Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering

Bioinformatics
Xiaolin HaoTing Chen

Abstract

With the advancements of next-generation sequencing technology, it is now possible to study samples directly obtained from the environment. Particularly, 16S rRNA gene sequences have been frequently used to profile the diversity of organisms in a sample. However, such studies are still taxed to determine both the number of operational taxonomic units (OTUs) and their relative abundance in a sample. To address these challenges, we propose an unsupervised Bayesian clustering method termed Clustering 16S rRNA for OTU Prediction (CROP). CROP can find clusters based on the natural organization of data without setting a hard cut-off threshold (3%/5%) as required by hierarchical clustering methods. By applying our method to several datasets, we demonstrate that CROP is robust against sequencing errors and that it produces more accurate results than conventional hierarchical clustering methods. Source code freely available at the following URL: http://code.google.com/p/crop-tingchenlab/, implemented in C++ and supported on Linux and MS Windows.

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Citations

Aug 31, 2013·Bioinformatics·Jiajie ZhangAlexandros Stamatakis
Mar 13, 2012·Nucleic Acids Research·Lu ChengJukka Corander
Jul 25, 2012·BMC Bioinformatics·Dan WeiShengrui Wang
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Nov 29, 2012·PloS One·Xiaolin Hao, Ting Chen
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