Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica

PLoS Genetics

Volumn 14, Issue 5, 2018, Pages

  Wheeler, Nicole E ^a,b   Gardner, Paul P ^b,c   Barquist, Lars ^d,e  

^a WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^b UNIVERSITY OF CANTERBURY   (New Zealand)

^c UNIVERSITY OF OTAGO   (New Zealand)

^d UNIVERSITY OF WÜRZBURG   (Germany)

^e Helmholtz Institute for RNA Based Infection Research (HIRI)   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

PENICILLIN BINDING PROTEIN; BACTERIAL PROTEIN;

ADAPTATION; AFRICA SOUTH OF THE SAHARA; ANAEROBIC METABOLISM; ARTICLE; BACTERIAL GENE; BACTERIAL GENOME; BACTERIAL LOAD; BACTERIAL METABOLISM; BACTERIAL MUTATION; BACTERIAL STRAIN; BACTERIUM ISOLATE; CLASSIFIER; DECISION TREE; DIVERSIFYING SELECTION; GASTROINTESTINAL INFECTION; GENE IDENTIFICATION; GENE MUTATION; HOST RANGE; IMMUNOCOMPROMISED PATIENT; MOLECULAR PHYLOGENY; MUTATION ACCUMULATION; NONHUMAN; ORTHOLOGY; PATHOTYPE; RANDOM FOREST; SALMONELLA ENTERICA; SALMONELLA ENTERICA SEROVAR DUBLIN; SALMONELLA ENTERICA SEROVAR ENTERITIDIS; SALMONELLA ENTERICA SEROVAR TYPHIMURIUM; SALMONELLOSIS; SYSTEMIC DISEASE; ANIMAL; ANIMAL SALMONELLOSIS; CLASSIFICATION; GENETICS; HUMAN; MACHINE LEARNING; MICROBIOLOGY; MUTATION; PATHOGENICITY; PHYLOGENY; VIRULENCE;

ADAPTATION, PHYSIOLOGICAL; ANIMALS; BACTERIAL PROTEINS; HOST SPECIFICITY; HUMANS; MACHINE LEARNING; MUTATION; PHYLOGENY; SALMONELLA ENTERICA; SALMONELLA INFECTIONS; SALMONELLA INFECTIONS, ANIMAL; VIRULENCE;

EID: 85048244156     PISSN: 15537390     EISSN: 15537404     Source Type: Journal    
DOI: 10.1371/journal.pgen.1007333     Document Type: Article

References (84)

