The use of statistical phylogenetics in virology

Cover Page


Cite item

Full Text

Abstract

Molecular phylogenetics, particularly statistical phylogenetics, is widely used to solve the fundamental and applied problems in virology. Bayesian, or statistical, phylogenetic methods, which came into practice 10—15 years ago, markedly expanded the range of questions that can be answered based on analyzing nucleotide and amino acid sequences. An opportunity of using various evolution models allows inferring the chronology, geography and dynamics of the infection spreading. For example, analysis of globally distributed HIV group M by Bayesian methods demonstrated with a probability of 99% that the most recent common ancestor of these viruses existed in the surroundings of the city of Kinshasa (Democratic Republic of the Congo) in the early 1920s. Another study showed that H9N2 influenza virus most likely passed on to humans from wild ducks in Hong Kong in the late 1960s. In addition, using of the Bayesian analysis allows to evaluating the effect of measures taken on the development of the epidemic process. For example, it was shown retrospectively that the rate of hepatitis C virus infection cases in Egypt increased by several orders of magnitude in the mid-20th century. A sharp rise in new case rate is associated with the treatment for schistosomiasis by using non-sterile repeatedly used syringes. A set of Bayesian analysis methods has been applied in tens of thousands of researches describing various aspects of the occurrence and spread of infectious diseases in humans and animals. This was facilitated by the development and accessibility of software that implements these methods. The complexity of Bayesian phylogenetic methods imposes strict requirements on the data being analyzed. The correctness of the phylogenetic analysis data depends on various factors. For example, it is necessary to choose an evolutionary model that most adequately describes the studied objects. A mandatory step in formulating the results is the justification of the selected model. For viruses, the acquisition of genetic elements from other organisms is typical, therefore, the genomes even from closely related viruses may have non-homologous regions unsuitable for phylogenetic analysis. Another aspect is the creation of a representative dataset. Sometimes, all stages of the analysis are not indicated in publications, so that the data obtained can be interpreted ambiguously. The correct use of statistical phylogenetics methods in virology is possible only upon understanding their principles, proper methods of data preparation and evolutionary model selection criteria.

About the authors

Yu. A. Vakulenko

Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov First Moscow State Medical University; Lomonosov Moscow State University

Email: vjulia94@gmail.com
ORCID iD: 0000-0003-2791-1835

Junior ResearcherMIMP, Tropical and Vector Borne Diseases, Sechenov First MSMU PhD-student, Faculty of Biology, Lomonosov MSU.

Moscow

Russian Federation

A. N. Lukashev

Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov First Moscow State Medical University; Institute of Molecular Medicine, Sechenov First Moscow State Medical University

Email: alexander_lukashev@hotmail.com
ORCID iD: 0000-0001-7365-0352

PhD, MD (Medicine), RAS Full Member, Director of Martsinovsky Institute of Medical Parasitology, Tropical and Vector Borne Diseases, Sechenov First MSMU; Leading Researcher Institute of Molecular Medicine, Sechenov First MSMU.

Moscow

Russian Federation

A. A. Deviatkin

Institute of Molecular Medicine, Sechenov First Moscow State Medical University

Author for correspondence.
Email: andreideviatkin@gmail.com
ORCID iD: 0000-0003-0789-4601

Andrei A. Deviatkin - PhD (Biology), Senior Researcher.

