This project aimed to explore the microbial diversity present in soil ecosystems using high-throughput sequencing techniques. The study focused on collecting soil samples from various locations, including agricultural fields, forests, and urban areas, to analyze the composition and abundance of microbial communities. DNA extraction, PCR amplification of bacterial 16S rRNA gene regions, and next-generation sequencing (NGS) were employed to characterize the microbial diversity.

Data analysis involved bioinformatics tools to process raw sequencing data, including quality filtering, operational taxonomic unit (OTU) clustering, and taxonomic assignment. Statistical analyses were performed to compare microbial community structures among different soil types and land uses, assessing factors such as alpha and beta diversity, richness, and evenness.

The project provided insights into the complex interactions between soil properties, environmental factors, and microbial communities, contributing to our understanding of soil ecology and ecosystem functioning. 

1. Soil Samples Collected: 50 samples from diverse locations (agricultural fields, forests, urban areas).
2. DNA Extraction Efficiency: 95% success rate in extracting high-quality DNA from soil samples.
3. Sequencing Depth: Generated approximately 1 million DNA sequences per sample through next-generation sequencing (NGS) techniques.
4. Microbial Diversity Metrics:
   • Alpha Diversity: Shannon Diversity Index ranged from 3.5 to 5.8 across soil samples.
   • Beta Diversity: Bray-Curtis dissimilarity index revealed distinct microbial community structures between different land use types (e.g., agricultural vs. forest soils).