Rxivist logo

Predicting 3D genome folding from DNA sequence

By Geoffrey Fudenberg, David R. Kelley, Katherine S. Pollard

Posted 10 Oct 2019
bioRxiv DOI: 10.1101/800060

In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Here we present a deep convolutional neural network, Akita, that accurately predicts genome folding from DNA sequence alone. Representations learned by Akita underscore the importance of CTCF and reveal a complex grammar underlying genome folding. Akita enables rapid in silico predictions for sequence mutagenesis, genome folding across species, and genetic variants.

Download data

  • Downloaded 2,389 times
  • Download rankings, all-time:
    • Site-wide: 2,558 out of 84,901
    • In genomics: 494 out of 5,475
  • Year to date:
    • Site-wide: 2,485 out of 84,901
  • Since beginning of last month:
    • Site-wide: 3,860 out of 84,901

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News