1. Frank SA, Schmid-Hempel P, Mechanisms of pathogenesis and the evolution of parasite virulence. J Evol Biol. 2008;21: 396–404. doi: 10.1111/j.1420-9101.2007.01480.x 18179516
2. Fauci AS, Morens DM, The perpetual challenge of infectious diseases. N Engl J Med. 2012;366: 454–461. doi: 10.1056/NEJMra1108296 22296079
3. Pallen MJ, Wren BW, Bacterial pathogenomics. Nature. nature.com; 2007;449: 835–842. doi: 10.1038/nature06248 17943120
4. Loman NJ, Pallen MJ, Twenty years of bacterial genome sequencing. Nat Rev Microbiol. 2015;13: 787–794. doi: 10.1038/nrmicro3565 26548914
5. McNally A, Thomson NR, Reuter S, Wren BW, “Add, stir and reduce”: Yersinia spp. as model bacteria for pathogen evolution. Nat Rev Microbiol. 2016;14: 177–190. doi: 10.1038/nrmicro.2015.29 26876035
6. The HC, Thanh DP, Holt KE, Thomson NR, Baker S, The genomic signatures of Shigella evolution, adaptation and geographical spread. Nat Rev Microbiol. nature.com; 2016; doi: 10.1038/nrmicro.2016.10 26923111
7. Merhej V, Georgiades K, Raoult D, Postgenomic analysis of bacterial pathogens repertoire reveals genome reduction rather than virulence factors. Brief Funct Genomics. 2013;12: 291–304. doi: 10.1093/bfgp/elt015 23814139
8. Reuter S, Connor TR, Barquist L, Walker D, Feltwell T, Harris SR, et al. Parallel independent evolution of pathogenicity within the genus Yersinia. Proc Natl Acad Sci U S A. 2014;111: 6768–6773. doi: 10.1073/pnas.1317161111 24753568
9. Marvig RL, Sommer LM, Molin S, Johansen HK, Convergent evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nat Genet. 2015;47: 57–64. doi: 10.1038/ng.3148 25401299
10. Klemm EJ, Gkrania-Klotsas E, Hadfield J, Forbester JL, Harris SR, Hale C, et al. Emergence of host-adapted Salmonella Enteritidis through rapid evolution in an immunocompromised host. Nat Microbiol. 2016;1: 15023.
11. Feasey NA, Dougan G, Kingsley RA, Heyderman RS, Gordon MA, Invasive non-typhoidal salmonella disease: an emerging and neglected tropical disease in Africa. Lancet. 2012;379: 2489–2499. doi: 10.1016/S0140-6736(11)61752-2 22587967
12. Rabsch W, Andrews HL, Kingsley RA, Prager R, Tschäpe H, Adams LG, et al. Salmonella enterica serotype Typhimurium and its host-adapted variants. Infect Immun. 2002;70: 2249–2255. doi: 10.1128/IAI.70.5.2249-2255.2002 11953356
13. Bäumler A, Fang FC, Host specificity of bacterial pathogens. Cold Spring Harb Perspect Med. 2013;3: a010041. doi: 10.1101/cshperspect.a010041 24296346
14. Parkhill J, Dougan G, James KD, Thomson NR, Pickard D, Wain J, et al. Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18. Nature. 2001;413: 848–852. doi: 10.1038/35101607 11677608
15. McClelland M, Sanderson KE, Clifton SW, Latreille P, Porwollik S, Sabo A, et al. Comparison of genome degradation in Paratyphi A and Typhi, human-restricted serovars of Salmonella enterica that cause typhoid. Nat Genet. 2004;36: 1268–1274. doi: 10.1038/ng1470 15531882
16. Thomson NR, Clayton DJ, Windhorst D, Vernikos G, Davidson S, Churcher C, et al. Comparative genome analysis of Salmonella Enteritidis PT4 and Salmonella Gallinarum 287/91 provides insights into evolutionary and host adaptation pathways. Genome Res. 2008;18: 1624–1637. doi: 10.1101/gr.077404.108 18583645
17. Nuccio S-P, Bäumler AJ, Comparative Analysis of Salmonella Genomes Identifies a Metabolic Network for Escalating Growth in the Inflamed Gut. MBio. 2014;5: e00929–14–e00929–14. doi: 10.1128/mBio.00929-14 24643865
18. Langridge GC, Fookes M, Connor TR, Feltwell T, Feasey N, Parsons BN, et al. Patterns of genome evolution that have accompanied host adaptation in Salmonella. Proc Natl Acad Sci U S A. 2015;112: 863–868. doi: 10.1073/pnas.1416707112 25535353
