satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect

The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does...

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Detalles Bibliográficos
Autores principales: Hoskins, Ian, Sun, Song, Cote, Atina, Roth, Frederick P., Cenik, Can
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116734/
https://www.ncbi.nlm.nih.gov/pubmed/37081510
http://dx.doi.org/10.1186/s13059-023-02922-z
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author Hoskins, Ian
Sun, Song
Cote, Atina
Roth, Frederick P.
Cenik, Can
author_facet Hoskins, Ian
Sun, Song
Cote, Atina
Roth, Frederick P.
Cenik, Can
author_sort Hoskins, Ian
collection PubMed
description The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02922-z.
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spelling pubmed-101167342023-04-21 satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect Hoskins, Ian Sun, Song Cote, Atina Roth, Frederick P. Cenik, Can Genome Biol Method The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02922-z. BioMed Central 2023-04-20 /pmc/articles/PMC10116734/ /pubmed/37081510 http://dx.doi.org/10.1186/s13059-023-02922-z Text en © The Author(s) 2023 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
Hoskins, Ian
Sun, Song
Cote, Atina
Roth, Frederick P.
Cenik, Can
satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect
title satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect
title_full satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect
title_fullStr satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect
title_full_unstemmed satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect
title_short satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect
title_sort satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10116734/
https://www.ncbi.nlm.nih.gov/pubmed/37081510
http://dx.doi.org/10.1186/s13059-023-02922-z
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