Next, we used the Hicks dataset to benchmark FATHMM against four other computational prediction algorithms (using their native alignments) [Adzhubei et al., 2010; Ng and Henikoff, 2001; Reva et al., 2011; Tavtigian et al., 2006] (Table 3). Overall, Align-GVGD [Tavtigian et al., 2006] appears to be the best performing method. However, Align-GVGD employs gene-specific alignments and its performance is severely affected when automatically generated alignments are used [Hicks et al., 2011]. These results appear to indicate that our unweighted method is more specific than either Align-GVGD or SIFT; however, we also noted higher false positive rates when compared with the other prediction methods. In general, and perhaps more surprisingly, it appears that the performance of all trained/weighted computational prediction methods is inferior across the four genes when compared to their theoretical/unweighted counterparts. Again, although no one trained/weighted prediction method performs best across the four genes, it would appear that our weighted method is, on average, the most specific/least sensitive.
