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DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies
Genome Biology ( IF 12.3 ) Pub Date : 2020-08-17 , DOI: 10.1186/s13059-020-02091-3
Andre J Faure 1 , Jörn M Schmiedel 1 , Pablo Baeza-Centurion 1 , Ben Lehner 1, 2, 3
Affiliation  

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.

中文翻译:

DiMSum:用于分析深度突变扫描数据和诊断常见实验病理的错误模型和管道

深度突变扫描 (DMS) 能够对蛋白质、RNA 和调控元件的数千种变体的影响进行多重测量。在这里,我们提出了一个可定制的管道 DiMSum,它代表了一个端到端的解决方案,用于从原始测序数据中获取变异适应度和错误估计。DiMSum 的一项关键创新是使用可解释的错误模型,该模型捕获 DMS 工作流程中出现的主要可变性来源,其性能优于以前的方法。DiMSum 可作为 R/Bioconda 软件包提供,并提供总结报告以帮助研究人员诊断常见的 DMS 病理并在他们的分析中采取补救措施。
更新日期:2020-08-17
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