FATHMM-XF: accurate prediction of pathogenic point mutations via extended features

By Mark F. Rogers, Hashem A. Shihab, Matthew E. Mort, David N. Cooper, Tom R. Gaunt, Colin Campbell. 2017

417 citations recorded by the source corpus.

Summary We present FATHMM‐XF, a method for predicting pathogenic point mutations in the human genome. Drawing on an extensive feature set, FATHMM‐XF outperforms competitors on benchmark tests, particularly in non‐coding regions where the majority of pathogenic mutations are likely to be found. Availability and implementation The FATHMM‐XF web server is available at http://fathmm.biocompute.org.uk/fathmm‐xf/, and as tracks on the Genome Tolerance Browser: http://gtb.biocompute.org.uk. Predictions are provided for human genome version GRCh37/hg19. The data used for this project can be downloaded from: http://fathmm.biocompute.org.uk/fathmm‐xf/

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, methodology): “Cross validation test of DeepSAV showed that it yielded better performance (measured by area under the ROC (receiver operating characteristic) curve (AUC)) to differentiate pathogenic from benign SAVs than most stand-alone programs such as SIFT [38], PolyPhen-2 [39], FATHMM-XF [40], PROVEAN [41], CA”
dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, background): “1 added 18 deleteriousness prediction scores (BayesDel_addAF and BayesDel_noAF [15], CADD_hg19 [16], ClinPred [17], DEOGEN2 [18], Eigen and Eigen PC [19], FATHMM-XF [20], GenoCanyon [21], LINSIGHT [22], LIST-S2 [23], M-CAP [24], MPC [25], MutPred [26], MVP [27], PrimateAI [28], REVEL [29], SIFT4G [3”