19. Lerat E, Ochman H, Recognizing the pseudogenes in bacterial genomes. Nucleic Acids Res. 2005;33: 3125–3132. doi: 10.1093/nar/gki631 15933207
20. Kuo C-H, Ochman H, The extinction dynamics of bacterial pseudogenes. PLoS Genet. 2010;6. doi: 10.1371/journal.pgen.1001050 20700439
21. Wheeler NE, Barquist L, Kingsley RA, Gardner PP, A profile-based method for identifying functional divergence of orthologous genes in bacterial genomes. Bioinformatics. 2016;32: 3566–3574. doi: 10.1093/bioinformatics/btw518 27503221
22. Kingsley RA, Kay S, Connor T, Barquist L, Sait L, Holt KE, et al. Genome and transcriptome adaptation accompanying emergence of the definitive type 2 host-restricted Salmonella enterica serovar Typhimurium pathovar. MBio. 2013;4: e00565–13. doi: 10.1128/mBio.00565-13 23982073
23. Dutilh BE, Backus L, Edwards RA, Wels M, Bayjanov JR, van Hijum SAFT, Explaining microbial phenotypes on a genomic scale: GWAS for microbes. Brief Funct Genomics. 2013;12: 366–380. doi: 10.1093/bfgp/elt008 23625995
24. Pappu V, Pardalos PM, Aleskerov F, Goldengorin B, Pardalos PM, High-Dimensional Data Classification. In: Clusters, Orders, and Trees: Methods and Applications. Springer New York; 2014. pp. 119–150.
25. Touw WG, Bayjanov JR, Overmars L, Backus L, Boekhorst J, Wels M, et al. Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Brief Bioinform. 2013;14: 315–326. doi: 10.1093/bib/bbs034 22786785
26. Wei W-H, Hemani G, Haley CS, Detecting epistasis in human complex traits. Nat Rev Genet. 2014;15: 722–733. doi: 10.1038/nrg3747 25200660
27. Bayjanov JR, Molenaar D, Tzeneva V, Siezen RJ, van Hijum SAFT, PhenoLink—a web-tool for linking phenotype to ~omics data for bacteria: application to gene-trait matching for Lactobacillus plantarum strains. BMC Genomics. 2012;13: 170. doi: 10.1186/1471-2164-13-170 22559291
28. Laabei M, Recker M, Rudkin JK, Aldeljawi M, Gulay Z, Sloan TJ, et al. Predicting the virulence of MRSA from its genome sequence. Genome Res. 2014;24: 839–849. doi: 10.1101/gr.165415.113 24717264
29. Alam MT, Petit RA, 3rdCrispell EK, Thornton TA, Conneely KN, Jiang Y, et al. Dissecting vancomycin-intermediate resistance in staphylococcus aureus using genome-wide association. Genome Biol Evol. 2014;6: 1174–1185. doi: 10.1093/gbe/evu092 24787619
30. Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44: D286–93. doi: 10.1093/nar/gkv1248 26582926
31. Breiman L, Random Forests. Mach Learn. Kluwer Academic Publishers; 2001;45: 5–32.
32. Tolosi L, Lengauer T, Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics. 2011;27: 1986–1994. doi: 10.1093/bioinformatics/btr300 21576180
33. Kthiri F, Gautier V, Le H-T, Prère M-F, Fayet O, Malki A, et al. Translational defects in a mutant deficient in YajL, the bacterial homolog of the parkinsonism-associated protein DJ-1. J Bacteriol. 2010;192: 6302–6306. doi: 10.1128/JB.01077-10 20889753
34. Le H-T, Gautier V, Kthiri F, Malki A, Messaoudi N, Mihoub M, et al. YajL, prokaryotic homolog of parkinsonism-associated protein DJ-1, functions as a covalent chaperone for thiol proteome. J Biol Chem. 2012;287: 5861–5870. doi: 10.1074/jbc.M111.299198 22157000
35. Roth JR, Lawrence JG, Bobik TA, Cobalamin (coenzyme B12): synthesis and biological significance. Annu Rev Microbiol. 1996;50: 137–181. doi: 10.1146/annurev.micro.50.1.137 8905078
36. Phan G, Remaut H, Wang T, Allen WJ, Pirker KF, Lebedev A, et al. Crystal structure of the FimD usher bound to its cognate FimC-FimH substrate. Nature. 2011;474: 49–53. doi: 10.1038/nature10109 21637253
