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Prabhu, Puniethaa
- In Silico Identification of Genes, Coding Sequences for Iron Source and Drug Targets from Haemophilus Ducreyi
Authors
1 Department of Biotechnology, K.S. Rangasamy College of Technology, Tiruchengode-637215, IN
Source
Biometrics and Bioinformatics, Vol 3, No 10 (2011), Pagination: 477-480Abstract
Haemophilus ducreyi strain 35000HD is responsible for causing chancroid, one of the most common sexually transmitted disease prevailing globally. Although extensive researches are in progress in order to control the transmission of the disease and to develop drugs against the pathogen, till date no effective vaccine or specific drug are developed and only antibiotic treatment is in use. Due to excess use of antibiotics, several resistant strains such as Herpes simplex, chlamydia trachomatis virus have been found. In the present study, candidate drug and vaccine targets are identified from Haemophilus ducreyi virulent strain 35000HD using in silico subtractive genomics approach. Essential genes are responsible for virulence nature and support the growth of microorganism. 531 essential genes of Haemophilus ducreyi are expressed beneath in vivo. Out of 531 essential genes, 125 genes are identified as essential, 12 are iron source coding genes. These essential enzymes are found to be the potential drug targets from the host pathogen. From the identified proteins, Ferritin and transferrin are expected to be better possible drug targets. Screening of the functional inhibitors against these drug targets may result in discovery of novel therapeutic drugs that can be effective for the antibiotic resistant strains.Keywords
Haemophilus Ducreyi, Essential Genes, Genes Coding Iron Source, Candidate Vaccine Targets.
- Comparative Analysis of Soil Microbiome Diversity using QIIME and CloVR Pipelines
Authors
1 Department of Biotechnology, K.S.Rangasamy College of Technology, Tiruchengode – 637 215, IN
2 Department of Biotechnology, K.S.Rangasamy College of Technology, Tiruchengode – 637 215, IN
Source
Biometrics and Bioinformatics, Vol 9, No 1 (2017), Pagination: 11-19Abstract
The study of 16s rRNA sequences through Next Generation Sequencing (NGS) have revolutionized the understanding of the microbial community composition and its structure. The massive data production and substantial cost reduction in NGS technologies have led to rapid growth of metagenomic research both quantitatively and qualitatively. Soil Metagenomics is a discipline that enables the genomic study of uncultured organisms in the soil samples. Quantitative Insights Into Microbial Ecology (QIIME) and Cloud Virtual Resource (CloVR) processes metagenomic data from a high-throughput 16S rRNA sequencing platform, beginning with multiplexed sequence reads, then Operational taxonomic units picking, Summarizing taxonomies, phylogenetic relationships and analyzing the alpha and finally beta diversities through plots. The proposed study demonstrates the analysis of microbial composition present in the study sample using QIIME and CloVR metagenomic pipelines. The soil metagenomic Datasets applied for the present study are retrived from European Nucleotide Archive (ENA) under the sample ID ERP001958.The interrealtionships among the OTUs is studied through the network analysis. A comparative analysis of the metagenomic pipelines is also performed with the gene clustering algorithms for understanding the concept of OUT clustering in metagenomic analysis. This research underscores the usefulness of next-generation sequencing techniques both to understand the ecological impact of contamination and to identify potential molecular proxies for detection of natural attenuation.