanming; Schafer, Daniel W; Wilhelm, Larry J; Fox, Samuel E; Sullivan, Christopher M; Curzon, Aron D; Carrington, James C; Mockler, Todd C; Chang, Jeff H

2011-01-01

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

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Jason S Cumbie

Full Text Available GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.

Analysis of Single-cell Gene Transcription by RNA Fluorescent In Situ Hybridization (FISH)

DEFF Research Database (Denmark)

Ronander, Elena; Bengtsson, Dominique C; Joergensen, Louise

2012-01-01

Adhesion of Plasmodium falciparum infected erythrocytes (IE) to human endothelial receptors during malaria infections is mediated by expression of PfEMP1 protein variants encoded by the var genes. The haploid P. falciparum genome harbors approximately 60 different var genes of which only one has...... been believed to be transcribed per cell at a time during the blood stage of the infection. How such mutually exclusive regulation of var gene transcription is achieved is unclear, as is the identification of individual var genes or sub-groups of var genes associated with different receptors...... fluorescent in situ hybridization (FISH) analysis of var gene transcription by the parasite in individual nuclei of P. falciparum IE(1). Here, we present a detailed protocol for carrying out the RNA-FISH methodology for analysis of var gene transcription in single-nuclei of P. falciparum infected human...

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

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Chen, Edward Y; Tan, Christopher M; Kou, Yan; Duan, Qiaonan; Wang, Zichen; Meirelles, Gabriela Vaz; Clark, Neil R; Ma'ayan, Avi

2013-04-15

System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

CANDIDATE GENE ANALYSIS IN ISRAELI SOLDIERS WITH STRESS FRACTURES

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Ran Yanovich

2012-03-01

Full Text Available To investigate the association of polymorphisms within candidate genes which we hypothesized may contribute to stress fracture predisposition, a case-control, cross- sectional study design was employed. Genotyping 268 Single Nucleotide Polymorphisms- SNPs within 17 genes in 385 Israeli young male and female recruits (182 with and 203 without stress fractures. Twenty-five polymorphisms within 9 genes (NR3C1, ANKH, VDR, ROR2, CALCR, IL6, COL1A2, CBG, and LRP4 showed statistically significant differences (p < 0.05 in the distribution between stress fracture cases and non stress fracture controls. Seventeen genetic variants were associated with an increased stress fracture risk, and eight variants with a decreased stress fracture risk. None of the SNP associations remained significant after correcting for multiple comparisons (false discovery rate- FDR. Our findings suggest that genes may be involved in stress fracture pathogenesis. Specifically, the CALCR and the VDR genes are intriguing candidates. The putative involvement of these genes in stress fracture predisposition requires analysis of more cases and controls and sequencing the relevant genomic regions, in order to define the specific gene mutations

Semi-supervised consensus clustering for gene expression data analysis

OpenAIRE

Wang, Yunli; Pan, Youlian

2014-01-01

Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

Gene Expression Signature in Endemic Osteoarthritis by Microarray Analysis

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Xi Wang

2015-05-01

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

Identification of Key Pathways and Genes in Advanced Coronary Atherosclerosis Using Bioinformatics Analysis

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Xiaowen Tan

2017-01-01

Full Text Available Background. Coronary artery atherosclerosis is a chronic inflammatory disease. This study aimed to identify the key changes of gene expression between early and advanced carotid atherosclerotic plaque in human. Methods. Gene expression dataset GSE28829 was downloaded from Gene Expression Omnibus (GEO, including 16 advanced and 13 early stage atherosclerotic plaque samples from human carotid. Differentially expressed genes (DEGs were analyzed. Results. 42,450 genes were obtained from the dataset. Top 100 up- and downregulated DEGs were listed. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG identification were performed. The result of functional and pathway enrichment analysis indicted that the immune system process played a critical role in the progression of carotid atherosclerotic plaque. Protein-protein interaction (PPI networks were performed either. Top 10 hub genes were identified from PPI network and top 6 modules were inferred. These genes were mainly involved in chemokine signaling pathway, cell cycle, B cell receptor signaling pathway, focal adhesion, and regulation of actin cytoskeleton. Conclusion. The present study indicated that analysis of DEGs would make a deeper understanding of the molecular mechanisms of atherosclerosis development and they might be used as molecular targets and diagnostic biomarkers for the treatment of atherosclerosis.

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

Science.gov (United States)

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

2014-06-01

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

Tracking difference in gene expression in a time-course experiment using gene set enrichment analysis.

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Pui Shan Wong

Full Text Available Fistulifera sp. strain JPCC DA0580 is a newly sequenced pennate diatom that is capable of simultaneously growing and accumulating lipids. This is a unique trait, not found in other related microalgae so far. It is able to accumulate between 40 to 60% of its cell weight in lipids, making it a strong candidate for the production of biofuel. To investigate this characteristic, we used RNA-Seq data gathered at four different times while Fistulifera sp. strain JPCC DA0580 was grown in oil accumulating and non-oil accumulating conditions. We then adapted gene set enrichment analysis (GSEA to investigate the relationship between the difference in gene expression of 7,822 genes and metabolic functions in our data. We utilized information in the KEGG pathway database to create the gene sets and changed GSEA to use re-sampling so that data from the different time points could be included in the analysis. Our GSEA method identified photosynthesis, lipid synthesis and amino acid synthesis related pathways as processes that play a significant role in oil production and growth in Fistulifera sp. strain JPCC DA0580. In addition to GSEA, we visualized the results by creating a network of compounds and reactions, and plotted the expression data on top of the network. This made existing graph algorithms available to us which we then used to calculate a path that metabolizes glucose into triacylglycerol (TAG in the smallest number of steps. By visualizing the data this way, we observed a separate up-regulation of genes at different times instead of a concerted response. We also identified two metabolic paths that used less reactions than the one shown in KEGG and showed that the reactions were up-regulated during the experiment. The combination of analysis and visualization methods successfully analyzed time-course data, identified important metabolic pathways and provided new hypotheses for further research.

