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Updated: May 11, 2026
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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
Published on: August 29, 2014
CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization
Kyuin Hwang1, Jeongsu Oh, Tae-Kyung Kim
1Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea.
Plos One
|May 8, 2013
Summary
A new algorithm, CLUSTOM, accurately clusters 16S ribosomal RNA (rRNA) gene sequences from microbial communities by leveraging uneven sequence distance distributions. This method improves microbial diversity analysis and outperforms existing clustering tools.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- High-throughput sequencing has generated vast microbial community data, necessitating efficient analysis of 16S ribosomal RNA (rRNA) gene sequences.
- Current clustering algorithms for 16S rRNA gene sequences face challenges with overlapping sequence spaces, hindering accurate phylotype assignment and microbial diversity studies.
Purpose of the Study:
- To develop a novel sequence clustering algorithm, CLUSTOM, that minimizes overlaps between adjacent clusters for more accurate microbial community analysis.
- To leverage the principle of uneven genetic distance distribution within taxonomic units for improved 16S rRNA gene sequence clustering.
Main Methods:
- Developed CLUSTOM, a new sequence clustering algorithm based on identifying core sequences at the centers of genetic distance distributions.
- Evaluated CLUSTOM's performance against existing algorithms like ESPRIT-Tree and mothur using SILVA database reference sequences and pyrosequencing datasets.
Main Results:
- CLUSTOM demonstrated higher accuracy in clustering 16S rRNA gene sequences compared to ESPRIT-Tree and mothur.
- The study confirmed that utilizing the uneven distribution of sequence distances is an effective strategy for accurate 16S rRNA gene sequence clustering.
Conclusions:
- CLUSTOM offers a more accurate approach to clustering 16S rRNA gene sequences, enhancing microbial diversity and community structure analysis.
- The algorithm's availability as a web and standalone application facilitates its adoption in microbial genomics research.
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