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A phenotype centric benchmark of variant prioritisation tools
Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from aligned sequencing reads, variant prioritisation too...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799157/ https://www.ncbi.nlm.nih.gov/pubmed/29423277 http://dx.doi.org/10.1038/s41525-018-0044-9 |
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author | Anderson, Denise Lassmann, Timo |
author_facet | Anderson, Denise Lassmann, Timo |
author_sort | Anderson, Denise |
collection | PubMed |
description | Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from aligned sequencing reads, variant prioritisation tools are used to examine the conservation or potential functional consequences of variants. We hypothesised that the performance of variant prioritisation tools may vary by disease phenotype. To test this we created benchmark data sets for variants associated with different disease phenotypes. We found that performance of 24 tested tools is highly variable and differs by disease phenotype. The task of identifying a causative variant amongst a large number of benign variants is challenging for all tools, highlighting the need for further development in the field. Based on our observations, we recommend use of five top performers found in this study (FATHMM, M-CAP, MetaLR, MetaSVM and VEST3). In addition we provide tables indicating which analytical approach works best in which disease context. Variant prioritisation tools are best suited to investigate variants associated with well-studied genetic diseases, as these variants are more readily available during algorithm development than variants associated with rare diseases. We anticipate that further development into disease focussed tools will lead to significant improvements. |
format | Online Article Text |
id | pubmed-5799157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57991572018-02-08 A phenotype centric benchmark of variant prioritisation tools Anderson, Denise Lassmann, Timo NPJ Genom Med Article Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from aligned sequencing reads, variant prioritisation tools are used to examine the conservation or potential functional consequences of variants. We hypothesised that the performance of variant prioritisation tools may vary by disease phenotype. To test this we created benchmark data sets for variants associated with different disease phenotypes. We found that performance of 24 tested tools is highly variable and differs by disease phenotype. The task of identifying a causative variant amongst a large number of benign variants is challenging for all tools, highlighting the need for further development in the field. Based on our observations, we recommend use of five top performers found in this study (FATHMM, M-CAP, MetaLR, MetaSVM and VEST3). In addition we provide tables indicating which analytical approach works best in which disease context. Variant prioritisation tools are best suited to investigate variants associated with well-studied genetic diseases, as these variants are more readily available during algorithm development than variants associated with rare diseases. We anticipate that further development into disease focussed tools will lead to significant improvements. Nature Publishing Group UK 2018-02-05 /pmc/articles/PMC5799157/ /pubmed/29423277 http://dx.doi.org/10.1038/s41525-018-0044-9 Text en © The Author(s) 2018 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 Anderson, Denise Lassmann, Timo A phenotype centric benchmark of variant prioritisation tools |
title | A phenotype centric benchmark of variant prioritisation tools |
title_full | A phenotype centric benchmark of variant prioritisation tools |
title_fullStr | A phenotype centric benchmark of variant prioritisation tools |
title_full_unstemmed | A phenotype centric benchmark of variant prioritisation tools |
title_short | A phenotype centric benchmark of variant prioritisation tools |
title_sort | phenotype centric benchmark of variant prioritisation tools |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799157/ https://www.ncbi.nlm.nih.gov/pubmed/29423277 http://dx.doi.org/10.1038/s41525-018-0044-9 |
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