%0 Journal Article %1 Jia2011MetaBinG %A Jia, Peng %A Xuan, Liming %A Liu, Lei %A Wei, Chaochun %D 2011 %I Public Library of Science %J PLoS ONE %K gpu metagenomics %N 11 %P e25353+ %R 10.1371/journal.pone.0025353 %T MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification %U http://dx.doi.org/10.1371/journal.pone.0025353 %V 6 %X 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.

@article{Jia2011MetaBinG, abstract = {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.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Jia, Peng and Xuan, Liming and Liu, Lei and Wei, Chaochun}, biburl = {https://www.bibsonomy.org/bibtex/2ab4586125986259935b5752b06252a0c/karthikraman}, citeulike-article-id = {10063261}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pone.0025353}, citeulike-linkout-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3223155/}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/22132069}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=22132069}, day = 23, doi = {10.1371/journal.pone.0025353}, interhash = {cb56b2512200e64000ec9e4111f236fd}, intrahash = {ab4586125986259935b5752b06252a0c}, issn = {1932-6203}, journal = {PLoS ONE}, keywords = {gpu metagenomics}, month = nov, number = 11, pages = {e25353+}, pmcid = {PMC3223155}, pmid = {22132069}, posted-at = {2011-12-16 06:37:33}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {{MetaBinG}: Using {GPUs} to Accelerate Metagenomic Sequence Classification}, url = {http://dx.doi.org/10.1371/journal.pone.0025353}, volume = 6, year = 2011 }