Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 15, Issue 2, Pages 441-451Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2016.2535326
Keywords
Clustering algorithm; operational taxonomic unit (OTU); pyrosequencing; metagenomics; 16s rRNA
Categories
Funding
- National Research Foundation of Korea (NRF) - Korea government (Ministry of Science, ICT and Future Planning, MSIP) [2011-0009963, 2014M3C9A3063541]
- Industrial Core Technology Development Program [10040176]
- Ministry of Trade, Industry and Energy (MOTIE, Korea)
- Samsung Electronics Co., Ltd.
- NRF - Korea government (MSIP) [2013R1A1A1057949, 2014R1A4A1007895]
- National Research Foundation of Korea [2014M3C9A3063541, 22A20151713442, 2014R1A4A1007895, 2013R1A1A1057949] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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To assess the genetic diversity of an environmental sample in metagenomics studies, the amplicon sequences of 16s rRNA genes need to be clustered into operational taxonomic units (OTUs). Many existing tools for OTU clustering trade off between accuracy and computational efficiency. We propose a novel OTU clustering algorithm, hc-OTU, which achieves high accuracy and fast runtime by exploiting homopolymer compaction and k-mer profiling to significantly reduce the computing time for pairwise distances of amplicon sequences. We compare the proposed method with other widely used methods, including UCLUST, CD-HIT, MOTHUR, ESPRIT, ESPRIT-TREE, and CLUSTOM, comprehensively, using nine different experimental datasets and many evaluation metrics, such as normalized mutual information, adjusted Rand index, measure of concordance, and F-score. Our evaluation reveals that the proposed method achieves a level of accuracy comparable to the respective accuracy levels of MOTHUR and ESPRIT-TREE, two widely used OTU clustering methods, while delivering orders-of-magnitude speedups.
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