Predicting 3D genome folding from DNA sequence with Akita

Nat Methods. 2020 Nov;17(11):1111-1117. doi: 10.1038/s41592-020-0958-x. Epub 2020 Oct 12.

Abstract

In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key regulators; perturbing the levels of either greatly disrupts genome-wide folding as assayed by chromosome conformation capture methods. Still, how a given DNA sequence encodes a particular locus-specific folding pattern remains unknown. Here we present a convolutional neural network, Akita, that accurately predicts genome folding from DNA sequence alone. Representations learned by Akita underscore the importance of an orientation-specific grammar for CTCF binding sites. Akita learns predictive nucleotide-level features of genome folding, revealing effects of nucleotides beyond the core CTCF motif. Once trained, Akita enables rapid in silico predictions. Accounting for this, we demonstrate how Akita can be used to perform in silico saturation mutagenesis, interpret eQTLs, make predictions for structural variants and probe species-specific genome folding. Collectively, these results enable decoding genome function from sequence through structure.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • CCCTC-Binding Factor / genetics*
  • Cell Cycle Proteins / genetics*
  • Chromosomal Proteins, Non-Histone / genetics*
  • DNA-Binding Proteins / genetics*
  • Gene Expression Regulation
  • Genome, Human*
  • Humans
  • Models, Genetic
  • Neural Networks, Computer*
  • Sequence Analysis, DNA / methods*

Substances

  • CCCTC-Binding Factor
  • CTCFL protein, human
  • Cell Cycle Proteins
  • Chromosomal Proteins, Non-Histone
  • DNA-Binding Proteins
  • cohesins