Predicting A/B compartments from histone modifications using deep learning

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dc.citation.articleNumber109570
dc.citation.issueNumber5
dc.citation.journalTitleiScience
dc.citation.volumeNumber27
dc.contributor.authorZheng, Suchen
dc.contributor.authorThakkar, Nitya
dc.contributor.authorHarris, Hannah L.
dc.contributor.authorLiu, Susanna
dc.contributor.authorZhang, Megan
dc.contributor.authorGerstein, Mark
dc.contributor.authorAiden, Erez Lieberman
dc.contributor.authorRowley, M. Jordan
dc.contributor.authorNoble, William Stafford
dc.contributor.authorGürsoy, Gamze
dc.contributor.authorSingh, Ritambhara
dc.date.accessioned2024-07-25T20:55:15Z
dc.date.available2024-07-25T20:55:15Z
dc.date.issued2024
dc.description.abstractThe three-dimensional organization of genomes plays a crucial role in essential biological processes. The segregation of chromatin into A and B compartments highlights regions of activity and inactivity, providing a window into the genomic activities specific to each cell type. Yet, the steep costs associated with acquiring Hi-C data, necessary for studying this compartmentalization across various cell types, pose a significant barrier in studying cell type specific genome organization. To address this, we present a prediction tool called compartment prediction using recurrent neural networks (CoRNN), which predicts compartmentalization of 3D genome using histone modification enrichment. CoRNN demonstrates robust cross-cell-type prediction of A/B compartments with an average AuROC of 90.9%. Cell-type-specific predictions align well with known functional elements, with H3K27ac and H3K36me3 identified as highly predictive histone marks. We further investigate our mispredictions and found that they are located in regions with ambiguous compartmental status. Furthermore, our model’s generalizability is validated by predicting compartments in independent tissue samples, which underscores its broad applicability.
dc.identifier.citationZheng, S., Thakkar, N., Harris, H. L., Liu, S., Zhang, M., Gerstein, M., Aiden, E. L., Rowley, M. J., Noble, W. S., Gürsoy, G., & Singh, R. (2024). Predicting A/B compartments from histone modifications using deep learning. iScience, 27(5). https://doi.org/10.1016/j.isci.2024.109570
dc.identifier.digitalPIIS2589004224007922
dc.identifier.doihttps://doi.org/10.1016/j.isci.2024.109570
dc.identifier.urihttps://hdl.handle.net/1911/117499
dc.language.isoeng
dc.publisherElsevier
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titlePredicting A/B compartments from histone modifications using deep learning
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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