What question did this study set out to answer?

Develop and validate a computational model, SURF, that leverages RegulomeDB functional genomics annotations to predict the functional consequences of noncoding variants in enhancer and promoter regions.

June 22, 2019Human MutationOpen Access

Predicting functional variants in enhancer and promoter elements using RegulomeDB

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Authors

SDShengcheng DongStanford University ABAlan P. BoyleUniversity of Michigan

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Implication

Computational modeling study demonstrates accurate prediction of enhancer and promoter variant effects, highlighting functional genomics annotations for identifying disease-associated mutations.

Key Points

Develop and validate a computational model, SURF, that leverages RegulomeDB functional genomics annotations to predict the functional consequences of noncoding variants in enhancer and promoter regions.
Trained the Score of Unified Regulatory Features (SURF) model on expression data generated from massively parallel reporter assays.
Integrated functional genomics annotations from RegulomeDB, including DNase footprints and ChIP-seq signals, alongside sequence-based deep learning transfer representations.
Evaluated model accuracy using the Fifth Critical Assessment of Genome Interpretation Regulation Saturation challenge and allele-specific transcription factor binding benchmarks.
SURF secured the top performance ranking in the Fifth Critical Assessment of Genome Interpretation Regulation Saturation challenge.
Functional genomic features from RegulomeDB improved predictive accuracy beyond sequence-derived deep learning models, with DNase footprints coupled to ChIP-seq data being among the most informative.
The model exhibited strong accuracy in predicting allele-specific transcription factor binding events across regulatory elements.

Cite This Study

Dong et al. (2019) studied this question.

synapsesocial.com/papers/69daa4038988aeabbe68713c https://doi.org/10.1002/humu.23791

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Implication

Computational modeling study demonstrates accurate prediction of enhancer and promoter variant effects, highlighting functional genomics annotations for identifying disease-associated mutations.

Predicting functional variants in enhancer and promoter elements using RegulomeDB

Discussion

Member takes

Implication

Key Points

Cite This Study

Predicting functional variants in enhancer and promoter elements using RegulomeDB

Discussion

Member takes

Implication

Key Points

Cite This Study

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