Phylogenomic analysis of vertebrate thrombospondins reveals fish-specific paralogues, ancestral gene relationships and a tetrapod innovation

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Adams Josephine C

2006-04-01

Full Text Available Abstract Background Thrombospondins (TSPs are evolutionarily-conserved, extracellular, calcium-binding glycoproteins with important roles in cell-extracellular matrix interactions, angiogenesis, synaptogenesis and connective tissue organisation. Five TSPs, designated TSP-1 through TSP-5, are encoded in the human genome. All but one have known roles in acquired or inherited human diseases. To further understand the roles of TSPs in human physiology and pathology, it would be advantageous to extend the repertoire of relevant vertebrate models. In general the zebrafish is proving an excellent model organism for vertebrate biology, therefore we set out to evaluate the status of TSPs in zebrafish and two species of pufferfish. Results We identified by bioinformatics that three fish species encode larger numbers of TSPs than vertebrates, yet all these sequences group as homologues of TSP-1 to -4. By phylogenomic analysis of neighboring genes, we uncovered that, in fish, a TSP-4-like sequence is encoded from the gene corresponding to the tetrapod TSP-5 gene. Thus, all TSP genes show conservation of synteny between fish and tetrapods. In the human genome, the TSP-1, TSP-3, TSP-4 and TSP-5 genes lie within paralogous regions that provide insight into the ancestral genomic context of vertebrate TSPs. Conclusion A new model for TSP evolution in vertebrates is presented. The TSP-5 protein sequence has evolved rapidly from a TSP-4-like sequence as an innovation in the tetrapod lineage. TSP biology in fish is complicated by the presence of additional lineage- and species-specific TSP paralogues. These novel results give deeper insight into the evolution of TSPs in vertebrates and open new directions for understanding the physiological and pathological roles of TSP-4 and TSP-5 in humans.

Analysis of mammary specific gene locus regulation in differentiated cells derived by somatic cell fusion

International Nuclear Information System (INIS)

Robinson, Claire; Kolb, Andreas F.

2009-01-01

The transcriptional regulation of a gene is best analysed in the context of its normal chromatin surroundings. However, most somatic cells, in contrast to embryonic stem cells, are refractory to accurate modification by homologous recombination. We show here that it is possible to introduce precise genomic modifications in ES cells and to analyse the phenotypic consequences in differentiated cells by using a combination of gene targeting, site-specific recombination and somatic cell fusion. To provide a proof of principle, we have analysed the regulation of the casein gene locus in mammary gland cells derived from modified murine ES cells by somatic cell fusion. A β-galactosidase reporter gene was inserted in place of the β-casein gene and the modified ES cells, which do not express the reporter gene, were fused with the mouse mammary gland cell line HC11. The resulting cell clones expressed the β-galactosidase gene to a similar extent and with similar hormone responsiveness as the endogenous gene. However, a reporter gene under the control of a minimal β-casein promoter (encompassing the two consensus STAT5 binding sites which mediate the hormone response of the casein genes) was unable to replicate expression levels or hormone responsiveness of the endogenous gene when inserted into the same site of the casein locus. As expected, these results implicate sequences other than the STAT5 sites in the regulation of the β-casein gene

Impact of Docosahexaenoic Acid on Gene Expression during Osteoclastogenesis in Vitro—A Comprehensive Analysis

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Ikuo Morita

2013-08-01

Full Text Available Polyunsaturated fatty acids (PUFAs, especially n-3 polyunsaturated fatty acids, docosahexaenoic acid (DHA and eicosapentaenoic acid (EPA, are known to protect against inflammation-induced bone loss in chronic inflammatory diseases, such as rheumatoid arthritis, periodontitis and osteoporosis. We previously reported that DHA, not EPA, inhibited osteoclastogenesis induced by the receptor activator of nuclear factor-κB ligand (sRANKL in vitro. In this study, we performed gene expression analysis using microarrays to identify genes affected by the DHA treatment during osteoclastogenesis. DHA strongly inhibited osteoclastogenesis at the late stage. Among the genes upregulated by the sRANKL treatment, 4779 genes were downregulated by DHA and upregulated by the EPA treatment. Gene ontology analysis identified sets of genes related to cell motility, cell adhesion, cell-cell signaling and cell morphogenesis. Quantitative PCR analysis confirmed that DC-STAMP, an essential gene for the cell fusion process in osteoclastogenesis, and other osteoclast-related genes, such as Siglec-15, Tspan7 and Mst1r, were inhibited by DHA.

Selection of Reference Genes for qRT-PCR Analysis of Gene Expression in Stipa grandis during Environmental Stresses.

