PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants

Functional characterization of the noncoding genome is essential for biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring), which predicts the functional impact of noncoding variants by integrating...

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Autores principales: Bodea, Corneliu A., Mitchell, Adele A., Bloemendal, Alex, Day-Williams, Aaron G., Runz, Heiko, Sunyaev, Shamil R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6203199/
https://www.ncbi.nlm.nih.gov/pubmed/30359302
http://dx.doi.org/10.1186/s13059-018-1546-6
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author Bodea, Corneliu A.
Mitchell, Adele A.
Bloemendal, Alex
Day-Williams, Aaron G.
Runz, Heiko
Sunyaev, Shamil R.
author_facet Bodea, Corneliu A.
Mitchell, Adele A.
Bloemendal, Alex
Day-Williams, Aaron G.
Runz, Heiko
Sunyaev, Shamil R.
author_sort Bodea, Corneliu A.
collection PubMed
description Functional characterization of the noncoding genome is essential for biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring), which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. PINES enables analyses to be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1546-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-62031992018-11-01 PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants Bodea, Corneliu A. Mitchell, Adele A. Bloemendal, Alex Day-Williams, Aaron G. Runz, Heiko Sunyaev, Shamil R. Genome Biol Software Functional characterization of the noncoding genome is essential for biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring), which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. PINES enables analyses to be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1546-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-25 /pmc/articles/PMC6203199/ /pubmed/30359302 http://dx.doi.org/10.1186/s13059-018-1546-6 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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
Bodea, Corneliu A.
Mitchell, Adele A.
Bloemendal, Alex
Day-Williams, Aaron G.
Runz, Heiko
Sunyaev, Shamil R.
PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
title PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
title_full PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
title_fullStr PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
title_full_unstemmed PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
title_short PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
title_sort pines: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6203199/
https://www.ncbi.nlm.nih.gov/pubmed/30359302
http://dx.doi.org/10.1186/s13059-018-1546-6
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