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

Ben Lehner,Andre J Faure,Pablo Baeza-Centurion,Jörn M Schmiedel

doi:10.1186/s13059-020-02091-3

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.

Highlights

Deep mutational scanning (DMS), known as massively parallel reporter assays (MPRAs) and multiplex assays of variant effect (MAVEs), enables parallel measurement of the effects of thousands of mutations in the same experiment [1, 2]
The high-throughput nature of DMS facilitates the comprehensive study of combinations of mutations and their genetic interactions where fitness effects of individual mutations depend on the presence of other mutations [3]
Overview of the DiMSum pipeline The DiMSum pipeline is implemented as an R/Bioconda package and a command-line tool that can be configured to handle a variety of DMS experimental designs

Summary

Introduction

Deep mutational scanning (DMS), known as massively parallel reporter assays (MPRAs) and multiplex assays of variant effect (MAVEs), enables parallel measurement of the effects of thousands of mutations in the same experiment [1, 2]. DMS experiment, a library of sequence variants is constructed and deep sequencing before and after selection for an in vitro or in vivo activity is used to quantify the relative activity (“molecular fitness”) of each genotype. The high-throughput nature of DMS facilitates the comprehensive study of combinations of mutations and their genetic interactions (epistasis) where fitness effects of individual mutations depend on the presence of other (background) mutations [3]. In recognition of the growing number and importance of DMS assays in biomedical research, a dedicated platform for sharing, accessing, and visualizing these datasets has recently been launched [28]

Methods

Results

Conclusion