Abstract
Most current Salmonella subtyping analyses rely on whole genome sequencing (WGS), which focuses on the high-resolution analysis of single genomes or multiple single genomes from the isolated colonies on microbiological agar plates. In this study, we introduce bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multiple Salmonella serovars at the same time. bettercallsal is one of the first analysis tools to identify multiple Salmonella enterica serotypes from metagenomic or quasi-metagenomic datasets with high accuracy, allowing these isolate-independent methods to be incorporated into surveillance and root cause investigations. It was tested on an in silico benchmark dataset comprising 29 unique Salmonella serovars, 46 non-Salmonella bacterial genomes, and 10 viral genomes at varying read depths and on previously well-characterized and sequenced non-selective primary and selective enrichments of papaya and peach samples from separate outbreak investigations that resulted in the identification of multiple Salmonella serovars using traditional isolate culturing and WGS as well as nucleic acid assays. Analyses were also conducted on these datasets using a custom-built k-mer tool, SeqSero2, and Kallisto to compare serotype calling to bettercallsal. The in silico dataset analyzed with bettercallsal achieved the maximum precision, recall, and accuracy of 100, 83, and 94%, respectively. In the papaya outbreak samples, bettercallsal identified the presence of multiple serovars in agreement with the Luminex® xMAP assay results and also identified more serovars per sample, as evidenced by NCBI SNP clustering. In peach outbreak samples, bettercallsal identified two serovars in concordance with k-mer analysis and the Luminex xMAP assay. The genome hit reported by bettercallsal clustered with the chicken isolate genome, as reported by the FDA peach outbreak investigation from sequenced isolates (WGS). Overall, bettercallsal outperformed k-mer, Seqsero2, and Kallisto in identifying multiple serovars from enrichment cultures using shotgun metagenomic sequencing.