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Best practices for variant calling in clinical sequencing
Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Jus...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7586657/ https://www.ncbi.nlm.nih.gov/pubmed/33106175 http://dx.doi.org/10.1186/s13073-020-00791-w |
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author | Koboldt, Daniel C. |
author_facet | Koboldt, Daniel C. |
author_sort | Koboldt, Daniel C. |
collection | PubMed |
description | Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the “best practice” principles in this review should be relevant to clinical variant calling in the long term. |
format | Online Article Text |
id | pubmed-7586657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75866572020-10-26 Best practices for variant calling in clinical sequencing Koboldt, Daniel C. Genome Med Review Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the “best practice” principles in this review should be relevant to clinical variant calling in the long term. BioMed Central 2020-10-26 /pmc/articles/PMC7586657/ /pubmed/33106175 http://dx.doi.org/10.1186/s13073-020-00791-w Text en © The Author(s) 2020 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Review Koboldt, Daniel C. Best practices for variant calling in clinical sequencing |
title | Best practices for variant calling in clinical sequencing |
title_full | Best practices for variant calling in clinical sequencing |
title_fullStr | Best practices for variant calling in clinical sequencing |
title_full_unstemmed | Best practices for variant calling in clinical sequencing |
title_short | Best practices for variant calling in clinical sequencing |
title_sort | best practices for variant calling in clinical sequencing |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7586657/ https://www.ncbi.nlm.nih.gov/pubmed/33106175 http://dx.doi.org/10.1186/s13073-020-00791-w |
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