Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics


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. 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. Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets yielded considerably lower accuracy than observed for other, potentially more conflicted datasets.

Human Genomics