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Predicting 3D genome folding from DNA sequence

View ORCID ProfileGeoff Fudenberg, View ORCID ProfileDavid R. Kelley, Katherine S. Pollard
doi: https://doi.org/10.1101/800060
Geoff Fudenberg
1Gladstone Institutes for Data Science and Biotechnology, San Francisco, USA
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  • For correspondence: geoff.fudenberg@gladstone.ucsf.edu drk@calicolabs.com katherine.pollard@gladstone.ucsf.edu
David R. Kelley
2Calico Life Sciences LLC, South San Francisco, CA, USA
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  • For correspondence: geoff.fudenberg@gladstone.ucsf.edu drk@calicolabs.com katherine.pollard@gladstone.ucsf.edu
Katherine S. Pollard
1Gladstone Institutes for Data Science and Biotechnology, San Francisco, USA
3Department of Epidemiology & Biostatistics, Institute for Human Genetics, Quantitative Biology Institute, and Institute for Computational Health Sciences, University of California, San Francisco, CA, USA
4Chan-Zuckerberg Biohub, San Francisco, CA, USA
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  • For correspondence: geoff.fudenberg@gladstone.ucsf.edu drk@calicolabs.com katherine.pollard@gladstone.ucsf.edu
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Abstract

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 10, 2019.
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Predicting 3D genome folding from DNA sequence
Geoff Fudenberg, David R. Kelley, Katherine S. Pollard
bioRxiv 800060; doi: https://doi.org/10.1101/800060
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Predicting 3D genome folding from DNA sequence
Geoff Fudenberg, David R. Kelley, Katherine S. Pollard
bioRxiv 800060; doi: https://doi.org/10.1101/800060

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