Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/339818
Title: Efficient and accurate sequence clustering methods for metagenomics data
Researcher: Ashaq Hussain Bhat
Guide(s): Puniethaa Prabhu
Keywords: Life Sciences
Biology and Biochemistry
Developmental Biology
Metagenomics data
Clustering methods
University: Anna University
Completed Date: 2020
Abstract: The high-throughput Next Generation Sequencing (NGS) or amplicon sequencing of 16S ribosomal Ribonucleic acid (16S rRNA) NGS produces millions of sequences because of its parallel nature. Microbes play an indispensable role in processes as diverse as human health and biogeochemical activities critical to existence of life in all environments on the earth. The human microbiome particularly gut microbiome and human genome somehow form a mutualistic symbiotic relationship to a certain extent that they are dependent with each other where the disruption of one may affect the well-being of the others, for example, microbiome provide enzymes for digestion, and overgrowth of intestinal flora may cause irritable bowel syndrome. Therefore, the approach to look at microorganisms at their native environments is crucial for understanding their functions and characteristics. Taxonomic profiling, using hyper-variable regions of 16S rRNA, is one of the important goals in metagenomics analysis. Operational Taxonomic Unit (OTU) clustering algorithms are performing taxonomic profiling by grouping 16S rRNA sequence reads into OTU clusters. The metabarcoding or metagenomics analysis is generally divided into three steps: (i) pre-processing, (ii) Operational Taxonomic Unit (OTU) clustering and (iii) downstream processing. The pre-processing step involves demultiplexing, filtering, and error removal tasks, while the downstream processing involves statistics, visualization, etc. The most important phase is the clustering phase, which has received lot of attention and is still an active area of research.The purpose of clustering is to find the natural arrangement or clusters within the data, which are similar together but different from other clusters. Clustering is used for taxonomic profiling of microbial communities by binning the 16S rRNA amplicon reads into bins called as Operational Taxonomic Units. A number of clustering algorithms has been given to explore the unknown microbial world, but to anticipate the increasing number of se
Pagination: xxi,153 p.
URI: http://hdl.handle.net/10603/339818
Appears in Departments:Faculty of Science and Humanities

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