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Dongli Wan

Full Text Available Stipa grandis P. Smirn. is a dominant plant species in the typical steppe of the Xilingole Plateau of Inner Mongolia. Selection of suitable reference genes for the quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR is important for gene expression analysis and research into the molecular mechanisms underlying the stress responses of S. grandis. In the present study, 15 candidate reference genes (EF1 beta, ACT, GAPDH, SamDC, CUL4, CAP, SNF2, SKIP1, SKIP5, SKIP11, UBC2, UBC15, UBC17, UCH, and HERC2 were evaluated for their stability as potential reference genes for qRT-PCR under different stresses. Four algorithms were used: GeNorm, NormFinder, BestKeeper, and RefFinder. The results showed that the most stable reference genes were different under different stress conditions: EF1beta and UBC15 during drought and salt stresses; ACT and GAPDH under heat stress; SKIP5 and UBC17 under cold stress; UBC15 and HERC2 under high pH stress; UBC2 and UBC15 under wounding stress; EF1beta and UBC17 under jasmonic acid treatment; UBC15 and CUL4 under abscisic acid treatment; and HERC2 and UBC17 under salicylic acid treatment. EF1beta and HERC2 were the most suitable genes for the global analysis of all samples. Furthermore, six target genes, SgPOD, SgPAL, SgLEA, SgLOX, SgHSP90 and SgPR1, were selected to validate the most and least stable reference genes under different treatments. Our results provide guidelines for reference gene selection for more accurate qRT-PCR quantification and will promote studies of gene expression in S. grandis subjected to environmental stress.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

Science.gov (United States)

Cava, Claudia; Bertoli, Gloria; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Castiglioni, Isabella

2018-01-06

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network. We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study. Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.

Science.gov (United States)

Mustafin, Zakhar Sergeevich; Lashin, Sergey Alexandrovich; Matushkin, Yury Georgievich; Gunbin, Konstantin Vladimirovich; Afonnikov, Dmitry Arkadievich

2017-01-27

There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape ( http://cytoscape.org/ ) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged 'network evolution' found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results. Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface. Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

Identification of conserved drought-adaptive genes using a cross-species meta-analysis approach.

Science.gov (United States)

Shaar-Moshe, Lidor; Hübner, Sariel; Peleg, Zvi

2015-05-03

Drought is the major environmental stress threatening crop-plant productivity worldwide. Identification of new genes and metabolic pathways involved in plant adaptation to progressive drought stress at the reproductive stage is of great interest for agricultural research. We developed a novel Cross-Species meta-Analysis of progressive Drought stress at the reproductive stage (CSA:Drought) to identify key drought adaptive genes and mechanisms and to test their evolutionary conservation. Empirically defined filtering criteria were used to facilitate a robust integration of 17 deposited microarray experiments (148 arrays) of Arabidopsis, rice, wheat and barley. By prioritizing consistency over intensity, our approach was able to identify 225 differentially expressed genes shared across studies and taxa. Gene ontology enrichment and pathway analyses classified the shared genes into functional categories involved predominantly in metabolic processes (e.g. amino acid and carbohydrate metabolism), regulatory function (e.g. protein degradation and transcription) and response to stimulus. We further investigated drought related cis-acting elements in the shared gene promoters, and the evolutionary conservation of shared genes. The universal nature of the identified drought-adaptive genes was further validated in a fifth species, Brachypodium distachyon that was not included in the meta-analysis. qPCR analysis of 27, randomly selected, shared orthologs showed similar expression pattern as was found by the CSA:Drought.In accordance, morpho-physiological characterization of progressive drought stress, in B. distachyon, highlighted the key role of osmotic adjustment as evolutionary conserved drought-adaptive mechanism. Our CSA:Drought strategy highlights major drought-adaptive genes and metabolic pathways that were only partially, if at all, reported in the original studies included in the meta-analysis. These genes include a group of unclassified genes that could be involved

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

Semantic integration of gene expression analysis tools and data sources using software connectors

Science.gov (United States)

2013-01-01

Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools

Genome-wide analysis of the expansin gene superfamily reveals grapevine-specific structural and functional characteristics.

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Silvia Dal Santo

Full Text Available BACKGROUND: Expansins are proteins that loosen plant cell walls in a pH-dependent manner, probably by increasing the relative movement among polymers thus causing irreversible expansion. The expansin superfamily (EXP comprises four distinct families: expansin A (EXPA, expansin B (EXPB, expansin-like A (EXLA and expansin-like B (EXLB. There is experimental evidence that EXPA and EXPB proteins are required for cell expansion and developmental processes involving cell wall modification, whereas the exact functions of EXLA and EXLB remain unclear. The complete grapevine (Vitis vinifera genome sequence has allowed the characterization of many gene families, but an exhaustive genome-wide analysis of expansin gene expression has not been attempted thus far. METHODOLOGY/PRINCIPAL FINDINGS: We identified 29 EXP superfamily genes in the grapevine genome, representing all four EXP families. Members of the same EXP family shared the same exon-intron structure, and phylogenetic analysis confirmed a closer relationship between EXP genes from woody species, i.e. grapevine and poplar (Populus trichocarpa, compared to those from Arabidopsis thaliana and rice (Oryza sativa. We also identified grapevine-specific duplication events involving the EXLB family. Global gene expression analysis confirmed a strong correlation among EXP genes expressed in mature and green/vegetative samples, respectively, as reported for other gene families in the recently-published grapevine gene expression atlas. We also observed the specific co-expression of EXLB genes in woody organs, and the involvement of certain grapevine EXP genes in berry development and post-harvest withering. CONCLUSION: Our comprehensive analysis of the grapevine EXP superfamily confirmed and extended current knowledge about the structural and functional characteristics of this gene family, and also identified properties that are currently unique to grapevine expansin genes. Our data provide a model for the

Sequence analysis of putative swrW gene required for surfactant ...

African Journals Online (AJOL)

owner

2012-07-17

Jul 17, 2012 ... These nucleotide and protein sequence analysis of the putative swrW gene provides vital information on the versatility .... chain reaction (PCR) products were stored at 4°C. Presence of ... identical to the same gene with an E-value of 0.0. .... The Prokaryotes-A Handbook on the Biol. of Bacteria:Ecophysiol.

Genome-Wide Analysis of the NAC Gene Family in Physic Nut (Jatropha curcas L.).

