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Nat Rev Genet. 2013 May;14(5):333-46. doi: 10.1038/nrg3433.

Computational solutions for omics data.

Author information

  • 1Department of Mathematics and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. bab@mit.edu

Abstract

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.

PMID:
23594911
[PubMed - indexed for MEDLINE]
PMCID:
PMC3966295
Free PMC Article
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