An analysis of non-cultivable bacteria using WEKA

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. Background: Multiple methods have been developed to culture different kinds of microorganisms. However, the availability of complex growth medium has helped in isolating only 1% of microorganisms from distinct habitats. Thus, 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]. Low cost of sequencing, advances in the field of Bioinformatics, development of data library, tools and databases for metagenome has impact on metagenomics study [1].