1887
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.

Phylogenetic Concepts and Tools Applied to Epidemiologic Investigations of Infectious Diseases *

MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.
  • Author: Daniel Janies1
  • Editors: Lee W. Riley2, Ronald E. Blanton3
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223; 2: Divisions of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, CA; 3: Center for Global Health & Diseases, Case Western Reserve University, Cleveland, OH
  • Source: microbiolspec July 2019 vol. 7 no. 4 doi:10.1128/microbiolspec.AME-0006-2018
  • Received 13 June 2018 Accepted 10 May 2019 Published 19 July 2019
  • Daniel Janies, [email protected]
image of Phylogenetic Concepts and Tools Applied to Epidemiologic Investigations of Infectious Diseases<span class="xref">
<a href="#fn1">*</a>
</span>
    Preview this microbiology spectrum article:
    Zoom in
    Zoomout

    Phylogenetic Concepts and Tools Applied to Epidemiologic Investigations of Infectious Diseases * , Page 1 of 2

    | /docserver/preview/fulltext/microbiolspec/7/4/AME-0006-2018-1.gif /docserver/preview/fulltext/microbiolspec/7/4/AME-0006-2018-2.gif
  • Abstract:

    In this review, which is a part of the Curated Collection: Advances in Molecular Epidemiology of Infectious Diseases, I present an overview of the principles used to classify organisms in the field of phylogenetics, highlight the methods used to infer the interrelationships of organisms, and summarize how these concepts are applied to molecular epidemiologic analyses. I present steps in analyses that come downstream of the assembly of a set of genomes or genes and the production of a multiple-sequence alignment or other matrices of putative orthologs for comparison. I focus on the history of the problem of phylogenetic reconstruction and debates within the field about the most appropriate methods. I illustrate methods that bridge the gap between molecular epidemiology and traditional epidemiology, including phylogenetic character evolution and geographic visualization. Finally, I provide practical advice on how to conduct an example analysis in the appendix.

    *This article is part of a curated collection.

  • Citation: Janies D. 2019. Phylogenetic Concepts and Tools Applied to Epidemiologic Investigations of Infectious Diseases * . Microbiol Spectrum 7(4):AME-0006-2018. doi:10.1128/microbiolspec.AME-0006-2018.

