Computational solutions for omics data
Name
Berger_Computational solutions.pdf
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1.57 MB
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Adobe PDF
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4208e859252f7e6850ebc013fc3e5c01
Author(s)
Berger, Bonnie
Peng, Jian
Singh, Mona
Date Issued
April 2013
Journal
Nature Reviews Genetics
Publisher
Nature Publishing Group
Citation
Berger, Bonnie, Jian Peng, and Mona Singh. “Computational Solutions for Omics Data.” Nature Reviews Genetics 14, no. 5 (April 18, 2013): 333–346.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/nrg3433
