Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.

ABSTRACT:

Background

Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches.

Methodology/principal findings

Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1)

SUBMITTER: Hudson NJ 

PROVIDER: S-EPMC2749936 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature