SparkBLAST: scalable BLAST processing using in-memory operations

作者：de Castro Marcelo Rodrigo; Tostes Catherine dos Santos; Davila Alberto M R; Senger Hermes; da Silva Fabricio A B^*

来源：BMC Bioinformatics, 2017, 18(1): 318.

DOI：10.1186/s12859-017-1723-8

• Cloud computing
• Comparative genomics
• Scalability
• Spark

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

• 出版日期2017-6-27