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Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotatio...

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Autores principales: Vitsios, Dimitrios, Dhindsa, Ryan S., Middleton, Lawrence, Gussow, Ayal B., Petrovski, Slavé
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7940646/
https://www.ncbi.nlm.nih.gov/pubmed/33686085
http://dx.doi.org/10.1038/s41467-021-21790-4
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author Vitsios, Dimitrios
Dhindsa, Ryan S.
Middleton, Lawrence
Gussow, Ayal B.
Petrovski, Slavé
author_facet Vitsios, Dimitrios
Dhindsa, Ryan S.
Middleton, Lawrence
Gussow, Ayal B.
Petrovski, Slavé
author_sort Vitsios, Dimitrios
collection PubMed
description Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome.
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spelling pubmed-79406462021-03-28 Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning Vitsios, Dimitrios Dhindsa, Ryan S. Middleton, Lawrence Gussow, Ayal B. Petrovski, Slavé Nat Commun Article Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome. Nature Publishing Group UK 2021-03-08 /pmc/articles/PMC7940646/ /pubmed/33686085 http://dx.doi.org/10.1038/s41467-021-21790-4 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Vitsios, Dimitrios
Dhindsa, Ryan S.
Middleton, Lawrence
Gussow, Ayal B.
Petrovski, Slavé
Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_full Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_fullStr Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_full_unstemmed Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_short Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_sort prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7940646/
https://www.ncbi.nlm.nih.gov/pubmed/33686085
http://dx.doi.org/10.1038/s41467-021-21790-4
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