Science.gov (United States)

Wu, Zhenying; Xu, Xueqin; Xiong, Wangdan; Wu, Pingzhi; Chen, Yaping; Li, Meiru; Wu, Guojiang; Jiang, Huawu

2015-01-01

The NAC proteins (NAM, ATAF1/2 and CUC2) are plant-specific transcriptional regulators that have a conserved NAM domain in the N-terminus. They are involved in various biological processes, including both biotic and abiotic stress responses. In the present study, a total of 100 NAC genes (JcNAC) were identified in physic nut (Jatropha curcas L.). Based on phylogenetic analysis and gene structures, 83 JcNAC genes were classified as members of, or proposed to be diverged from, 39 previously predicted orthologous groups (OGs) of NAC sequences. Physic nut has a single intron-containing NAC gene subfamily that has been lost in many plants. The JcNAC genes are non-randomly distributed across the 11 linkage groups of the physic nut genome, and appear to be preferentially retained duplicates that arose from both ancient and recent duplication events. Digital gene expression analysis indicates that some of the JcNAC genes have tissue-specific expression profiles (e.g. in leaves, roots, stem cortex or seeds), and 29 genes differentially respond to abiotic stresses (drought, salinity, phosphorus deficiency and nitrogen deficiency). Our results will be helpful for further functional analysis of the NAC genes in physic nut.

Bioinformatics Analysis of MAPKKK Family Genes in Medicago truncatula

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Wei Li

2016-04-01

Full Text Available Mitogen‐activated protein kinase kinase kinase (MAPKKK is a component of the MAPK cascade pathway that plays an important role in plant growth, development, and response to abiotic stress, the functions of which have been well characterized in several plant species, such as Arabidopsis, rice, and maize. In this study, we performed genome‐wide and systemic bioinformatics analysis of MAPKKK family genes in Medicago truncatula. In total, there were 73 MAPKKK family members identified by search of homologs, and they were classified into three subfamilies, MEKK, ZIK, and RAF. Based on the genomic duplication function, 72 MtMAPKKK genes were located throughout all chromosomes, but they cluster in different chromosomes. Using microarray data and high‐throughput sequencing‐data, we assessed their expression profiles in growth and development processes; these results provided evidence for exploring their important functions in developmental regulation, especially in the nodulation process. Furthermore, we investigated their expression in abiotic stresses by RNA‐seq, which confirmed their critical roles in signal transduction and regulation processes under stress. In summary, our genome‐wide, systemic characterization and expressional analysis of MtMAPKKK genes will provide insights that will be useful for characterizing the molecular functions of these genes in M. truncatula.

Gene expression analysis of FABP4 in gastric cancer

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Abdulkarim Yasin Karim

2016-06-01

Full Text Available Purpose: Gastric cancer has high incidence and mortality rate in several countries and is still one of the most frequent and lethal disease. In this study, we aimed to determine diagnostic markers in gastric cancer by molecular techniques; include mRNA expression analysis of FABP4 gene. Fatty acid binding protein 4 (FABP4 gene encodes the fatty acid binding protein found in adipocytes. The protein encoded by FABP4 are a family of small, highly conserved, cytoplasmic proteins that bind long-chain fatty acids and other hydrophobic ligands. It is thought that FABPs roles include fatty acid uptake, transport, and metabolism. Material and Methods: Total RNA were extracted from paired tumor and normal tissues of 47 gastric cancer. The mRNA expression level of FABP4 was measured employing semi- quantitative reverse transcription- polymerase chain reaction (RT- PCR. Results: The mRNA expression level of FABP4 was significantly decreased (down- regulated. Conclusion: Down-regulation of FABP4 gene seems to occur at the initial steps of gastric cancer development. In order to confirm the relationship between the gastric tumor and FABP4 gene, further analysis like immunohistochemistry and epigenetc techniques are necessary. [Cukurova Med J 2016; 41(2.000: 248-252

Economic solvency in the context of violence against women: a concept analysis.

Science.gov (United States)

Gilroy, Heidi; Symes, Lene; McFarlane, Judith

2015-03-01

The aim of this concept analysis is to define economic solvency in the context of violence against women. Poverty, or lack of resources, is often discussed as a risk factor for intimate partner violence. The concept of economic solvency, which may be a protective factor for women, is less often discussed and not well defined. Databases searched for the analysis included EBSCOhost, CINAHL, PubMed and Gender Watch. The Rodgers evolutionary method was used to perform the concept analysis. A total of 134 articles were retrieved, using the specified search terms 'economic solvency and women', 'economic self-reliance and women' and 'economic self-sufficiency and women'. Articles were included if they were peer reviewed, contained the keywords with sufficient context to determine the author's intended meaning, and focused on women only or contrasted men to women. Thirty-five articles were used in the concept analysis. The definition of economic solvency drawn from the concept analysis is: a long-term state that occurs when there is societal structure that supports gender equity and external resources are available and can be used by a woman who has necessary human capital, sustainable employment and independence. Just as poverty and violence are cyclical, so are economic solvency and empowerment of women. To decrease women's risk of intimate partner violence around the world and further improve the status of women, we recommend continued research on economic solvency, including the individual, family, community and societal resources required to obtain economic solvency and the human capital characteristics needed for sustainability. © 2014 John Wiley & Sons Ltd.

Fine mapping and candidate gene analysis of the virescent gene v 1 in Upland cotton (Gossypium hirsutum).

