Evidence map›Paper›PMID 39283462›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Prediction of Enhancer-Gene Interactions Using Chromatin-Conformation Capture and Epigenome Data Using STARE.

Dennis Hecker, Marcel H Schulz

Abstract read
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Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Dennis HeckerInstitute for Computational Genomic Medicine and Institute of Cardiovascular Regeneration, Goethe University Frankfurt am Main, Frankfurt, Germany.ORCID 0000-0003-0272-243X
Marcel H SchulzInstitute for Computational Genomic Medicine and Institute of Cardiovascular Regeneration, Goethe University Frankfurt am Main, Frankfurt, Germany. marcel.schulz@em.uni-frankfurt.de.ORCID 0000-0002-1252-3656

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Disentangling the relationship of enhancers and genes is an ongoing challenge in epigenomics. We present STARE, our software to quantify the strength of enhancer-gene interactions based on enhancer activity and chromatin contact data. It implements the generalized Activity-by-Contact (gABC) score, which allows predicting putative target genes of candidate enhancers over any desired genomic distance. The only requirement for its application is a measurement of enhancer activity. In addition to regulatory interactions, STARE calculates transcription factor (TF) affinities on gene level. We illustrate its usage on a public single-cell data set of the human heart by predicting regulatory interactions on cell type level, by giving examples on how to integrate them with other data modalities, and by constructing TF affinity matrices.

Indexed as

ChromatinEnhancer Elements, GeneticEpigenomicsSoftwareComputational BiologyEpigenomeHumansTranscription FactorsChromatinTranscription FactorsChromatin contact dataEnhancer–gene linksGene regulationRegulatory regionsTranscription factors

Identifiers

What OpenQuestion holds

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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 OpenQuestion graph.