Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications

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Abstract

The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera. Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes. Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications. The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation. Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes. There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question. In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.

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We present Neighbor-Net, a distance based method for constructing phylogenetic networks that is based on the Neighbor-Joining (NJ) algorithm of Saitou and Nei. Neighbor-Net provides a snapshot of the data that can guide more detailed analysis. Unlike split decomposition, Neighbor-Net scales well and can quickly produce detailed and informative networks for several hundred taxa. We illustrate the method by reanalyzing three published data sets: a collection of 110 highly recombinant Salmonella multi-locus sequence typing sequences, the 135 "African Eve" human mitochondrial sequences published by Vigilant et al., and a collection of 12 Archeal chaperonin sequences demonstrating strong evidence for gene conversion. Neighbor-Net is available as part of the SplitsTree4 software package.

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A decade after the beginning of the genomic era, the question of how genomics can describe a bacterial species has not been fully addressed. Experimental data have shown that in some species new genes are discovered even after sequencing the genomes of several strains. Mathematical modeling predicts that new genes will be discovered even after sequencing hundreds of genomes per species. Therefore, a bacterial species can be described by its pan-genome, which is composed of a "core genome" containing genes present in all strains, and a "dispensable genome" containing genes present in two or more strains and genes unique to single strains. Given that the number of unique genes is vast, the pan-genome of a bacterial species might be orders of magnitude larger than any single genome.

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BIGSdb: Scalable analysis of bacterial genome variation at the population level

Keith Jolley, Martin Maiden (2010)

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.