Evidence map›Paper›PMID 31221079›Full record

ArticleBMC genomics2019

Genome-wide enhancer annotations differ significantly in genomic distribution, evolution, and function.

Mary Lauren Benton, Sai Charan Talipineni, Dennis Kostka, John A Capra

Abstract readComparative Study
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–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

40 citing papers in PubMed.

  1. UnlockingNAR genomics and bioinformatics · 2026
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  9. InterpretingbioRxiv : the preprint server for biology · 2024
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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

4 authors.

Mary Lauren BentonDepartment of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37235, USA.
Sai Charan TalipineniDepartment of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA.
Dennis KostkaDepartment of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA. kostka@pitt.edu.
John A CapraDepartment of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37235, USA. tony.capra@vanderbilt.edu.

Funding

The Evolution of Gene Regulation and Human DiseaseR35GM127087 · NIGMS · VANDERBILT UNIVERSITY · PI John Anthony Capra · 2018 to 2026
$3.2M
BIDS: Vanderbilt Training Program in BIg Biomedical Data ScienceT32LM012412 · NLM · VANDERBILT UNIVERSITY · PI BLUME, JEFFREY D., GADD, CYNTHIA S · 2016 to 2020
$1.5M
Modeling the Dynamics of Genome-Scale Data Across TreesR01GM115836 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CAPRA, JOHN ANTHONY, KOSTKA, DENNIS · 2015 to 2017
$1.1M
Foundation for the National Institutes of Health R01GM115836Foundation for the National Institutes of Health R01GMGM115836Foundation for the National Institutes of Health R35GM127087March of Dimes Foundation Basil O'Connor Starter ScholarshipMarch of Dimes Foundation Innovation Catalyst AwardNIGMS NIH HHS R01 GM115836NIGMS NIH HHS R35 GM127087NLM NIH HHS T32 LM012412U.S. National Library of Medicine T32LM012412
6 · The paper itself

Abstract

backgroundNon-coding gene regulatory enhancers are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. Given the differences in the biological signals assayed, some variation in the enhancers identified by different methods is expected; however, the concordance of enhancers identified by different methods has not been comprehensively evaluated. This is critically needed, since in practice, most studies consider enhancers identified by only a single method. Here, we compare enhancer sets from eleven representative strategies in four biological contexts.

resultsAll sets we evaluated overlap significantly more than expected by chance; however, there is significant dissimilarity in their genomic, evolutionary, and functional characteristics, both at the element and base-pair level, within each context. The disagreement is sufficient to influence interpretation of candidate SNPs from GWAS studies, and to lead to disparate conclusions about enhancer and disease mechanisms. Most regions identified as enhancers are supported by only one method, and we find limited evidence that regions identified by multiple methods are better candidates than those identified by a single method. As a result, we cannot recommend the use of any single enhancer identification strategy in all settings.

conclusionsOur results highlight the inherent complexity of enhancer biology and identify an important challenge to mapping the genetic architecture of complex disease. Greater appreciation of how the diverse enhancer identification strategies in use today relate to the dynamic activity of gene regulatory regions is needed to enable robust and reproducible results.

Indexed as

Enhancer Elements, GeneticCell LineDatabases, GeneticEvolution, MolecularGene Expression RegulationGenomicsHumansLiverMolecular Sequence AnnotationMyocardiumCis-regulatory elementsEnhancer identificationGene regulation

Identifiers

PMID31221079
PMCPMC6585034

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.