References

1. Graffelman AW, Knuistingh Neven A, le Cessie S, Kroes AC, Springer MP, van den Broek PJ. 2004. Pathogens involved in lower respiratory tract infections in general practice. Br J Gen Pract 54:15–19.
2. Carroll LN, Au AP, Detwiler LT, Fu TC, Painter IS, Abernethy NF. 2014. Visualization and analytics tools for infectious disease epidemiology: a systematic review. J Biomed Inform 51:287–298. http://dx.doi.org/10.1016/j.jbi.2014.04.006. [PubMed]
3. Kong LY, Eyre D, Walker AS, Corbeil J, Wilcox M, Bourgault A-M, Dascal A, Oughton M, Michaud S, Toye B, Frost E, Poirier L, Brassard P, Turgeon N, Gilca R, Loo V. 2016. Comparison of pulsed-field gel electrophoresis and whole genome sequencing in Clostridium difficile typing. Open Forum Infect Dis 3(Suppl 1) :2063. http://dx.doi.org/10.1093/ofid/ofw172.1611.
4. Salipante SJ, SenGupta DJ, Cummings LA, Land TA, Hoogestraat DR, Cookson BT, Tang Y-W. 2015. Application of whole-genome sequencing for bacterial strain typing in molecular epidemiology. J Clin Microbiol 53:1072–1079. http://dx.doi.org/10.1128/JCM.03385-14. [PubMed]
5. Janies DA, Voronkin IO, Das M, Hardman J, Treseder TW, Studer J. 2010. Genome informatics of influenza A: from data sharing to shared analytical capabilities. Anim Health Res Rev 11:73–79. http://dx.doi.org/10.1017/S1466252310000083. [PubMed]
6. Janies D, Pomeroy L, Krueger C, Zhang Y, Senturk I, Kaya KÇ, Çatalyürek ÜV. 2015. Phylogenetic visualization of the spread of H7 influenza A viruses. Cladistics 31:679–691. http://dx.doi.org/10.1111/cla.12107.
7. Worby CJ, Lipsitch M, Hanage WP. 2014. Within-host bacterial diversity hinders accurate reconstruction of transmission networks from genomic distance data. PLOS Comput Biol 10:e1003549. http://dx.doi.org/10.1371/journal.pcbi.1003549. [PubMed]
8. Gardy JL, Brunham RC. 2010. Navigating transmission networks with genomics and phylogenetic trees. Future Virol 5:251–253. http://dx.doi.org/10.2217/fvl.10.15.
9. Hoffmann M, Luo Y, Monday SR, Gonzalez-Escalona N, Ottesen AR, Muruvanda T, Wang C, Kastanis G, Keys C, Janies D, Senturk IF, Catalyurek UV, Wang H, Hammack TS, Wolfgang WJ, Schoonmaker-Bopp D, Chu A, Myers R, Haendiges J, Evans PS, Meng J, Strain EA, Allard MW, Brown EW. 2016. Tracing origins of the Salmonella Bareilly strain causing a food-borne outbreak in the United States. J Infect Dis 213:502–508. http://dx.doi.org/10.1093/infdis/jiv297. [PubMed]
10. Grad YH, Godfrey P, Cerquiera GC, Mariani-Kurkdjian P, Gouali M, Bingen E, Shea TP, Haas BJ, Griggs A, Young S, Zeng Q, Lipsitch M, Waldor MK, Weill FX, Wortman JR, Hanage WP. 2013. Comparative genomics of recent Shiga toxin-producing Escherichia coli O104:H4: short-term evolution of an emerging pathogen. mBio 4:e00452-12. http://dx.doi.org/10.1128/mBio.00452-12. [PubMed]
11. Bokhari SH, Pomeroy LW, Janies DA. 2012. Reassortment networks and the evolution of pandemic H1N1 swine-origin influenza. IEEE/ACM Trans Comput Biol Bioinformatics 9:214–227. http://dx.doi.org/10.1109/TCBB.2011.95. [PubMed]
12. Wheeler WC. 2015. Phylogenetic network analysis as a parsimony optimization problem. BMC Bioinformatics 16:296. http://dx.doi.org/10.1186/s12859-015-0675-0. [PubMed]
13. Ahrenfeldt J, Skaarup C, Hasman H, Pedersen AG, Aarestrup FM, Lund O. 2017. Bacterial whole genome-based phylogeny: construction of a new benchmarking dataset and assessment of some existing methods. BMC Genomics 18:19. http://dx.doi.org/10.1186/s12864-016-3407-6. [PubMed]
