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Microb Genom. 2018 Nov 22. doi: 10.1099/mgen.0.000220. [Epub ahead of print]

PANINI: Pangenome Neighbour Identification for Bacterial Populations.

Author information

1
1​Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK.
2
2​School of Veterinary Medicine, University of Surrey, Guildford, UK.
3
3​Department of Mathematics and Statistics, Helsinki Institute of Information Technology, University of Helsinki, FI-00014 Helsinki, Finland.
4
4​Pathogen Genomics, Wellcome Trust Sanger Institute, Hinxton, UK.
5
5​Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
6
6​Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, N-0317 Oslo, Norway.
7
7​Big Data Institute, Li Ka Shing Centre for Health Informatics, University of Oxford, Oxford, UK.

Abstract

The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, 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 (Pangenome Neighbour Identification for Bacterial Populations), a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding based on the t-SNE (t-distributed stochastic neighbour embedding) algorithm. panini is browser-based and integrates with the Microreact platform for rapid online visualization and exploration of both core and accessory genome evolutionary signals, together with relevant epidemiological, geographical, temporal and other metadata. Several case studies with single- and multi-clone pneumococcal populations are presented to demonstrate the ability to identify biologically important signals from gene content data. panini is available at http://panini.pathogen.watch and code at http://gitlab.com/cgps/panini.

PMID:
30465642
DOI:
10.1099/mgen.0.000220
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