Dataset Information

Inferring condition-specific transcription factor function from DNA binding and gene expression data.

ABSTRACT: Numerous genomic and proteomic datasets are permitting the elucidation of transcriptional regulatory networks in the yeast Saccharomyces cerevisiae. However, predicting the condition dependence of regulatory network interactions has been challenging, because most protein-DNA interactions identified in vivo are from assays performed in one or a few cellular states. Here, we present a novel method to predict the condition-specific functions of S. cerevisiae transcription factors (TFs) by integrating 1327 microarray gene expression data sets and either comprehensive TF binding site data from protein binding microarrays (PBMs) or in silico motif data. Importantly, our method does not impose arbitrary thresholds for calling target regions 'bound' or genes 'differentially expressed', but rather allows all the information derived from a TF binding or gene expression experiment to be considered. We show that this method can identify environmental, physical, and genetic interactions, as well as distinct sets of genes that might be activated or repressed by a single TF under particular conditions. This approach can be used to suggest conditions for directed in vivo experimentation and to predict TF function.

SUBMITTER: McCord RP

PROVIDER: S-EPMC1865582 | biostudies-other | 2007

REPOSITORIES: biostudies-other

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Inferring condition-specific transcription factor function from DNA binding and gene expression data.

McCord Rachel Patton RP Berger Michael F MF Philippakis Anthony A AA Bulyk Martha L ML

Molecular systems biology 20070417

Numerous genomic and proteomic datasets are permitting the elucidation of transcriptional regulatory networks in the yeast Saccharomyces cerevisiae. However, predicting the condition dependence of regulatory network interactions has been challenging, because most protein-DNA interactions identified in vivo are from assays performed in one or a few cellular states. Here, we present a novel method to predict the condition-specific functions of S. cerevisiae transcription factors (TFs) by integrati ...[more]

PMID: 17437025

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Project description:Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis. One possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions or execute final biological functions in pathways, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.

Dataset Information

Inferring condition-specific transcription factor function from DNA binding and gene expression data.

Publications

Inferring condition-specific transcription factor function from DNA binding and gene expression data.

Similar Datasets

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