Food-Borne Pathogen Detection From Direct Nanopore Sequencing Data Using Galaxy Platform

Abstract

Food safety is a critical global concern, with food-borne pathogens posing significant threats to public health and economic stability. Rapid and accurate detection methods are essential to mitigate these risks. This study explored the use of direct nanopore sequencing data combined with the Galaxy platform for the identification of food-borne pathogens. The National Center for Biotechnology Information (NCBI) database was used to find samples used for the data analysis in fulfilment of objectives 2 and 3 as well as reference genome. The workflow integrated real-time sequencing capabilities and bioinformatics tools available in Galaxy to analyze raw sequencing data for pathogen identification. The tools used included but not limited to FastQC, Nanoplot, Fastp, Porechop, MultiQC, Kraken2, Krona Pie Chart, Pavian Visualisation, ABRicate, Flye, etc. Results demonstrated the method’s effectiveness in detecting and classifying various food-borne pathogens such as Salmonella enterica and Staphylococcus aureus based on numerous methods that rely on the metagenome’s taxonomy profile, pathogenetic gene composition and allele variation with high sensitivity and specificity. In conclusion, this approach was able to accurately determine the food-borne pathogens existing within the samples using the tools in the Galaxy platform.