MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification
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}
}
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.
January 11, 2012 by hgpu