Science.gov (United States)

Mao, Guangzhi; Ma, Qiang; Wei, Hengling; Su, Junji; Wang, Hantao; Ma, Qifeng; Fan, Shuli; Song, Meizhen; Zhang, Xianlong; Yu, Shuxun

2018-02-01

The young leaves of virescent mutants are yellowish and gradually turn green as the plants reach maturity. Understanding the genetic basis of virescent mutants can aid research of the regulatory mechanisms underlying chloroplast development and chlorophyll biosynthesis, as well as contribute to the application of virescent traits in crop breeding. In this study, fine mapping was employed, and a recessive gene (v 1 ) from a virescent mutant of Upland cotton was narrowed to an 84.1-Kb region containing ten candidate genes. The GhChlI gene encodes the cotton Mg-chelatase I subunit (CHLI) and was identified as the candidate gene for the virescent mutation using gene annotation. BLAST analysis showed that the GhChlI gene has two copies, Gh_A10G0282 and Gh_D10G0283. Sequence analysis indicated that the coding region (CDS) of GhChlI is 1269 bp in length, with three predicted exons and one non-synonymous nucleotide mutation (G1082A) in the third exon of Gh_D10G0283, with an amino acid (AA) substitution of arginine (R) to lysine (K). GhChlI-silenced TM-1 plants exhibited a lower GhChlI expression level, a lower chlorophyll content, and the virescent phenotype. Analysis of upstream regulatory elements and expression levels of GhChlI showed that the expression quantity of GhChlI may be normal, and with the development of the true leaf, the increase in the Gh_A10G0282 dosage may partially make up for the deficiency of Gh_D10G0283 in the v 1 mutant. Phylogenetic analysis and sequence alignment revealed that the protein sequence encoded by the third exon of GhChlI is highly conserved across diverse plant species, in which AA substitutions among the completely conserved residues frequently result in changes in leaf color in various species. These results suggest that the mutation (G1082A) within the GhChlI gene may cause a functional defect of the GhCHLI subunit and thus the virescent phenotype in the v 1 mutant. The GhChlI mutation not only provides a tool for understanding the

Analysis of pan-genome to identify the core genes and essential genes of Brucella spp.

Science.gov (United States)

Yang, Xiaowen; Li, Yajie; Zang, Juan; Li, Yexia; Bie, Pengfei; Lu, Yanli; Wu, Qingmin

2016-04-01

Brucella spp. are facultative intracellular pathogens, that cause a contagious zoonotic disease, that can result in such outcomes as abortion or sterility in susceptible animal hosts and grave, debilitating illness in humans. For deciphering the survival mechanism of Brucella spp. in vivo, 42 Brucella complete genomes from NCBI were analyzed for the pan-genome and core genome by identification of their composition and function of Brucella genomes. The results showed that the total 132,143 protein-coding genes in these genomes were divided into 5369 clusters. Among these, 1710 clusters were associated with the core genome, 1182 clusters with strain-specific genes and 2477 clusters with dispensable genomes. COG analysis indicated that 44 % of the core genes were devoted to metabolism, which were mainly responsible for energy production and conversion (COG category C), and amino acid transport and metabolism (COG category E). Meanwhile, approximately 35 % of the core genes were in positive selection. In addition, 1252 potential essential genes were predicted in the core genome by comparison with a prokaryote database of essential genes. The results suggested that the core genes in Brucella genomes are relatively conservation, and the energy and amino acid metabolism play a more important role in the process of growth and reproduction in Brucella spp. This study might help us to better understand the mechanisms of Brucella persistent infection and provide some clues for further exploring the gene modules of the intracellular survival in Brucella spp.

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.

Identification of candidate genes for human pituitary development by EST analysis

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Xiao Huasheng

2009-03-01

Full Text Available Abstract Background The pituitary is a critical neuroendocrine gland that is comprised of five hormone-secreting cell types, which develops in tandem during the embryonic stage. Some essential genes have been identified in the early stage of adenohypophysial development, such as PITX1, FGF8, BMP4 and SF-1. However, it is likely that a large number of signaling molecules and transcription factors essential for determination and terminal differentiation of specific cell types remain unidentified. High-throughput methods such as microarray analysis may facilitate the measurement of gene transcriptional levels, while Expressed sequence tag (EST sequencing, an efficient method for gene discovery and expression level analysis, may no-redundantly help to understand gene expression patterns during development. Results A total of 9,271 ESTs were generated from both fetal and adult pituitaries, and assigned into 961 gene/EST clusters in fetal and 2,747 in adult pituitary by homology analysis. The transcription maps derived from these data indicated that developmentally relevant genes, such as Sox4, ST13 and ZNF185, were dominant in the cDNA library of fetal pituitary, while hormones and hormone-associated genes, such as GH1, GH2, POMC, LHβ, CHGA and CHGB, were dominant in adult pituitary. Furthermore, by using RT-PCR and in situ hybridization, Sox4 was found to be one of the main transcription factors expressed in fetal pituitary for the first time. It was expressed at least at E12.5, but decreased after E17.5. In addition, 40 novel ESTs were identified specifically in this tissue. Conclusion The significant changes in gene expression in both tissues suggest a distinct and dynamic switch between embryonic and adult pituitaries. All these data along with Sox4 should be confirmed to further understand the community of multiple signaling pathways that act as a cooperative network that regulates maturation of the pituitary. It was also suggested that EST

rpb2 is a reliable reference gene for quantitative gene expression analysis in the dermatophyte Trichophyton rubrum.

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Jacob, Tiago R; Peres, Nalu T A; Persinoti, Gabriela F; Silva, Larissa G; Mazucato, Mendelson; Rossi, Antonio; Martinez-Rossi, Nilce M

2012-05-01

The selection of reference genes used for data normalization to quantify gene expression by real-time PCR amplifications (qRT-PCR) is crucial for the accuracy of this technique. In spite of this, little information regarding such genes for qRT-PCR is available for gene expression analyses in pathogenic fungi. Thus, we investigated the suitability of eight candidate reference genes in isolates of the human dermatophyte Trichophyton rubrum subjected to several environmental challenges, such as drug exposure, interaction with human nail and skin, and heat stress. The stability of these genes was determined by geNorm, NormFinder and Best-Keeper programs. The gene with the most stable expression in the majority of the conditions tested was rpb2 (DNA-dependent RNA polymerase II), which was validated in three T. rubrum strains. Moreover, the combination of rpb2 and chs1 (chitin synthase) genes provided for the most reliable qRT-PCR data normalization in T. rubrum under a broad range of biological conditions. To the best of our knowledge this is the first report on the selection of reference genes for qRT-PCR data normalization in dermatophytes and the results of these studies should permit further analysis of gene expression under several experimental conditions, with improved accuracy and reliability.

