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Samplot: a platform for structural variant visual validation and automated filtering
Visual validation is an important step to minimize false-positive predictions from structural variant (SV) detection. We present Samplot, a tool for creating images that display the read depth and sequence alignments necessary to adjudicate purported SVs across samples and sequencing technologies. T...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145817/ https://www.ncbi.nlm.nih.gov/pubmed/34034781 http://dx.doi.org/10.1186/s13059-021-02380-5 |
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author | Belyeu, Jonathan R. Chowdhury, Murad Brown, Joseph Pedersen, Brent S. Cormier, Michael J. Quinlan, Aaron R. Layer, Ryan M. |
author_facet | Belyeu, Jonathan R. Chowdhury, Murad Brown, Joseph Pedersen, Brent S. Cormier, Michael J. Quinlan, Aaron R. Layer, Ryan M. |
author_sort | Belyeu, Jonathan R. |
collection | PubMed |
description | Visual validation is an important step to minimize false-positive predictions from structural variant (SV) detection. We present Samplot, a tool for creating images that display the read depth and sequence alignments necessary to adjudicate purported SVs across samples and sequencing technologies. These images can be rapidly reviewed to curate large SV call sets. Samplot is applicable to many biological problems such as SV prioritization in disease studies, analysis of inherited variation, or de novo SV review. Samplot includes a machine learning package that dramatically decreases the number of false positives without human review. Samplot is available at https://github.com/ryanlayer/samplot. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02380-5. |
format | Online Article Text |
id | pubmed-8145817 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81458172021-05-25 Samplot: a platform for structural variant visual validation and automated filtering Belyeu, Jonathan R. Chowdhury, Murad Brown, Joseph Pedersen, Brent S. Cormier, Michael J. Quinlan, Aaron R. Layer, Ryan M. Genome Biol Method Visual validation is an important step to minimize false-positive predictions from structural variant (SV) detection. We present Samplot, a tool for creating images that display the read depth and sequence alignments necessary to adjudicate purported SVs across samples and sequencing technologies. These images can be rapidly reviewed to curate large SV call sets. Samplot is applicable to many biological problems such as SV prioritization in disease studies, analysis of inherited variation, or de novo SV review. Samplot includes a machine learning package that dramatically decreases the number of false positives without human review. Samplot is available at https://github.com/ryanlayer/samplot. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02380-5. BioMed Central 2021-05-25 /pmc/articles/PMC8145817/ /pubmed/34034781 http://dx.doi.org/10.1186/s13059-021-02380-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Method Belyeu, Jonathan R. Chowdhury, Murad Brown, Joseph Pedersen, Brent S. Cormier, Michael J. Quinlan, Aaron R. Layer, Ryan M. Samplot: a platform for structural variant visual validation and automated filtering |
title | Samplot: a platform for structural variant visual validation and automated filtering |
title_full | Samplot: a platform for structural variant visual validation and automated filtering |
title_fullStr | Samplot: a platform for structural variant visual validation and automated filtering |
title_full_unstemmed | Samplot: a platform for structural variant visual validation and automated filtering |
title_short | Samplot: a platform for structural variant visual validation and automated filtering |
title_sort | samplot: a platform for structural variant visual validation and automated filtering |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145817/ https://www.ncbi.nlm.nih.gov/pubmed/34034781 http://dx.doi.org/10.1186/s13059-021-02380-5 |
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