14. Timme RE, Rand H, Shumway M, Trees EK, Simmons M, Agarwala R, Davis S, Tillman GE, Defibaugh-Chavez S, Carleton HA, Klimke WA, Katz LS. 2017. Benchmark datasets for phylogenomic pipeline validation, applications for foodborne pathogen surveillance. PeerJ 5:e3893. http://dx.doi.org/10.7717/peerj.3893. [PubMed]
15. Wheeler W. 2012. Systematics: A Course of Lectures. Wiley-Blackwell, Hoboken, NJ. http://dx.doi.org/10.1002/9781118301081.
16. Camin JH, Sokal RR. 1965. A method for deducing branching sequences in phylogeny. Evolution 19:311–326. http://dx.doi.org/10.1111/j.1558-5646.1965.tb01722.x.
17. Kluge AG, Farris JS. 1969. Quantitative phyletics and the evolution of anurans. Syst Zool 18:1–32. http://dx.doi.org/10.2307/2412407.
18. Farris JS. 1970. Methods for computing Wagner trees. Syst Biol 19:83–92. http://dx.doi.org/10.1093/sysbio/19.1.83.
19. Fitch WM. 1971. Toward defining the course of evolution: minimal change for a specific tree topology. Syst Zool 20:406–416. http://dx.doi.org/10.2307/2412116.
20. Farris JS. 1977. Phylogenetic analysis under Dollo’s law. Syst Zool 26:77–88. http://dx.doi.org/10.2307/2412867.
21. Felsenstein J. 1983. Parsimony in systematics: biological and statistical issues. Annu Rev Ecol Syst 14:313–333. http://dx.doi.org/10.1146/annurev.es.14.110183.001525.
22. Goldman N. 1990. Maximum likelihood of phylogenetic trees, with special reference to Poisson process models of DNA substitution and to parsimony analysis. Syst Zool 39:345–361. http://dx.doi.org/10.2307/2992355.
23. Hillis DM, Moritz C, Mable BK. 1996. Molecular Systematics, 2nd ed. Sinauer Associates, Inc, Sunderland, MA.
24. Hillis DM, Moritz C, Mable BK (ed). 1996. Molecular systematics, second edition. Sinauer Associates, Inc, Sunderland, MA.
25. Graur D, Wen-Hsiung L. 2000. Fundamentals of Molecular Evolution, 2nd ed. Sinauer Associates, Inc, Sunderland, MA.
26. Nei M, Kumar S. 2000. Molecular Evolution and Phylogenetics. Oxford University Press, New York, NY.
27. Felsenstein J. 2004. Inferring Phylogenies. Sinauer Associates, Sunderland, MA.
28. Owen R. 1843. Lectures on Comparative Anatomy and Physiology of the Invertebrate Animals, Delivered at the Royal College of Surgeons in 1843. Longman, Brown, Green and Longmans, London, United Kingdom.
29. Darwin C. 1859. On the Origin of Species: Or the Preservation of Favoured Races in the Struggle for Life, 2nd ed. John Murray, London, United Kingdom.
30. Hennig W. 1966. Phylogenetic Systematics. University of Illinois Press, Urbana, IL. [English translation (Davis DD, Zangerl R) of original entitled Grundzüge einer Theorie der Phylogenetischen Systematik, Deutcher Zentralverlag, Berlin, Germany.)
31. Nelson G, Platnick N. 1981. Systematics and Biogeography: Cladistics and Vicariance. Columbia University Press, New York, NY.
32. Watrous L, Wheeler Q. 1981. The out-group comparison method of character analysis. Syst Biol 30:1–11. http://dx.doi.org/10.1093/sysbio/30.1.1.
33. Kluge A. 1989. A concern for evidence and a phylogenetic hypothesis of relationships among Epicrates (Boidae, Serpentes). Syst Biol 38:7–25. http://dx.doi.org/10.1093/sysbio/38.1.7.
34. Maddison WP, Maddison D. 2018. MESQUITE: a modular system for evolutionary analysis. http://mesquiteproject.org.
35. Habib F, Johnson AD, Bundschuh R, Janies D. 2007. Large scale genotype-phenotype correlation analysis based on phylogenetic trees. Bioinformatics 23:785–788. http://dx.doi.org/10.1093/bioinformatics/btm003. [PubMed]
