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 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.

journal_name

Hum Mutat

journal_title

Human mutation

authors

Dong S,Boyle AP

doi

10.1002/humu.23791

subject

Has Abstract

pub_date

2019-09-01 00:00:00

pages

1292-1298

issue

9

eissn

1059-7794

issn

1098-1004

journal_volume

40

pub_type

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