Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.

ABSTRACT: Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent valid

SUBMITTER: Gray VE 

PROVIDER: S-EPMC5799033 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature