Spark-based parallelization of basic local alignment search tool

被引：2

作者：

Wang H. ^{[1
,2
]}

Li L. ^{[1
,2
]}

Zhou C. ^{[1
,2
]}

Lin H. ^{[1
,2
]}

Deng D. ^{[1
,2
]}

机构：

[1] College of Data Science and Application, Inner Mongolia University of Technology, Hohhot

[2] Inner Mongolia Autonomous Region Engineering and, Technology Research Center of Big Data Based Software Service, Inner Mongolia University of Technology, Hohhot

来源：

International Journal Bioautomation | 2020年 / 24卷 / 01期

关键词：

Basic local alignment search tool; Parallelization; Sequence alignment; Spark; Speedup;

D O I：

10.7546/ijba.2020.24.1.000767

中图分类号：

学科分类号：

摘要：

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. © 2020 by the authors.

引用

页码：87 / 98

页数：11

共 22 条

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