36. Handelman SK, Aaronson JM, Seweryn M, Voronkin I, Kwiek JJ, Sadee W, Verducci JS, Janies DA. 2015. Cladograms with Path to Event (ClaPTE): a novel algorithm to detect associations between genotypes or phenotypes using phylogenies. Comput Biol Med 58:1–13. http://dx.doi.org/10.1016/j.compbiomed.2014.12.013. [PubMed]
37. Harvey P. 2001. Phylogeny and systematics, p 11405–11411. In Baltes NJ (ed), International Encyclopedia of the Social and Behavioral Sciences. Elsevier, Amsterdam, the Netherlands. http://dx.doi.org/10.1016/B0-08-043076-7/03133-8.
38. Elder RO, Keen JE, Siragusa GR, Barkocy-Gallagher GA, Koohmaraie M, Laegreid WW. 2000. Correlation of enterohemorrhagic Escherichia coli O157 prevalence in feces, hides, and carcasses of beef cattle during processing. Proc Natl Acad Sci U S A 97:2999–3003. http://dx.doi.org/10.1073/pnas.97.7.2999. [PubMed]
39. Allard MW, Strain E, Melka D, Bunning K, Musser SM, Brown EW, Timme R. 2016. Practical value of food pathogen traceability through building a whole-genome sequencing network and database. J Clin Microbiol 54:1975–1983. http://dx.doi.org/10.1128/JCM.00081-16. [PubMed]
41. Pettengill JB, Pightling AW, Baugher JD, Rand H, Strain E. 2016. Real-time pathogen detection in the era of whole-genome sequencing and big data: comparison of k-mer and site-based methods for inferring the genetic distances among tens of thousands of Salmonella samples. PLoS One 11:e0166162. http://dx.doi.org/10.1371/journal.pone.0166162. [PubMed]
42. FDA. 2018. GenomeTrakr data. ftp.ncbi.nlm.nih.gov/pathogen/Results/.
43. Colijn C, Gardy J. 2014. Phylogenetic tree shapes resolve disease transmission patterns. Evol Med Public Health 2014:96–108. http://dx.doi.org/10.1093/emph/eou018. [PubMed]
44. Cheng JM, Hiscoe L, Pollock SL, Hasselback P, Gardy JL, Parker R. 2015. A clonal outbreak of tuberculosis in a homeless population in the interior of British Columbia, Canada, 2008–2015. Epidemiol Infect 143:3220–3226. http://dx.doi.org/10.1017/S0950268815000825. [PubMed]
45. Janies D, Hill AW, Guralnick R, Habib F, Waltari E, Wheeler WC. 2007. Genomic analysis and geographic visualization of the spread of avian influenza (H5N1). Syst Biol 56:321–329. http://dx.doi.org/10.1080/10635150701266848. [PubMed]
46. Janies D, Habib F, Alexandrov B, Hill A, Pol D. 2008. Evolution of genomes, host shifts and the geographic spread of SARS-CoV and related coronaviruses. Cladistics 24:111–130. http://dx.doi.org/10.1111/j.1096-0031.2008.00199.x.
47. Janies D, Treseder T, Alexandrov B, Habib F, Chen J, Ferreira R, Çatalyürek Ü, Varón A, Wheeler W. 2011. The Supramap project: linking pathogen genomes with geography to fight emergent infectious diseases. Cladistics 27:61–66. http://dx.doi.org/10.1111/j.1096-0031.2010.00314.x.
48. Janies DA, Ford C, Damodaran L, Witter Z. 2017. Spread of Middle East respiratory coronavirus: genetic versus epidemiological data. Online J Public Health Inform 9:e004. http://dx.doi.org/10.5210/ojphi.v9i1.7581.
49. Ezeoke I, Galac MR, Lin Y, Liem AT, Roth PA, Kilianski A, Gibbons HS, Bloch D, Kornblum J, Del Rosso P, Janies DA, Weiss D. 2018. Tracking a serial killer: integrating phylogenetic relationships, epidemiology, and geography for two invasive meningococcal disease outbreaks. PLoS One 13:e0202615. http://dx.doi.org/10.1371/journal.pone.0202615. [PubMed]
50. Phillips A, Janies D, Wheeler W. 2000. Multiple sequence alignment in phylogenetic analysis. Mol Phylogenet Evol 16:317–330. http://dx.doi.org/10.1006/mpev.2000.0785. [PubMed]
51. Goloboff P. 1999. Analyzing large data sets in reasonable times: solutions for composite optima. Cladistics 14:415–428. http://dx.doi.org/10.1111/j.1096-0031.1999.tb00278.x.
