Development of an Expert Knowledge-Based Genomic Variant Prioritisation Platform.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) generates vast amounts of data, posing challenges for researchers without specialized bioinformatics expertise.
• Exploiting NGS data for genetic diagnosis requires sophisticated analytical skills, limiting accessibility for many scientists.

Purpose of the Study:

• To develop a user-friendly analytical tool for non-bioinformatics experts to process and interpret NGS genomic data.
• To enable automated prioritization and annotation of genomic variants, facilitating genetic diagnosis.

Main Methods:

• A user-centered, iterative development methodology was employed to create the analytical tool.
• The tool integrates pre-processing pipelines, data filtering, and annotation using diverse databases.
• An expert-based scoring system assists in prioritizing relevant genomic variants.

Main Results:

• The developed tool, CRIBOMICS, was found to be easy to learn, use, and interact with by end-users.
• The system effectively automates the prioritization of genomic variants.
• Analysis time and potential errors in variant prioritization for genetic diagnosis were significantly reduced.

Conclusions:

• CRIBOMICS empowers non-bioinformatics experts to confidently analyze NGS data.
• The tool streamlines the process of variant prioritization, enhancing efficiency in genetic diagnosis.
• This user-friendly approach democratizes access to advanced genomic data analysis.