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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...

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Autores principales: Belyeu, Jonathan R., Chowdhury, Murad, Brown, Joseph, Pedersen, Brent S., Cormier, Michael J., Quinlan, Aaron R., Layer, Ryan M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
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.
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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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