Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.

ABSTRACT: Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state

SUBMITTER: Shojaie A 

PROVIDER: S-EPMC3938831 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature