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

Abstract

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.

Keywords

• Deep mutational scanning
• Bioinformatic pipeline
• Statistical model
• Variant effect prediction
• R package
• Bioconda