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Predicting functional variants in enhancer and promoter elements using RegulomeDB.

Authors
  • Dong, Shengcheng1
  • Boyle, Alan P1, 2
  • 1 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.
  • 2 Department of Human Genetics, University of Michigan, Ann Arbor, Michigan.
Type
Published Article
Journal
Human Mutation
Publisher
Wiley (John Wiley & Sons)
Publication Date
Sep 01, 2019
Volume
40
Issue
9
Pages
1292–1298
Identifiers
DOI: 10.1002/humu.23791
PMID: 31228310
Source
Medline
Keywords
Language
English
License
Unknown

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 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. © 2019 Wiley Periodicals, Inc.

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