Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators.

ABSTRACT:

Background

Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes.

Results

Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and

SUBMITTER: Padi M 

PROVIDER: S-EPMC4650867 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature