PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria

Abstract

Background Cataloguing the distribution of genes within natural bacterial populations is essential for understanding evolutionary processes and the genetic basis of adaptation. Advances in whole genome sequencing technologies have led to a vast expansion in the amount of bacterial genomes deposited in public databases. There is a pressing need for software solutions which are able to cluster, catalogue and characterise genes, or other features, in increasingly large genomic datasets. Results Here we present a pangenomics toolbox, PIRATE (Pangenome Iterative Refinement and Threshold Evaluation), which identifies and classifies orthologous gene families in bacterial pangenomes over a wide range of sequence similarity thresholds. PIRATE builds upon recent scalable software developments to allow for the rapid interrogation of thousands of isolates. PIRATE clusters genes (or other annotated features) over a wide range of amino acid or nucleotide identity thresholds and uses the clustering information to rapidly identify paralogous gene families and putative fission/fusion events. Furthermore, PIRATE orders the pangenome using a directed graph, provides a measure of allelic variation, and estimates sequence divergence for each gene family. Conclusions We demonstrate that PIRATE scales linearly with both number of samples and computation resources, allowing for analysis of large genomic datasets, and compares favorably to other popular tools. PIRATE provides a robust framework for analysing bacterial pangenomes, from largely clonal to panmictic species.

Precise Species Identification and Taxonomy Update for the Genus Kluyvera With Reporting Kluyvera sichuanensis sp. nov.

“…Bar, value indicates the nucleotide substitution per site. PIRATE v1.0.4 (Bayliss et al, 2019) with default settings and were defined as core genes (n = 684). Nucleotide sequences of core genes were aligned and concatenated using MAFFT v7.313 (Katoh et al, 2002) and AMAS v0.98 (Borowiec, 2016) prior to being fed into RAxML v8.2.12 (Stamatakis, 2014) for inferring a maximum-likelihood phylogenomic tree (Figure 2) under GTRGAMMA model with a 1,000-bootstrap test.…”

“…Genomes labeled as Kluyvera and those of type strains of other species in Enterobacteriaceae ( Supplementary Table S1) were annotated using Prokka v1.14.6 (Seemann, 2014) prior to being fed into PIRATE v1.0.4 (Bayliss et al, 2019) with 85% amino acid identity as the cutoff for comparative genomic analysis. Universal primers to amplify the complete sequence of the ferric reductase-encoding gene fes from all Kluyvera genomes were designed manually by examining the upstream and downstream sequences.…”

Accurate reconstruction of bacterial pan- and core genomes with PEPPAN

“…A separate tool, PEPPAN_parser, generates analyses of the estimated pangenome based on the GFF3 outputs from PEPPAN (details can be found at https://github.com/ zheminzhou/PEPPAN/blob/master/docs/source/usage/outputs .rst). Similar to Roary (Page et al 2015) and PIRATE (Bayliss et al 2019), these include rarefaction curves, gene presence matrices, and gene presence trees. In addition, PEPPAN_parser can also calculate a core genome tree based on allelic differences of genes that are conserved in most genomes.…”

“…We assessed the absolute performance of PEPPAN and compared it with other, recently described pipelines for pangenome construction (Roary [Page et al 2015]; panX [Ding et al 2018]; PIRATE [Bayliss et al 2019]), as well as with a classical, small-scale pipeline (OrthoMCL [Li et al 2003]). It is important to examine multiple aspects of genomic diversity for these comparisons because the evolutionary history of bacterial pangenomes can be highly complex.…”

“…However, such large numbers of genomes exacerbate the scalability problem. Fortunately, at least three recent pipelines (Roary [Page et al 2015]; panX [Ding et al 2018]; PIRATE [Bayliss et al 2019]) exist for constructing pangenomes from large and representative data sets (Alikhan et al 2018;Ding et al 2018).…”