PT  - JOURNAL ARTICLE
AU  - Fudenberg, Geoff
AU  - Kelley, David R.
AU  - Pollard, Katherine S.
TI  - Predicting 3D genome folding from DNA sequence
AID  - 10.1101/800060
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 800060
4099  - http://biorxiv.org/content/early/2019/10/10/800060.short
4100  - http://biorxiv.org/content/early/2019/10/10/800060.full
AB  - 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.