Fast and flexible bacterial genomic epidemiology with PopPUNK

Show full item record



Lees , J A , Harris , S R , Tonkin-Hill , G , Gladstone , R A , Lo , S W , Weiser , J N , Corander , J , Bentley , S D & Croucher , N J 2019 , ' Fast and flexible bacterial genomic epidemiology with PopPUNK ' , Genome Research , vol. 29 , no. 2 , pp. 304-316 .

Title: Fast and flexible bacterial genomic epidemiology with PopPUNK
Author: Lees, John A.; Harris, Simon R.; Tonkin-Hill, Gerry; Gladstone, Rebecca A.; Lo, Stephanie W.; Weiser, Jeffrey N.; Corander, Jukka; Bentley, Stephen D.; Croucher, Nicholas J.
Contributor organization: Helsinki Institute for Information Technology
Jukka Corander / Principal Investigator
Department of Mathematics and Statistics
Biostatistics Helsinki
Date: 2019-02
Language: eng
Number of pages: 13
Belongs to series: Genome Research
ISSN: 1088-9051
Abstract: The routine use of genomics for disease surveillance provides the opportunity for high-resolution bacterial epidemiology. Current whole-genome clustering and multilocus typing approaches do not fully exploit core and accessory genomic variation, and they cannot both automatically identify, and subsequently expand, clusters of significantly similar isolates in large data sets spanning entire species. Here, we describe PopPUNK (Population Partitioning Using Nucleotide K-mers), a 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 data from both simulations and genomic collections representing 10 taxonomically widespread species. Connections between closely related isolates of the same strain are robustly identified, despite interspecies variation in the pairwise distance distributions that reflects species' diverse evolutionary patterns. PopPUNK can process 10(3)-10(4) genomes in a single batch, with minimal memory use and runtimes up to 200-fold faster than existing model-based methods. Clusters of strains remain consistent as new batches of genomes are added, which is achieved without needing to reanalyze all genomes de novo. This facilitates real-time surveillance with consistent cluster naming between studies and allows for outbreak detection using hundreds of genomes in minutes. Interactive visualization and online publication is streamlined through the 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.
1182 Biochemistry, cell and molecular biology
111 Mathematics
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

Files in this item

Total number of downloads: Loading...

Files Size Format View
Genome_Res._2019_Lees_304_16.pdf 9.186Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record