Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. chewBBACA performs the schema creation and allele calls on complete or draft genomes resulting from de novo assemblers. The chewBBACA software uses Python 3.4 or higher and can run on a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA.

Salmonella enterica ser. Typhimurium monophasic variant 4,[5],12:i:- has been associated with food-borne epidemics worldwide and swine appeared to be the main reservoir in most of the countries of isolation. However, the monomorphic nature of this serovar has, so far, hindered identification of the source due to expansion of clonal lineages in multiple hosts and food producing systems. Since geographically structured genetic signals can shape bacterial populations, identification of biogeographical markers in S. 1,4,[5],12:i:- genomes can contribute to improving source attribution. In this study, the phylogeographical structure of 148 geographically and temporally related Italian S. 1,4,[5],12:i:- has been investigated. The Italian isolates belong to a large population of clonal S. Typhimurium/1,4,[5],12:i:- isolates collected worldwide in two decades showing up to 2.5% of allele differences. Phylogenetic reconstruction revealed that isolates from the same geographical origin form highly supported monophyletic groups, suggesting discrete geographical segregation. These monophyletic groups are characterized by the gene content of a large sopE-containing prophage. Within this prophage, genome-wide comparison identified several genes overrepresented in strains of Italian origin. This suggests that certain lineages may be characterized by the acquisition of specific accessory genetic markers useful for improving identification of the source in ongoing epidemics.

Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. The software can run in a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA or as a docker image at https://hub.docker.com/r/ummidock/chewbbaca/.DATA SUMMARYAssembled genomes used for the tutorial were downloaded from NCBI in August 2016 by selecting those submitted as Streptococcus agalactiae taxon or sub-taxa. All the assemblies have been deposited as a zip file in FigShare (https://figshare.com/s/9cbe1d422805db54cd52), where a file with the original ftp link for each NCBI directory is also available.Code for the chewBBACA suite is available at https://github.com/B-UMMI/chewBBACA while the tutorial example is found at https://github.com/B-UMMI/chewBBACA_tutorial.I/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. ⊠IMPACT STATEMENTThe chewBBACA software offers a computational solution for the creation, evaluation and use of whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) schemas. It allows researchers to develop wg/cgMLST schemes for any bacterial species from a set of genomes of interest. The alleles identified by chewBBACA correspond to potential coding sequences, possibly offering insights into the correspondence between the genetic variability identified and phenotypic variability. The software performs allele calling in a matter of seconds to minutes per strain in a laptop but is easily scalable for the analysis of large datasets of hundreds of thousands of strains using multiprocessing options. The chewBBACA software thus provides an efficient and freely available open source solution for gene-by-gene methods. Moreover, the ability to perform these tasks locally is desirable when the submission of raw data to a central repository or web services is hindered by data protection policies or ethical or legal concerns.

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