Background: Multilocus Sequence Typing (MLST) is a frequently used typing method for the analysis of the clonal relationships among strains of several clinically relevant microbial species. MLST is based on the sequence of housekeeping genes that result in each strain having a distinct numerical allelic profile, which is abbreviated to a unique identifier: the sequence type (ST). The relatedness between two strains can then be inferred by the differences between allelic profiles. For a more comprehensive analysis of the possible patterns of evolutionary descent, a set of rules were proposed and implemented in the eBURST algorithm. These rules allow the division of a data set into several clusters of related strains, dubbed clonal complexes, by implementing a simple model of clonal expansion and diversification. Within each clonal complex, the rules identify which links between STs correspond to the most probable pattern of descent. However, the eBURST algorithm is not globally optimized, which can result in links, within the clonal complexes, that violate the rules proposed. Results: Here, we present a globally optimized implementation of the eBURST algorithm – goeBURST. The search for a global optimal solution led to the formalization of the problem as a graphic matroid, for which greedy algorithms that provide an optimal solution exist. Several public data sets of MLST data were tested and differences between the two implementations were found and are discussed for five bacterial species: Enterococcus faecium, Streptococcus pneumoniae, Burkholderia pseudomallei, Campylobacter jejuni and Neisseria spp.. A novel feature implemented in goeBURST is the representation of the level of tiebreak rule reached before deciding if a link should be drawn, which can used to visually evaluate the reliability of the represented hypothetical pattern of descent. Conclusion: goeBURST is a globally optimized implementation of the eBURST algorithm, that identifies alternative patterns of descent for several bacterial species. Furthermore, the algorithm can be applied to any multilocus typing data based on the number of differences between numeric profiles. A software implementation is available at http://goeBURST.phyloviz.net.

Background: With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it. Results: PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available. Conclusions: PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net.

Summary: High Throughput Sequencing provides a cost effective means of generating high resolution data for hundreds or even thousands of strains, and is rapidly superseding methodologies based on a few genomic loci. The wealth of genomic data deposited on public databases such as Sequence Read Archive/European Nucleotide Archive provides a powerful resource for evolutionary analysis and epidemiological surveillance. However, many of the analysis tools currently available do not scale well to these large datasets, nor provide the means to fully integrate ancillary data. Here we present PHYLOViZ 2.0, an extension of PHYLOViZ tool, a platform independent Java tool that allows phylogenetic inference and data visualization for large datasets of sequence based typing methods, including Single Nucleotide Polymorphism (SNP) and whole genome/core genome Multilocus Sequence Typing (wg/cgMLST) analysis. PHYLOViZ 2.0 incorporates new data analysis algorithms and new visualization modules, as well as the capability of saving projects for subsequent work or for dissemination of results.

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.

The studies that correlate the results obtained by different typing methodologies rely solely on qualitative comparisons of the groups defined by each methodology. We propose a framework of measures for the quantitative assessment of correspondences between different typing methods as a first step to the global mapping of type equivalences. A collection of 325 macrolide-resistant Streptococcus pyogenes isolates associated with pharyngitis cases in Portugal was used to benchmark the proposed measures. All isolates were characterized by macrolide resistance phenotyping, T serotyping, emm sequence typing, and pulsed-field gel electrophoresis (PFGE), using SmaI or Cfr9I and SfiI. A subset of 41 isolates, representing each PFGE cluster, was also characterized by multilocus sequence typing (MLST). The application of Adjusted Rand and Wallace indices allowed the evaluation of the strength and the directionality of the correspondences between the various typing methods and showed that if PFGE or MLST data are available one can confidently predict the emm type (Wallace coefficients of 0.952 for both methods). In contrast, emm typing was a poor predictor of PFGE cluster or MLST sequence type (Wallace coefficients of 0.803 and 0.655, respectively). This was confirmed by the analysis of the larger data set available from http://spyogenes.mlst.net and underscores the necessity of performing PFGE or MLST to unambiguously define clones in S. pyogenes .

We propose a new coefficient, the adjusted Wallace coefficient ( AW ), and corresponding confidence intervals (CI) as quantitative measures of congruence between typing methods. The performance of the derived CI was evaluated using simulated data. Published microbial typing data were used to demonstrate the advantages of AW over the Wallace coefficient.