SuRFing the genomics wave: an R package for prioritising SNPs by functionality.

Area of Science:

• Genetics
• Bioinformatics

Background:

• Identifying functional non-coding variants is a significant challenge in genetic research.
• Non-coding variants play crucial roles in gene regulation and disease etiology.

Purpose of the Study:

• To introduce SuRFR, an R package designed to prioritize candidate functional non-coding variants.
• To provide a robust tool that integrates functional annotation and biological knowledge for variant prioritization.

Main Methods:

• Development of the SuRFR R package.
• Integration of functional annotation data and prior biological knowledge.
• Benchmarking using a scalable dataset for model training and validation.

Main Results:

• SuRFR demonstrates high sensitivity and specificity in identifying functional variants.
• The package is modular, flexible, fast, and user-friendly.
• A widely applicable benchmarking dataset is provided.

Conclusions:

• SuRFR is an effective tool for prioritizing functional non-coding variants.
• The package facilitates genetic research by addressing a key unmet challenge.
• SuRFR offers a scalable and accessible solution for variant analysis.