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Accelerating the quality control of genetic sequences through stream processing
dc.contributor.author | Castellanos Rodríguez, Óscar | |
dc.contributor.author | Expósito, Roberto R. | |
dc.contributor.author | Touriño, Juan | |
dc.date.accessioned | 2024-02-29T18:51:46Z | |
dc.date.available | 2024-02-29T18:51:46Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Óscar Castellanos-Rodríguez, Roberto R. Expósito, and Juan Touriño. 2023. Accelerating the quality control of genetic sequences through stream processing. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23). Association for Computing Machinery, New York, NY, USA, 398–401. https://doi.org/10.1145/3555776.3577785 | es_ES |
dc.identifier.isbn | 978-1-4503-9517-5 | |
dc.identifier.uri | http://hdl.handle.net/2183/35759 | |
dc.description | This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23). Association for Computing Machinery, New York, NY, USA, 398–401. https://doi.org/10.1145/3555776.3577785. | es_ES |
dc.description.abstract | [Abstract]: Quality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing. In this paper we present SeQual-Stream, a Big Data tool that allows performing quality control on genomic datasets in a fast, distributed and scalable way. To do so, our tool relies on the Apache Spark framework and the Hadoop Distributed File System (HDFS) to fully exploit the stream paradigm and accelerate the preprocessing of large datasets as they are being downloaded and/or copied to HDFS. The experimental results have shown significant improvements when compared to a batch processing tool, providing a maximum speedup of 2.7x. | es_ES |
dc.description.sponsorship | This work was supported by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00 / AEI / 10.13039 / 501100011033), and by Xunta de Galicia and FEDER funds of the European Union (Centro de Investigación de Galicia accreditation 2019-2022, ref. ED431G 2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/30 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Association for Computing Machinery | 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.uri | https://doi.org/10.1145/3555776.3577785 | es_ES |
dc.rights | Todos os dereitos reservados. All rights reserved. | es_ES |
dc.subject | Big Data | es_ES |
dc.subject | Stream processing | es_ES |
dc.subject | Next Generation Sequencing (NGS) | es_ES |
dc.subject | Quality control | es_ES |
dc.subject | Apache Spark | es_ES |
dc.title | Accelerating the quality control of genetic sequences through stream processing | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.volume | 2023 Proceeding | es_ES |
UDC.startPage | 398 | es_ES |
UDC.endPage | 401 | es_ES |
dc.identifier.doi | 10.1145/3555776.3577785 | |
UDC.conferenceTitle | ACM/SIGAPP Symposium on Applied Computing, 38th, SAC '23 | es_ES |