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SeQual-Stream: approaching stream processing to quality control of NGS datasets
dc.contributor.author | Castellanos Rodríguez, Óscar | |
dc.contributor.author | Expósito, Roberto R. | |
dc.contributor.author | Touriño, Juan | |
dc.date.accessioned | 2024-02-29T18:07:46Z | |
dc.date.available | 2024-02-29T18:07:46Z | |
dc.date.issued | 2023-10 | |
dc.identifier.citation | Castellanos-Rodríguez, Ó., Expósito, R.R. & Touriño, J. SeQual-Stream: approaching stream processing to quality control of NGS datasets. BMC Bioinformatics 24, 403 (2023). https://doi.org/10.1186/s12859-023-05530-7 | es_ES |
dc.identifier.issn | 1471-2105 | |
dc.identifier.uri | http://hdl.handle.net/2183/35758 | |
dc.description.abstract | [Abstract]: Background 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. Results In this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations on genomic datasets in a fast, distributed and scalable way. To do so, our approach 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 in the execution times of SeQual-Stream when compared to a batch processing tool with similar quality control features, providing a maximum speedup of 2.7 when processing a dataset with more than 250 million DNA sequences, while also demonstrating good scalability features. Conclusion Our solution provides a more scalable and higher performance way to carry out quality control of large genomic datasets by taking advantage of stream processing features. The tool is distributed as free open-source software released under the GNU AGPLv3 license and is publicly available to download at https://github.com/UDC-GAC/SeQual-Stream. | es_ES |
dc.description.sponsorship | Grants PID2019-104184RB-I00 and PID2022-136435NB-I00, funded by MCIN/AEI/10.13039/501100011033, PID2022 also funded by “ERDF A way of making Europe”, EU. Grant ED431C 2021/30, funded by Xunta de Galicia under the Consolidation Program of Competitive Reference Groups. Predoctoral grant of Oscar Castellanos-Rodriguez ref. ED481A 2022/067, also funded by Xunta de Galicia. The funding agencies did not participate in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/30 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A 2022/067 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | BMC | 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 | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136435NB-I00/ES/RESULTADOS DE INVESTIGACIÓN PROYECTOS ARQUITECTURAS, FRAMEWORKS Y APLICACIONES DE LA COMPUTACION DE ALTAS PRESTACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1186/s12859-023-05530-7 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Quality control | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Stream processing | es_ES |
dc.subject | Apache Spark | es_ES |
dc.subject | Next generation sequencing (NGS) | es_ES |
dc.title | SeQual-Stream: approaching stream processing to quality control of NGS datasets | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | BMC Bioinformatics | es_ES |
UDC.volume | 24 | es_ES |
UDC.issue | 403 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 22 | es_ES |
dc.identifier.doi | 10.1186/s12859-023-05530-7 |
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