TBC: A clustering algorithm based on prokaryotic taxonomy

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

Research output: Contribution to journalArticle

17 Citations (Scopus)

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, ESPRIT-Tree, 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.

Original languageEnglish
Pages (from-to)181-185
Number of pages5
JournalJournal of Microbiology
Volume50
Issue number2
DOIs
Publication statusPublished - 2012 Apr
Externally publishedYes

Keywords

  • BLASTClust
  • CD-HIT
  • ESPRIT-Tree
  • MOTHUR
  • OTU
  • TBC
  • UCLUST
  • clustering algorithm
  • metagenome
  • pyrosequencing

ASJC Scopus subject areas

  • Microbiology
  • Applied Microbiology and Biotechnology

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