Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies.

ABSTRACT: Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress resp

SUBMITTER: Koch C 

PROVIDER: S-EPMC5515301 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature