SparkBLAST: scalable BLAST processing using in-memory operations

• Published in: BMC bioinformatics Vol. 18; no. 1; p. 318
• Main Authors: de Castro, Marcelo Rodrigo, Tostes, Catherine Dos Santos, Dávila, Alberto M R, Senger, Hermes, da Silva, Fabricio A B
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.06.2017; BioMed Central; BMC
• Subjects: Algorithms; Alignment; Bacteria; Bioinformatics; Cloud Computing; Comparative Genomic Hybridization; Comparative genomics; Computer applications; Data processing; Databases, Factual; Fault tolerance; Genetic algorithms; Genomes; Genomics; Nucleotide sequence; Parallel processing; Provisioning; Radioisotopes; Scalability; Scheduling; Sequence Alignment; Similarity; Software; Spark; Brazil; Comparative genomics; Cloud computing; Spark; Scalability
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1723-8

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