Metagenome techniques allow analyses of microorganisms and their genes present in a given environment without isolation and culture. Thus, metagenomics has become a broadly applied tool to study various environments and elucidate the relationship between diseases and the host microbiota. With continuous improvement in the performance of genome sequencers, the number of sequence reads generated has increased exponentially; thus, methods for the efficient processing of such large numbers of sequences are required. To this end, we developed the pipeline system, GHOSTMEGAN, to speed up the processing of large-scale whole genome shotgun metagenome analysis, which integrates the sequence homology search tool GHOSTZ-GPU and the analyzing tool MEGAN. Assuming a cluster-type computer with a job scheduling system, the multi-node parallel processing of GHOSTZ-GPU and MEGAN was pipelined. Performance evaluation of GHOSTMEGAN with a whole genome sequence dataset, the oral metagenome demonstrated that execution of 128 nodes in parallel, which required 15 h on a single node, could be completed in only 20 min, thereby achieving about 45 times faster calculation. This pipeline is expected to greatly accelerate the field of metagenomics and broaden its application potential.
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