Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Department of Bioinformatics, Genentech, Inc., 1 DNA Way, M.S. 93, South San Francisco, CA 94080, USA.
Human Genetics (Impact Factor: 4.82). 06/2009; 126(4):481-98. DOI: 10.1007/s00439-009-0677-y
Source: PubMed


Cancer is a genetic disease that results from a variety of genomic alterations. Identification of some of these causal genetic events has enabled the development of targeted therapeutics and spurred efforts to discover the key genes that drive cancer formation. Rapidly improving sequencing and genotyping technology continues to generate increasingly large datasets that require analytical methods to identify functional alterations that deserve additional investigation. This review examines statistical and computational approaches for the identification of functional changes among sets of single-nucleotide substitutions. Frequency-based methods identify the most highly mutated genes in large-scale cancer sequencing efforts while bioinformatics approaches are effective for independent evaluation of both non-synonymous mutations and polymorphisms. We also review current knowledge and tools that can be utilized for analysis of alterations in non-protein-coding genomic sequence.

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    • "These methods are now commonly used to assess the potential functional consequences of discovered somatic variants in genome-wide scans. Depending on the specifics of training data and the availability of functional information, they can perform strongly (Kaminker et al. 2007; Torkamani and Schork 2008; Carter et al. 2009; Lee et al. 2009a). Many of the bioinformatic approaches to predict the functional effects of missense mutations were initially developed for germline variation and have been applied to better understand common and rare disease variants as well as evolution (Ng and Henikoff 2001; Ramensky et al. 2002; Bromberg and Rost 2007; Kryukov et al. 2007). "
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