SparkBLAST: scalable BLAST processing using in-memory operationsReport as inadecuate

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BMC Bioinformatics

, 18:318

Sequence analysis applications


BackgroundThe 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.

ResultsExperiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times.

ConclusionsThe superior performance of SparkBLAST is mainly due to the in-memory operations available through the Spark framework, consequently reducing the number of local I-O operations required for distributed BLAST processing.

KeywordsCloud computing Comparative genomics Scalability Spark AbbreviationsDFSDistributed file system

NGSNext generation sequencing

RDDResilient distribution datasets

RBHReciprocal best hits

SMCScalable MapReduce computation

vCPUVirtual CPU

Electronic supplementary materialThe online version of this article doi:10.1186-s12859-017-1723-8 contains supplementary material, which is available to authorized users.

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Author: Marcelo Rodrigo de Castro - Catherine dos Santos Tostes - Alberto M. R. Dávila - Hermes Senger - Fabricio A. B. da Silva



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