Abstract
The study of metagenomics from high throughput sequencing data processed through Waikato Environment for Knowledge Analysis (WEKA) is gaining momentum in recent years. Therefore, we report an analysis of metagenome data generated using T-RFLP followed by using the SMO (Sequential minimal optimization) algorithm in WEKA to identify the total amount of cultured and uncultured microorganism present in the sample collected from multiple sources.
Highlights
Multiple methods have been developed to culture different kinds of microorganisms
It is fully implemented in the Java programming language and runs on almost any modern computing platform
There are very few researchers who work on Support Vector Machine (SVM)
Summary
Multiple methods have been developed to culture different kinds of microorganisms. The availability of complex growth medium has helped in isolating only 1% of microorganisms from distinct habitats. 99% of microorganisms are still not cultured [1]. Metagenomics helps to uncover these unknown species [2] Sequencing is important for metagenomics analysis especially where species are not indetified. Sequencing technologies has revolutionized Biology [3]. Advances in the field of Bioinformatics, development of data library, tools and databases for metagenome has impact on metagenomics study [1]
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