Comparative analysis of regulatory information and circuits across distant species
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Kellis_Comparative analysis.pdf
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Author(s)
Kheradpour, Pouya
Kundaje, Anshul
Wu, Yi-Chieh
Kellis, Manolis
Date Issued
August 2014
Journal
Nature
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Boyle, Alan P., Carlos L. Araya, Cathleen Brdlik, Philip Cayting, Chao Cheng, Yong Cheng, Kathryn Gardner, et al. “Comparative Analysis of Regulatory Information and Circuits Across Distant Species.” Nature 512, no. 7515 (August 27, 2014): 453–456.
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Author's final manuscript
Abstract
Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/nature13668
