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PANINI: Pangenome Neighbor Identification for Bacterial Populations

Khalil Abudahab, Joaquín M. Prada, Zhirong Yang, Stephen D. Bentley, Nicholas J. Croucher, Jukka Corander, David M. Aanensen
doi: https://doi.org/10.1101/174409
Khalil Abudahab
1Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Cambridgeshire, UK
2Department of Infectious Disease Epidemiology, Imperial College London, W2 1PJ, UK
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Joaquín M. Prada
3Mathematics Institute, University of Warwick, Coventry, CV4 7AL
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Zhirong Yang
4HIIT, Helsinki Institute of Information Technology, Department of Mathematics and Statistics, University of Helsinki, FI-00014 Helsinki, Finland
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Stephen D. Bentley
5Pathogen Genomics, Wellcome Trust Sanger Institute
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Nicholas J. Croucher
2Department of Infectious Disease Epidemiology, Imperial College London, W2 1PJ, UK
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Jukka Corander
4HIIT, Helsinki Institute of Information Technology, Department of Mathematics and Statistics, University of Helsinki, FI-00014 Helsinki, Finland
5Pathogen Genomics, Wellcome Trust Sanger Institute
6Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, N-0317 Oslo, Norway
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David M. Aanensen
1Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Cambridgeshire, UK
2Department of Infectious Disease Epidemiology, Imperial College London, W2 1PJ, UK
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ABSTRACT

The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, in the current era of population genomics, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here we introduce PANINI, a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding. PANINI is browser-based and integrates with the Microreact platform for rapid online visualisation and exploration of both core and accessory genome evolutionary signals together with relevant epidemiological, geographic, temporal and other metadata. Several case studies with single-and multi-clone pneumococcal populations are presented to demonstrate ability to identify biologically important signals from gene content data. PANINI is available at http://panini.wgsa.net/ and code at http://gitlab.com/cgps/panini

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 21, 2017.
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PANINI: Pangenome Neighbor Identification for Bacterial Populations
Khalil Abudahab, Joaquín M. Prada, Zhirong Yang, Stephen D. Bentley, Nicholas J. Croucher, Jukka Corander, David M. Aanensen
bioRxiv 174409; doi: https://doi.org/10.1101/174409
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PANINI: Pangenome Neighbor Identification for Bacterial Populations
Khalil Abudahab, Joaquín M. Prada, Zhirong Yang, Stephen D. Bentley, Nicholas J. Croucher, Jukka Corander, David M. Aanensen
bioRxiv 174409; doi: https://doi.org/10.1101/174409

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