No control genes required: Bayesian analysis of qRT-PCR data.

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Mikhail V Matz

Full Text Available Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process.In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts. Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests.Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.

No control genes required: Bayesian analysis of qRT-PCR data.

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Matz, Mikhail V; Wright, Rachel M; Scott, James G

2013-01-01

Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.

Expression and functional analysis of apoptosis-related gene ...

African Journals Online (AJOL)

Administrator

2011-10-19

Oct 19, 2011 ... conducted a molecular cloning and functional analysis to study a specific silkworm gene BmICAD related to apoptosis. .... blocking with 5% non-fat milk for 1 h at room temperature, the .... requirements for all next experiments.

Enablement in health care context: a concept analysis.

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Hudon, Catherine; St-Cyr Tribble, Denise; Bravo, Gina; Poitras, Marie-Eve

2011-02-01

The enablement process is defined as a professional intervention aiming to recognize, support and emphasize the patient's capacity to have control over her or his health and life. The purpose of this article was to study the enablement concept through a concept analysis in the health care context to identify: (1) its attributes and (2) its antecedents and consequents. A concept analysis was performed according to the method of Rodgers. The literature was reviewed from 1980 to June 2008, using search strategies adapted to the databases Cinahl, Medline, Embase, PsycInfo and Social Works Abstract, and hand searching. All articles contributing to a deeper understanding of the concept were included. The analysis was carried out according to a thematic analysis procedure, as described by Miles & Huberman. The search identified 1305 citations. After in-depth assessment of 148 potentially eligible citations, 61 articles were included in the review. Five articles were added with hand searching. Sixty-seven per cent of these articles were related to nursing. The attributes of the enablement concept included: contribution to the therapeutic relationship; consideration of the person as a whole; facilitation of learning; valorization of the person's strengths; implication and support to decision making; and broadening of the possibilities. These attributes could be used as a basis for other studies on enablement. Conceptual and empirical work is still needed to better position this concept among others such as patient-centred care, shared decision making and patient's participation. © 2010 Blackwell Publishing Ltd.

Temporal-pattern similarity analysis reveals the beneficial and detrimental effects of context reinstatement on human memory.

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Staudigl, Tobias; Vollmar, Christian; Noachtar, Soheyl; Hanslmayr, Simon

2015-04-01

A powerful force in human memory is the context in which memories are encoded (Tulving and Thomson, 1973). Several studies suggest that the reinstatement of neural encoding patterns is beneficial for memory retrieval (Manning et al., 2011; Staresina et al., 2012; Jafarpour et al., 2014). However, reinstatement of the original encoding context is not always helpful, for instance, when retrieving a memory in a different contextual situation (Smith and Vela, 2001). It is an open question whether such context-dependent memory effects can be captured by the reinstatement of neural patterns. We investigated this question by applying temporal and spatial pattern similarity analysis in MEG and intracranial EEG in a context-match paradigm. Items (words) were tagged by individual dynamic context stimuli (movies). The results show that beta oscillatory phase in visual regions and the parahippocampal cortex tracks the incidental reinstatement of individual context trajectories on a single-trial level. Crucially, memory benefitted from reinstatement when the encoding and retrieval contexts matched but suffered from reinstatement when the contexts did not match. Copyright © 2015 the authors 0270-6474/15/355373-12$15.00/0.

Male-biased genes in catfish as revealed by RNA-Seq analysis of the testis transcriptome.

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Fanyue Sun

Full Text Available BACKGROUND: Catfish has a male-heterogametic (XY sex determination system, but genes involved in gonadogenesis, spermatogenesis, testicular determination, and sex determination are poorly understood. As a first step of understanding the transcriptome of the testis, here, we conducted RNA-Seq analysis using high throughput Illumina sequencing. METHODOLOGY/PRINCIPAL FINDINGS: A total of 269.6 million high quality reads were assembled into 193,462 contigs with a N50 length of 806 bp. Of these contigs, 67,923 contigs had hits to a set of 25,307 unigenes, including 167 unique genes that had not been previously identified in catfish. A meta-analysis of expressed genes in the testis and in the gynogen (double haploid female allowed the identification of 5,450 genes that are preferentially expressed in the testis, providing a pool of putative male-biased genes. Gene ontology and annotation analysis suggested that many of these male-biased genes were involved in gonadogenesis, spermatogenesis, testicular determination, gametogenesis, gonad differentiation, and possibly sex determination. CONCLUSION/SIGNIFICANCE: We provide the first transcriptome-level analysis of the catfish testis. Our analysis would lay the basis for sequential follow-up studies of genes involved in sex determination and differentiation in catfish.

[BIOINFORMATIC SEARCH AND PHYLOGENETIC ANALYSIS OF THE CELLULOSE SYNTHASE GENES OF FLAX (LINUM USITATISSIMUM)].

