A collection of five human mutation datasets from online databases and the literature were downloaded and used in this study (Table 1). First, inherited disease-causing AASs annotated as DMs (damaging mutations) in the Human Gene Mutation Database [Stenson et al., 2009] (HGMD—November 2011; http://www.hgmd.org) and inherited putative functionally neutral AASs in the UniProt database [Apweiler et al., 2004] (UniProt—November 2011; http://www.uniprot.org/docs/humsavar) were downloaded and used to calculate the pathogenicity weights implemented in our weighted/species-specific method. Next, we obtained two human mutation datasets to assess the performance of FATHMM against the performance of other computational prediction algorithms previously reported in the literature: the VariBench database (VariBench—November 2011; http://bioinf.uta.fi/VariBench) used in a comprehensive review [Thusberg et al., 2011] of nine other computational prediction methods [Adzhubei et al., 2010; Bao et al., 2005; Bromberg and Rost, 2007; Calabrese et al., 2009; Capriotti et 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
