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Parka: A Parallel Implementation of BLAST with MapReduce

  • Li ZhangEmail author
  • Bing Tang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 686)

Abstract

Bioinformatics applications have become more data-intensive and compute-intensive, which requires an effective method to implement parallel computing and get a high-throughput. Although there exists some tools to realize parallelization of BLAST, but most of them depend on complex platforms or software. A parallel BLAST is implemented using Spark, which is called Parka. The parallel execution time and speedup of Parka are evaluated in a cluster environment. Then, it is compared with Hadoop-based parallelization method. Results show that it is a scalable and effective parallelization approach for sequence alignment.

Keywords

BLAST MapReduce Data-intensive computing Bioinformatics applications 

Notes

Acknowledgments

This work is partly supported by the National Natural Science Foundation of China (No. 61602169), the Natural Science Foundation of Hunan Province (No. 2015JJ3071), and the Scientific Research Fund of Hunan Provincial Education Department (No. 16C0643).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina

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