SparkBLAST: scalable BLAST processing using in-memory operations

dc.creatorCastro, Marcelo Rodrigo de
dc.creatorTostes, Catherine dos Santos
dc.creatorDávila, Alberto M. R.
dc.creatorSenger, Hermes
dc.creatorSilva, Fabricio A. B. da
dc.date2017-11-28T13:15:23Z
dc.date2017-11-28T13:15:23Z
dc.date2017
dc.date.accessioned2023-09-26T20:59:23Z
dc.date.available2023-09-26T20:59:23Z
dc.identifierCASTRO, Marcelo Rodrigo de; et al. SparkBLAST: scalable BLAST processing using in-memory operations. BMC Bioinformatics, v.18:318, 13p, 2017.
dc.identifier1471-2105
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/23410
dc.identifier10.1186/s12859-017-1723-8
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8867347
dc.descriptionThe demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient computational systems. In this paper we propose SparkBLAST, a parallelization of a sequence alignment application (BLAST) that employs cloud computing for the provisioning of computational resources and Apache Spark as the coordination framework. As a proof of concept, some radionuclide-resistant bacterial genomes were selected for similarity analysis.
dc.formatapplication/pdf
dc.languageeng
dc.publisherBioMed Central
dc.rightsopen access
dc.subjectComputação em Nuvem
dc.subjectGenômica comparativa
dc.subjectEscalabilidade
dc.subjectFaísca
dc.subjectCloud computing
dc.subjectComparative genomics
dc.subjectScalability
dc.subjectSpark
dc.titleSparkBLAST: scalable BLAST processing using in-memory operations
dc.typeArticle


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