37. Typas A, Banzhaf M, Gross CA, Vollmer W, From the regulation of peptidoglycan synthesis to bacterial growth and morphology. Nat Rev Microbiol. ncbi.nlm.nih.gov; 2011;10: 123–136. doi: 10.1038/nrmicro2677 22203377
38. Pepper ED, Farrell MJ, Finkel SE, Role of penicillin-binding protein 1b in competitive stationary-phase survival of Escherichia coli. FEMS Microbiol Lett. 2006;263: 61–67. doi: 10.1111/j.1574-6968.2006.00418.x 16958852
39. Langridge GC, Phan M-D, Turner DJ, Perkins TT, Parts L, Haase J, et al. Simultaneous assay of every Salmonella Typhi gene using one million transposon mutants. Genome Res. 2009;19: 2308–2316. doi: 10.1101/gr.097097.109 19826075
40. Crawford RW, Rosales-Reyes R, Ramírez-Aguilar M de la L, Chapa-Azuela O, Alpuche-Aranda C, Gunn JS, Gallstones play a significant role in Salmonella spp. gallbladder colonization and carriage. Proc Natl Acad Sci U S A. 2010;107: 4353–4358. doi: 10.1073/pnas.1000862107 20176950
41. Blondel CJ, Jiménez JC, Contreras I, Santiviago CA, Comparative genomic analysis uncovers 3 novel loci encoding type six secretion systems differentially distributed in Salmonella serotypes. BMC Genomics. 2009;10: 354. doi: 10.1186/1471-2164-10-354 19653904
42. Blondel CJ, Jiménez JC, Leiva LE, Alvarez SA, Pinto BI, Contreras F, et al. The type VI secretion system encoded in Salmonella pathogenicity island 19 is required for Salmonella enterica serotype Gallinarum survival within infected macrophages. Infect Immun. 2013;81: 1207–1220. doi: 10.1128/IAI.01165-12 23357385
43. Mulder DT, Cooper CA, Coombes BK, Type VI secretion system-associated gene clusters contribute to pathogenesis of Salmonella enterica serovar Typhimurium. Infect Immun. Am Soc Microbiol; 2012;80: 1996–2007.
44. Kingsley RA, Bäumler AJ, Host adaptation and the emergence of infectious disease: the Salmonella paradigm. Mol Microbiol. 2000;36: 1006–1014. 10844686
45. Harvey RR, Friedman CR, Crim SM, Judd M, Barrett KA, Tolar B, et al. Epidemiology of Salmonella enterica Serotype Dublin Infections among Humans, United States, 1968–2013. Emerging Infectious Disease journal. 2017;23: 1493.
46. Kingsley RA, Msefula CL, Thomson NR, Kariuki S, Holt KE, Gordon MA, et al. Epidemic multiple drug resistant Salmonella Typhimurium causing invasive disease in sub-Saharan Africa have a distinct genotype. Genome Res. 2009;19: 2279–2287. doi: 10.1101/gr.091017.109 19901036
47. Okoro CK, Kingsley RA, Connor TR, Harris SR, Parry CM, Al-Mashhadani MN, et al. Intracontinental spread of human invasive Salmonella Typhimurium pathovariants in sub-Saharan Africa. Nat Genet. 2012;44: 1215–1221. doi: 10.1038/ng.2423 23023330
48. Feasey NA, Hadfield J, Keddy KH, Dallman TJ, Jacobs J, Deng X, et al. Distinct Salmonella Enteritidis lineages associated with enterocolitis in high-income settings and invasive disease in low-income settings. Nat Genet. 2016;48: 1211–1217. doi: 10.1038/ng.3644 27548315
49. Uche IV, MacLennan CA, Saul A, A Systematic Review of the Incidence, Risk Factors and Case Fatality Rates of Invasive Nontyphoidal Salmonella (iNTS) Disease in Africa (1966 to 2014). PLoS Negl Trop Dis. 2017;11: e0005118. doi: 10.1371/journal.pntd.0005118 28056035
50. Ao TT, Feasey NA, Gordon MA, Heddy KH, Angulo FJ, Crump JA, Global Burden of Invasive Nontyphoidal Salmonella Disease, 20101. Emerging Infectious Disease journal. 2015;21: 941.
