Identifying functional non-coding variants is one of the greatest unmet challenges in genetics. To help address this, we introduce an R package, SuRFR, which integrates functional annotation and prior biological knowledge to prioritise candidate functional variants. SuRFR is publicly available, modular, flexible, fast, and simple to use. We demonstrate that SuRFR performs with high sensitivity and specificity and provide a widely applicable and scalable benchmarking dataset for model training and validation.
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- Deanery of Molecular, Genetic and Population Health Sciences - Reader
- Centre for Genomic and Experimental Medicine
- Edinburgh Neuroscience
Person: Academic: Research Active