A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data

Kakuta, Masanori; Suzuki, Shuji; Izawa, Kazuki; Ishida, Takashi; Akiyama, Yutaka

doi:10.3390/ijms18102124

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A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data

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Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo 152-8550, Japan

Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 4259 J3-141 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo 152-8550, Japan

Author to whom correspondence should be addressed.

Int. J. Mol. Sci. 2017, 18(10), 2124; https://doi.org/10.3390/ijms18102124

Received: 31 August 2017 / Revised: 3 October 2017 / Accepted: 6 October 2017 / Published: 11 October 2017

(This article belongs to the Special Issue Deciphering the Human Microbiota: Methods and Impact on Human Health)

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Abstract

Sequence similarity searches have been widely used in the analyses of metagenomic sequencing data. Finding homologous sequences in a reference database enables the estimation of taxonomic and functional characteristics of each query sequence. Because current metagenomic sequencing data consist of a large number of nucleotide sequences, the time required for sequence similarity searches account for a large proportion of the total time. This time-consuming step makes it difficult to perform large-scale analyses. To analyze large-scale metagenomic data, such as those found in the human oral microbiome, we developed GHOST-MP (Genome-wide HOmology Search Tool on Massively Parallel system), a parallel sequence similarity search tool for massively parallel computing systems. This tool uses a fast search algorithm based on suffix arrays of query and database sequences and a hierarchical parallel search to accelerate the large-scale sequence similarity search of metagenomic sequencing data. The parallel computing efficiency and the search speed of this tool were evaluated. GHOST-MP was shown to be scalable over 10,000 CPU (Central Processing Unit) cores, and achieved over 80-fold acceleration compared with mpiBLAST using the same computational resources. We applied this tool to human oral metagenomic data, and the results indicate that the oral cavity, the oral vestibule, and plaque have different characteristics based on the functional gene category. View Full-Text

Keywords: database search; sequence similarity search; metagenomics; human oral microbiome

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Kakuta, M.; Suzuki, S.; Izawa, K.; Ishida, T.; Akiyama, Y. A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data. Int. J. Mol. Sci. 2017, 18, 2124. https://doi.org/10.3390/ijms18102124

AMA Style

Kakuta M, Suzuki S, Izawa K, Ishida T, Akiyama Y. A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data. International Journal of Molecular Sciences. 2017; 18(10):2124. https://doi.org/10.3390/ijms18102124

Chicago/Turabian Style

Kakuta, Masanori, Shuji Suzuki, Kazuki Izawa, Takashi Ishida, and Yutaka Akiyama. 2017. "A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data" International Journal of Molecular Sciences 18, no. 10: 2124. https://doi.org/10.3390/ijms18102124

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A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data

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