Evaluation of Serotype Prediction With Multiplex Nanopore Sequencing.

The use of whole genome sequencing (WGS) data generated by the long-read sequencing platform Oxford Nanopore Technologies (ONT) has been shown to provide reliable results for serotype prediction in a previous study. To further meet the needs of industry for accurate, rapid, and cost-efficient confirmation and serotype classification, we evaluated the serotype prediction accuracy of using WGS data from multiplex ONT sequencing with three, four, five, seven, or ten isolates (each isolate represented one serotype) pooled in one R9.4.1 flow cell. Each multiplexing strategy was repeated with five flow cells, and the loaded samples were sequenced simultaneously in a GridION sequencer for 48 h. serotype prediction was performed using both SeqSero2 (for raw reads and genome assemblies) and SISTR (for genome assemblies) software suites. An average of 10.63 Gbp of clean sequencing data was obtained per flow cell. We found that the unevenness of data yield among each multiplexed isolate was a major barrier for shortening sequencing time. Using genome assemblies, both SeqSero2 and SISTR accurately predicted all the multiplexed isolates under each multiplexing strategy when depth of genome coverage ≥50× for each isolate. We identified that cross-sample barcode assignment was a major cause of prediction errors when raw sequencing data were used for prediction. This study also demonstrated that, (i) sequence data generated by ONT multiplex sequencing can be used to simultaneously predict serotype for three to ten isolates, (ii) with three to ten isolates multiplexed, genome coverage at ≥50× per isolate was obtained within an average of 6 h of ONT multiplex sequencing, and (iii) with five isolates multiplexed, the cost per isolate might be reduced to 23% of that incurred with single ONT sequencing. This study is a starting point for future validation of multiplex ONT WGS as a cost-efficient and rapid confirmation and serotype classification tool for the food industry.