Predicting 3D chromatin interactions from DNA sequence using Deep Learning

Robert S. Piecyk, Luca Schlegel, Frank Johannes

Research output: Contribution to journalReview articlepeer-review

12 Scopus citations

Abstract

Gene regulation in eukaryotes is profoundly shaped by the 3D organization of chromatin within the cell nucleus. Distal regulatory interactions between enhancers and their target genes are widespread and many causal loci underlying heritable agricultural or clinical traits have been mapped to distal cis-regulatory elements. Dissecting the sequence features that mediate such distal interactions is key to understanding their underlying biology. Deep Learning (DL) models coupled with genome-wide 3C-based sequencing data have emerged as powerful tools to infer the DNA sequence grammar underlying such distal interactions. In this review we show that most DL models have remarkably high prediction accuracy, which indicates that DNA sequence features are important determinants of chromatin looping. However, DL model training has so far been limited to a small set of human cell lines, raising questions about the generalization of these predictions to other tissue-types and species. Furthermore, we find that the model architecture seems less relevant for model performance than the training strategy and the data preparation step. Transfer learning, coupled with functionally curated interactions, appear to be the most promising approach to learn cell-type specific and possibly species- specific sequence features in future applications.

Original languageEnglish
Pages (from-to)3439-3448
Number of pages10
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
StatePublished - Jan 2022

Keywords

  • 3D Chromatin Interaction
  • Chromosome conformation capture (3C)
  • Deep Learning
  • Epigenetics
  • Genome folding

Fingerprint

Dive into the research topics of 'Predicting 3D chromatin interactions from DNA sequence using Deep Learning'. Together they form a unique fingerprint.

Cite this