TBC: A Clustering Algorithm Based on Prokaryotic Taxonomy

Jae-Hak Lee; Hana Yi; Yoon-Seong Jeon; Sungho Won; Jongsik Chun

doi:10.1007/s12275-012-1214-6

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TBC: A Clustering Algorithm Based on Prokaryotic Taxonomy: Jae-Hak Lee ¹, Hana Yi ², Yoon-Seong Jeon ^1,3, Sungho Won ⁴, Jongsik Chun ^1,2,5; Journal of Microbiology 2012;50(2):181-185.
DOI: https://doi.org/10.1007/s12275-012-1214-6
Published online: April 27, 2012

¹Interdisciplinary Graduate Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea, ²Inst. of Molecular Biology and Genetics, Seoul National University, Seoul 151-742, Republic of Korea, ³Chunlab, Inc., Bldg 138 Rm 318, Seoul National University, Seoul 151-742, Republic of Korea, ⁴Department of Statistics, Chung-Ang University, Seoul 156-756, Republic of Korea, ⁵School of Biological Sciences and Advanced Inst. of Convergence Tech., Seoul National University, Seoul 151-742, Republic of Korea¹Interdisciplinary Graduate Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea, ²Inst. of Molecular Biology and Genetics, Seoul National University, Seoul 151-742, Republic of Korea, ³Chunlab, Inc., Bldg 138 Rm 318, Seoul National University, Seoul 151-742, Republic of Korea, ⁴Department of Statistics, Chung-Ang University, Seoul 156-756, Republic of Korea, ⁵School of Biological Sciences and Advanced Inst. of Convergence Tech., Seoul National University, Seoul 151-742, Republic of Korea

Corresponding author: Jongsik Chun , Tel: +82-2-880-8153,

Received: 25 April 2011 • Accepted: 7 November 2011

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Abstract

High-throughput DNA sequencing technologies have revolutionized the study of microbial ecology. Massive sequencing of PCR amplicons of the 16S rRNA gene has been widely used to understand the microbial community structure of a variety of environmental samples. The resulting sequencing reads are clustered into operational taxonomic units that are then used to calculate various statistical indices that represent the degree of species diversity in a given sample. Several algorithms have been developed to perform this task, but they tend to produce different outcomes. Herein, we propose a novel sequence clustering algorithm, namely Taxonomy-Based Clustering (TBC). This algorithm incorporates the basic concept of prokaryotic taxonomy in which only comparisons to the type strain are made and used to form species while omitting full-scale multiple sequence alignment. The clustering quality of the proposed method was compared with those of MOTHUR, BLASTClust, ESPRITTree, CD-HIT, and UCLUST. A comprehensive comparison using three different experimental datasets produced by pyrosequencing demonstrated that the clustering obtained using TBC is comparable to those obtained using MOTHUR and ESPRIT-Tree and is computationally efficient. The program was written in JAVA and is available from http://sw. ezbiocloud.net/tbc.

Keywords: TBC;clustering algorithm;OTU;CD-HIT;UCLUST;MOTHUR;ESPRIT-Tree;BLASTClust;pyrosequencing;metagenome

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TBC: A Clustering Algorithm Based on Prokaryotic Taxonomy

J. Microbiol. 2012;50(2):181-185. Published online April 27, 2012

DOI: https://doi.org/10.1007/s12275-012-1214-6

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