Evidence map›Paper›PMID 42301918›Full record

ArticleNucleic acids research2026

Integrated flexible DNA methylation-chromatin segmentation modeling enhances epigenomic state annotation.

Nihit Aggarwal, Johanna Elena Schmitz, Lukas Laufer, Sven Rahmann, Jörn Walter, Abdulrahman Salhab

Abstract read
In one paragraph

Article in Nucleic acids research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. EpiATLAS - a reference for human epigenomic research.bioRxiv : the preprint server for biology · 2026
    Article
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

6 authors.

Nihit AggarwalDepartment of Genetics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0000-0003-4875-8219
Johanna Elena SchmitzAlgorithmic Bioinformatics, Center for Bioinformatics Saar, Saarland Informatics Campus, 66123 Saarbrücken, Germany.ORCID 0009-0002-6377-2561
Lukas LauferDepartment of Genetics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0009-0008-0197-1948
Sven RahmannAlgorithmic Bioinformatics, Center for Bioinformatics Saar, Saarland Informatics Campus, 66123 Saarbrücken, Germany.ORCID 0000-0002-8536-6065
Jörn WalterDepartment of Genetics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0000-0003-0563-7417
Abdulrahman SalhabDepartment of Genetics, Saarland University, 66123 Saarbrücken, Germany.ORCID 0000-0002-9649-6922

Funding

Deutsche ForschungsgemeinschaftELIXIR-DE W-de.NBI-021
6 · The paper itself

Abstract

DNA methylation and histone modifications together shape the cell-type-specific epigenomic landscape. To enhance genome-wide annotation, we developed EpiSegMixMeth (ESMM), the first integrative segmentation model combining chromatin marks and DNA methylation. ESMM improves upon hidden Markov models by incorporating flexible read count distributions and state duration modeling. Applied to 154 high-quality human epigenomes from the IHEC EpiATLAS, ESMM enhances the annotation of broad heterochromatic regions-over 60% of the genome-that are often missed by chromatin-only models. It accurately defines narrow regulatory element boundaries and captures local chromatin state transitions during cell differentiation. Notably, we show that DNA methylation can replace missing repressive histone marks in segmentation, ensuring robust results across various cell types. In developing memory B cells, ESMM reveals chromatin shifts that align with 3D genome architecture changes, providing a valuable resource for studying cell-type-specific epigenomic regulation.

Indexed as

ChromatinDNA MethylationEpigenesis, GeneticEpigenomeEpigenomicsCell DifferentiationGenome, HumanHidden Markov ModelsHistone CodeHistonesHumansMolecular Sequence AnnotationChromatinHistones

Identifiers

PMID42301918
PMCPMC13270199

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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.