The performance of FATHMM was compared against the performance of other computational prediction algorithms reported in two previously published reviews [Hicks et al., 2011; Thusberg et al., 2011]. First, the VariBench database was used to benchmark our method against nine alternative computational prediction algorithms [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] (Table 2). Typically, the performance of trained/weighted computational prediction algorithms is superior to that of theoretical/unweighted algorithms. Therefore, to allow for a fair comparison to be made, we opted to compare our unweighted/species-independent method against other theoretical/unweighted computational algorithms and our weighted/species-specific method against other trained/weighted computational prediction algorithms. From Table 2, and in terms of performance accuracies, PANTHER [Thomas et al., 2003] appears to be the best performing theoretical/unweighted prediction method with an accuracy of 76%. It appears that both SIFT [Ng and Henikoff, 2001] (another sequence-based method) and our unweighted method perform less favorably
