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Evaluation of real-time nanopore sequencing for Salmonella serotype prediction

Publication

Date: 5th February 2020 | Source: Food Microbiology

Authors: Feng Xu, Chongtao Ge, Hao Luo, Shaoting Li, Martin Wiedmann, Xiangyu Deng, Guangtao Zhang, Abigail Stevenson, Robert C. Baker, Silin Tang.

The use of whole genome sequencing (WGS) data generated by short-read sequencing technologies such as the Illumina sequencing platforms has been shown to provide reliable results for Salmonella serotype prediction. Emerging long-read sequencing platforms developed by Oxford Nanopore Technologies (ONT) provide an alternative WGS method to meet the needs of industry for rapid and accurate Salmonella confirmation and serotype classification. Advantages of the ONT sequencing platforms include portability, real-time base-calling and long-read sequencing.

To explore whether WGS data generated by an ONT sequencing platform could accurately predict Salmonella serotypes, 38 Salmonella strains representing 34 serotypes were sequenced using R9.4 flow cells on an ONT sequencer for up to 2 h.

The downstream bioinformatics analysis was performed using pipelines with different assemblers including Canu, Wdbtg2 combined with Racon, or Miniasm combined with Racon. In silico serotype prediction programs were carried out using both SeqSero2 (raw reads and genome assemblies) and SISTR (genome assemblies). The WGS data of the same strains were also obtained from Illumina Hiseq (200 x depth of coverage per genome) as a benchmark of accurate serotype prediction. Predictions using WGS data generated after 30 min, 45 min, 1 h, and 2 h of ONT sequencing time all matched the prediction results from Illumina WGS data.

This study demonstrated the comparable accuracy of WGS-based serotype prediction between ONT and Illumina sequencing platforms. This study also sets a start point for future validation of ONT WGS as a rapid Salmonella confirmation and serotype classification tool for the food industry.

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