+1 Recommend
1 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      PANINI: Pangenome Neighbour Identification for Bacterial Populations

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          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.

          Related collections

          Most cited references 9

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

          Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at .
            • Record: found
            • Abstract: not found
            • Article: not found

            Visualizing Datausing t-SNE

              • Record: found
              • Abstract: found
              • Article: not found

              Population genomics of post-vaccine changes in pneumococcal epidemiology

              Whole genome sequencing of 616 asymptomatically carried pneumococci was used to study the impact of the 7-valent pneumococcal conjugate vaccine. Comparison of closely related isolates revealed the role of transformation in facilitating capsule switching to non-vaccine serotypes and the emergence of drug resistance. However, such recombination was found to occur at significantly different rates across the species, and the evolution of the population was primarily driven by changes in the frequency of distinct genotypes extant pre-vaccine. These alterations resulted in little overall effect on accessory genome composition at the population level, contrasting with the fall in pneumococcal disease rates after the vaccine’s introduction.

                Author and article information

                Microb Genom
                Microb Genom
                Microbial Genomics
                Microbiology Society
                April 2019
                22 November 2018
                22 November 2018
                : 5
                : 4
                [ 1]Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus , Hinxton, UK
                [ 2]School of Veterinary Medicine, University of Surrey , Guildford, UK
                [ 3]Department of Mathematics and Statistics, Helsinki Institute of Information Technology, University of Helsinki , FI-00014 Helsinki, Finland
                [ 4]Pathogen Genomics, Wellcome Trust Sanger Institute , Hinxton, UK
                [ 5]Department of Infectious Disease Epidemiology, Imperial College London , London, UK
                [ 6]Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo , N-0317 Oslo, Norway
                [ 7]Big Data Institute, Li Ka Shing Centre for Health Informatics, University of Oxford , Oxford, UK
                Author notes
                *Correspondence: Jukka Corander, jukka.corander@ 123456medisin.uio.no

                These authors contributed equally to this work.

                © 2019 The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funded by: Wellcome Trust
                Award ID: 099202
                Funded by: Medical Research Council
                Award ID: MR/N019296/1
                Funded by: Bill and Melinda Gates Foundation
                Award ID: NTD Modelling Consortium
                Funded by: Royal Society of Biology
                Award ID: 104169/z/14/z
                Funded by: European Research Council
                Award ID: 742158
                Research Article
                Microbial Evolution and Epidemiology: Population Genomics
                Custom metadata


                Comment on this article