SparkBLAST: scalable BLAST processing using in-memory operations

BMC Bioinformatics

Software

Hermes Senger¹  Marcelo Rodrigo de Castro¹  Alberto M. R. Dávila²  Catherine dos Santos Tostes²  Fabricio A. B. da Silva³ 

[1] Computer Science Department, Federal University of São Carlos, Rod. Washington Luís, Km 235, 21040-900, São Carlos, Brazil;LBCS-IOC, Oswaldo Cruz Foundation, Av Brasil 4365, 21040-900, Rio de Janeiro, Brazil;PROCC, Oswaldo Cruz Foundation, Av. Brasil 4365, 21040-900, Rio de Janeiro, Brazil;

关键词: Cloud computing;    Comparative genomics;    Scalability;    Spark;   

DOI  :  10.1186/s12859-017-1723-8

 received in 2016-10-05, accepted in 2017-06-12,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2017