Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/339423
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dc.coverage.spatialEfficient and accurate sequence clustering methods for metagenomics data
dc.date.accessioned2021-09-07T05:43:31Z-
dc.date.available2021-09-07T05:43:31Z-
dc.identifier.urihttp://hdl.handle.net/10603/339423-
dc.description.abstractThe 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 s
dc.format.extentxxi,153 p.
dc.languageEnglish
dc.relationp.131-152
dc.rightsuniversity
dc.titleEfficient and accurate sequence clustering methods for metagenomics data
dc.title.alternative
dc.creator.researcherAshaq Hussain Bhat
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering
dc.subject.keywordEngineering Manufacturing
dc.subject.keywordMetagenomics data
dc.subject.keywordClustering methods
dc.description.note
dc.contributor.guidePuniethaa Prabhu
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Technology
dc.date.registered
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Technology

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08_listoftables.pdf7.6 kBAdobe PDFView/Open
09_listoffigures.pdf118.04 kBAdobe PDFView/Open
10_listofabbreviations.pdf225.58 kBAdobe PDFView/Open
11_chapter1.pdf799.33 kBAdobe PDFView/Open
12_chapter2.pdf480.82 kBAdobe PDFView/Open
13_chapter3.pdf630.46 kBAdobe PDFView/Open
14_chapter4.pdf778.01 kBAdobe PDFView/Open
15_chapter5.pdf1.91 MBAdobe PDFView/Open
16_chapter6.pdf2.53 MBAdobe PDFView/Open
17_conclusion.pdf123.28 kBAdobe PDFView/Open
18_references.pdf326.54 kBAdobe PDFView/Open
19_listofpublications.pdf109.95 kBAdobe PDFView/Open
80_recommendation.pdf94.29 kBAdobe PDFView/Open


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