Fast and flexible bacterial genomic epidemiology with PopPUNK
Simon R Harris,
Rebecca A. Gladstone,
Stephanie W. Lo,
Jeffrey N. Weiser,
Stephen D. Bentley,
Nicholas J Croucher
Posted 06 Jul 2018
bioRxiv DOI: 10.1101/360917 (published DOI: 10.1101/gr.241455.118)
Posted 06 Jul 2018
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 103-104 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.
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