OTU Clustering: A window to analyse uncultured microbial world

Ashaq Hussain Bhat¹ , Puniethaa Prabhu²

1. Department of Biotechnology K. S. Rangasamy College of Technology, Tiruchengode, India.
2. Department of Biotechnology K. S. Rangasamy College of Technology, Tiruchengode, India.

Correspondence should be addressed to: ashaq11bhat@gmail.com.

Section:Review Paper, Product Type: Isroset-Journal
Vol.5 , Issue.6 , pp.62-68, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i6.6268

Online published on Dec 31, 2017

Copyright © Ashaq Hussain Bhat, Puniethaa Prabhu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Clustering is the technique used to deal with higher amounts of data by partitioning the data into some groups based on some attributes. Clustering technique has many applications in different fields of science and technology. It is an important tool in genomics and metagenomics which performs taxonomic profiling of the microbial world by grouping 16S RDNA amplicon reads into clusters called as Operational Taxonomic Units (OTUs). With the help of Next Generation Sequencing (NGS) tools and clustering it has become easy for scientists to find the microbial diversities in different environments without culturing the microbes. Assignment of 16s rDNA sequences to the clusters called as OTUs is the main task in metagenomics algorithms and is also the main bottleneck for analysing microbial communities. Taxonomic profiling of 16S rDNA is an important step in Metagenomic pipeline analysis. There are several OTU clustering algorithms which clusters the amplicon reads of 16S rDNA into OTUs, each algorithm use a specific type of clustering technique to cluster the sequence reads. Some of the mostly used algorithms are Uclust, swarm, SUMACLUST, SortMeRNA, USEARCH. In this paper, we first give a brief overview of major clustering techniques and their types. Furthermore, we provide a comprehensive overview of OTU clustering algorithms.

Key-Words / Index Term :
16S rDNA; OTUs; Uclust; SUMACLUST; SortMeRNA; USEARCH; taxonomic profiling

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