ArticleBMC bioinformatics2007
Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts.PloS one · 2011Article
- Signatures of murine B-cell development implicate Yy1 as a regulator of the germinal center-specific program.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
- TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information.BMC bioinformatics · 2010Article
- Gene set-based module discovery in the breast cancer transcriptome.BMC bioinformatics · 2009Article
- DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites.BMC bioinformatics · 2008Article
- Relating gene expression data on two-component systems to functional annotations in Escherichia coli.BMC bioinformatics · 2008Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene sets can provide clues on which functional themes or regulatory sequence motifs are associated with individual gene clusters. In spite of the potential utility, gene clustering and enrichment analysis have been used in separate platforms, thus, the development of integrative algorithm linking both methods is highly challenging.
resultsIn this study, we propose an algorithm for discovery of molecular functions and elucidation of transcriptional logics using two kinds of gene information, functional and regulatory motif gene sets. The algorithm, termed gene set expression coherence analysis first selects functional gene sets with significantly high expression coherences. Those candidate gene sets are further processed into a number of functionally related themes or functional clusters according to the expression similarities. Each functional cluster is then, investigated for the enrichment of transcriptional regulatory motifs using modified gene set enrichment analysis and regulatory motif gene sets. The method was tested for two publicly available expression profiles representing murine myogenesis and erythropoiesis. For respective profiles, our algorithm identified myocyte- and erythrocyte-related molecular functions, along with the putative transcriptional regulators for the corresponding molecular functions.
conclusionAs an integrative and comprehensive method for the analysis of large-scaled gene expression profiles, our method is able to generate a set of testable hypotheses: the transcriptional regulator X regulates function Y under cellular condition Z. GSECA algorithm is implemented into freely available software package.
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