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.
Metagenomics is the branch of science which does the direct analysis of genetic material of environmental samples. Maximum microorganisms present in the ecosystem are unculturable. Metagenomics with the expansion in the field of Next Generation Sequencing (NGS) has generated large amount of raw sequence reads of uncultured microbes from different ecosystem. This combination opens door for new metabolites, drug targets, new species and unknown world of new genes could be utilized for the therapeutics. Biomarkers could be utilized as an indicator for the identification of any biological processes, disease genes, metabolites, enzymes, and antibiotics while biosensor is an analytical devise for the detection of analyte with the help of biological component (biomarkers) and physicochemical detector. Current study tried to find probable biomarkers which could be utilized for biosensor development especially for uncultured microorganisms from fresh water ecosystems. We collected all fresh water raw reads from publically available different repositories. After preliminary screening on huge data sets, we performed quality analysis on raw read sequences, generated 10 different conserve domains by multiple sequence alignment. These CDs could be promising biomarkers for the identification of uncultured microbes from fresh water ecosystems which may be pathogenic for humans. DNA hybridization based Biosensor could be synthesized by using by using this biomarker sequences for detecting the presence of uncultured microorganisms (pathogenic/non-pathogenic) in fresh water ecosystem.
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