51. Okoro CK, Barquist L, Connor TR, Harris SR, Clare S, Stevens MP, et al. Signatures of Adaptation in Human Invasive Salmonella Typhimurium ST313 Populations from Sub-Saharan Africa. PLoS Negl Trop Dis. 2015;9: e0003611. doi: 10.1371/journal.pntd.0003611 25803844
52. Parsons BN, Humphrey S, Salisbury AM, Mikoleit J, Hinton JCD, Gordon MA, et al. Invasive non-typhoidal Salmonella typhimurium ST313 are not host-restricted and have an invasive phenotype in experimentally infected chickens. PLoS Negl Trop Dis. journals.plos.org; 2013;7: e2487. doi: 10.1371/journal.pntd.0002487 24130915
53. Ramachandran G, Panda A, Higginson EE, Ateh E, Lipsky MM, Sen S, et al. Virulence of invasive Salmonella Typhimurium ST313 in animal models of infection. PLoS Negl Trop Dis. 2017;11: e0005697. doi: 10.1371/journal.pntd.0005697 28783750
54. Ramachandran G, Perkins DJ, Schmidlein PJ, Tulapurkar ME, Tennant SM, Invasive Salmonella Typhimurium ST313 with naturally attenuated flagellin elicits reduced inflammation and replicates within macrophages. PLoS Negl Trop Dis. 2015;9: e3394. doi: 10.1371/journal.pntd.0003394 25569606
55. Carden S, Okoro C, Dougan G, Monack D, Non-typhoidal Salmonella Typhimurium ST313 isolates that cause bacteremia in humans stimulate less inflammasome activation than ST19 isolates associated with gastroenteritis. Pathog Dis. 2015;73. doi: 10.1093/femspd/ftu023 25808600
56. Singletary LA, Karlinsey JE, Libby SJ, Mooney JP, Lokken KL, Tsolis RM, et al. Loss of Multicellular Behavior in Epidemic African Nontyphoidal Salmonella enterica Serovar Typhimurium ST313 Strain D23580. MBio. 2016;7. doi: 10.1128/mBio.02265-15 26933058
57. Carden SE, Walker GT, Honeycutt J, Lugo K, Pham T, Jacobson A, et al. Pseudogenization of the Secreted Effector Gene sseI Confers Rapid Systemic Dissemination of S. Typhimurium ST313 within Migratory Dendritic Cells. Cell Host Microbe. 2017;21: 182–194. doi: 10.1016/j.chom.2017.01.009 28182950
58. Ashton PM, Owen SV, Kaindama L, Rowe WPM, Lane C, Larkin L, et al. Salmonella enterica Serovar Typhimurium ST313 Responsible For Gastroenteritis In The UK Are Genetically Distinct From Isolates Causing Bloodstream Infections In Africa [Internet]. bioRxiv. 2017. p. 139576. doi: 10.1101/139576
59. Almeida F, Seribelli AA, da Silva P, Medeiros MIC, Dos Prazeres Rodrigues D, Moreira CG, et al. Multilocus sequence typing of Salmonella Typhimurium reveals the presence of the highly invasive ST313 in Brazil. Infect Genet Evol. 2017;51: 41–44. doi: 10.1016/j.meegid.2017.03.009 28288927
60. Painter JA, Mølbak K, Sonne-Hansen J, Barrett T, Wells JG, Tauxe RV, Salmonella-based rodenticides and public health. Emerg Infect Dis. 2004;10: 985–987. doi: 10.3201/eid1006.030790 15207046
61. Pasmans F, Van Immerseel F, Hermans K, Heyndrickx M, Collard J-M, Ducatelle R, et al. Assessment of virulence of pigeon isolates of Salmonella enterica subsp. enterica serovar typhimurium variant copenhagen for humans. J Clin Microbiol. 2004;42: 2000–2002. doi: 10.1128/JCM.42.5.2000-2002.2004 15131161
62. Lawson B, Hughes LA, Peters T, de Pinna E, John SK, Macgregor SK, et al. Pulsed-field gel electrophoresis supports the presence of host-adapted Salmonella enterica subsp. enterica serovar Typhimurium strains in the British garden bird population. Appl Environ Microbiol. 2011;77: 8139–8144. doi: 10.1128/AEM.00131-11 21948838
63. Mather AE, Lawson B, de Pinna E, Wigley P, Parkhill J, Thomson NR, et al. Genomic Analysis of Salmonella enterica Serovar Typhimurium from Wild Passerines in England and Wales. Appl Environ Microbiol. 2016;82: 6728–6735. doi: 10.1128/AEM.01660-16 27613688
