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Open Access Gold möglich sobald Verlagsversion bei der ZB eingereicht worden ist.
Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility.
NAR Gen. Bioinfo. 5:lqad026 (2023)
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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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Transcription Factors; Gene-expression; Enhancers; Binding; Genome
ISSN (print) / ISBN
2631-9268
e-ISSN
2631-9268
Zeitschrift
NAR: Genomics and Bioinformatics
Quellenangaben
Band: 5,
Heft: 2,
Artikelnummer: lqad026
Verlag
Oxford University Press
Verlagsort
Great Clarendon St, Oxford Ox2 6dp, England
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)