Project Description

In Tuberculosis (TB), we focus on identifying genomic mutations associated with antimicrobial resistance, utilizing machine learning for multi-drug resistance classification, and conducting genomic and phylogenetic analyses to understand Mycobacterium tuberculosis (Mtb) evolution. Our efforts extend to uncovering common genetic factors in distinct phylogenetic clades contributing to pathogenicity. With a database of 20,408 Illumina raw sequence read datasets and laboratory-based phenotypic AST data, we integrate information from public sources using custom scripts. Real-time tracking of Mtb's anti-microbial resistance, mutations, and genomic evolution leverages Geographic Information System (GIS) tools in the front end, with database connection managed by open-source GIS tools like PostGIS. Anticipated outcomes include timely genomic investigations, providing critical insights into evolutionary positions, transmission routes, and resistance patterns. This project is poised to deliver real-time surveillance tools and geospatial comparative genomics, offering innovative preventive approaches against tuberculosis. Highlighting the effectiveness of an integrated Whole Genome Sequencing (WGS) approach, the project aims to characterize genomic evolution, antimicrobial resistance, and associated mutations in real time, supported by advanced graphical visualization. Ultimately, a bioinformatics web server will provide a high-resolution comparative genomics interface for real-time monitoring of Mtb evolution.