gyōza: a Snakemake workflow for modular analysis of deep-mutational scanning data
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Deep-mutational scanning (DMS) is a powerful technique that allows screening large libraries of mutants at high throughput. It has been used in many applications, including to estimate the fitness impact of all single mutants of entire proteins, to catalog drug resistance mutations and even to predict protein structures. Here, we present gyōza, a Snakemake-based workflow to analyze DMS data. gyōza requires little programming knowledge and comes with comprehensive documentation to help the user go from raw sequencing data to functional impact scores. Complete with quality control and an automatically generated HTML report, this new pipeline should facilitate the analysis of time-series DMS experiments. gyōza is freely available on GitHub ( https://github.com/durr1602/gyoza ).
Article summary
Deep-mutational scanning (DMS) refers to molecular biology methods used to generate many genetic variants and evaluate their effect on adaptive fitness. It is used both in fundamental and applied research, with implications in genomic medicine. Many data processing steps are needed to transform the output of DMS, high-throughput sequencing data, into scores that quantify the fitness effect of each variant. The analysis is tailored to the type of experimental design, which can vary a lot. To facilitate such analysis and make it accessible to people with limited knowledge in bioinformatics, we have developed gyōza, a free and easy-to-use program to analyze DMS data.
