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GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

Shuji Suzuki, Takashi Ishida, Ken Kurokawa, Yutaka Akiyama
Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo, Japan
PLoS ONE 7(5): e36060, 2012

@article{suzuki2012ghostm,

   title={GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics},

   author={Suzuki, S. and Ishida, T. and Kurokawa, K. and Akiyama, Y.},

   journal={PLoS ONE},

   volume={7},

   number={5},

   pages={e36060},

   year={2012},

   publisher={Public Library of Science}

}

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BACKGROUND: A large number of sensitive homology searches are required for mapping DNA sequence fragments to known protein sequences in public and private databases during metagenomic analysis. BLAST is currently used for this purpose, but its calculation speed is insufficient, especially for analyzing the large quantities of sequence data obtained from a next-generation sequencer. However, faster search tools, such as BLAT, do not have sufficient search sensitivity for metagenomic analysis. Thus, a sensitive and efficient homology search tool is in high demand for this type of analysis. METHODOLOGY/PRINCIPAL FINDINGS: We developed a new, highly efficient homology search algorithm suitable for graphics processing unit (GPU) calculations that was implemented as a GPU system that we called GHOSTM. The system first searches for candidate alignment positions for a sequence from the database using pre-calculated indexes and then calculates local alignments around the candidate positions before calculating alignment scores. We implemented both of these processes on GPUs. The system achieved calculation speeds that were 130 and 407 times faster than BLAST with 1 GPU and 4 GPUs, respectively. The system also showed higher search sensitivity and had a calculation speed that was 4 and 15 times faster than BLAT with 1 GPU and 4 GPUs. CONCLUSIONS: We developed a GPU-optimized algorithm to perform sensitive sequence homology searches and implemented the system as GHOSTM. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We developed GHOSTM, which is a cost-efficient tool, and offer this tool as a potential solution to this problem.
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