REPRODUCIBLE BACTERIAL GENOMICS ON MODEST HARDWARE: AN AUTOMATION AND OFFLOADING FRAMEWORK FOR ANTIMICROBIAL RESISTANCE AND PATHOGEN WHOLE GENOME ANALYSIS, WITH ESBL PRODUCING ESCHERICHIA COLI AS A CASE STUDY

Abstract

Whole genome sequencing (WGS) has become central to bacterial epidemiology and antimicrobial resistance (AMR) research, but the analytical workflow that turns raw genomes into interpretable results is fragmented across many command line tools and is frequently run by hand. Manual execution is slow. It is hard to reproduce and error prone. The problem is sharpest for the many laboratories particularly in low  and middle income settings that work on modest desktop hardware rather than high performance computing (HPC) clusters. Here we present a practical framework for converting fragmented bacterial WGS analyses into automated, reproducible workflows that remain feasible on a standard laptop. We first map the canonical comparative genomics pipeline. This pipeline will help in genome retrieval, quality control, annotation, resistome, virulome and plasmidome profiling, sequence typing, pan genome reconstruction, phylogenetics and integration. It will identify the recurring bottlenecks at each stage. We then describe a lightweight automation pattern built from widely available Linux and bioinformatics tools. A single machine readable manifest as the source of truth, configuration driven and modular scripts, pinned software environments, fixed random seeds and structured logging as an audit trail. The framework's distinctive element is an explicit local versus offload decision rule. The memory  and CPU light steps are automated locally. While a small number of resource intensive steps such as core genome phylogenetics offloaded to a free web platform (Galaxy) or the cloud. We illustrate the framework with a completed comparative genomic study of 100 ESBL producing E. coli genomes performed end to end on an 8 GB laptop.  Every step ran locally except the maximum likelihood phylogeny, which exceeded available memory and was offloaded successfully. We provide a decision table and reproducibility checklist that researchers can adopt directly. The framework lowers the hardware  barrier to reproducible pathogen genomics without requiring migration to a full workflow management system, while remaining fully compatible with such systems as projects scale.