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A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data

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...

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Autores principales: Kakuta, Masanori, Suzuki, Shuji, Izawa, Kazuki, Ishida, Takashi, Akiyama, Yutaka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5666806/
https://www.ncbi.nlm.nih.gov/pubmed/29019934
http://dx.doi.org/10.3390/ijms18102124
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author Kakuta, Masanori
Suzuki, Shuji
Izawa, Kazuki
Ishida, Takashi
Akiyama, Yutaka
author_facet Kakuta, Masanori
Suzuki, Shuji
Izawa, Kazuki
Ishida, Takashi
Akiyama, Yutaka
author_sort Kakuta, Masanori
collection PubMed
description 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.
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spelling pubmed-56668062017-11-09 A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data Kakuta, Masanori Suzuki, Shuji Izawa, Kazuki Ishida, Takashi Akiyama, Yutaka Int J Mol Sci Article 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. MDPI 2017-10-11 /pmc/articles/PMC5666806/ /pubmed/29019934 http://dx.doi.org/10.3390/ijms18102124 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kakuta, Masanori
Suzuki, Shuji
Izawa, Kazuki
Ishida, Takashi
Akiyama, Yutaka
A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data
title A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data
title_full A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data
title_fullStr A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data
title_full_unstemmed A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data
title_short A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data
title_sort massively parallel sequence similarity search for metagenomic sequencing data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5666806/
https://www.ncbi.nlm.nih.gov/pubmed/29019934
http://dx.doi.org/10.3390/ijms18102124
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