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ParRADMeth: Identification of Differentially Methylated Regions on Multicore Clusters
dc.contributor.author | Fernández Fraga, Alejandro | |
dc.contributor.author | González-Domínguez, Jorge | |
dc.contributor.author | Touriño, Juan | |
dc.date.accessioned | 2023-12-21T10:13:04Z | |
dc.date.available | 2023-12-21T10:13:04Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | A. Fernandez-Fraga, J. Gonzalez-Dominguez and J. Tourino, "ParRADMeth: Identification of Differentially Methylated Regions on Multicore Clusters" in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 03, pp. 2041-2049, 2023. https://doi.org/10.1109/TCBB.2022.3230473 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/34586 | |
dc.description | © 2023 IEEE. This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/TCBB.2022.3230473 | es_ES |
dc.description | Versión aceptada final de: A. Fernandez-Fraga, J. Gonzalez-Dominguez and J. Tourino, "ParRADMeth: Identification of Differentially Methylated Regions on Multicore Clusters" in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 03, pp. 2041-2049, 2023. https://doi.org/10.1109/TCBB.2022.3230473 | es_ES |
dc.description.abstract | [Abstract]: The discovery of Differentially Methylated (DM) regions is an important research field in biology, as it can help to anticipate the risk of suffering from specific diseases. Nevertheless, the high computational cost of the bioinformatic tools developed for this purpose prevents their application to large-scale datasets. Hence, much faster tools are required to further progress in this research field. In this work we present ParRADMeth, a parallel tool that applies beta-binomial regression for the identification of these DM regions. It is based on the state-of-the-art sequential tool RADMeth, which proved superior biological accuracy compared to counterparts in previous experimental evaluations. ParRADMeth provides the same DM regions as RADMeth but at significantly reduced runtime thanks to exploiting the compute capabilities of common multicore CPU clusters. For example, our tool is up to 189 times faster for real data experiments on a cluster with 16 nodes, each one containing two eight-core processors. The source code of ParRADMeth, as well as a reference manual, are available at https://github.com/UDC-GAC/ParRADMeth. | es_ES |
dc.description.sponsorship | This work was supported in part by the Ministry of Science and Innovation of Spain under Grants PID2019-104184RB-I00 and / AEI / 10.13039/501100011033, and in part by the Xunta de Galicia and FEDER funds (Centro de Investigacion de Galicia accreditation 2019-2022 and Consolidation Program of Competitive Reference Groups) under Grants ED431 G 2019/01 and ED431 C 2021/30. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431 G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431 C 2021/30 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104184RB-I00/ES/DESAFIOS ACTUALES EN HPC: ARQUITECTURAS, SOFTWARE Y APLICACIONES | es_ES |
dc.relation.isversionof | https://doi.org/10.1109/TCBB.2022.3230473 | |
dc.relation.uri | https://doi.org/10.1109/TCBB.2022.3230473 | es_ES |
dc.rights | © 2023 IEEE. | es_ES |
dc.subject | Differential methylation | es_ES |
dc.subject | bioinformatics | es_ES |
dc.subject | high performance computing | es_ES |
dc.subject | MPI | es_ES |
dc.subject | OpenMP | es_ES |
dc.title | ParRADMeth: Identification of Differentially Methylated Regions on Multicore Clusters | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | IEEE/ACM Transactions on Computational Biology and Bioinformatics | es_ES |
UDC.volume | 20 | es_ES |
UDC.issue | 3 | es_ES |
UDC.startPage | 2041 | es_ES |
UDC.endPage | 2049 | es_ES |
dc.identifier.doi | 10.1109/TCBB.2022.3230473 |
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