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Data-Parallel Computational Model for Next Generation Sequencing on Commodity Clusters

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Parallel Computing Technologies (PaCT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11657))

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Abstract

It is obvious that the next generation sequencing (NGS) technologies, are poised to be the next big revolution in personalized healthcare, and caused the amount of available sequencing data growing exponentially. While NGS data processing has become a major challenge for individual genomic research, commodity computers as a cost-effective platform for distributed and parallel processing in laboratories can help processing such huge volume of data. To deploy sequence-processing methods on these platforms, in this paper we present a parallel computational model for BLAST on commodity clusters that works in a data parallel manner. The suggested model has a master-worker paradigm. The master stores temporarily incoming requests and splits the database to chunks according to the number of available workers. Each worker pulls, formats, and searches queries against a unique chunk of the database. To show that our model works well, we used queries with different lengths to search against a small database (i.e. UniProtKB/SWISS-PROT) and a large database (i.e. UniProtKB/TrEMBL). The results were equal with the output of the golden method (i.e. NCBI BLAST) and the performance of our model outperformed the most popular distributed form of BLAST (i.e. mpiBLAST) with 25% higher performance.

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Notes

  1. 1.

    GenomeTools. Available at: http://genometools.org

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Authors and Affiliations

  1. Department of Electrical and Information Technology, Iranian Research Organization for Science and Technology, Tehran, Iran

    Majid Hajibaba & Saeid Gorgin

  2. Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Majid Hajibaba & Mohsen Sharifi

Authors
  1. Majid Hajibaba
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  2. Mohsen Sharifi
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  3. Saeid Gorgin
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Corresponding author

Correspondence to Majid Hajibaba .

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Editors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk State Technical University, Novosibirsk, Russia

    Victor Malyshkin

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Hajibaba, M., Sharifi, M., Gorgin, S. (2019). Data-Parallel Computational Model for Next Generation Sequencing on Commodity Clusters. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2019. Lecture Notes in Computer Science(), vol 11657. Springer, Cham. https://doi.org/10.1007/978-3-030-25636-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-25636-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25635-7

  • Online ISBN: 978-3-030-25636-4

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