INTRODUCTION:
antibiotic resistance is a growing global health threat, contributing to increased morbidity, mortality, and healthcare costs worldwide. the burden is especially severe in resource-limited settings, where genomic surveillance capacity is limited. although large volumes of microbial genomic data are publicly available, they remain underutilized due to the absence of accessible, standardized, and reproducible computational pipelines. this gap hinders timely detection of resistance trends and delays evidence-based interventions. developing scalable computational tools tailored for low-resource contexts is therefore critical to strengthen surveillance and public health responses.

METHODS:
this proposed study will develop a computational pipeline for genomic surveillance of antibiotic resistance gene mutations using publicly available bacterial genomes. the pipeline will integrate sequence retrieval from ncbi, quality control, resistance gene identification using the card database, multiple sequence alignment with mafft, and phylogenetic tree construction using iq-tree. mutation profiling will compare gene sequences to reference alleles, and outputs will be standardized for epidemiological interpretation. the workflow will be automated with open-source tools and workflow managers to ensure reproducibility, modularity, and low computational requirements suitable for resource-limited settings. benchmarking will evaluate usability, runtime efficiency, and consistency across datasets. results (expected) the expected outcome is a validated, user-friendly computational pipeline capable of systematically identifying resistance genes and mutations across diverse bacterial genomes. outputs will include mutation tables, alignment files, and phylogenetic trees suitable for integration into public health surveillance systems. the pipeline is anticipated to empower low-resource regions to monitor resistance trends and contribute meaningfully to global surveillance efforts.

CONCLUSIONS:
this proposed pipeline addresses critical gaps in genomic surveillance, promotes equitable access to computational tools, and strengthens evidence-based public health decision-making. it offers a reproducible and scalable framework and has the potential to transform how low-resource regions monitor resistance trends and respond to emerging antimicrobial threats.