Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.

Eran Segal; Michael Shapira; Aviv Regev; Dana Pe'er; David Botstein; Daphne Koller; Nir Friedman

doi:10.1038/ng1165

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Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.

Eran Segal·Michael Shapira·Aviv Regev·Dana Pe'er·David Botstein·Daphne Koller·Nir Friedman

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Abstract

Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.

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Algorithms Databases, Genetic Gene Expression Profiling Gene Expression Regulation, Fungal Genes, Fungal Genes, Regulator Models, Genetic Models, Statistical Mutation Oligonucleotide Array Sequence Analysis Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Journal Article Research Support, Non-U.S. Gov't

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Eran Segal, Michael Shapira, Aviv Regev, Dana Pe'er, David Botstein, Daphne Koller, Nir Friedman. 2003. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.. https://doi.org/10.1038/ng1165

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