ProbioMinServer is a platform designed to help researchers access information on probiotics regarding a wide variety of characteristics, such as safety (e.g. antimicrobial resistance, virulence, pathogenic, plasmid, and prophage genes) and functionality (e.g. functional classes, carbohydrate-active enzyme, and metabolite gene cluster profile). Because probiotics are functional foods, their safety and functionality are a crucial part of health care. Genomics has become a crucial methodology for investigating the safety and functionality of probiotics in food and feed. This shift is primarily attributed to the growing affordability of next-generation sequencing technologies. However, no integrated platform is available for simultaneously evaluating probiotic strain safety, investigating probiotic functionality, and identifying known phylogenetically related strains.

5NosoAE is a webserver that can be used for nosocomial bacterial analysis including the identification of similar strains based on antimicrobial resistance profiles (antibiogram) and the spatiotemporal distribution visualization and phylogenetic analysis of identified strains with similar antibiograms. The extensive use of antibiotics has caused many pathogenic bacteria to develop multiple drug resistance, resulting in clinical infection treatment challenges and posing a major threat to global public health. Relevant studies have investigated the key determinants of antimicrobial resistance in the whole-genome sequence of bacteria. However, a web server is currently not available for performing large-scale strain searches according to antimicrobial resistance profiles and visualizing epidemiological information including the spatiotemporal distribution, antibiogram heatmap, and phylogeny of identified strains. Here, we implemented these functions in the new server, referred to as 5NosoAE. This server accepts the genome sequence file in the FASTA format of five nosocomial bacteria, namely Acinetobacter baumannii, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecium and Staphylococcus aureus for query. All visualizations are implemented in JavaScript and PHP. This server will be useful for physicians and epidemiologists involved in research on infectious disease. 

Listeria monocytogenes can cause listeriosis, and people with hypoimmunity such as pregnant women, infants and fetuses are at high risk of invasive infection. Although the incidence of listeriosis is low, the fatality rate is high. Therefore, continual surveillance and rapid epidemiological investigation are crucial for addressing L. monocytogenes. Because of the popularity of next-generation sequencing, obtaining the whole-genome sequence of a bacterium is easy. Several genome-based typing methods are available, and core-genome multilocus sequence typing (cgMLST) is the most recognized methods. Using cgMLST typing to compare L. monocytogenes whole-genome sequences (WGS) with those obtained across distinct regions is beneficial. However, the concern is how to incorporate the powerful cgMLST method into investigations, such as by using source tracing. Herein, we present an easy-to-use web service called-LmTraceMap that can help public-health professionals rapidly trace closely related isolates worldwide and visually inspect them in search results on a world map with labeled epidemiological data. We expect the proposed service to improve the convenience of public health investigations.

With the evolution of next generation sequencing (NGS) technologies, whole-genome sequencing of bacterial isolates is increasingly employed to investigate epidemiology. Phylogenetic analysis is the common method for using NGS data, usually for comparing closeness between bacterial isolates to detect probable outbreaks. However, interpreting a phylogenetic tree is not easy without training in evolutionary biology. Therefore, developing an easy-to-use tool that can assist people who wish to use a phylogenetic tree to investigate epidemiological relatedness is crucial. In this paper, we present a tool called OutbreakFinder that can accept a distance matrix in csv format; alignment files from Lyve-SET, Parsnp, and ClustalOmega; and a tree file in Newick format as inputs to compute a cluster-labeled two-dimensional plot based on multidimensional-scaling dimension reduction coupled with affinity propagation clustering.

Extended multi-locus sequence typing (eMLST) methods have become popular in the field of genomic epidemiology. Before eMLST methods can be applied in epidemiological investigations, the selection of a suitable scheme is critical. The core genome scheme (cgMLST) has become the most popular eMLST approach for strain typing in the epidemiological domain. In addition to strain typing, many public health researchers and clinical microbiologists wish to investigate which genes cause genetic differences between compared strains. Therefore, a tool that can be used to extract canonical genes with an eMLST scheme would be particularly useful. In this study, we present cano-eMLST, a well-designed program that applies a feature-selection methodology to create a canonical locus combination with discriminatory power by traversing a genetic relatedness tree based on a user-selected scheme. The cano-eMLST program is provided mainly to help infectious disease laboratory researchers identify potential factors related to bacterial pathogenesis. The core program (tree-traversing approach) of cano-eMLST is implemented in Perl and Python. All the necessary dependencies and environmental settings are provided in the encapsulated version (VirtualBox or VMware) and self-installation version (all use source code and libraries).

With the decreasing cost of next-generation sequencing, whole-genome sequence-based bacterial genome comparisons are expected to become a mainstream process in the public health domain. Extended multilocus sequence typing (MLST) methods are becoming increasingly popular for use in comparing bacterial genetic relatedness in epidemiological investigations. Several extended MLST schemes based on biological signatures have been reported. Among them, whole-genome MLST (wgMLST) has gradually become one of the most widely used approaches for bacterial strain typing. In addition to using bacterial typing, many researchers aim to identify differences in the genes of compared strains. Because these differences might provide insights into critical bacterial functions, such as virulence and pathogenicity, researchers usually study these genes that differ between strains. Hence, we designed a web service tool based on wgMLST-constructed tree topology coupled with the feature selection method to create the "canonical wgMLST (cano-wgMLST) tree." The genes that differ between strains are shown at each split of the tree, thereby directly providing information for performing a comparative genomic analysis for each strain pair. We demonstrated that this web service tool could be operated efficiently on two datasets consisting of 22 Campylobacter jejuni isolates and 59 S. Heidelberg isolates, respectively. We imposed this tool on a designated web server, cano-wgMLST_BacCompare, to enable users to create a wgMLST tree and canonical wgMLST tree automatically from their uploaded bacterial genomes for not only epidemiological investigation but also comparative genomic analysis. Additionally, detailed information on how to use this service is provided.

With the advance of next generation sequencing techniques, whole genome sequencing (WGS) is expected to become the optimal method for molecular subtyping of bacterial isolates. To use WGS as a general subtyping method for disease outbreak investigation and surveillance, the layout of WGS-based typing must be comparable among laboratories. Whole genome multilocus sequence typing (wgMLST) is an approach that achieves this requirement. To apply wgMLST as a standard subtyping approach, a pan-genome allele database (PGAdb) for the population of a bacterial organism must first be established. We present a free web service tool, PGAdb-builder, for the construction of bacterial PGAdb. The effectiveness of PGAdb-builder was tested by constructing a pan-genome allele database for Salmonella enterica serovar Typhimurium, with the database being applied to create a wgMLST tree for a panel of epidemiologically well-characterized S. Typhimurium isolates. The performance of the wgMLST-based approach was as high as that of the SNP-based approach in Leekitcharoenphon’s study used for discerning among epidemiologically related and non-related isolates.

We built a pan-genome allele database with 395 genomes of Salmonella enterica serovar Enteritidis and developed computer tools for analysis of whole genome sequencing (WGS) data of bacterial isolates for disease cluster identification. A web server was set up with the database and the tools, allowing users to upload WGS data to generate whole genome multilocus sequence typing (wgMLST) profiles and to perform cluster analysis of wgMLST profiles. The usefulness of the database in disease cluster identification was demonstrated by analyzing a panel of genomes from 55 epidemiologically well-defined S. Enteritidis isolates provided by the Minnesota Department of Health. The wgMLST-based cluster analysis revealed distinct clades that were concordant with the epidemiologically defined outbreaks. Thus, using a common pan-genome allele database, wgMLST can be a promising WGS-based subtyping approach for disease surveillance and outbreak investigation across laboratories.