DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies

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• dc.contributor.author Faure, Andre J.
• dc.contributor.author Schmiedel, Jörn M.
• dc.contributor.author Baeza Centurión, Pablo, 1989-
• dc.contributor.author Lehner, Ben, 1978-
• dc.date.accessioned 2020-10-28T07:01:18Z
• dc.date.available 2020-10-28T07:01:18Z
• dc.date.issued 2020
• dc.description.abstract Deep mutational scanning (DMS) enables multiplexed measurement of the effects of thousands of variants of proteins, RNAs, and regulatory elements. Here, we present a customizable pipeline, DiMSum, that represents an end-to-end solution for obtaining variant fitness and error estimates from raw sequencing data. A key innovation of DiMSum is the use of an interpretable error model that captures the main sources of variability arising in DMS workflows, outperforming previous methods. DiMSum is available as an R/Bioconda package and provides summary reports to help researchers diagnose common DMS pathologies and take remedial steps in their analyses.
• dc.description.sponsorship This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012-0208), the Bettencourt Schueller Foundation, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322.), and the CERCA Program/Generalitat de Catalunya. We also acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and the Centro de Excelencia Severo Ochoa. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 752809 (J.M.S.).
• dc.format.mimetype application/pdf
• dc.identifier.citation Faure AJ, Schmiedel JM, Baeza-Centurion P, Lehner B. DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies. Genome Biol. 2020; 21(1):207. DOI: 10.1186/s13059-020-02091-3
• dc.identifier.doi http://dx.doi.org/10.1186/s13059-020-02091-3
• dc.identifier.issn 1474-7596
• dc.identifier.uri http://hdl.handle.net/10230/45593
• dc.language.iso eng
• dc.publisher BioMed Central
• dc.relation.ispartof Genome Biol. 2020; 21(1):207
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/616434
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/BFU2017-89488-P
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/752809
• dc.rights © The Author(s). 2020 Open Access This 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 ma
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Bioconda
• dc.subject.keyword Bioinformatic pipeline
• dc.subject.keyword Deep mutational scanning
• dc.subject.keyword R package
• dc.subject.keyword Statistical model
• dc.subject.keyword Variant effect prediction
• dc.title DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion