Variant scoring tools for deep mutational scanning

Abstract

Deep mutational scanning (DMS) can systematically assess the effects of thousands of genetic variants in a single assay, providing insights into protein function, evolution, host-pathogen interactions, and clinical impacts. Accurate scoring of variant effects is crucial, yet the diversity of tools and experimental designs contributes considerable heterogeneity that complicates data analysis. Here, we review and compare 12 computational tools for processing DMS sequencing data and scoring variant effects. We systematically outline each tool's statistical approaches, supported experimental designs, input/output requirements, software implementation, visualisation capabilities, and key assumptions. By highlighting the strengths and limitations of these tools, we hope to guide researchers in selecting methods appropriate for their specific experiments. Furthermore, we discuss current challenges, including the need for standardised analysis protocols and sustainable software maintenance, as well as opportunities for future methods development. Ultimately, this review seeks to advance the application and adoption of DMS, facilitating deeper biological understanding and improved clinical translation.

Keywords: Bioinformatics; Deep Mutational Scanning; Functional Genomics; Multiplexed Assays of Variant Effect; Software.