64. Barrick JE, Lenski RE, Genome dynamics during experimental evolution. Nat Rev Genet. 2013;14: 827–839. doi: 10.1038/nrg3564 24166031
65. Quick J, Loman NJ, Duraffour S, Simpson JT, Severi E, Cowley L, et al. Real-time, portable genome sequencing for Ebola surveillance. Nature. 2016;530: 228–232. doi: 10.1038/nature16996 26840485
66. Aanensen DM, Feil EJ, Holden MTG, Dordel J, Yeats CA, Fedosejev A, et al. Whole-Genome Sequencing for Routine Pathogen Surveillance in Public Health: a Population Snapshot of Invasive Staphylococcus aureus in Europe. MBio. 2016;7. doi: 10.1128/mBio.00444-16 27150362
67. Schürch AC, Schaik W, Challenges and opportunities for whole-genome sequencing—based surveillance of antibiotic resistance. Ann N Y Acad Sci. Wiley Online Library; 2017;1388: 108–120. doi: 10.1111/nyas.13310 28134443
68. Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D, FaST linear mixed models for genome-wide association studies. Nat Methods. 2011;8: 833–835. doi: 10.1038/nmeth.1681 21892150
69. Lees JA, Vehkala M, Välimäki N, Harris SR, Chewapreecha C, Croucher NJ, et al. Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes. Nat Commun. 2016;7: 12797. doi: 10.1038/ncomms12797 27633831
70. Earle SG, Wu C-H, Charlesworth J, Stoesser N, Gordon NC, Walker TM, et al. Identifying lineage effects when controlling for population structure improves power in bacterial association studies. Nat Microbiol. 2016;1: 16041. doi: 10.1038/nmicrobiol.2016.41 27572646
71. Chen PE, Shapiro BJ, The advent of genome-wide association studies for bacteria. Curr Opin Microbiol. 2015;25: 17–24. doi: 10.1016/j.mib.2015.03.002 25835153
72. Power RA, Parkhill J, de Oliveira T, Microbial genome-wide association studies: lessons from human GWAS. Nat Rev Genet. 2017;18: 41–50. doi: 10.1038/nrg.2016.132 27840430
73. Lupolova N, Dallman TJ, Holden NJ, Gally DL, Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli. Microbial Genomics. Microbiology Society; 2017; doi: 10.1099/mgen.0.000135 29177093
74. Roberts DR, Bahn V, Ciuti S, Boyce MS, Elith J, Guillera-Arroita G, et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography. Blackwell Publishing Ltd; 2017;40: 913–929.
75. Stephan J, Stegle O, Beyer A, A random forest approach to capture genetic effects in the presence of population structure. Nat Commun. 2015;6: 7432. doi: 10.1038/ncomms8432 26109276
76. Stamatakis A, RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30: 1312–1313. doi: 10.1093/bioinformatics/btu033 24451623
77. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31: 3691–3693. doi: 10.1093/bioinformatics/btv421 26198102
78. Liaw A, Wiener M, Classification and regression by randomForest. R news. 2002;2: 18–22.
79. Breiman L, Friedman J, Stone CJ, Olshen RA, Classification and Regression Trees. Chapman and Hall/CRC; 1984.
80. Li H, Durbin R, Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25: 1754–1760. doi: 10.1093/bioinformatics/btp324 19451168
81. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20: 1297–1303. doi: 10.1101/gr.107524.110 20644199
82. Li H, A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27: 2987–2993. doi: 10.1093/bioinformatics/btr509 21903627
83. Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA, Bentley SD, et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 2015;43: e15. doi: 10.1093/nar/gku1196 25414349
84. Hadfield J, Croucher NJ, Goater RJ, Abudahab K, Aanensen DM, Harris SR, Phandango: an interactive viewer for bacterial population genomics. Bioinformatics. 2017; doi: 10.1093/bioinformatics/btx610 29028899