Removing noise from pyrosequenced amplicons

Quince, Christopher, Lanzen, Anders, Davenport, Russell J. and Turnbaugh, Peter J. (2011) Removing noise from pyrosequenced amplicons. BMC Bioinformatics, 12. ISSN 1471-2105

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Background: In many environmental genomics applications a homologous region of DNA from a diverse sample is first amplified by PCR and then sequenced. The next generation sequencing technology, 454 pyrosequencing, has allowed much larger read numbers from PCR amplicons than ever before. This has revolutionised the study of microbial diversity as it is now possible to sequence a substantial fraction of the 16S rRNA genes in a community. However, there is a growing realisation that because of the large read numbers and the lack of consensus sequences it is vital to distinguish noise from true sequence diversity in this data. Otherwise this leads to inflated estimates of the number of types or operational taxonomic units (OTUs) present. Three sources of error are important: sequencing error, PCR single base substitutions and PCR chimeras. We present AmpliconNoise, a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors. We also introduce a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data. We use data sets where samples of known diversity have been amplified and sequenced to quantify the effect of each of the sources of error on OTU inflation and to validate these algorithms.Results: AmpliconNoise outperforms alternative algorithms substantially reducing per base error rates for both the GS FLX and latest Titanium protocol. All three sources of error lead to inflation of diversity estimates. In particular, chimera formation has a hitherto unrealised importance which varies according to amplification protocol. We show that AmpliconNoise allows accurate estimates of OTU number. Just as importantly AmpliconNoise generates the right OTUs even at low sequence differences. We demonstrate that Perseus has very high sensitivity, able to find 99% of chimeras, which is critical when these are present at high frequencies.Conclusions: AmpliconNoise followed by Perseus is a very effective pipeline for the removal of noise. In addition the principles behind the algorithms, the inference of true sequences using Expectation-Maximization (EM), and the treatment of chimera detection as a classification or 'supervised learning' problem, will be equally applicable to new sequencing technologies as they appear.

Item Type: Article
Additional Information: Funding Information: CQ is supported by an Engineering and Physical Sciences Research Council Career Acceleration Fellowship (EP/H003851/1). PT is supported by NIH P50 GM068763. We would like to thank Jens Reeder and Rob Knight for providing the DeNoiser processed data sets and Sue Huse for a copy of the single-linkage preclustering algorithm. We also thank two anonymous reviewers and Dr Keith Harris for constructive comments on the manuscript.
Uncontrolled Keywords: structural biology,biochemistry,molecular biology,computer science applications,applied mathematics ,/dk/atira/pure/subjectarea/asjc/1300/1315
Faculty \ School: Faculty of Science > School of Biological Sciences
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Depositing User: LivePure Connector
Date Deposited: 12 Sep 2022 10:32
Last Modified: 20 Oct 2022 19:32
DOI: 10.1186/1471-2105-12-38

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