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

Marco Salvatore, Marc Horlacher, Annalisa Marsico, Ole Winther, Robin Andersson*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

6 Citations (Scopus)
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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.

Original languageEnglish
Article number026
JournalNAR Genomics and Bioinformatics
Volume5
Issue number2
Number of pages10
DOIs
Publication statusPublished - 2023

Keywords

  • TRANSCRIPTION FACTORS
  • GENE-EXPRESSION
  • ENHANCERS
  • BINDING
  • GENOME

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