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

Abstract

Genetic modifiers are variants modulating phenotypic outcomes of a primary detrimental variant. They contribute to rare diseases phenotypic variability, but their identification is challenging. Genetic screening with model organisms is a widely used method for demystifying genetic modifiers. Forward genetics screening followed by whole genome sequencing allows the detection of variants throughout the genome but typically produces thousands of candidate variants making the interpretation and prioritization process very time-consuming and tedious. Despite whole genome sequencing is more time and cost-efficient, usage of computational pipelines specific to modifier identification remains a challenge for biological-experiment-focused laboratories doing research with model organisms. To facilitate a broader implementation of whole genome sequencing in genetic screens, we have developed Model Organism Modifier or MOM, a pipeline as a user-friendly Galaxy workflow. Model Organism Modifier analyses raw short-read whole genome sequencing data and implements tailored filtering to provide a Candidate Variant List short enough to be further manually curated. We provide a detailed tutorial to run the Galaxy workflow Model Organism Modifier and guidelines to manually curate the Candidate Variant Lists. We have tested Model Organism Modifier on published and validated Caenorhabditis elegans modifiers screening datasets. As whole genome sequencing facilitates high-throughput identification of genetic modifiers in model organisms, Model Organism Modifier provides a user-friendly solution to implement the bioinformatics analysis of the short-read datasets in laboratories without expertise or support in Bioinformatics.

Keywords: Caenorhabditis elegans; bioinformatics pipeline; galaxy; genetic screening; modifiers; short-read whole genome sequencing.

Fig. 1.

Schematic representation of the main bioinformatics steps implemented in the Galaxy workflow MOM.

Fig. 2.

Overview of Galaxy and the workflow webpage. (a) Annotated screenshot of the main page on Galaxy showing on the left the toolshed, and on the right side, the History—displaying jobs status and files. (b) Annotated screenshot of the window allowing uploading files into a History on Galaxy. (c) Annotated screenshot of the Galaxy Workflow MOM main page displaying all necessary input files.

Fig. 3.

Overview of the Galaxy history. (a) Annotated screenshot of a History in Galaxy showing the different settings available to the user. (b) Annotated screenshot of a History in Galaxy showing some of the different statuses that a job can display: pending, running, done.

Fig. 4.

Overview of the IGV and how to use it for manual curation through visualization. (a) Annotated screenshot of the Search bar on IGV. (b) Annotated screenshot of the tracks as displayed on IGV when visualizing WGS datasets. (c) Screenshots of homozygous SNVs of good-quality as visualized on IGV. (d) Screenshots of SNVs of bad quality as visualized on IGV.