Fast and flexible bacterial genomic epidemiology with PopPUNK

Abstract

The routine use of genomics for disease surveillance provides the opportunity for high-resolution bacterial epidemiology.However, current whole-genome clustering and multi-locus typing approaches do not fully exploit core and accessory genomic variation, and cannot both automatically identify, and subsequently expand, clusters of significantly-similar isolates in large datasets and across species.Here we describe PopPUNK (Population Partitioning Using Nucleotide K-mers; https://poppunk.readthedocs.io/en/latest/ ), software implementing scalable and expandable annotation-and alignment-free methods for population analysis and clustering.Variable-length k -mer comparisons are used to distinguish isolates' divergence in shared sequence and gene content, which we demonstrate to be accurate over multiple orders of magnitude using both simulated data and real datasets from ten taxonomically-widespread species. Connections between closely-related isolates of the same strain are robustly identified, despite variation in the discontinuous pairwise distance distributions that reflects species' diverse evolutionary patterns. PopPUNK can process 10 3 -10 4 genomes as single batch, with minimal memory use and runtimes up to 200-fold faster than existing methods. Clusters of strains remain consistent as new batches of genomes are added, which is achieved without needing to re-analyse all genomes de novo.This facilitates real-time surveillance with stable cluster naming and allows for outbreak detection using hundreds of genomes in minutes. Interactive visualisation and online publication is streamlined through automatic output of results to multiple platforms.PopPUNK has been designed as a flexible platform that addresses important issues with currently used whole-genome clustering and typing methods, and has potential uses across bacterial genetics and public health research.

Pan-genome analysis ofMycobacterium africanum: insights to dynamics and evolution

“…Additionally, the pan-genome functional analysis was carried out 120 utilizing KEGG, COG metabolic and functional pathways, which were all visualised with LibreOffice Calc plot functions. Panaroo (26) and PopPUNK (27) were also employed for pangenome investigation of gene profiles and clustering core and accessory genomes.…”

The effect of recombination on the evolution of a population ofNeisseria meningitidis

“…To assess the population structure of the collection of carrier isolates, we clustered the sequence assemblies using Pop-PUNK (Lees et al, 2019). For 4 clusters among the 9 largest carried forward for further analysis, an Oxford Nanopore sequenced reference was not available.…”

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

“…Bacterial populations vary greatly in their sequence content, and mapping short variation within their core genes (coding sequences shared by all members of the population) is generally insufficient to capture all of the variation within the samples. In particular, accessory gene content has been shown to both vary independently of core variation (32) , associated with clinically relevant phenotypes (12,33) , and be useful for predicting the evolution of the population (34,35) .…”

“…Any method which produces discrete cluster membership definitions independent of cluster frequency is suitable for this purpose, such as sequence type, clonal complex or percentage identity cutoff. We opted to use the 'strain' definition provided by the PopPUNK software throughout our analysis due to its speed and biological basis (32) , but our implementation allows any preferred definition of cluster membership to be used.…”

“…Where a split into training and test data was needed, this was done at random in the ratio 2:1. When including a cluster assignment to account for population structure we used the previous assignments from PopPUNK where available (32) . For TB we used the major lineage.…”