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VarSAn: associating pathways with a set of genomic variants using network analysis
There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes re...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421213/ https://www.ncbi.nlm.nih.gov/pubmed/34313777 http://dx.doi.org/10.1093/nar/gkab624 |
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author | Xie, Xiaoman Kendzior, Matthew C Ge, Xiyu Mainzer, Liudmila S Sinha, Saurabh |
author_facet | Xie, Xiaoman Kendzior, Matthew C Ge, Xiyu Mainzer, Liudmila S Sinha, Saurabh |
author_sort | Xie, Xiaoman |
collection | PubMed |
description | There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect. |
format | Online Article Text |
id | pubmed-8421213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84212132021-09-09 VarSAn: associating pathways with a set of genomic variants using network analysis Xie, Xiaoman Kendzior, Matthew C Ge, Xiyu Mainzer, Liudmila S Sinha, Saurabh Nucleic Acids Res Computational Biology There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect. Oxford University Press 2021-07-27 /pmc/articles/PMC8421213/ /pubmed/34313777 http://dx.doi.org/10.1093/nar/gkab624 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Computational Biology Xie, Xiaoman Kendzior, Matthew C Ge, Xiyu Mainzer, Liudmila S Sinha, Saurabh VarSAn: associating pathways with a set of genomic variants using network analysis |
title | VarSAn: associating pathways with a set of genomic variants using network analysis |
title_full | VarSAn: associating pathways with a set of genomic variants using network analysis |
title_fullStr | VarSAn: associating pathways with a set of genomic variants using network analysis |
title_full_unstemmed | VarSAn: associating pathways with a set of genomic variants using network analysis |
title_short | VarSAn: associating pathways with a set of genomic variants using network analysis |
title_sort | varsan: associating pathways with a set of genomic variants using network analysis |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8421213/ https://www.ncbi.nlm.nih.gov/pubmed/34313777 http://dx.doi.org/10.1093/nar/gkab624 |
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