Evidence map›Paper›PMID 31228310›Full record

ArticleHuman mutation2019

Predicting functional variants in enhancer and promoter elements using RegulomeDB.

Shengcheng Dong, Alan P Boyle

Abstract read
In one paragraph

Article in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Genome-wide association study of aromatase inhibitor discontinuation due to musculoskeletal symptoms.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2022
    Trial
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  9. Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025
    Review
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  11. Article
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  13. Review
  14. Article
  15. Article
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  17. Article
  18. Dissecting the genetic overlap between severe mental disorders and markers of cellular aging: Identification of pleiotropic genes and druggable targets.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Article
  19. Review
  20. Genetic and environmental factors driving congenital solitary functioning kidney.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2024
    Article

5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Shengcheng DongDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.ORCID 0000-0001-5728-8090
Alan P BoyleDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.

Funding

Resource Informatics and ProductionU41HG009293 · NHGRI · STANFORD UNIVERSITY · PI BOYLE, ALAN P, CHERRY, J. MICHAEL · 2017 to 2019
$2.2M
Component 2 - Resource ProjectU41HG007346 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E · 2015 to 2017
$1.7M
Critical Assessment of Genome Interpretation ConferenceR13HG006650 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E · 2011 to 2018
$165k
NHGRI NIH HHS R13 HG006650NHGRI NIH HHS U41 HG007346NHGRI NIH HHS U41 HG009293NHGRI NIH HHS U41HG009293NIH HHS R13HG006650NIH HHS U41HG007346
6 · The paper itself

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.

Indexed as

Enhancer Elements, GeneticGenetic VariationPromoter Regions, GeneticDeep LearningGenetic Predisposition to DiseaseGenome, HumanGenomicsHumansModels, GeneticSequence Analysis, DNAfunctional genomicsgene regulationmachine learningMPRAvariation

Identifiers

PMID31228310
PMCPMC6744346

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.