NDLI: Rapid and Robust Denoising of Pyrosequenced Amplicons for Metagenomics

Content Provider	IEEE Xplore Digital Library
Author	Byunghan Lee Joonhong Park Sungroh Yoon
Copyright Year	2012
Abstract	Metagenomic sequencing has become a crucial tool for obtaining a gene catalogue of operational taxonomic units (OTUs) in a microbial community. High-throughput pyrosequencing is a next-generation sequencing technique very popular in microbial community analysis due to its longer read length compared to alternative methods. Computational tools are inevitable to process raw data from pyrosequencers, and in particular, noise removal is a critical data-mining step to obtain robust sequence reads. However, the slow rate of existing denoisers has bottlenecked the whole pyrosequencing process, let alone hindering efforts to improve robustness. To address these, we propose a new approach that can accelerate the denoising process substantially. By using our approach, it now takes only about 2 hours to denoise 62,873 pyrosequenced amplicons from a mixture of 91 full-length 16S rRNA clones. It would otherwise take nearly 2.5 days if existing software tools were used. Furthermore, our approach can effectively reduce overestimating the number of OTUs, producing 6.7 times fewer species-level OTUs on average than a state-of-the-art alternative under the same condition. Leveraged by our approach, we hope that metagenomic sequencing will become an even more appealing tool for microbial community analysis.
Starting Page	954
Ending Page	959
File Size	306133
Page Count	6
File Format	PDF
ISBN	9781467346498
ISSN	15504786
DOI	10.1109/ICDM.2012.68
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics processing units Instruction sets Noise Robustness Noise reduction Communities Acceleration metagenomics pyrosequencing amplicons cluster analysis GPU biomedical informatics
Content Type	Text
Resource Type	Article

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