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

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. AVAI...

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Detalles Bibliográficos
Autores principales: Rogers, Mark F, Shihab, Hashem A, Mort, Matthew, Cooper, David N, Gaunt, Tom R, Campbell, Colin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860356/
https://www.ncbi.nlm.nih.gov/pubmed/28968714
http://dx.doi.org/10.1093/bioinformatics/btx536
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author Rogers, Mark F
Shihab, Hashem A
Mort, Matthew
Cooper, David N
Gaunt, Tom R
Campbell, Colin
author_facet Rogers, Mark F
Shihab, Hashem A
Mort, Matthew
Cooper, David N
Gaunt, Tom R
Campbell, Colin
author_sort Rogers, Mark F
collection PubMed
description 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/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-58603562018-03-21 FATHMM-XF: accurate prediction of pathogenic point mutations via extended features Rogers, Mark F Shihab, Hashem A Mort, Matthew Cooper, David N Gaunt, Tom R Campbell, Colin Bioinformatics Applications Notes 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/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-02-01 2017-09-05 /pmc/articles/PMC5860356/ /pubmed/28968714 http://dx.doi.org/10.1093/bioinformatics/btx536 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Rogers, Mark F
Shihab, Hashem A
Mort, Matthew
Cooper, David N
Gaunt, Tom R
Campbell, Colin
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features
title FATHMM-XF: accurate prediction of pathogenic point mutations via extended features
title_full FATHMM-XF: accurate prediction of pathogenic point mutations via extended features
title_fullStr FATHMM-XF: accurate prediction of pathogenic point mutations via extended features
title_full_unstemmed FATHMM-XF: accurate prediction of pathogenic point mutations via extended features
title_short FATHMM-XF: accurate prediction of pathogenic point mutations via extended features
title_sort fathmm-xf: accurate prediction of pathogenic point mutations via extended features
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860356/
https://www.ncbi.nlm.nih.gov/pubmed/28968714
http://dx.doi.org/10.1093/bioinformatics/btx536
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