The performance of FATHMM was compared to the performances of alternative computational prediction methods previously reported in two published reviews [Hicks et al., 2011; Thusberg et al., 2011]. Furthermore, we performed our own independent benchmark comparing the performance of FATHMM against the performance of other computational prediction methods. In two benchmarks (VariBench/SwissVar), the performance of our unweighted method is comparable to another sequence-based method: SIFT [Ng and Henikoff, 2001], and to a sequence/structure-based method: PolyPhen-1 [Ramensky et al., 2002]. This performance reaffirms the ability of FATHMM to recognize important structural and/or evolutionary constraints (via priors) modeled within manually curated HMMs representing the alignment of conserved protein domains: SUPERFAMILY [Gough et al., 2001] and Pfam [Sonnhammer et al., 1997]. A detailed analysis of four cancer-associated genes (Hicks; BRCA1, MSH2, MLH1, and TP53) shows Align-GVGD [Tavtigian et al., 2006] to be the best performing prediction method. However, this can be attributed to the manually curated (gene-specific) sequence alignments employed in the prediction method. On average, the performance of our unweighted method in this benchmark is comparable to SIFT.
