Leveraging Third Generation Sequencing And Novel Sequence Analysis Algorithms For Rapid And Efficient Amplicon-Based Detection Of Foodborne Pathogens

Current databases for taxonomic classification of bacteria are very large, computationally expensive and allow little to no customization. This study served to decrease computational time and memory required to taxonomically identify foodborne pathogens through incorporating the novel ColorID algorithm with the Nanopore MinION™ and to determine the limit of detection of Salmonella Enteritidis and Listeria monocytogenes. Various fecal samples were prepared into 16S libraries and sequenced via the MinION™. The sequencing speed, computational power and efficiency were determined with ColorID using an “all-bacteria” database compared with a database consisting of relevant foodborne pathogens. Analyses were compared to bioinformatic pipelines within QIIME2 for Illumina data. The MinION™/ColorID method using a “pathogen-specific” database was more computationally efficient than an “all-bacteria” database or QIIME2. The limit of detection of the MinION™/ColorID method was 1.7 log and 4.1 log CFU/ml for Salmonella Enteritidis and Listeria monocytogenes, respectively. These findings could greatly reduce computational time and resources needed to detect pathogens, which could be used for many applications related to food safety.