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scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing
Identifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimall...
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/PMC8103760/ https://www.ncbi.nlm.nih.gov/pubmed/33962667 http://dx.doi.org/10.1186/s13059-021-02364-5 |
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author | Wilson, Gavin W. Derouet, Mathieu Darling, Gail E. Yeung, Jonathan C. |
author_facet | Wilson, Gavin W. Derouet, Mathieu Darling, Gail E. Yeung, Jonathan C. |
author_sort | Wilson, Gavin W. |
collection | PubMed |
description | Identifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimally detect variant co-expression. Herein, we introduce scSNV designed from the ground up to “collapse” molecular duplicates and accurately identify variants and their co-expression. We demonstrate that scSNV is fast, with a reduced false-positive variant call rate, and enables the co-detection of genetic variants and A>G RNA edits across twenty-two samples. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02364-5. |
format | Online Article Text |
id | pubmed-8103760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81037602021-05-10 scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing Wilson, Gavin W. Derouet, Mathieu Darling, Gail E. Yeung, Jonathan C. Genome Biol Method Identifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimally detect variant co-expression. Herein, we introduce scSNV designed from the ground up to “collapse” molecular duplicates and accurately identify variants and their co-expression. We demonstrate that scSNV is fast, with a reduced false-positive variant call rate, and enables the co-detection of genetic variants and A>G RNA edits across twenty-two samples. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02364-5. BioMed Central 2021-05-07 /pmc/articles/PMC8103760/ /pubmed/33962667 http://dx.doi.org/10.1186/s13059-021-02364-5 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 Wilson, Gavin W. Derouet, Mathieu Darling, Gail E. Yeung, Jonathan C. scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing |
title | scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing |
title_full | scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing |
title_fullStr | scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing |
title_full_unstemmed | scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing |
title_short | scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing |
title_sort | scsnv: accurate dscrna-seq snv co-expression analysis using duplicate tag collapsing |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103760/ https://www.ncbi.nlm.nih.gov/pubmed/33962667 http://dx.doi.org/10.1186/s13059-021-02364-5 |
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