期刊
HUMAN MUTATION
卷 40, 期 9, 页码 1292-1298出版社
WILEY
DOI: 10.1002/humu.23791
关键词
functional genomics; gene regulation; machine learning; MPRA; variation
资金
- NHGRI NIH HHS [U41 HG009293, U41 HG007346, R13 HG006650, U41HG009293] Funding Source: Medline
- NIH HHS [R13HG006650, U41HG007346] Funding Source: Medline
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.
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