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dc.contributor.authorLi, Yu
dc.contributor.authorChen, Siyuan
dc.contributor.authorRapakoulia, Trisevgeni
dc.contributor.authorKuwahara, Hiroyuki
dc.contributor.authorYip, Kevin Y
dc.contributor.authorGao, Xin
dc.date.accessioned2022-04-13T09:10:53Z
dc.date.available2022-04-13T09:10:53Z
dc.date.issued2022-04-12
dc.identifier.citationLi, Y., Chen, S., Rapakoulia, T., Kuwahara, H., Yip, K. Y., & Gao, X. (2022). Deep learning identifies and quantifies recombination hotspot determinants. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac234
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.pmid35412589
dc.identifier.doi10.1093/bioinformatics/btac234
dc.identifier.urihttp://hdl.handle.net/10754/676242
dc.description.abstractMotivation Recombination is one of the essential genetic processes for sexually reproducing organisms, which can happen more frequently in some regions, called recombination hotspots. Although several factors, such as PRDM9 binding motifs, are known to be related to the hotspots, their contributions to the recombination hotspots have not been quantified, and other determinants are yet to be elucidated. Here, we propose a computational method, RHSNet, based on deep learning and signal processing, to identify and quantify the hotspot determinants in a purely data-driven manner, utilizing datasets from various studies, populations, sexes, and species. Results RHSNet can significantly outperforms other sequence-based methods on multiple datasets across different species, sexes, and studies. In addition to being able to identify hotspot regions and the well-known determinants accurately, more importantly, RHSNet can quantify the determinants that contribute significantly to the recombination hotspot formation in the relation between PRDM9 binding motif, histone modification, and GC content. Further cross-sex, cross-population, and cross-species studies suggest that the proposed method has the generalization power and potential to identify and quantify the evolutionary determinant motifs.
dc.description.sponsorshipFigure 1 is created by Heno Hwang, a scientific illustrator at King Abdullah University of Science and Technology (KAUST).
dc.description.sponsorshipSupported by the KAUST Office of Sponsored Research(OSR)
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac234/6567357
dc.rightsArchived with thanks to Bioinformatics under a Creative Commons license, details at: https://creativecommons.org/licenses/by-nc/4.0/
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleDeep learning identifies and quantifies recombination hotspot determinants
dc.typeArticle
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
dc.contributor.departmentKAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.identifier.journalBioinformatics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
dc.contributor.institutionThe CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen 518057, China
dc.contributor.institutionMax Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany
kaust.personLi, Yu
kaust.personChen, Siyuan
kaust.personKuwahara, Hiroyuki
kaust.personGao, Xin
kaust.grant.numberBAS/1/1624-01
kaust.grant.numberFCC/1/1976-23-01
kaust.grant.numberFCC/1/1976-26-01
kaust.grant.numberREI/1/0018-01-01
kaust.grant.numberREI/1/4216-01-01
kaust.grant.numberREI/1/4437-01-01
kaust.grant.numberREI/1/4473-01-01
kaust.grant.numberURF/1/4098-01-01
kaust.grant.numberREI/1/4742-01-01
dc.relation.issupplementedbygithub:frankchen121212/RHSNet
refterms.dateFOA2022-04-13T09:19:28Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: frankchen121212/RHSNet: Tensorflow and Keras implementation of RHSNet for recombination hotspot identification and quantification. Publication Date: 2021-05-14. github: <a href="https://github.com/frankchen121212/RHSNet" >frankchen121212/RHSNet</a> Handle: <a href="http://hdl.handle.net/10754/676686" >10754/676686</a></a></li></ul>
kaust.acknowledged.supportUnitOffice of Sponsored Research(OSR)


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Archived with thanks to Bioinformatics under a Creative Commons license, details at: https://creativecommons.org/licenses/by-nc/4.0/
Except where otherwise noted, this item's license is described as Archived with thanks to Bioinformatics under a Creative Commons license, details at: https://creativecommons.org/licenses/by-nc/4.0/