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Cited by 269Genomics and Chromatin DynamicsGenetic Associations and EpidemiologyEpigenetics and DNA Methylation

A sequence-based global map of regulatory activity for deciphering human genetics

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Chen, Kathleen M., Wong, Aaron K., Troyanskaya, Olga G., Zhou, Jian·2022·Nature Genetics
DOI 10.1038/s41588-022-01102-2PMC PMC9279145

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Abstract

Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. We address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. We provide Sei as a resource to elucidate the regulatory basis of human health and disease.

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  • Thus far, sequence-based models have focused on assays in which measured activity is proportional to local sequencing read counts.

    “Thus far, such models have focused on assays in which measured activity is proportional to local sequencing read counts.”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [10]

    The Main section says earlier sequence models focused on predicting transcription-factor binding and histone modifications. Results describes Sei’s chromatin-profile targets, but Methods (Deep learning sequence model training; page unavailable) says its training labels are binary peak calls. The paper does not establish that measured activity in these assays is proportional to local sequencing read counts. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Machine-learning models trained to predict function from DNA sequences have been successful at characterizing regulatory syntax and interpreting genetic variant effects.

    “Machine-learning models trained to predict function from DNA sequences have been successful at characterizing regulatory syntax and interpreting genetic variant effects.”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [10]

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Cited by

Sorted by citations

Authors
Chen, K. M., Wong, A. K., Troyanskaya, O. G., & Zhou, J.
Affiliations
  • Princeton University (US)
  • Simons Foundation (US)
  • The University of Texas Southwestern Medical Center (US)
Corresponding author
  • Troyanskaya, Olga G.
  • Zhou, Jian
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2022-07
Volume
54
Issue
7
Pages
940-949
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-022-01102-2
PMC
PMC9279145
OpenAlex
W4285006295
Cited by
269
References
6
Citations by year
2026: 68 · 2025: 65 · 2024: 68 · 2023: 54 · 2022: 14
FWCI
16.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Health & Human Services | National Institutes of Health (R01HG005998, U54HL117798, R01GM071966, DP2GM146336)
  • U.S. Department of Health and Human Services (HHSN272201000054C)
  • Simons Foundation (395506)
  • Cancer Prevention and Research Institute of Texas (RR190071)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
Keywords
regulatory action, enhancer function, cell type-specific gene regulation, expression quantitative trait loci, evolutionary constraints, tissue-specific gene expression, complex traits, pathogenic variants, deep learning, epigenomics, human genetics
MeSH terms
Human Genetics, Chromatin, Humans, Regulatory Sequences, Nucleic Acid, Quantitative Trait Loci, Epigenomics

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