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Tonkin-Hill G, Lees JA, Bentley SD, Frost SDW, Corander J. Fast hierarchical Bayesian analysis of population structure. Nucleic Acids Res 2019; 47:5539-5549. [PMID: 31076776 PMCID: PMC6582336 DOI: 10.1093/nar/gkz361] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 04/29/2019] [Indexed: 12/16/2022] Open
Abstract
We present fastbaps, a fast solution to the genetic clustering problem. Fastbaps rapidly identifies an approximate fit to a Dirichlet process mixture model (DPM) for clustering multilocus genotype data. Our efficient model-based clustering approach is able to cluster datasets 10-100 times larger than the existing model-based methods, which we demonstrate by analyzing an alignment of over 110 000 sequences of HIV-1 pol genes. We also provide a method for rapidly partitioning an existing hierarchy in order to maximize the DPM model marginal likelihood, allowing us to split phylogenetic trees into clades and subclades using a population genomic model. Extensive tests on simulated data as well as a diverse set of real bacterial and viral datasets show that fastbaps provides comparable or improved solutions to previous model-based methods, while being significantly faster. The method is made freely available under an open source MIT licence as an easy to use R package at https://github.com/gtonkinhill/fastbaps.
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Affiliation(s)
- Gerry Tonkin-Hill
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - John A Lees
- Department of Microbiology, New York University School of Medicine, NY 10016, USA
| | - Stephen D Bentley
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
| | - Simon D W Frost
- Department of Veterinary Medicine, University of Cambridge, Cambridge, CB3 0ES, UK
- The Alan Turing Institute, London, NW1 2DB, UK
| | - Jukka Corander
- Parasites and Microbes, Wellcome Sanger Institute, Cambridge, CB10 1SA, UK
- Department of Biostatistics, University of Oslo, Blindern 0317, Norway
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Aalto FI-00076, Finland
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Lees JA, Harris SR, Tonkin-Hill G, Gladstone RA, Lo SW, Weiser JN, Corander J, Bentley SD, Croucher NJ. Fast and flexible bacterial genomic epidemiology with PopPUNK. Genome Res 2019; 29:304-316. [PMID: 30679308 PMCID: PMC6360808 DOI: 10.1101/gr.241455.118] [Citation(s) in RCA: 187] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 12/10/2018] [Indexed: 12/02/2022]
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 103–104 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.
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Affiliation(s)
- John A Lees
- Department of Microbiology, New York University School of Medicine, New York, New York 10016, USA
| | - Simon R Harris
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Gerry Tonkin-Hill
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Rebecca A Gladstone
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Stephanie W Lo
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom
| | - Jeffrey N Weiser
- Department of Microbiology, New York University School of Medicine, New York, New York 10016, USA
| | - Jukka Corander
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom.,Department of Biostatistics, University of Oslo, 0372 Oslo, Norway.,Helsinki Institute of Information Technology, Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
| | - Stephen D Bentley
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, United Kingdom.,Institute of Infection and Global Health, University of Liverpool, Liverpool L7 3EA, United Kingdom.,Department of Pathology, University of Cambridge, Cambridge CB2 1QP, United Kingdom
| | - Nicholas J Croucher
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, United Kingdom
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