Construction of regulatory networks using expression time-series data of a genotyped population.

ABSTRACT: The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-e

SUBMITTER: Yeung KY 

PROVIDER: S-EPMC3228453 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature