| Autor: |
Yuyang Qiao, Ben Jia, Zhiqiang Hu, Chen Sun, Yijin Xiang, Chaochun Wei |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Biology Direct, Vol 13, Iss 1, Pp 1-21 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1745-6150 |
| DOI: |
10.1186/s13062-018-0220-y |
| Popis: |
Abstract Background Many methods have been developed for metagenomic sequence classification, and most of them depend heavily on genome sequences of the known organisms. A large portion of sequencing sequences may be classified as unknown, which greatly impairs our understanding of the whole sample. Result Here we present MetaBinG2, a fast method for metagenomic sequence classification, especially for samples with a large number of unknown organisms. MetaBinG2 is based on sequence composition, and uses GPUs to accelerate its speed. A million 100 bp Illumina sequences can be classified in about 1 min on a computer with one GPU card. We evaluated MetaBinG2 by comparing it to multiple popular existing methods. We then applied MetaBinG2 to the dataset of MetaSUB Inter-City Challenge provided by CAMDA data analysis contest and compared community composition structures for environmental samples from different public places across cities. Conclusion Compared to existing methods, MetaBinG2 is fast and accurate, especially for those samples with significant proportions of unknown organisms. Reviewers This article was reviewed by Drs. Eran Elhaik, Nicolas Rascovan, and Serghei Mangul. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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