title: Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  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.
authors.affiliation: Department of Biology, University of Southern California, University Park, Los Angeles, CA 90089, USA.
authors.forename: Xiaolin
authors.initials: X
authors.lastname: Hao
authors.forename: Rui
authors.initials: R
authors.lastname: Jiang
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D006801:Humans; D054892:Metagenome; D015233:Models, Statistical; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012867:Skin / Q000382:microbiology
chemical_list: D012336:RNA, Ribosomal, 16S