Predicting functional variants in enhancer and promoter elements using RegulomeDB.

ABSTRACT: Here we present a computational model, Score of Unified Regulatory Features (SURF), that predicts functional variants in enhancer and promoter elements. SURF is trained on data from massively parallel reporter assays and predicts the effect of variants on reporter expression levels. It achieved the top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" challenge. We also show that features queried through RegulomeDB, which are direct annotations from functional genomics data, help improve prediction accuracy beyond transfer learning features from DNA sequence-based deep learning models. Some of the most important features include DNase footprints, especially when coupled with complementary ChIP-seq data. Furthermore, we found our model achieved go

SUBMITTER: Dong S 

PROVIDER: S-EPMC6744346 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature