A graph-based approach for the visualisation and analysis of bacterial pangenomes

Abstract

Background The advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. Results Starting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. Conclusions We anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

Contingency, repeatability, and predictability in the evolution of a prokaryotic pangenome

“…Several software programs have been developed to find coevolving gene pairs and to infer coevolving modules ( 17 – 22 ). However, gene presence or absence in a genome may be influenced by a mix of positive and negative intragenomic effects beyond just pairwise correlations.…”

Temporal Dynamics of Genetically Heterogeneous Extended-Spectrum Cephalosporin ResistantEscherichia coliBloodstream Infections

“…We performed a principal component analysis of this distance matrix using base R functions. The graph network analysis was accomplished using the same gene_presence_absence.csv input using graphia (56, 57). Edge transformations using a k-nearest neighbors algorithm (n=8) were employed to reduce the number of edges from 44k to 1724.…”

Multi-genome synteny detection using minimizer graph mappings

“…From pangenomics initiatives, such as the Human Pangenome Project 2 , to programs assembling a wide variety of non-model organisms, like the Earth BioGenome Project 3 , researchers now have unprecedented access to high-quality reference genomes. Robust bioinformatics tools are crucial to leverage this influx of data for comparative genomics studies, which can enable important insights into genomic influences on phenotypes, genomic diversity, and genome synteny [4][5][6][7] .…”

“…From pangenomics initiatives, such as the Human Pangenome Project 2 , to programs assembling a wide variety of non-model organisms, like the Earth BioGenome Project 3 , researchers now have unprecedented access to high-quality reference genomes. Robust bioinformatics tools are crucial to leverage this influx of data for comparative genomics studies, which can enable important insights into genomic influences on phenotypes, genomic diversity, and genome synteny [4][5][6][7] .Genome synteny studies center on analyzing the conservation of genome structure within or between species [8][9][10][11] . The analysis can focus on large-scale macrosynteny, which tolerates smaller rearrangements within synteny blocks, or more fine-grained microsynteny [12][13][14] .…”