Document Type

Article

Publication Date

3-3-2020

Keywords

JGM, JAXCC

JAX Source

Nat Commun 2020 Mar 3; 11(1):1173

Volume

11

Issue

1

First Page

1173

Last Page

1173

ISSN

2041-1723

PMID

32127534

DOI

https://doi.org/10.1038/s41467-020-14974-x

Grant

Jackson Laboratory Director's Innovation Fund, Jackson Laboratory Cancer Center New Investigator Award,GM133562, CA034196,HG009409

Abstract

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub-compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training.

Comments

We thank Drs. Sara Cassidy, Carmen Robinett, and Stephen Sampson from The Jackson Laboratory Research Program Development for editing this paper. We thank The Jackson Laboratory Computational Sciences and Research IT team for technical support and discussion.

This open access article is licensed under a Creative Commons Attribution 4.0 International License.

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