1887

Abstract

Visualization is frequently used to aid our interpretation of complex datasets. Within microbial genomics, visualizing the relationships between multiple genomes as a tree provides a framework onto which associated data (geographical, temporal, phenotypic and epidemiological) are added to generate hypotheses and to explore the dynamics of the system under investigation. Selected static images are then used within publications to highlight the key findings to a wider audience. However, these images are a very inadequate way of exploring and interpreting the richness of the data. There is, therefore, a need for flexible, interactive software that presents the population genomic outputs and associated data in a user-friendly manner for a wide range of end users, from trained bioinformaticians to front-line epidemiologists and health workers. Here, we present Microreact, a web application for the easy visualization of datasets consisting of any combination of trees, geographical, temporal and associated metadata. Data files can be uploaded to Microreact directly via the web browser or by linking to their location (e.g. from Google Drive/Dropbox or via API), and an integrated visualization via trees, maps, timelines and tables provides interactive querying of the data. The visualization can be shared as a permanent web link among collaborators, or embedded within publications to enable readers to explore and download the data. Microreact can act as an end point for any tool or bioinformatic pipeline that ultimately generates a tree, and provides a simple, yet powerful, visualization method that will aid research and discovery and the open sharing of datasets.

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/content/journal/mgen/10.1099/mgen.0.000093
2016-11-30
2024-03-19
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References

  1. Aanensen D. M., Feil E. J., Holden M. T., Dordel J., Yeats C. A., Fedosejev A., Goater R., Castillo-Ramírez S., Corander J. et al. 2016; Whole-genome sequencing for routine pathogen surveillance in public health: a population snapshot of invasive Staphylococcus aureus in Europe. MBio 7:e00444-16 [View Article][PubMed]
    [Google Scholar]
  2. Cochrane G., Karsch-Mizrachi I., Takagi T. International Nucleotide Sequence Database Collaboration 2016; The International Nucleotide Sequence Database Collaboration. Nucleic Acids Res 44:D48–D50 [View Article][PubMed]
    [Google Scholar]
  3. Crellen T., Allan F., David S., Durrant C., Huckvale T., Holroyd N., Emery A. M., Rollinson D., Aanensen D. M. et al. 2016; Whole genome resequencing of the human parasite Schistosoma mansoni reveals population history and effects of selection. Sci Rep 6:20954 [View Article][PubMed]
    [Google Scholar]
  4. Croucher N. J., Finkelstein J. A., Pelton S. I., Parkhill J., Bentley S. D., Lipsitch M., Hanage W. P. 2015; Population genomic datasets describing the post-vaccine evolutionary epidemiology of Streptococcus pneumoniae . Sci Data 2:150058 [View Article][PubMed]
    [Google Scholar]
  5. Currie T. E., Meade A., Guillon M., Mace R. 2013; Cultural phylogeography of the Bantu languages of sub-Saharan Africa. Proc Biol Sci 280:20130695 [View Article][PubMed]
    [Google Scholar]
  6. Faria N. R., Azevedo R. S., Kraemer M. U., Souza R., Cunha M. S., Hill S. C., Thézé J., Bonsall M. B., Bowden T. A. et al. 2016; Zika virus in the Americas: early epidemiological and genetic findings. Science 352:345–349 [View Article][PubMed]
    [Google Scholar]
  7. Gardy J., Loman N. J., Rambaut A. 2015; Real-time digital pathogen surveillance – the time is now. Genome Biol 16:155 [View Article][PubMed]
    [Google Scholar]
  8. Gibson R., Alako B., Amid C., Cerdeño-Tárraga A., Cleland I., Goodgame N., Ten Hoopen P., Jayathilaka S., Kay S. et al. 2016; Biocuration of functional annotation at the European nucleotide archive. Nucleic Acids Res 44:D58–D66 [View Article][PubMed]
    [Google Scholar]
  9. Goodwin S., McPherson J. D., McCombie W. R. 2016; Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17:333–351 [View Article][PubMed]
    [Google Scholar]
  10. Hallast P., Batini C., Zadik D., Maisano Delser P., Wetton J. H., Arroyo-Pardo E., Cavalleri G. L., de Knijff P., Destro Bisol G. et al. 2015; The Y-chromosome tree bursts into leaf: 13 000 high-confidence SNPs covering the majority of known clades. Mol Biol Evol 32:661–673 [View Article][PubMed]
    [Google Scholar]
  11. Imamura H., Downing T., Van den Broeck F., Sanders M. J., Rijal S., Sundar S., Mannaert A., Vanaerschot M., Berg M. et al. 2016; Evolutionary genomics of epidemic visceral leishmaniasis in the Indian subcontinent. Elife 5:e12613 [View Article][PubMed]
    [Google Scholar]
  12. Knetsch C. W., Connor T. R., Mutreja A., van Dorp S. M., Sanders I. M., Browne H. P., Harris D., Lipman L., Keessen E. C. et al. 2014; Whole genome sequencing reveals potential spread of Clostridium difficile between humans and farm animals in the Netherlands, 2002 to 2011. Euro Surveill 19:20954 [View Article][PubMed]
    [Google Scholar]
  13. Njamkepo E., Fawal N., Tran-Dien A., Hawkey J., Strockbine N., Jenkins C., Talukder K. A., Bercion R., Kuleshov K. et al. 2016; Global phylogeography and evolutionary history of Shigella dysenteriae type 1. Nat Microbiol 1:16027 [View Article][PubMed]
    [Google Scholar]
  14. Page A. J., Cummins C. A., Hunt M., Wong V. K., Reuter S., Holden M. T., Fookes M., Falush D., Keane J. A., Parkhill J. 2015; Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31:3691–3693 [View Article][PubMed]
    [Google Scholar]
  15. Quick J., Loman N. J., Duraffour S., Simpson J. T., Severi E., Cowley L., Bore J. A., Koundouno R., Dudas G. et al. 2016; Real-time, portable genome sequencing for Ebola surveillance. Nature 530:228–232 [View Article][PubMed]
    [Google Scholar]
  16. Reuter S., Corander J., de Been M., Harris S., Cheng L., Hall M., Thomson N. R., McNally A. 2015; Directional gene flow and ecological separation in Yersinia enterocolitica . Microbial Genomics 1:3 [View Article]
    [Google Scholar]
  17. Vernikos G., Medini D., Riley D. R., Tettelin H. 2015; Ten years of pan-genome analyses. Curr Opin Microbiol 23:148–154 [View Article][PubMed]
    [Google Scholar]
  18. Wong V. K., Baker S., Pickard D. J., Parkhill J., Page A. J., Feasey N. A., Kingsley R. A., Thomson N. R., Keane J. A. et al. 2015; Phylogeographical analysis of the dominant multidrug-resistant H58 clade of Salmonella Typhi identifies inter- and intracontinental transmission events. Nat Genet 47:632–639 [View Article][PubMed]
    [Google Scholar]
  19. Xiao J., Zhang Z., Wu J., Yu J. 2015; A brief review of software tools for pangenomics. Genomics Proteomics Bioinformatics 13:73–76 [View Article][PubMed]
    [Google Scholar]
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