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Mapping Functional Transcription Factor Networks from Gene Expression Data  

2013-06-22 09:04:31|  分类: 生物信息分析 |  标签: |举报 |字号 订阅

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A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein-DNA interactions have been identified for most TFs by ChIP-chip and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: the response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: the best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.

Mapping Functional Transcription Factor Networks from Gene Expression Data - 喜欢吃桃子 - wangyufeng的博客

 Figure S3. Same as Figure 2 except that additional expression profiles from stress conditions and overexpression of 55 TFs are included in the analysis along with the TF deletions. LASSO regression was carried out on each of the three data sets separately and the resulting regression coefficients were averaged. For differential expression analysis, data from TF overexpression was used only for those TFs for which no deletion data were available. Several other methods of combining the data were tried but all were less accurate.

FULL TEXT:http://genome.cshlp.org/content/early/2013/04/25/gr.150904.112.abstract

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