119048, Moscow, Trubetskaya str., 8/2, Phone: +7 (495) 609-14-00

Russian Federation

References

  1. Лукашов В.В. Молекулярная эволюция и филогенетический анализ. М.: БИНОМ. Лаборатория знаний, 2009. 256 с.
  2. Abascal F., Zardoya R., Telford M.J. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res., 2010, vol. 38: W7-13. doi: 10.1093/nar/gkq291
  3. Akaike H. A new look at the statistical model identification. IEEE Trans. Automat. Contr., 1974, vol. 19, no. 6, pp. 716—723. doi: 10.1109/TAC.1974.1100705
  4. Anderson R.M., May R.M. Population biology of infectious diseases: part I. Nature, 1979, vol. 280, no. 5721, pp. 361—367. doi: 10.1038/280361a0
  5. Anderson R.M., May R.M., Jackson H.C., Smith A.M. Population dynamics of fox rabies in Europe. Nature, 1981, vol. 289, no. 5800, pp. 765-771. doi: 10.1038/289765a0
  6. Arenas M. Trends in substitution models of molecular evolution. Front. Genet, 2015, vol. 6: 319. doi: 10.3389/fgene.2015.00319
  7. Avise J.C. Phylogeography: retrospect and prospect. J. Biogeogr., 2009, vol. 36, no. 1, pp. 3-15. doi: 10.1111/j.1365-2699.2008.02032.x
  8. Berry I.M., Ribeiro R., Kothari M., Athreya G., Daniels M., Lee H.Y., Bruno W., Leitner T. Unequal evolutionary rates in the human immunodeficiency virus type 1 (HIV-1) pandemic: the evolutionary rate of HIV-1 slows down when the epidemic rate increases. J. Virol., 2007, vol. 81, no. 19, pp. 10625-10635. doi: 10.1128/jvi.00985-07
  9. Botvinkin A., Kosenko M. Rabies in the european parts of Russia, Belarus and Ukraine. In: Historical perspective of rabies in Europe and the Mediterranean Basin: a testament to rabies. OIE: Paris, France, 2004, pp. 47-65.
  10. Bouckaert R. Phylogeography by diffusion on a sphere: whole world phylogeography. Peer J., 2016, vol. 4: e2406. doi: 10.7717/peerj.2406
  11. Bouckaert R., Heled J., Kuhnert D., Vaughan T., Wu C.H., Xie D., Suchard M.A., Rambaut A., Drummond A.J. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol., 2014, vol. 10, no. 4, pp. 1-6. doi: 10.1371/journal.pcbi.1003537
  12. Bouckaert R.R., Drummond A.J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol., 2017, vol. 17, no. 1, pp. 1-11. doi: 10.1186/s12862-017-0890-6
  13. Bouckaert R., Vaughan T.G., Barido-Sottani J., Duchene S., Fourment M., Gavryushkina A., Heled J., Jones G., Kuhnert D., Maio De N., Matschiner M., Mendes F.K., Muller N.F., Ogilvie H.A., Plessis du L., Popinga A., Rambaut A., Rasmussen D., Siveroni I., Suchard M.A., Wu C.H., Xie D., Zhang Ch., Stadler T., Drummond A.J. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput. Biol., 2019, vol. 15, no. 4: e1006650. doi: 10.1371/journal.pcbi.1006650
  14. Choudhuri S. Phylogenetic Analysis. In: Bioinformatics for Beginners. Elsevier, 2014. pp 209-218.
  15. Colijn C., Plazzotta G. A Metric on phylogenetic tree shapes. Syst. Biol., 2018, vol. 67, no. 1, pp. 113-126. doi: 10.1093/sysbio/syx046
  16. Dayrat B. The roots of phylogeny: how did haeckel build his trees? Syst. Biol., 2003, vol. 52, no. 4, pp. 515-527. doi: 10.1080/10635150390218277
  17. Deviatkin A.A., Kholodilov I.S., Vakulenko Yu.A., Karganova G.G., Lukashev A.N. Tick-Borne encephalitis virus: an emerging ancient zoonosis? Viruses, 2020, vol. 12, no. 2: 247. doi: 10.3390/v12020247
