Predicting A/B compartments from histone modifications using deep learning

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dc.citation.articleNumber109570en_US
dc.citation.issueNumber5en_US
dc.citation.journalTitleiScienceen_US
dc.citation.volumeNumber27en_US
dc.contributor.authorZheng, Suchenen_US
dc.contributor.authorThakkar, Nityaen_US
dc.contributor.authorHarris, Hannah L.en_US
dc.contributor.authorLiu, Susannaen_US
dc.contributor.authorZhang, Meganen_US
dc.contributor.authorGerstein, Marken_US
dc.contributor.authorAiden, Erez Liebermanen_US
dc.contributor.authorRowley, M. Jordanen_US
dc.contributor.authorNoble, William Stafforden_US
dc.contributor.authorGürsoy, Gamzeen_US
dc.contributor.authorSingh, Ritambharaen_US
dc.date.accessioned2024-07-25T20:55:15Zen_US
dc.date.available2024-07-25T20:55:15Zen_US
dc.date.issued2024en_US
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.en_US
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.2024.109570en_US
dc.identifier.digitalPIIS2589004224007922en_US
dc.identifier.doihttps://doi.org/10.1016/j.isci.2024.109570en_US
dc.identifier.urihttps://hdl.handle.net/1911/117499en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
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.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titlePredicting A/B compartments from histone modifications using deep learningen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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