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.
Highlights
Most microbes are difficult to isolate and cultivate [1]
We developed a new massively parallel sequence similarity search tool for large-scale metagenomic sequencing data, such as the human oral microbiome
GHOST-MP achieved faster sequence similarity searches than mpiBLAST, enabling large-scale functional analyses to be performed within a short period of time
Summary
Most microbes are difficult to isolate and cultivate [1]. The metagenomic approach with direct sequencing of microbial genomes from environmental samples is a culture-independent way to identify uncultured microbes. The mpiBLAST software searches in parallel using multiple processes on a distributed memory system with thousands of CPU cores to reduce the search time Both approaches accelerate the similarity search process, the acceleration of only one approach is insufficient for large-scale analyses. We developed a new massively parallel sequence similarity search tool for large-scale metagenomic sequencing data, such as the human oral microbiome. The system consists of a parallel sequence similarity search on a massively parallel distributed memory system, named GHOST-MP This enables the analysis of large-scale metagenomic data consisting of hundreds of sets of environmental sequencing data. GHOST-MP achieved faster sequence similarity searches than mpiBLAST, enabling large-scale functional analyses to be performed within a short period of time. The GHOST-MP program is implemented in C++, and is available under the BSD (Berkeley Software Distribution) License from http://www.bi.cs.titech.ac.jp/ghostmp/
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