title: Computational solutions for omics data.
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.
authors.affiliation: Department of Mathematics and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. bab@mit.edu
authors.forename: Bonnie
authors.initials: B
authors.lastname: Berger
authors.forename: Jian
authors.initials: J
authors.lastname: Peng
authors.forename: Mona
authors.initials: M
authors.lastname: Singh
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software