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Cited by 13Genomics and Chromatin DynamicsMachine Learning in BioinformaticsChromatin Remodeling and Cancer

Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility

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Salvatore, Marco, Horlacher, Marc, Marsico, Annalisa, Winther, Ole, Andersson, Robin·2023·NAR Genomics and Bioinformatics
DOI 10.1093/nargab/lqad026PMC PMC10052367

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Abstract

Dysfunction of regulatory elements through genetic variants is a central mechanism in the pathogenesis of disease. To better understand disease etiology, there is consequently a need to understand how DNA encodes regulatory activity. Deep learning methods show great promise for modeling of biomolecular data from DNA sequence but are limited to large input data for training. Here, we develop ChromTransfer, a transfer learning method that uses a pre-trained, cell-type agnostic model of open chromatin regions as a basis for fine-tuning on regulatory sequences. We demonstrate superior performances with ChromTransfer for learning cell-type specific chromatin accessibility from sequence compared to models not informed by a pre-trained model. Importantly, ChromTransfer enables fine-tuning on small input data with minimal decrease in accuracy. We show that ChromTransfer uses sequence features matching binding site sequences of key transcription factors for prediction. Together, these results demonstrate ChromTransfer as a promising tool for learning the regulatory code.

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Cited by

Sorted by citations

Authors
Salvatore, M., Horlacher, M., Marsico, A., Winther, O., & Andersson, R.
Affiliations
  • University of Copenhagen (DK)
  • Helmholtz Munich (DE)
  • Broad Institute (US)
Corresponding author
Andersson, Robin
ORCID
Venue
NAR Genomics and Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2022-06-01
Volume
5
Issue
2
Pages
lqad026-lqad026
ISSN
2631-9268 (ISSN-L)
DOI
10.1093/nargab/lqad026
PMC
PMC10052367
OpenAlex
W4361853159
Cited by
13
References
9
Citations by year
2026: 2 · 2025: 3 · 2024: 6 · 2023: 1 · 2022: 1
FWCI
0.90
Type
journal article
Language
en
Open access
Licence
Locations
  • NAR Genomics and Bioinformatics (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion, cc-by-nc-nd)
  • mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
  • Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by-nc)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
Funders
  • Novo Nordisk Foundation (NNF18OC0052570, NNF20OC0059796, NNF21SA0072102, NNF20OC0062606)
  • Munich School for Data Science – MUDS
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Keywords
chromatin accessibility, cell type-specific gene regulation, transfer learning, deep learning, transcription factor binding sites, open chromatin, limited training data, genetic variants, etiology

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