Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics
Khalid Mahmood, Chol-hee Jung, Gayle Philip, Peter Georgeson, Jessica Chung, Bernard J Pope, Daniel J Park
HUMAN GENOMICS | BMC | Published : 2017
BACKGROUND: Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools. RESULTS: Apparent accuracies of variant effect prediction tools were influenced signific..View full abstract
The authors are supported by Melbourne Bioinformatics (formerly the Victorian Life Sciences Computation Initiative).