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Applied and Environmental Microbiology, May 2009, p. 3263-3270, Vol. 75, No. 10
0099-2240/09/$08.00+0     doi:10.1128/AEM.01931-08
Copyright © 2009, American Society for Microbiology. All Rights Reserved.

Effects of Experimental Choices and Analysis Noise on Surveys of the "Rare Biosphere"{triangledown} ,{dagger}

Timothy J. Hamp,1 W. Joe Jones,2 and Anthony A. Fodor1*

Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina,1 Environmental Genomics Core Facility, University of South Carolina at Columbia, Columbia, South Carolina2

Received 19 August 2008/ Accepted 25 February 2009

When planning a survey of 16S rRNA genes from a complex environment, investigators face many choices including which primers to use and how to taxonomically classify sequences. In this study, we explored how these choices affected a survey of microbial diversity in a sample taken from the aerobic basin of the activated sludge of a North Carolina wastewater treatment plant. We performed pyrosequencing reactions on PCR products generated from primers targeting the V1-V2, V6, and V6-V7 variable regions of the 16S rRNA gene. We compared these sequences to 16S rRNA gene sequences found in a whole-genome shotgun pyrosequencing run performed on the same sample. We found that sequences generated from primers targeting the V1-V2 variable region had the best match to the whole-genome shotgun reaction across a range of taxonomic classifications from phylum to family. Pronounced differences between primer sets, however, occurred in the "rare biosphere" involving taxa that we observed in fewer than 11 sequences. We also examined the results of analysis strategies comparing a classification scheme using a nearest-neighbor approach to directly classifying sequences with a naïve Bayesian algorithm. Again, we observed pronounced differences between these analysis schemes in infrequently observed taxa. We conclude that if a study is meant to probe the rare biosphere, both the experimental conditions and analysis choices will have a profound impact on the observed results.

* Corresponding author. Mailing address: Bioinformatics Resource Center, University of North Carolina—Charlotte, Cameron 212, 9201 University City Boulevard, Charlotte, NC 28223. Phone: (704) 687-8214. Fax: (704) 687-6610. E-mail: anthony.fodor{at}gmail.com

{triangledown} Published ahead of print on 6 March 2009.

{dagger} Supplemental material for this article may be found at http://aem.asm.org/.

Applied and Environmental Microbiology, May 2009, p. 3263-3270, Vol. 75, No. 10
0099-2240/09/$08.00+0     doi:10.1128/AEM.01931-08
Copyright © 2009, American Society for Microbiology. All Rights Reserved.





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