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Pydiura, N A; Bayer, G Ya; Galinousky, D V; Yemets, A I; Pirko, Ya V; Podvitski, T A; Anisimova, N V; Khotyleva, L V; Kilchevsky, A V; Blume, Ya B

2015-01-01

A bioinformatic search of sequences encoding cellulose synthase genes in the flax genome, and their comparison to dicots orthologs was carried out. The analysis revealed 32 cellulose synthase gene candidates, 16 of which are highly likely to encode cellulose synthases, and the remaining 16--cellulose synthase-like proteins (Csl). Phylogenetic analysis of gene products of cellulose synthase genes allowed distinguishing 6 groups of cellulose synthase genes of different classes: CesA1/10, CesA3, CesA4, CesA5/6/2/9, CesA7 and CesA8. Paralogous sequences within classes CesA1/10 and CesA5/6/2/9 which are associated with the primary cell wall formation are characterized by a greater similarity within these classes than orthologous sequences. Whereas the genes controlling the biosynthesis of secondary cell wall cellulose form distinct clades: CesA4, CesA7, and CesA8. The analysis of 16 identified flax cellulose synthase gene candidates shows the presence of at least 12 different cellulose synthase gene variants in flax genome which are represented in all six clades of cellulose synthase genes. Thus, at this point genes of all ten known cellulose synthase classes are identify in flax genome, but their correct classification requires additional research.

Genome-wide analysis of the WRKY gene family in physic nut (Jatropha curcas L.).

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Xiong, Wangdan; Xu, Xueqin; Zhang, Lin; Wu, Pingzhi; Chen, Yaping; Li, Meiru; Jiang, Huawu; Wu, Guojiang

2013-07-25

The WRKY proteins, which contain highly conserved WRKYGQK amino acid sequences and zinc-finger-like motifs, constitute a large family of transcription factors in plants. They participate in diverse physiological and developmental processes. WRKY genes have been identified and characterized in a number of plant species. We identified a total of 58 WRKY genes (JcWRKY) in the genome of the physic nut (Jatropha curcas L.). On the basis of their conserved WRKY domain sequences, all of the JcWRKY proteins could be assigned to one of the previously defined groups, I-III. Phylogenetic analysis of JcWRKY genes with Arabidopsis and rice WRKY genes, and separately with castor bean WRKY genes, revealed no evidence of recent gene duplication in JcWRKY gene family. Analysis of transcript abundance of JcWRKY gene products were tested in different tissues under normal growth condition. In addition, 47 WRKY genes responded to at least one abiotic stress (drought, salinity, phosphate starvation and nitrogen starvation) in individual tissues (leaf, root and/or shoot cortex). Our study provides a useful reference data set as the basis for cloning and functional analysis of physic nut WRKY genes. Copyright © 2013 Elsevier B.V. All rights reserved.

[Genome-wide identification and expression analysis of the WRKY gene family in peach].

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Gu, Yan-bing; Ji, Zhi-rui; Chi, Fu-mei; Qiao, Zhuang; Xu, Cheng-nan; Zhang, Jun-xiang; Zhou, Zong-shan; Dong, Qing-long

2016-03-01

The WRKY transcription factors are one of the largest families of transcriptional regulators and play diverse regulatory roles in biotic and abiotic stresses, plant growth and development processes. In this study, the WRKY DNA-binding domain (Pfam Database number: PF03106) downloaded from Pfam protein families database was exploited to identify WRKY genes from the peach (Prunus persica 'Lovell') genome using HMMER 3.0. The obtained amino acid sequences were analyzed with DNAMAN 5.0, WebLogo 3, MEGA 5.1, MapInspect and MEME bioinformatics softwares. Totally 61 peach WRKY genes were found in the peach genome. Our phylogenetic analysis revealed that peach WRKY genes were classified into three Groups: Ⅰ, Ⅱ and Ⅲ. The WRKY N-terminal and C-terminal domains of Group Ⅰ (group I-N and group I-C) were monophyletic. The Group Ⅱ was sub-divided into five distinct clades (groupⅡ-a, Ⅱ-b, Ⅱ-c, Ⅱ-d and Ⅱ-e). Our domain analysis indicated that the WRKY regions contained a highly conserved heptapeptide stretch WRKYGQK at its N-terminus followed by a zinc-finger motif. The chromosome mapping analysis showed that peach WRKY genes were distributed with different densities over 8 chromosomes. The intron-exon structure analysis revealed that structures of the WRKY gene were highly conserved in the peach. The conserved motif analysis showed that the conserved motifs 1, 2 and 3, which specify the WRKY domain, were observed in all peach WRKY proteins, motif 5 as the unknown domain was observed in group Ⅱ-d, two WRKY domains were assigned to GroupⅠ. SqRT-PCR and qRT-PCR results indicated that 16 PpWRKY genes were expressed in roots, stems, leaves, flowers and fruits at various expression levels. Our analysis thus identified the PpWRKY gene families, and future functional studies are needed to reveal its specific roles.

Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules.

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Curtis, Ross E; Kim, Seyoung; Woolford, John L; Xu, Wenjie; Xing, Eric P

2013-03-21

Association analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant. While eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case of the Ribi group

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.

Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

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Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

2015-06-01

To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

The utility of optical detection system (qPCR) and bioinformatics methods in reference gene expression analysis

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Skarzyńska, Agnieszka; Pawełkowicz, Magdalena; PlÄ der, Wojciech; Przybecki, Zbigniew

2016-09-01

Real-time quantitative polymerase chain reaction is consider as the most reliable method for gene expression studies. However, the expression of target gene could be misinterpreted due to improper normalization. Therefore, the crucial step for analysing of qPCR data is selection of suitable reference genes, which should be validated experimentally. In order to choice the gene with stable expression in the designed experiment, we performed reference gene expression analysis. In this study genes described in the literature and novel genes predicted as control genes, based on the in silico analysis of transcriptome data were used. Analysis with geNorm and NormFinder algorithms allow to create the ranking of candidate genes and indicate the best reference for flower morphogenesis study. According to the results, genes CACS and CYCL were characterised the most stable expression, but the least suitable genes were TUA and EF.

