Abstract
BackgroundSalmonella enterica is a major cause of bacterial food-borne disease worldwide. Immunological serotyping is the most commonly used typing method to characterize S. enterica isolates, but is time-consuming and requires expensive reagents. Here, we developed an R package CSESA (CRISPR-based Salmonella enterica Serotype Analyzer) to predict the serotype based on the CRISPR loci of S. enterica.ResultsCSESA has implemented the CRISPR typing method CLSPT and extended its coverage on diverse S. enterica serotypes. This package takes CRISPR sequences or the genome sequences as input and provides users with the predicted serotypes. CSESA has shown excellent performance with currently available sequences of S. enterica.ConclusionsCSESA is a convenient and useful tool for the prediction of S. enterica serotypes. The application of CSESA package can improve the efficiency of serotyping for S. enterica and reduce the burden of manpower resources. CSESA is freely available from CRAN at https://cran.r-project.org/web/packages/CSESA/.
Highlights
Salmonella enterica is a major cause of bacterial food-borne disease worldwide
clustered regularly interspaced short palindromic repeat (CRISPR) locus spacer pair typing (CLSPT) distinguishes S. enterica isolates based on the pair of newly incorporated spacers in both CRISPR loci
We have extended the database of CRISPR-based Salmonella enterica Serotype Analyzer (CSESA) with diverse S. enterica isolates
Summary
Genome sequences of 5335 currently available S. enterica isolates with determined serotypes were downloaded from the NCBI database for the test of CSESA (accessed 24th March 2018). The accuracy of CSESA was evaluated by comparing the predicted result with provided serotype of each isolate. CSESA has shown excellent performance on prediction with common serotypes of S. enterica. Only 48.3% of the isolates of Bareilly got the matched serotype in CSESA due to the limitation of current dictionary. The results revealed that 1665 of 5335 genomes were correctly identified by CCT, which had a significantly lower accuracy of serotype prediction than CSESA (Chi-square test, p < 0.001). To Further improve CSESA’s performance in clinical situations, these correlations identified in the downloaded S. enterica genomes from NCBI have been integrated into the dictionary. A total of 388 correlations covering 137 serotypes and 323 newly incorporated spacer pair polymorphisms are currently documented
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