52. Mau B, Newton MA, Larget B. 1999. Bayesian phylogenetic inference via Markov chain Monte Carlo methods. Biometrics 55:1–12. http://dx.doi.org/10.1111/j.0006-341X.1999.00001.x. [PubMed]
53. Janies DA, Wheeler WC. 2001. Efficiency of parallel direct optimization. Cladistics 17:S71–S82. http://dx.doi.org/10.1111/j.1096-0031.2001.tb00106.x.
54. Wheeler W, Aagesen L, Arango C, Faivovich J, Grant T, D’Haese C, Janies D, Smith WL, Varón A, Giribet G. 2006. Dynamic Homology and Phylogenetic Systematics: A Unified Approach Using POY. American Museum of Natural History, New York, NY.
55. Sankoff D, Cedergren RJ, Lapalme G. 1976. Frequency of insertion, deletion, transversion, and transition in the evolution of 5S ribosomal RNA. J Mol Evol 7:133–149. http://dx.doi.org/10.1007/BF01732471. [PubMed]
56. Slowinski J. 1993. “Unordered” versus “ordered” characters. Syst Biol 42:155–165. http://dx.doi.org/10.1093/sysbio/42.2.155.
57. Wheeler W. 1993. The triangle inequality and character analysis. Mol Biol Evol 10:707–712.
58. Huelsenbeck J. 1995. Performance of phylogenetic methods in simulation. Syst Biol 44:17–48. http://dx.doi.org/10.1093/sysbio/44.1.17.
59. Siddall M. 1998. Success of parsimony in the four-taxon case: long-branch repulsion by likelihood in the Farris zone. Cladistics 14:209–220. http://dx.doi.org/10.1111/j.1096-0031.1998.tb00334.x.
60. Steel M, Penny D. 2000. Parsimony, likelihood, and the role of models in molecular phylogenetics. Mol Biol Evol 17:839–850. http://dx.doi.org/10.1093/oxfordjournals.molbev.a026364. [PubMed]
61. Swofford DL, Waddell PJ, Huelsenbeck JP, Foster PG, Lewis PO, Rogers JS. 2001. Bias in phylogenetic estimation and its relevance to the choice between parsimony and likelihood methods. Syst Biol 50:525–539. http://dx.doi.org/10.1080/10635150117959. [PubMed]
62. Kolaczkowski B, Thornton JW. 2004. Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous. Nature 431:980–984. http://dx.doi.org/10.1038/nature02917. [PubMed]
63. Philippe H, Zhou Y, Brinkmann H, Rodrigue N, Delsuc F. 2005. Heterotachy and long-branch attraction in phylogenetics. BMC Evol Biol 5:50. http://dx.doi.org/10.1186/1471-2148-5-50. [PubMed]
64. Hovmöller R, Alexandrov B, Hardman J, Janies D. 2010. Tracking the geographic spread of avian influenza (H5N1) with multiple phylogenetic trees. Cladistics 26:1–13. http://dx.doi.org/10.1111/j.1096-0031.2009.00297.x.
65. Hill AW, Guralnick RP, Wilson MJ, Habib F, Janies D. 2009. Evolution of drug resistance in multiple distinct lineages of H5N1 avian influenza. Infect Genet Evol 9:169–178. http://dx.doi.org/10.1016/j.meegid.2008.10.006. [PubMed]
66. Schneider A, Malone R, Guo J, Homan J, Linchangco G, Witter Z, Vinesett D, Damodaran L, Janies D. 2016. Molecular evolution of Zika virus as it crossed the Pacific to the Americas. Cladistics 33:1–20. http://dx.doi.org/10.1111/cla.12178.
67. Jukes T, Cantor C. 1969. Evolution of protein molecules, p 21–132. In Munro H (ed), Mammalian Protein Metabolism. Academic Press, New York, NY. http://dx.doi.org/10.1016/B978-1-4832-3211-9.50009-7.
68. Felsenstein J. 1978. Cases in which parsimony or compatibility methods will be positively misleading. Syst Zool 27:401–410. http://dx.doi.org/10.2307/2412923.
69. Felsenstein J. 1981. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol 17:368–376. http://dx.doi.org/10.1007/BF01734359. [PubMed]
70. Kosiol C, Bofkin L, Whelan S. 2006. Phylogenetics by likelihood: evolutionary modeling as a tool for understanding the genome. J Biomed Inform 39:51–61. http://dx.doi.org/10.1016/j.jbi.2005.08.003. [PubMed]
