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Chunk #6 — Materials and Methods — The Mutation Datasets

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Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models.
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al., 2006; Li et al., 2009; Mort et al., 2010; Ng and Henikoff, 2001; Ramensky et al., 2002; Thomas et al., 2003] and 267 AASs in four cancer-associated genes (BRCA1, MSH2, MLH1, and TP53) used in a recent review [Hicks et al., 2011] of four alternative computational prediction algorithms [Adzhubei et al., 2010; Ng and Henikoff, 2001; Reva et al., 2011; Tavtigian et al., 2006]. Finally, we downloaded a human mutation dataset consisting of disease-associated and putative functionally neutral AASs from the SwissVar portal [Mottaz et al., 2010] (SwissVar—February 2011; http://swissvar.expasy.org) and performed an independent benchmark of FATHMM against eight other computational prediction algorithms [Adzhubei et al., 2010; Calabrese et al., 2009; Capriotti et al., 2006; Ferrer-Costa et al., 2004; Li et al., 2009; Mort et al., 2010; Ng and Henikoff, 2001; Ramensky et al., 2002; Thomas et al., 2003].