Although we recognize the importance of comparing prediction methods in relation to previously established benchmarks, we also conducted our own benchmark (using the SwissVar mutation dataset – see Materials & Methods) comparing the performance of FATHMM against eight published computational prediction methods [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] (Table 3—see Supp. Table S1). In contrast to the VariBench benchmark, and in terms of performance accuracies, it appears that both SIFT [Ng and Henikoff, 2001] and our own unweighted method outperform PANTHER [Thomas et al., 2003] (68%) with performance accuracies of 74% and 71%, respectively, indicating that SIFT is somewhat the better option. The best performing method is MutPred [Li et al., 2009; Mort et al., 2010] with a performance accuracy of 90%. However, the observed performances show that our weighted method once again performs favorably when compared to other state-of-the-art prediction methods: SNPs&GO [Calabrese et al., 2009], despite the domain-based
