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MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification

Peng Jia, Liming Xuan, Lei Liu, Chaochun Wei
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
PLoS ONE 6(11): e25353, 2011

@article{jia2011metabing,

   title={MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification},

   author={Jia, P. and Xuan, L. and Liu, L. and Wei, C.},

   journal={PloS one},

   volume={6},

   number={11},

   pages={e25353},

   year={2011},

   publisher={Public Library of Science}

}

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Metagenomic sequence classification is a procedure to assign sequences to their source genomes. It is one of the important steps for metagenomic sequence data analysis. Although many methods exist, classification of high-throughput metagenomic sequence data in a limited time is still a challenge. We present here an ultra-fast metagenomic sequence classification system (MetaBinG) using graphic processing units (GPUs). The accuracy of MetaBinG is comparable to the best existing systems and it can classify a million of 454 reads within five minutes, which is more than 2 orders of magnitude faster than existing systems. MetaBinG is publicly available at http://cbb.sjtu.edu.cn/~ccwei/pub/softwa?re/MetaBinG/MetaBinG.php.
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