Derek Lam, Sree Vadlamudi, Ria Poluru, Ashley Fang, Chujing Zheng, Connor Lee, Anna Zhang, Yujie Men, Linda Shi

Abstract: Antibiotic resistance is a growing threat to food safety and public health, with treated municipal wastewater serving as a major route for antibiotic resistance genes (ARGs) to enter agricultural systems and ultimately humans. Monitoring their movement is crucial for understanding transmission and developing risk-reducing strategies, but conventional approaches are slow, error-prone, and hard to scale for large datasets. To address this, an automated Bactopia pipeline was developed to analyze and track ARGs in streptomycin-resistant E. coli from wastewater effluent. Resistant bacteria were isolated from the secondary effluent of a local municipal wastewater treatment plant. Whole genomes were sequenced to identify ARGs. Three assemblers within Bactopia (SPAdes, SKESA, and MEGAHIT) were evaluated for performance, accuracy, and scalability. A Python automation program was implemented to streamline Bactopia execution, handle large datasets, and generate visualizations such as heat maps, sequence alignments, and interactive chord graphs. The automated pipeline efficiently detected and compared ARGs across data inputs, substantially reducing manual processing time while minimizing human error. Visualizations allowed rapid interpretation of resistome dynamics and potential transmission pathways. It helped provide insights into ARG patterns and the environmental spreading of ARGs. These findings demonstrate that automated genomic pipelines like Bactopia can provide scalable and interpretable tools for monitoring antibiotic resistance in agricultural reuse systems and broader environmental contexts.

Keywords: Antibiotic Resistance, Bactopia, Wastewater, Automation, Data Visualization, Genome Analysis

Date Published: December 19, 2025
DOI: 10.11159/jbeb.2025.016

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