Predicting A/B compartments from histone modifications using deep learning.

Autor:	Zheng S; Department of Computer Science, Brown University, Providence, RI, USA., Thakkar N; Department of Computer Science, Brown University, Providence, RI, USA., Harris HL; Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA., Liu S; Data Science and Statistics, Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA., Zhang M; Data Science and Statistics, Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA., Gerstein M; Computational Biology and Bioinformatics, Molecular Biophysics & Biochemistry, Data Science and Statistics, Computer Science, Yale University, New Haven, CT, USA., Aiden EL; Department of Genetics, Baylor College of Medicine, Department of Computer Science, Computational and Applied Mathematics, Rice University, Houston, TX, USA., Rowley MJ; Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA., Noble WS; Department of Genome Sciences, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA., Gürsoy G; Department of Biomedical Informatics, Columbia University, New York Genome Center, New York, NY, USA., Singh R; Department of Computer Science, Center for Computational Molecular Biology, Brown University, Providence, RI, USA.
Jazyk:	angličtina
Zdroj:	IScience [iScience] 2024 Mar 27; Vol. 27 (5), pp. 109570. Date of Electronic Publication: 2024 Mar 27 (Print Publication: 2024).
DOI:	10.1016/j.isci.2024.109570
Abstrakt:	The 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. Competing Interests: The authors declare no competing interests. (© 2024 The Author(s).)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu