Abstract
Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads.
Highlights
Sequence homology search is widely used in bioinformatics
The acceleration in processing speed is shown as the ratio of the time used for GHOSTZ-graphics processing units (GPUs) with an optimization relative to the time used for GHOSTZ-GPU with previous optimization and GHOSTZ. doi:10.1371/journal.pone.0157338.t005
GHOSTZ-GPU showed a greater increase in speed than GPU-based BLAST tools
Summary
Sequence homology search is widely used in bioinformatics. This method is needed to identify evolutionary relations among sequences. Environmental samples frequently include DNA sequences from several species, and the reference database often does not contain closely related homologous sequences. A sequence homology search is used to identify novel genes in these samples. In a typical metagenomic analysis, reads are translated into protein-coding sequences and assigned to protein families by means of a homology search in publicly available databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) [1, 2], COG [3, 4] and Pfam [5]. The BLASTX software [6, 7] is commonly used for such binning and classification
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