TBC: A Clustering Algorithm Based on Prokaryotic Taxonomy

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 clus...

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Bibliographic Details
Published in:	The journal of microbiology Vol. 50; no. 2; pp. 181 - 185
Main Authors:	Lee, J.H., Seoul National University, Seoul, Republic of Korea, Yi, H.N., Seoul National University, Seoul, Republic of Korea, Jeon, Y.S., Seoul National University, Seoul, Republic of Korea, Won, S.H., Chung-Ang University, Seoul, Republic of Korea, Chun, J.S., Seoul National University, Seoul, Republic of Korea
Format:	Journal Article
Language:	English
Published:	Heidelberg The Microbiological Society of Korea 01.04.2012 Springer Nature B.V 한국미생물학회
Subjects:	Algorithms Bacteria Bacteria - chemistry Bacteria - classification Bacteria - genetics Biomedical and Life Sciences BLASTClust CD-HIT chemistry classification Clustering clustering algorithm Community structure Ecological studies ESPRIT-Tree Genes genetics high-throughput nucleotide sequencing Life Sciences metagenome methods microbial communities Microbiology MOTHUR numerical taxonomy OTU Phylogenetics Phylogeny polymerase chain reaction Prokaryotic Cells Prokaryotic Cells - chemistry Prokaryotic Cells - classification pyrosequencing ribosomal RNA Sequence Alignment Sequence Analysis, DNA Sequence Analysis, DNA - methods Species diversity TAXONOMIA TAXONOMIE TAXONOMY TBC UCLUST 생물학 South Korea OTU clustering algorithm metagenome TBC MOTHUR ESPRIT-Tree UCLUST pyrosequencing BLASTClust CD-HIT
ISSN:	1225-8873, 1976-3794, 1976-3794
Online Access:	Get full text
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Summary:	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.
Bibliography:	A50 2013001311 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 G704-000121.2012.50.2.006
ISSN:	1225-8873 1976-3794 1976-3794
DOI:	10.1007/s12275-012-1214-6