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Nat Commun. 2016 Oct 26;7:13091. doi: 10.1038/ncomms13091.

Multi-omic data integration enables discovery of hidden biological regularities.

Author information

1
Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.
2
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220 DK-2800 Kongens Lyngby, Denmark.
3
Bioinformatics and Systems Biology Program, University of California, San Diego, California 92093, USA.
4
Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, USA.
5
Department of Pediatrics, University of California, San Diego, California 92093, USA.

Abstract

Rapid growth in size and complexity of biological data sets has led to the 'Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

PMID:
27782110
PMCID:
PMC5095171
DOI:
10.1038/ncomms13091
[Indexed for MEDLINE]
Free PMC Article

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