71. Goloboff P. 2003. Parsimony, likelihood, and simplicity. Cladistics 19:91–103. http://dx.doi.org/10.1111/j.1096-0031.2003.tb00297.x.
72. Giribet G, Wheeler WC. 1999. On gaps. Mol Phylogenet Evol 13:132–143. http://dx.doi.org/10.1006/mpev.1999.0643. [PubMed]
73. Bayes T. 1763. An essay towards solving a problem in the doctrine of chances. Philos T R Soc 53:370–418.
74. Huelsenbeck JP, Larget B, Miller RE, Ronquist F. 2002. Potential applications and pitfalls of Bayesian inference of phylogeny. Syst Biol 51:673–688. http://dx.doi.org/10.1080/10635150290102366. [PubMed]
75. Rannala B, Yang Z. 1996. Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference. J Mol Evol 43:304–311. http://dx.doi.org/10.1007/BF02338839. [PubMed]
76. Drummond AJ, Rambaut A. 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7:214. http://dx.doi.org/10.1186/1471-2148-7-214. [PubMed]
77. Ragonnet-Cronin M, Hué S, Hodcroft EB, Tostevin A, Dunn D, Fawcett T, Pozniak A, Brown AE, Delpech V, Brown AJL, UK HIV Drug Resistance Database. 2018. Non-disclosed men who have sex with men in UK HIV transmission networks: phylogenetic analysis of surveillance data. Lancet HIV 5:e309–e316. http://dx.doi.org/10.1016/S2352-3018(18)30062-6.
78. Meade A, Pagel M. November 2017. BayesTraits V3.0.1. http://www.evolution.rdg.ac.uk/BayesTraitsV3.0.1/BayesTraitsV3.0.1.html.
79. Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, Sagulenko P, Bedford T, Neher RA. 2018. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34:4121–4123. http://dx.doi.org/10.1093/bioinformatics/bty407. [PubMed]
80. Grenfell BT, Pybus OG, Gog JR, Wood JL, Daly JM, Mumford JA, Holmes EC. 2004. Unifying the epidemiological and evolutionary dynamics of pathogens. Science 303:327–332. http://dx.doi.org/10.1126/science.1090727. [PubMed]
81. Kermack W, McKendrick A. 1 August 1927. A contribution to the mathematical theory of epidemics. Proceed R Soc A Math Phys Eng Sci 115:700–721.
82. Volz EM, Koelle K, Bedford T. 2013. Viral phylodynamics. PLOS Comput Biol 9:e1002947. http://dx.doi.org/10.1371/journal.pcbi.1002947. [PubMed]
83. Alkhamis MA, Perez AM, Murtaugh MP, Wang X, Morrison RB. 2016. Applications of Bayesian phylodynamic methods in a recent U.S. porcine reproductive and respiratory syndrome virus outbreak. Front Microbiol 7:67. http://dx.doi.org/10.3389/fmicb.2016.00067. [PubMed]
84. Wang E, Ni H, Xu R, Barrett AD, Watowich SJ, Gubler DJ, Weaver SC. 2000. Evolutionary relationships of endemic/epidemic and sylvatic dengue viruses. J Virol 74:3227–3234. http://dx.doi.org/10.1128/JVI.74.7.3227-3234.2000. [PubMed]
85. Neher RA, Bedford T. 2015. nextflu: real-time tracking of seasonal influenza virus evolution in humans. Bioinformatics 31:3546–3548. http://dx.doi.org/10.1093/bioinformatics/btv381. [PubMed]
86. CDC. 2018. Pulsenet. https://www.cdc.gov/pulsenet/index.html.
87. Nadon C, Van Walle I, Gerner-Smidt P, Campos J, Chinen I, Concepcion-Acevedo J, Gilpin B, Smith AM, Man Kam K, Perez E, Trees E, Kubota K, Takkinen J, Nielsen EM, Carleton H, FWD-NEXT Expert Panel. 2017. PulseNet International: vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance. Euro Surveill 22:30544. http://dx.doi.org/10.2807/1560-7917.ES.2017.22.23.30544. [PubMed]
88. Kumar SB, Handelman SK, Voronkin I, Mwapasa V, Janies D, Rogerson SJ, Meshnick SR, Kwiek JJ. 2011. Different regions of HIV-1 subtype C env are associated with placental localization and in utero mother-to-child transmission. J Virol 85:7142–7152. http://dx.doi.org/10.1128/JVI.01955-10. [PubMed]
