Computational solutions for omics data

Bonnie Berger, Jian Peng, Mona Singh

Research output: Contribution to journalReview article

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.

Original languageEnglish (US)
Pages (from-to)333-346
Number of pages14
JournalNature Reviews Genetics
Volume14
Issue number5
DOIs
StatePublished - May 1 2013

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Sequence Analysis
Software
Technology
Datasets

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

Cite this

Computational solutions for omics data. / Berger, Bonnie; Peng, Jian; Singh, Mona.

In: Nature Reviews Genetics, Vol. 14, No. 5, 01.05.2013, p. 333-346.

Research output: Contribution to journalReview article

Berger, Bonnie ; Peng, Jian ; Singh, Mona. / Computational solutions for omics data. In: Nature Reviews Genetics. 2013 ; Vol. 14, No. 5. pp. 333-346.
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