Integrative metagenomics: compositional analysis, network based methods and computational pipeline development

This PhD thesis delves into the utilization and analysis of metagenomic data, employing next-generation sequencing (NGS) technologies to unravel microbial biodiversity and functions across diverse ecosystems. Central to the research is network analysis, revealing intricate relationships within microbial communities. Addressing challenges of compositional data and spurious correlations, the study develops theoretical methodologies, explores spurious correlations through simulations, and applies these approaches to various environmental contexts. Methodologically, the thesis focuses on minimizing spurious correlations in network construction, employing Aitchison's theory and the centered log-ratio transformation. Synthetic data generation models are developed to mimic real data characteristics. Case studies on wastewater and gut bacterial ecology showcase the effectiveness of tailored network methodologies, offering insights into temporal patterns and community dynamics. Computationally, the research harnesses High-Performance Computing (HPC) infrastructures, utilizing tools like Snakemake and Conda for efficient workflow management. In summary, the thesis advances metagenomics by elucidating microbial community interactions, tackling methodological hurdles, and introducing innovative computational approaches to unravel microbial ecology intricacies.