☆ 4.6 Article

MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification

PLOS ONE (2011)

期刊

PLOS ONE

卷 6, 期 11, 页码 -

出版社

PUBLIC LIBRARY SCIENCE

DOI: 10.1371/journal.pone.0025353

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类别

Multidisciplinary Sciences

资金

National High-Tech RD Program (863) [2009AA02Z310, 009AA02Z306]
National Natural Science Foundation of China [60970050]
Shanghai Pujiang Program [09PJ1407900]
Basic Science Foundation of Shanghai [08JC1416700]

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Protocol

Reagent

摘要

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/similar to ccwei/pub/software/MetaBinG/MetaBinG.php.

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