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

Mark Rogers; Hashem A Shihab; Matthew Mort; David N Cooper; Tom Gaunt; Colin Campbell

doi:10.1093/bioinformatics/btx536

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

Mark Rogers, Hashem A Shihab, Matthew Mort, David N Cooper, Tom Gaunt, Colin Campbell

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386 Citations (Scopus)

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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.

Original language	English
Article number	btx536
Pages (from-to)	511-513
Number of pages	3
Journal	Bioinformatics
Volume	34
Issue number	3
Early online date	5 Sept 2017
DOIs	https://doi.org/10.1093/bioinformatics/btx536
Publication status	Published - 1 Feb 2018

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This is the final published version of the article (version of record). It first appeared online via Oxford University Press at https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx536/4104409/FATHMMXF-accurate-prediction-of-pathogenic-point. Please refer to any applicable terms of use of the publisher.
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This is the final published version of the article (version of record). It first appeared online via Oxford University Press at https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx536/4104409/FATHMMXF-accurate-prediction-of-pathogenic-point. Please refer to any applicable terms of use of the publisher.
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title = "FATHMM-XF: accurate prediction of pathogenic point mutations via extended features",

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.",

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AU - Cooper, David N

AU - Gaunt, Tom

AU - Campbell, Colin

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N2 - 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.

AB - 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.

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

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Novel Methodology for Predicting the Functional Effects of Genetic Variation

An Active Learning Approach to Network Inference

IEU Theme 2

Dr I C G Campbell

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

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Novel Methodology for Predicting the Functional Effects of Genetic Variation

An Active Learning Approach to Network Inference

IEU Theme 2

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Dr I C G Campbell

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