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Multi-scale deep tensor factorization learns a latent representation of the human epigenome

Jacob Schreiber, Timothy Durham, Jeffrey Bilmes, William Stafford Noble
doi: https://doi.org/10.1101/364976
Jacob Schreiber
1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
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Timothy Durham
2Department of Genome Sciences, University of Washington, Seattle, USA
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Jeffrey Bilmes
1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
3Department of Electrical Engineering, University of Washington, Seattle, USA
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William Stafford Noble
1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
2Department of Genome Sciences, University of Washington, Seattle, USA
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  • For correspondence: wnoble@uw.edu
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Abstract

The human epigenome has been experimentally characterized by measurements of protein binding, chromatin acessibility, methylation, and histone modification in hundreds of cell types. The result is a huge compendium of data, consisting of thousands of measurements for every basepair in the human genome. These data are difficult to make sense of, not only for humans, but also for computational methods that aim to detect genes and other functional elements, predict gene expression, characterize polymorphisms, etc. To address this challenge, we propose a deep neural network tensor factorization method, Avocado, that compresses epigenomic data into a dense, information-rich representation of the human genome. We use data from the Roadmap Epigenomics Consortium to demonstrate that this learned representation of the genome is broadly useful: first, by imputing epigenomic data more accurately than previous methods, and second, by showing that machine learning models that exploit this representation outperform those trained directly on epigenomic data on a variety of genomics tasks. These tasks include predicting gene expression, promoter-enhancer interactions, replication timing, and an element of 3D chromatin architecture. Our findings suggest the broad utility of Avocado’s learned latent representation for computational genomics and epigenomics.

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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-ND 4.0 International license.
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Posted April 11, 2019.
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Multi-scale deep tensor factorization learns a latent representation of the human epigenome
Jacob Schreiber, Timothy Durham, Jeffrey Bilmes, William Stafford Noble
bioRxiv 364976; doi: https://doi.org/10.1101/364976
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Multi-scale deep tensor factorization learns a latent representation of the human epigenome
Jacob Schreiber, Timothy Durham, Jeffrey Bilmes, William Stafford Noble
bioRxiv 364976; doi: https://doi.org/10.1101/364976

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