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CROP | A method of unsupervised Bayesian clustering

Provides a clustering tool that automatically determines the best clustering result for 16S rRNA sequences at different phylogenetic levels. Our study shows that CROP gives accurate clustering results, both in terms of the number of clusters and their abundance levels, for various types of 16S rRNA datasets. In contrast, the standard hierarchical clustering strategy, even with the preclustering process and the average linkage method, still frequently overestimates the number of operational taxonomic units (OTUs) in the presence of sequencing errors, resulting in an underestimation of the abundance level of the underlying OTUs. 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.

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CROP classification

CROP specifications

Unique identifier:
Software type:
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Computer skills:
Clustering 16S rRNA for OTU Prediction
Command line interface
Operating system:
Unix/Linux, Windows
GNU General Public License version 2.0

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Molecular and Computational Biology Program, Department of Biology, University of Southern California, Los Angeles, CA, USA; MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China

Funding source(s)

National Institutes of Health; Center of Excellence in Genomic Sciences (CEGS) 2P50 HG002790-06; National Science Foundation of China (60805010)

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