Evidence map›Paper›PMID 37679846›Full record

ArticleGenome biology2023

ChromGene: gene-based modeling of epigenomic data.

Artur Jaroszewicz, Jason Ernst

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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

2 authors.

Artur JaroszewiczBioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Jason ErnstBioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA. jason.ernst@ucla.edu.ORCID http://orcid.org/0000-0003-4026-7853

Funding

Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotencyU01HG012079 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Chongyuan Luo, Kathrin Plath · 2021 to 2026
$6.9M
"Ribonomics" of Gene Regulation to predict Innate Immune ResponsesU01HG007912 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BLACK, DOUGLAS L, HOFFMANN, ALEXANDER · 2015 to 2017
$5.8M
Impact of coding and non-coding variation in progressive supranuclear palsyUG3NS104095 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DICKSON, DENNIS WILLIAM, GESCHWIND, DANIEL H · 2017 to 2019
$3.6M
Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing DataDP1DA044371 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ERNST, JASON · 2017 to 2021
$2.3M
Epigenomic control of RNA splicingR01ES024995 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOFFMANN, ALEXANDER, XING, YI · 2014 to 2015
$616k
NHGRI NIH HHS T32 HG002536NHGRI NIH HHS U01 HG007912NHGRI NIH HHS U01 HG012079NIDA NIH HHS DP1 DA044371NIEHS NIH HHS R01 ES024995NINDS NIH HHS UG3 NS104095
6 · The paper itself

Abstract

Various computational approaches have been developed to annotate epigenomes on a per-position basis by modeling combinatorial and spatial patterns within epigenomic data. However, such annotations are less suitable for gene-based analyses. We present ChromGene, a method based on a mixture of learned hidden Markov models, to annotate genes based on multiple epigenomic maps across the gene body and flanks. We provide ChromGene assignments for over 100 cell and tissue types. We characterize the mixture components in terms of gene expression, constraint, and other gene annotations. The ChromGene method and annotations will provide a useful resource for gene-based epigenomic analyses.

Indexed as

EpigenomeEpigenomicsHistocompatibility TestingLearningMolecular Sequence AnnotationChromatinEpigenomicsHidden Markov modelsHistone modificationsMachine learning

Identifiers

PMID37679846
PMCPMC10486095

What OpenQuestion holds

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LicenceCC BY
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.