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deSAMBA: fast and accurate classification of metagenomics long reads with sparse approximate matches

Gaoyang Li, Bo Liu, Yadong Wang
doi: https://doi.org/10.1101/736777
Gaoyang Li
1Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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Bo Liu
1Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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Yadong Wang
1Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
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  • For correspondence: ydwang@hit.edu.cn
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Abstract

Summary Long read sequencing technologies are promising to metagenomics studies. However, there is still lack of read classification tools to fast and accurately identify the taxonomies of noisy long reads, which is a bottleneck to the use of long read sequencing. Herein, we propose deSAMBA, a tailored long read classification approach that uses a novel sparse approximate match block (SAMB)-based pseudo alignment algorithm. Benchmarks on real datasets demonstrate that deSAMBA enables to simultaneously achieve fast speed and good classification yields, which outperforms state-of-the-art tools and has many potentials to cutting-edge metagenomics studies.

Availability and Implementation https://github.com/hitbc/deSAMBA.

Supplementary information:

Footnotes

  • ↵+ The authors should be regarded as Joint First Authors.

  • https://github.com/hitbc/deSAMBA-meta

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 18, 2019.
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deSAMBA: fast and accurate classification of metagenomics long reads with sparse approximate matches
Gaoyang Li, Bo Liu, Yadong Wang
bioRxiv 736777; doi: https://doi.org/10.1101/736777
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deSAMBA: fast and accurate classification of metagenomics long reads with sparse approximate matches
Gaoyang Li, Bo Liu, Yadong Wang
bioRxiv 736777; doi: https://doi.org/10.1101/736777

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