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

Rogers, Mark F.; Shihab, Hashem A.; Mort, Matthew; Cooper, D.N.; Gaunt, Tom R.; Campbell, Colin

doi:10.1093/bioinformatics/btx536

articleBioinformaticsSep 4, 2017HYBRID OA

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

MFMark F. Rogers HAHashem A. Shihab MMMatthew Mort DCD.N. Cooper TRTom R. Gaunt

University of Bristol · MRC Epidemiology Unit · +1 more institution

PubMed

Indexed incrossrefdoajpubmed

Abstract

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/. Contact: mark.rogers@bristol.ac.uk or c.campbell@bristol.ac.uk.…

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Topics & keywords

Topics

Keywords

Point mutation
Computer science
Point (geometry)
Computational biology
Mutation
Artificial intelligence
Algorithm
Genetics

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