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Science. 2016 Aug 19;353(6301):814-8. doi: 10.1126/science.aag1125.

Integration of omic networks in a developmental atlas of maize.

Author information

1
Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA. Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA.
2
Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
3
Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
4
Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
5
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA.
6
Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037, USA.
7
Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA. sbriggs@ucsd.edu.

Abstract

Coexpression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting functional roles of individual genes at a system-wide scale. To enable network reconstructions, we built a large-scale gene expression atlas composed of 62,547 messenger RNAs (mRNAs), 17,862 nonmodified proteins, and 6227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. Networks in which nodes are genes connected on the basis of highly correlated expression patterns of mRNAs were very different from networks that were based on coexpression of proteins. Roughly 85% of highly interconnected hubs were not conserved in expression between RNA and protein networks. However, networks from either data type were enriched in similar ontological categories and were effective in predicting known regulatory relationships. Integration of mRNA, protein, and phosphoprotein data sets greatly improved the predictive power of GRNs.

PMID:
27540173
PMCID:
PMC5808982
DOI:
10.1126/science.aag1125
[Indexed for MEDLINE]
Free PMC Article

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