DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies
DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies
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.
Lehner Ben、Schmiedel J?rn M.、Baeza-Centurion Pablo、Faure Andre J.
Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology||Universitat Pompeu Fabra (UPF)||Instituci¨? Catalana de Recerca i Estudis Avan?ats (ICREA)Center for Genomic Regulation (CRG), The Barcelona Institute of Science and TechnologyCenter for Genomic Regulation (CRG), The Barcelona Institute of Science and TechnologyCenter for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology
生物科学研究方法、生物科学研究技术分子生物学
Deep mutational scanningbioinformatic pipelinestatistical modelvariant effect predictionR packageBioconda
Lehner Ben,Schmiedel J?rn M.,Baeza-Centurion Pablo,Faure Andre J..DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies[EB/OL].(2025-03-28)[2025-04-26].https://www.biorxiv.org/content/10.1101/2020.06.25.171421.点此复制
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