Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies
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Author(s)
Koch, Christopher
Konieczka, Jay
Delorey, Toni
Socha, Amanda
Davis, Kathleen
Knaack, Sara A.
Thompson, Dawn
O'Shea, Erin K.
Regev, Aviv
Roy, Sushmita
Date Issued
May 2017
Journal
Cell Systems
Publisher
Elsevier
Citation
Koch, Christopher et al. “Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies.” Cell Systems 4, 5 (May 2017): 543–558 © 2017 The Author(s)
Version
Final published version
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 responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species. Keywords: regulatory networks;
network inference; evolution of gene regulatory networks; evolution of stress response; yeast; probabilistic graphical model; phylogeny; comparative functional genomics
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/J.CELS.2017.04.010
