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

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Deep mutational scanning (DMS) is a powerful technique for assessing the functional impact of genetic variants in DNA, RNA, and proteins.
• Accurate variant fitness and error estimation are critical for reliable interpretation of DMS data.
• Existing analysis pipelines may not fully capture or address the sources of variability inherent in DMS workflows.

Purpose of the Study:

• To introduce DiMSum, a customizable, end-to-end bioinformatics pipeline for analyzing deep mutational scanning data.
• To provide researchers with a robust solution for obtaining accurate variant fitness and error estimates from raw sequencing data.
• To enhance the interpretability and reliability of DMS results through an innovative error modeling approach.

Main Methods:

• Development of DiMSum, an integrated pipeline for processing raw sequencing data from DMS experiments.
• Implementation of an interpretable error model to identify and quantify sources of variability in DMS workflows.
• Creation of summary reports to aid researchers in diagnosing and mitigating common DMS analysis issues.

Main Results:

• DiMSum offers a comprehensive solution for variant fitness and error estimation from DMS data.
• The pipeline's interpretable error model demonstrates superior performance compared to previous methods in capturing DMS variability.
• DiMSum facilitates improved data quality assessment and troubleshooting for researchers using DMS.

Conclusions:

• DiMSum provides a significant advancement in the analysis of deep mutational scanning data.
• The pipeline's robust error modeling enhances the accuracy and interpretability of variant effect predictions.
• DiMSum, available as an R/Bioconda package, empowers researchers to conduct more reliable and insightful DMS studies.