Bacterial meningitis is an infectious disease of the brain that occurs worldwide and is a major public health challenge. A leading cause of this often-fatal disease is the bacterium Neisseria meningitidis, also called meningococcus. Genomics is having a transformational impact on medicine. It enables advances in accurate diagnosis, analysis and prediction of anti-microbial resistance, development and assessment of vaccines etc. PubMLST.org, developed and run by the authors, is a major open-access genomics reference database. It is an integrated collection of databases consisting of phenotype metadata linked to nucleotide sequence data including genome assemblies, for molecular typing of many bacterial species. This paper discusses visual analytics and visualization work conducted to explore and analyse Neisseria meningitidis data from PubMLST.

The bacterial core genome is of intense interest and the volume of whole genome sequence data in the public domain available to investigate it has increased dramatically. The aim of our study was to develop a model to estimate the bacterial core genome from next-generation whole genome sequencing data and use this model to identify novel genes associated with important biological functions. Five bacterial datasets were analysed, comprising 2096 genomes in total. We developed a Bayesian decision model to estimate the number of core genes, calculated pairwise evolutionary distances (p-distances) based on nucleotide sequence diversity, and plotted the median p-distance for each core gene relative to its genome location. We designed visually-informative genome diagrams to depict areas of interest in genomes. Case studies demonstrated how the model could identify areas for further study, e.g. 25% of the core genes with higher sequence diversity in the Campylobacter jejuni and Neisseria meningitidis genomes encoded hypothetical proteins. The core gene with the highest p-distance value in C. jejuni was annotated in the reference genome as a putative hydrolase, but further work revealed that it shared sequence homology with beta-lactamase/metallo-beta-lactamases (enzymes that provide resistance to a range of broad-spectrum antibiotics) and thioredoxin reductase genes (which reduce oxidative stress and are essential for DNA replication) in other C. jejuni genomes. Our Bayesian model of estimating the core genome is principled, easy to use and can be applied to large genome datasets. This study also highlighted the lack of knowledge currently available for many core genes in bacterial genomes of significant global public health importance.

BACKGROUND: The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner. RESULTS: The Bacterial Isolate Genome Sequence Database (BIGSDB) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens. The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses. Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches. LIMS functionality of the software enables linkage to and organisation of laboratory samples. The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database. Some of the applications of BIGSDB are illustrated with the genera Neisseria and Streptococcus.The BIGSDB source code and documentation are available at http://pubmlst.org/software/database/bigsdb/. CONCLUSIONS: Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies. BIGSDB represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach.

BACKGROUND: Bacterial typing schemes based on the sequences of genes encoding surface antigens require databases that provide a uniform, curated, and widely accepted nomenclature of the variants identified. Due to the differences in typing schemes, imposed by the diversity of genes targeted, creating these databases has typically required the writing of one-off code to link the database to a web interface. Here we describe agdbNet, widely applicable web database software that facilitates simultaneous BLAST querying of multiple loci using either nucleotide or peptide sequences. RESULTS: Databases are described by XML files that are parsed by a Perl CGI script. Each database can have any number of loci, which may be defined by nucleotide and/or peptide sequences. The software is currently in use on at least five public databases for the typing of Neisseria meningitidis, Campylobacter jejuni and Streptococcus equi and can be set up to query internal isolate tables or suitably-configured external isolate databases, such as those used for multilocus sequence typing. The style of the resulting website can be fully configured by modifying stylesheets and through the use of customised header and footer files that surround the output of the script. CONCLUSION: The software provides a rapid means of setting up customised Internet antigen sequence databases. The flexible configuration options enable typing schemes with differing requirements to be accommodated.

BACKGROUND: Multi-locus sequence typing (MLST) is a method of typing that facilitates the discrimination of microbial isolates by comparing the sequences of housekeeping gene fragments. The mlstdbNet software enables the implementation of distributed web-accessible MLST databases that can be linked widely over the Internet. RESULTS: The software enables multiple isolate databases to query a single profiles database that contains allelic profile and sequence definitions. This separation enables isolate databases to be established by individual laboratories, each customized to the needs of the particular project and with appropriate access restrictions, while maintaining the benefits of a single definitive source of profile and sequence information. Databases are described by an XML file that is parsed by a Perl CGI script. The software offers a large number of ways to query the databases and to further break down and export the results generated. Additional features can be enabled by installing third-party (freely available) tools. CONCLUSION: Development of a distributed structure for MLST databases offers scalability and flexibility, allowing participating centres to maintain ownership of their own data, without introducing duplication and data integrity issues.

UNLABELLED: The 32-bit Windows application START is implemented using Visual Basic and C(++) and performs analyses to aid in the investigation of bacterial population structure using multilocus sequence data. These analyses include data summary, lineage assignment, and tests for recombination and selection. AVAILABILITY: START is available at http://outbreak.ceid.ox.ac.uk/software.htm. CONTACT: [email protected]