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Predicting A/B compartments from histone modifications using deep learning

Suchen Zheng, Nitya Thakkar, Hannah L. Harris, Megan Zhang, Susanna Liu, Mark Gerstein, Erez Lieberman-Aiden, M. Jordan Rowley, William Stafford Noble, Gamze Gürsoy, Ritambhara Singh
doi: https://doi.org/10.1101/2022.04.19.488754
Suchen Zheng
1Department of Computer Science, Brown University
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Nitya Thakkar
1Department of Computer Science, Brown University
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Hannah L. Harris
2Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center
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Megan Zhang
5Data Science and Statistics, Yale University
6Molecular, Cellular, and Developmental Biology, Yale University
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Susanna Liu
5Data Science and Statistics, Yale University
6Molecular, Cellular, and Developmental Biology, Yale University
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Mark Gerstein
3Computational Biology and Bioinformatics, Yale University
4Molecular Biophysics & Biochemistry, Yale University
5Data Science and Statistics, Yale University
7Computer Science, Yale University
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Erez Lieberman-Aiden
8Department of Genetics, Baylor College of Medicine
9Department of Computer Science, Rice University
10Computational and Applied Mathematics, Rice University
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M. Jordan Rowley
2Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center
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William Stafford Noble
11Department of Genome Sciences, University of Washington
12Paul G. Allen School of Computer Science and Engineering, University of Washington
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Gamze Gürsoy
13Department of Biomedical Informatics, Columbia University
14New York Genome Center
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  • For correspondence: ritambhara@brown.edu
Ritambhara Singh
1Department of Computer Science, Brown University
15Center for Computational Molecular Biology, Brown University
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  • For correspondence: ritambhara@brown.edu
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ABSTRACT

Genomes in 3D are folded into organizational units that can influence critical biological functions. In particular, the organization of chromatin into A and B compartments segregates its active regions from the inactive regions. Compartments, evident in Hi-C contact matrices, have been used to describe cell-type specific changes in A/B organization. However, obtaining Hi-C data for all cell and tissue types of interest is prohibitively expensive, which has limited the widespread consideration of compartment status. We present a prediction tool called Compartment prediction using Recurrent Neural Network (CoRNN) that models the relationship between the compartmental organization of the genome and histone modification enrichment. Our model predicts A/B compartments, in a cross-cell type setting, with an average area under the ROC score of 90.9%. Our cell type specific compartment predictions show high overlap with known functional elements. We investigate our predictions by systematically removing combinations of histone marks and find that H3K27ac and H3K36me3 are the most predictive marks. We then perform a detailed investigation of loci where compartment status cannot be accurately predicted from these marks.These regions represent chromatin with ambiguous compartmental status, likely due to variations in status within the population of cells. As such these ambiguous loci also show highly variable compartmental status between biological replicates in the same GM12878 cell type. Our software and trained model are publicly available at https://github.com/rsinghlab/CoRNN.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://www.encodeproject.org/

Copyright 
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-NC 4.0 International license.
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Posted April 19, 2022.
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Predicting A/B compartments from histone modifications using deep learning
Suchen Zheng, Nitya Thakkar, Hannah L. Harris, Megan Zhang, Susanna Liu, Mark Gerstein, Erez Lieberman-Aiden, M. Jordan Rowley, William Stafford Noble, Gamze Gürsoy, Ritambhara Singh
bioRxiv 2022.04.19.488754; doi: https://doi.org/10.1101/2022.04.19.488754
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Predicting A/B compartments from histone modifications using deep learning
Suchen Zheng, Nitya Thakkar, Hannah L. Harris, Megan Zhang, Susanna Liu, Mark Gerstein, Erez Lieberman-Aiden, M. Jordan Rowley, William Stafford Noble, Gamze Gürsoy, Ritambhara Singh
bioRxiv 2022.04.19.488754; doi: https://doi.org/10.1101/2022.04.19.488754

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