89. Klase ZA, Khakhina S, Schneider AB, Callahan MV, Glasspool-Malone J, Malone R. 2016. Zika fetal neuropathogenesis: etiology of a viral syndrome. PLoS Negl Trop Dis 10:e0004877. http://dx.doi.org/10.1371/journal.pntd.0004877. [PubMed]
90. Chavali PL, Stojic L, Meredith LW, Joseph N, Nahorski MS, Sanford TJ, Sweeney TR, Krishna BA, Hosmillo M, Firth AE, Bayliss R, Marcelis CL, Lindsay S, Goodfellow I, Woods CG, Gergely F. 2017. Neurodevelopmental protein Musashi-1 interacts with the Zika genome and promotes viral replication. Science 357:83–88. http://dx.doi.org/10.1126/science.aam9243. [PubMed]
91. Hedge J, Wilson DJ. 2014. Bacterial phylogenetic reconstruction from whole genomes is robust to recombination but demographic inference is not. mBio 5:e02158-14. http://dx.doi.org/10.1128/mBio.02158-14. [PubMed]
92. Schneider AB. 2018. Arboviruses: The Hidden Path of an Imminent Threat. PhD dissertation. University of North Carolina, Charlotte, NC. Available from https://search.proquest.com/docview/2138348703.
93. Katoh K, Standley DM. 2013. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772–780. http://dx.doi.org/10.1093/molbev/mst010. [PubMed]
94. Goloboff P, Farris J, Nixon K. 2008. TNT, a free program for phylogenetic analysis. Cladistics 24:774–786. http://dx.doi.org/10.1111/j.1096-0031.2008.00217.x.
95. Stamatakis A. 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. http://dx.doi.org/10.1093/bioinformatics/btu033. [PubMed]
96. Swofford D. 2018. Phylogenetic Analysis Using PAUP. https://paup.phylosolutions.com/.
97. Witter Z, Janies D. 2015. gvgenerator. http://webpages.uncc.edu/djanies.
98. Graphvis. 2018. https://www.graphviz.org/.
99. Geogenes. 2015. https://geogenes.org/.
100. de Bernardi Schneider A, Ford C, Hostager R, Williams J, Cioce M, Çatalyürek U, Wertheim J, Janies D. 2018. StrainHub: A phylogenetic tool to construct pathogen transmission networks bioRxiv 650283. https://dx.doi.org/10.1101/650283.
Loading

Article metrics loading...

/content/journal/microbiolspec/10.1128/microbiolspec.AME-0006-2018
2019-07-19
2019-11-12

Abstract:

In this review, which is a part of the Curated Collection: Advances in Molecular Epidemiology of Infectious Diseases, I present an overview of the principles used to classify organisms in the field of phylogenetics, highlight the methods used to infer the interrelationships of organisms, and summarize how these concepts are applied to molecular epidemiologic analyses. I present steps in analyses that come downstream of the assembly of a set of genomes or genes and the production of a multiple-sequence alignment or other matrices of putative orthologs for comparison. I focus on the history of the problem of phylogenetic reconstruction and debates within the field about the most appropriate methods. I illustrate methods that bridge the gap between molecular epidemiology and traditional epidemiology, including phylogenetic character evolution and geographic visualization. Finally, I provide practical advice on how to conduct an example analysis in the appendix.

*This article is part of a curated collection.

Highlighted Text: Show | Hide
Loading full text...

Full text loading...

Supplemental Material

No supplementary material available for this content.

This is a required field
Please enter a valid email address
Please check the format of the address you have entered.
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error