Open AccessFATHMM-XF: accurate prediction of pathogenic point mutations via extended features
Author(s) -
Mark F. Rogers,
Hashem A. Shihab,
Matthew Mort,
D.N. Cooper,
Tom R. Gaunt,
Colin Campbell
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx536
Subject(s) - point mutation , computer science , point (geometry) , computational biology , mutation , artificial intelligence , algorithm , genetics , mathematics , biology , gene , geometry
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.
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