Model Organism Modifier (MOM): a user-friendly Galaxy workflow to detect modifiers from genome sequencing data using...

Area of Science:

• Genetics
• Bioinformatics
• Model Organism Research

Background:

• Genetic modifiers influence disease presentation and severity.
• Identifying these modifiers is key to understanding rare disease variability.
• Current methods using whole genome sequencing generate excessive data, complicating analysis.

Purpose of the Study:

• To develop a user-friendly computational pipeline for identifying genetic modifiers.
• To facilitate the broader adoption of whole genome sequencing in genetic screens.
• To streamline the analysis of short-read sequencing data for model organism research.

Main Methods:

• Developed the Model Organism Modifier (MOM) pipeline as a Galaxy workflow.
• MOM analyzes raw short-read whole genome sequencing data.
• Implemented tailored filtering to reduce candidate variant lists for manual curation.

Main Results:

• MOM provides a significantly reduced candidate variant list.
• The pipeline was successfully tested on published Caenorhabditis elegans modifier screening datasets.
• MOM simplifies the bioinformatics analysis for laboratories without dedicated bioinformatics support.

Conclusions:

• The Model Organism Modifier pipeline offers a user-friendly solution for genetic modifier identification.
• MOM enables efficient analysis of whole genome sequencing data in model organisms.
• This tool supports high-throughput genetic screens and rare disease research.