Spark-based Parallelization of Basic Local Alignment Search Tool

• Main Authors: Hui Wang, Leixiao Li, Chengdong Zhou, Hao Lin, Dan Deng
• Format: Article
• Language: English
• Published: Bulgarian Academy of Sciences 2020-03-01
• Series: International Journal Bioautomation
• Subjects: sequence alignment; basic local alignment search tool; spark; parallelization; speedup
• Online Access: http://www.biomed.bas.bg/bioautomation/2020/vol_24.1/files/24.1_08.pdf

Sequence alignment is a key link of bioinformatics analysis. The basic local alignment search tool (BLAST) is a popular sequence alignment algorithm with high accuracy. However, the BLAST is inefficient in comparing and analyzing a massive amount of gene sequencing data. To solve the problem, this paper designs a distributed parallel BLAST method called SparkBLAST, based on the big data technique Spark. Under the in-memory computing framework Spark, SparkBLAST identifies the task of sequence alignment, divides the sequence dataset, and compares the sequence data. The Apache Hadoop YARN was adopted to task scheduling and resource allocation. Finally, the SparkBLAST was compared with standalone BLAST through experiments. The results show that SparkBLAST realized the speedup ratio of 3.95, without sacrificing the accuracy. In other words, SparkBLAST greatly outshines the standalone BLAST in calculation efficiency. The research findings provide bioinformatics researchers a highly efficient tool for sequence alignment.