gyōza: a Snakemake workflow for modular analysis of deep-mutational scanning data.

Area of Science:

• Molecular Biology
• Bioinformatics
• Protein Engineering

Background:

• Deep-mutational scanning (DMS) is a high-throughput method for analyzing protein variants.
• Existing DMS analysis methods can be complex and require significant programming expertise.
• There is a need for accessible tools to interpret large-scale DMS datasets.

Purpose of the Study:

• To introduce gyōza, a Snakemake-based workflow for analyzing DMS data.
• To provide a user-friendly pipeline requiring minimal programming knowledge.
• To facilitate the interpretation of DMS experiments, including time-series analyses.

Main Methods:

• Developed a Snakemake workflow named gyōza.
• Integrated quality control measures.
• Automated the generation of HTML reports for data visualization.

Main Results:

• gyōza processes raw sequencing data into functional impact scores.
• The workflow includes comprehensive documentation for ease of use.
• The pipeline is designed for efficient analysis of time-series DMS experiments.

Conclusions:

• gyōza simplifies and standardizes DMS data analysis.
• The tool lowers the barrier to entry for researchers using DMS.
• gyōza is expected to accelerate discoveries in protein science and related fields.