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Hum Mutat. 2019 Sep;40(9):1292-1298. doi: 10.1002/humu.23791. Epub 2019 Jun 22.

Predicting functional variants in enhancer and promoter elements using RegulomeDB.

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Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.
Department of Human Genetics, University of Michigan, Ann Arbor, Michigan.


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 good performance in predicting allele-specific transcription factor binding events. As an extension to the current scoring system in RegulomeDB, we expect our computational model to prioritize variants in regulatory regions, thus help the understanding of functional variants in noncoding regions that lead to disease.


MPRA; functional genomics; gene regulation; machine learning; variation

[Available on 2020-09-01]

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