SuRFing the genomics wave: an R package for prioritising SNPs by functionality

Identifying functional non-coding variants is one of the greatest unmet challenges in genetics. To help address this, we introduce an R package, SuRFR, which integrates functional annotation and prior biological knowledge to prioritise candidate functional variants. SuRFR is publicly available, modu...

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Detalles Bibliográficos
Autores principales: Ryan, Niamh M, Morris, Stewart W, Porteous, David J, Taylor, Martin S, Evans, Kathryn L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4224693/
https://www.ncbi.nlm.nih.gov/pubmed/25400697
http://dx.doi.org/10.1186/s13073-014-0079-1
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author Ryan, Niamh M
Morris, Stewart W
Porteous, David J
Taylor, Martin S
Evans, Kathryn L
author_facet Ryan, Niamh M
Morris, Stewart W
Porteous, David J
Taylor, Martin S
Evans, Kathryn L
author_sort Ryan, Niamh M
collection PubMed
description Identifying functional non-coding variants is one of the greatest unmet challenges in genetics. To help address this, we introduce an R package, SuRFR, which integrates functional annotation and prior biological knowledge to prioritise candidate functional variants. SuRFR is publicly available, modular, flexible, fast, and simple to use. We demonstrate that SuRFR performs with high sensitivity and specificity and provide a widely applicable and scalable benchmarking dataset for model training and validation. Website: http://www.cgem.ed.ac.uk/resources/ ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-014-0079-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-42246932014-11-14 SuRFing the genomics wave: an R package for prioritising SNPs by functionality Ryan, Niamh M Morris, Stewart W Porteous, David J Taylor, Martin S Evans, Kathryn L Genome Med Software Identifying functional non-coding variants is one of the greatest unmet challenges in genetics. To help address this, we introduce an R package, SuRFR, which integrates functional annotation and prior biological knowledge to prioritise candidate functional variants. SuRFR is publicly available, modular, flexible, fast, and simple to use. We demonstrate that SuRFR performs with high sensitivity and specificity and provide a widely applicable and scalable benchmarking dataset for model training and validation. Website: http://www.cgem.ed.ac.uk/resources/ ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-014-0079-1) contains supplementary material, which is available to authorized users. BioMed Central 2014-10-14 /pmc/articles/PMC4224693/ /pubmed/25400697 http://dx.doi.org/10.1186/s13073-014-0079-1 Text en © Ryan et al.; licensee BioMed Central Ltd. 2014 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 use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Ryan, Niamh M
Morris, Stewart W
Porteous, David J
Taylor, Martin S
Evans, Kathryn L
SuRFing the genomics wave: an R package for prioritising SNPs by functionality
title SuRFing the genomics wave: an R package for prioritising SNPs by functionality
title_full SuRFing the genomics wave: an R package for prioritising SNPs by functionality
title_fullStr SuRFing the genomics wave: an R package for prioritising SNPs by functionality
title_full_unstemmed SuRFing the genomics wave: an R package for prioritising SNPs by functionality
title_short SuRFing the genomics wave: an R package for prioritising SNPs by functionality
title_sort surfing the genomics wave: an r package for prioritising snps by functionality
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4224693/
https://www.ncbi.nlm.nih.gov/pubmed/25400697
http://dx.doi.org/10.1186/s13073-014-0079-1
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