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI.

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Wang, Weijing; Jiang, Wenjie; Hou, Lin; Duan, Haiping; Wu, Yili; Xu, Chunsheng; Tan, Qihua; Li, Shuxia; Zhang, Dongfeng

2017-11-13

The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly

The Analysis of Communicative Context Adaptation in English Advertisements

Institute of Scientific and Technical Information of China (English)

向凤雅; 杨婧; 张明耀

2015-01-01

Communicative context adaptation,a non-linguistic context is one of Verschueren's Adaptation Theory.To master key elements of communicative of communicative context adaptation help us in the process of advertisements translation. Here,we will detail it from the three aspects -the adaptation to the physical world,the mental world and the social world.

Analyzing the Role of MicroRNAs in Schizophrenia in the Context of Common Genetic Risk Variants.

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Hauberg, Mads Engel; Roussos, Panos; Grove, Jakob; Børglum, Anders Dupont; Mattheisen, Manuel

2016-04-01

The recent implication of 108 genomic loci in schizophrenia marked a great advancement in our understanding of the disease. Against the background of its polygenic nature there is a necessity to identify how schizophrenia risk genes interplay. As regulators of gene expression, microRNAs (miRNAs) have repeatedly been implicated in schizophrenia etiology. It is therefore of interest to establish their role in the regulation of schizophrenia risk genes in disease-relevant biological processes. To examine the role of miRNAs in schizophrenia in the context of disease-associated genetic variation. The basis of this study was summary statistics from the largest schizophrenia genome-wide association study meta-analysis to date (83 550 individuals in a meta-analysis of 52 genome-wide association studies) completed in 2014 along with publicly available data for predicted miRNA targets. We examined whether schizophrenia risk genes were more likely to be regulated by miRNA. Further, we used gene set analyses to identify miRNAs that are regulators of schizophrenia risk genes. Results from association tests for miRNA targetomes and related analyses. In line with previous studies, we found that similar to other complex traits, schizophrenia risk genes were more likely to be regulated by miRNAs (P fragile X mental retardation homologue FXR1 and regulates dopamine D2 receptor density.

Genome analysis and identification of gelatinase encoded gene in Enterobacter aerogenes

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Shahimi, Safiyyah; Mutalib, Sahilah Abdul; Khalid, Rozida Abdul; Repin, Rul Aisyah Mat; Lamri, Mohd Fadly; Bakar, Mohd Faizal Abu; Isa, Mohd Noor Mat

2016-11-01

In this study, bioinformatic analysis towards genome sequence of E. aerogenes was done to determine gene encoded for gelatinase. Enterobacter aerogenes was isolated from hot spring water and gelatinase species-specific bacterium to porcine and fish gelatin. This bacterium offers the possibility of enzymes production which is specific to both species gelatine, respectively. Enterobacter aerogenes was partially genome sequenced resulting in 5.0 mega basepair (Mbp) total size of sequence. From pre-process pipeline, 87.6 Mbp of total reads, 68.8 Mbp of total high quality reads and 78.58 percent of high quality percentage was determined. Genome assembly produced 120 contigs with 67.5% of contigs over 1 kilo base pair (kbp), 124856 bp of N50 contig length and 55.17 % of GC base content percentage. About 4705 protein gene was identified from protein prediction analysis. Two candidate genes selected have highest similarity identity percentage against gelatinase enzyme available in Swiss-Prot and NCBI online database. They were NODE_9_length_26866_cov_148.013245_12 containing 1029 base pair (bp) sequence with 342 amino acid sequence and NODE_24_length_155103_cov_177.082458_62 which containing 717 bp sequence with 238 amino acid sequence, respectively. Thus, two paired of primers (forward and reverse) were designed, based on the open reading frame (ORF) of selected genes. Genome analysis of E. aerogenes resulting genes encoded gelatinase were identified.

Serial analysis of gene expression (SAGE) in normal human trabecular meshwork.

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Liu, Yutao; Munro, Drew; Layfield, David; Dellinger, Andrew; Walter, Jeffrey; Peterson, Katherine; Rickman, Catherine Bowes; Allingham, R Rand; Hauser, Michael A

2011-04-08

To identify the genes expressed in normal human trabecular meshwork tissue, a tissue critical to the pathogenesis of glaucoma. Total RNA was extracted from human trabecular meshwork (HTM) harvested from 3 different donors. Extracted RNA was used to synthesize individual SAGE (serial analysis of gene expression) libraries using the I-SAGE Long kit from Invitrogen. Libraries were analyzed using SAGE 2000 software to extract the 17 base pair sequence tags. The extracted sequence tags were mapped to the genome using SAGE Genie map. A total of 298,834 SAGE tags were identified from all HTM libraries (96,842, 88,126, and 113,866 tags, respectively). Collectively, there were 107,325 unique tags. There were 10,329 unique tags with a minimum of 2 counts from a single library. These tags were mapped to known unique Unigene clusters. Approximately 29% of the tags (orphan tags) did not map to a known Unigene cluster. Thirteen percent of the tags mapped to at least 2 Unigene clusters. Sequence tags from many glaucoma-related genes, including myocilin, optineurin, and WD repeat domain 36, were identified. This is the first time SAGE analysis has been used to characterize the gene expression profile in normal HTM. SAGE analysis provides an unbiased sampling of gene expression of the target tissue. These data will provide new and valuable information to improve understanding of the biology of human aqueous outflow.

Bioinformatics Analysis Reveals Genes Involved in the Pathogenesis of Ameloblastoma and Keratocystic Odontogenic Tumor.