Abstract
BackgroundGenome-wide expression profiles reflect the transcriptional networks specific to the given cell context. However, most statistical models try to estimate the average connectivity of the networks from a collection of gene expression data, and are unable to characterize the context-specific transcriptional regulations. We propose an approach for mining context-specific transcription networks from a large collection of gene expression fold-change profiles and composite gene-set information.ResultsUsing a composite gene-set analysis method, we combine the information of transcription factor binding sites, Gene Ontology or pathway gene sets and gene expression fold-change profiles for a variety of cell conditions. We then collected all the significant patterns and constructed a database of context-specific transcription networks for human (REGNET). As a result, context-specific roles of transcription factors as well as their functional targets are readily explored. To validate the approach, nine predicted targets of E2F1 in HeLa cells were tested using chromatin immunoprecipitation assay. Among them, five (Gadd45b, Dusp6, Mll5, Bmp2 and E2f3) were successfully bound by E2F1. c-JUN and the EMT transcription networks were also validated from literature.ConclusionsREGNET is a useful tool for exploring the ternary relationships among the transcription factors, their functional targets and the corresponding cell conditions. It is able to provide useful clues for novel cell-specific transcriptional regulations. The REGNET database is available at http://mgrc.kribb.re.kr/regnet.Electronic supplementary materialThe online version of this article (doi: 10.1186/1471-2164-15-450) contains supplementary material, which is available to authorized users.
Highlights
Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context
Using a composite gene-set analysis method (ADGO) [9,10], we combined the information of Transcription factor (TF) binding site (TFBS), Gene Ontology or KEGG pathway gene sets, and the gene expression fold-change profiles, and thereby tried to address the fundamental but largely open question of which TFs regulate which functions or pathways under differing cell conditions?
Since the majority of TFBSs are potentially false positives, we applied the following filtering criteria to identify reliable Transcription network (TN) as follows. (A) A TFBS gene set is required to have a significant overlap with a functional gene set. (B) The genes that overlap, as a whole, should exhibit significant expression changes. (C) Such changes should be observed across many microarray conditions
Summary
Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context. Elucidating the complex transcription networks (TNs) has been a daunting task in spite of the remarkable advances in both computational modeling and Taking these issues into account, we have developed an approach for identifying the context-specific TNs from a large collection of gene expression fold-change profiles. Toward this end, we collected 2,482 paired (test/control) human microarray datasets encompassing a variety of cell conditions. If the above three criteria are satisfied between a TFBS gene set and a functional gene set, we say the corresponding TF is associated with the functional gene set (Figure 1a), and the associated pairs as well as the corresponding conditions are output as context-specific TNs
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