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MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Bioinformatics, 37, 2021, 11, S. 1562-1570 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Lee, Byunghan Min, Hyeyoung Yoon, Sungroh Lee, Byunghan Min, Hyeyoung Yoon, Sungroh |
|---|---|
| author |
Lee, Byunghan Min, Hyeyoung Yoon, Sungroh |
| spellingShingle |
Lee, Byunghan Min, Hyeyoung Yoon, Sungroh Bioinformatics MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
lee, byunghan |
| spelling |
Lee, Byunghan Min, Hyeyoung Yoon, Sungroh 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bty096 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. A typical metagenomic sequencing produces a large amount of data (often in the order of terabytes or more), and computational tools are indispensable for efficient processing. In particular, error correction in metagenomics is crucial for accurate and robust genetic cataloging of microbial communities. However, many existing error-correction tools take a prohibitively long time and often bottleneck the whole analysis pipeline.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>To overcome this computational hurdle, we analyzed and exploited the data-level parallelism that exists in the error-correction procedure and proposed a tool named MUGAN that exploits both multi-core central processing units and multiple graphics processing units for co-processing. According to the experimental results, our approach reduced not only the time demand for denoising amplicons from approximately 59 h to only 46 min, but also the overestimation of the number of OTUs, estimating 6.7 times less species-level OTUs than the baseline. In addition, our approach provides web-based intuitive visualization of results. Given its efficiency and convenience, we anticipate that our approach would greatly facilitate denoising efforts in metagenomics studies.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>http://data.snu.ac.kr/pub/mugan</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment Bioinformatics |
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| title |
MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_unstemmed |
MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_full |
MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_fullStr |
MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_full_unstemmed |
MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_short |
MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_sort |
mugan: multi-gpu accelerated ampliconnoise server for rapid microbial diversity assessment |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/bty096 |
| publishDate |
2021 |
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1562-1570 |
| description |
<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Motivation</jats:title>
<jats:p>Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. A typical metagenomic sequencing produces a large amount of data (often in the order of terabytes or more), and computational tools are indispensable for efficient processing. In particular, error correction in metagenomics is crucial for accurate and robust genetic cataloging of microbial communities. However, many existing error-correction tools take a prohibitively long time and often bottleneck the whole analysis pipeline.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>To overcome this computational hurdle, we analyzed and exploited the data-level parallelism that exists in the error-correction procedure and proposed a tool named MUGAN that exploits both multi-core central processing units and multiple graphics processing units for co-processing. According to the experimental results, our approach reduced not only the time demand for denoising amplicons from approximately 59 h to only 46 min, but also the overestimation of the number of OTUs, estimating 6.7 times less species-level OTUs than the baseline. In addition, our approach provides web-based intuitive visualization of results. Given its efficiency and convenience, we anticipate that our approach would greatly facilitate denoising efforts in metagenomics studies.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Availability and implementation</jats:title>
<jats:p>http://data.snu.ac.kr/pub/mugan</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Supplementary information</jats:title>
<jats:p>Supplementary data are available at Bioinformatics online.</jats:p>
</jats:sec> |
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| author | Lee, Byunghan, Min, Hyeyoung, Yoon, Sungroh |
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| description | <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. A typical metagenomic sequencing produces a large amount of data (often in the order of terabytes or more), and computational tools are indispensable for efficient processing. In particular, error correction in metagenomics is crucial for accurate and robust genetic cataloging of microbial communities. However, many existing error-correction tools take a prohibitively long time and often bottleneck the whole analysis pipeline.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>To overcome this computational hurdle, we analyzed and exploited the data-level parallelism that exists in the error-correction procedure and proposed a tool named MUGAN that exploits both multi-core central processing units and multiple graphics processing units for co-processing. According to the experimental results, our approach reduced not only the time demand for denoising amplicons from approximately 59 h to only 46 min, but also the overestimation of the number of OTUs, estimating 6.7 times less species-level OTUs than the baseline. In addition, our approach provides web-based intuitive visualization of results. Given its efficiency and convenience, we anticipate that our approach would greatly facilitate denoising efforts in metagenomics studies.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>http://data.snu.ac.kr/pub/mugan</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> |
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| spelling | Lee, Byunghan Min, Hyeyoung Yoon, Sungroh 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bty096 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. A typical metagenomic sequencing produces a large amount of data (often in the order of terabytes or more), and computational tools are indispensable for efficient processing. In particular, error correction in metagenomics is crucial for accurate and robust genetic cataloging of microbial communities. However, many existing error-correction tools take a prohibitively long time and often bottleneck the whole analysis pipeline.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>To overcome this computational hurdle, we analyzed and exploited the data-level parallelism that exists in the error-correction procedure and proposed a tool named MUGAN that exploits both multi-core central processing units and multiple graphics processing units for co-processing. According to the experimental results, our approach reduced not only the time demand for denoising amplicons from approximately 59 h to only 46 min, but also the overestimation of the number of OTUs, estimating 6.7 times less species-level OTUs than the baseline. In addition, our approach provides web-based intuitive visualization of results. Given its efficiency and convenience, we anticipate that our approach would greatly facilitate denoising efforts in metagenomics studies.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>http://data.snu.ac.kr/pub/mugan</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment Bioinformatics |
| spellingShingle | Lee, Byunghan, Min, Hyeyoung, Yoon, Sungroh, Bioinformatics, MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_full | MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_fullStr | MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_full_unstemmed | MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_short | MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| title_sort | mugan: multi-gpu accelerated ampliconnoise server for rapid microbial diversity assessment |
| title_unstemmed | MUGAN: multi-GPU accelerated AmpliconNoise server for rapid microbial diversity assessment |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/bty096 |