ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes

Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial isolates. The increased resolution significantly enhances the possibility of tracing transmission routes and contamination sources of foodborne pathogens. In addition, machine learning (ML) on WGS data has shown promising applications for predicting important microbial traits such as virulence, growth potential, and resistance to antimicrobials. Many regulatory agencies have already adapted WGS and ML methods. However, the food industry hasn't followed a similarly enthusiastic implementation. Some possible reasons for this might be the lack of computational resources and limited expertise to analyse WGS and ML data and interpret the results. Here, we present ListPred, a ML tool to analyse WGS data of Listeria monocytogenes, a very concerning foodborne pathogen. ListPred relies on genomic markers and pre-trained ML models from two previous studies, and it is able to predict two important bacterial traits, namely virulence potential and disinfectant tolerance. ListPred only requires limited computational resources and practically no bioinformatic expertise, which is essential for a broad application in the food industry.