Evidence map›Paper›PMID 34991667›Full record

ArticleGenome biology2022

Universal annotation of the human genome through integration of over a thousand epigenomic datasets.

Ha Vu, Jason Ernst

Abstract read
In one paragraph

Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 1 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

90 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. EpiATLAS - a reference for human epigenomic research.bioRxiv : the preprint server for biology · 2026
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  9. Pervasive cryptic selection in the human noncoding genome.bioRxiv : the preprint server for biology · 2026
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30 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.

Ha VuBioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA.
Jason ErnstBioinformatics Interdepartmental Program, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0003-4026-7853

Funding

1/2 Genomic Strategies to Identify High-impact Psychiatric Risk VariantsU01MH105578 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FREIMER, NELSON B., GESCHWIND, DANIEL H · 2014 to 2018
$8.5M
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
SINGLE-CELL MULTI-OMIC APPROACHES TO MECHANISTICALLY CHARACTERIZE PSYCHIATRIC DISORDER RISK LOCI IN THE HUMAN BRAINR01MH125252 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LUO, CHONGYUAN · 2021 to 2025
$3.7M
Impact of coding and non-coding variation in progressive supranuclear palsyUH3NS104095 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DICKSON, DENNIS WILLIAM, GESCHWIND, DANIEL H · 2020 to 2021
$2.5M
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
NHGRI NIH HHS U01 HG012079NIDA NIH HHS DP1 DA044371NIMH NIH HHS R01 MH125252NIMH NIH HHS U01 MH105578NINDS NIH HHS UH3 NS104095
6 · The paper itself

Abstract

backgroundGenome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative "stacked modeling" approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.

resultsUsing a version of ChromHMM enhanced for large-scale applications, we apply the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we use in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type-specific activity and is more predictive of locations of external genomic annotations.

conclusionsThe full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.

Indexed as

EpigenomicsGenome, HumanChromatinGenomicsHumansMolecular Sequence AnnotationChromatin

Identifiers

PMID34991667
PMCPMC8734071

What OpenQuestion holds

Textmetadata
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.