  18. Deviatkin A.A., Lukashev A.N., Poleshchuk E.M., Dedkov V.G., Tkachev S.E., Sidorov G.N., Karganova G.G., Galkina I.V., Shchelkanov M.Yu., Shipulin G.A. The phylodynamics of the rabies virus in the Russian Federation. PLoS One, 2017, vol. 12, no. 2: e0171855. doi: 10.1371/journal.pone.0171855
  19. Domingo E., Sheldon J., Perales C. Viral quasispecies evolution. Microbiol. Mol. Biol. Rev., 2012, vol. 76, no. 2, pp. 159-216. doi: 10.1128/MMBR.05023-11
  20. Drake J.W., Holland J.J. Mutation rates among RNA viruses. Proc. Natl. Acad. Sci., 1999, vol. 96, no. 24, pp. 13910-13913. doi: 10.1073/pnas.96.24.13910
  21. Drummond A.J. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol., 2005, vol. 22, no. 5, pp. 1185-1192. doi: 10.1093/molbev/msi103
  22. Drummond A.J., Ho S.Y.W., Phillips M.J., Rambaut A. Relaxed phylogenetics and dating with confidence. PLoS Biol., 2006, vol. 4, no. 5: e88. doi: 10.1371/journal.pbio.0040088
  23. Drummond A.J., Pybus O.G., Rambaut A., Roald F., Rodrigo A.G. Measurably evolving populations. Trends Ecol. Evol., 2003, vol. 18, no. 9, pp. 481-488. doi: 10.1016/S0169-5347(03)00216-7
  24. Drummond A.J., Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol., 2007, vol. 7, no. 1: 214. doi: 10.1186/1471-2148-7-214
  25. Drummond A.J., Suchard M.A. Bayesian random local clocks, or one rate to rule them all. BMC Biol., 2010, vol. 8, no. 1: 114. doi: 10.1186/1741-7007-8-114
  26. Dudas G., Carvalho L.M., Rambaut A., Bedford T. MERS-CoV spillover at the camel-human interface. Elife, 2018, vol. 7: e31257. doi: 10.7554/eLife.31257
  27. Edgar R.C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res., 2004, vol. 32, no. 5, pp. 1792-1797. doi: 10.1093/nar/gkh340
  28. Edgar R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 2010, vol. 26, no. 19, pp. 2460-2461. doi: 10.1093/bioinformatics/btq461
  29. Fan Y., Wu R., Chen M.-H., Kuo L., Lewis P.O. Choosing among partition models in Bayesian phylogenetics. Mol. Biol. Evol., 2011, vol. 28, no. 1, pp. 523-532. doi: 10.1093/molbev/msq224
  30. Faria N.R., Rambaut A., Suchard M.A., Baele G., Bedford T., Ward M.J., Tatem A.J., Sousa J.D., Arinaminpathy N., Pepin J., Posada D., Peeters M., Pybus O.G., Lemey P. The early spread and epidemic ignition of HIV-1 in human populations. Science, 2014, vol. 346, no. 6205, pp. 56-61. doi: 10.1126/science.1256739
  31. Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol., 1981, vol. 17, no. 6, pp. 368-376. doi: 10.1007/BF01734359
  32. Fu L., Niu B., Zhu Z., Wu S., Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, vol. 28, no. 23, pp. 3150-3152. doi: 10.1093/bioinformatics/bts565
  33. Gaut B.S., Lewis P.O. Success of maximum likelihood phylogeny inference in the four-taxon case. Mol. Biol. Evol., 1995, vol. 12, no. 1, pp. 152-162. doi: 10.1093/oxfordjournals.molbev.a040183
  34. Gibbs M.J., Armstrong J.S., Gibbs A.J. Sister-scanning: a Monte Carlo procedure for assessing signals in rebombinant sequences. Bioinformatics, 2000, vol. 16, no. 7, pp. 573-582. doi: 10.1093/bioinformatics/16.7.573
  35. Gire S.K., Goba A., Andersen K.G., Sealfon R.S., Park D.J., Kanneh L., Jalloh S., Momoh M., Fullah M., Dudas G., Wohl S., Moses L.M., Yozwiak N.L., Winnicki S., Matranga C.B., Malboeuf C.M., Qu J., Gladden A.D., Schaffner S.F., Yang X., Jiang P.P., Nekoui M., Colubri A., Coomber M.R., Fonnie M., Moigboi A., Gbakie M., Kamara F.K., Tucker V., Konuwa E., Saffa S., Sellu J., Jalloh A.A., Kovoma A., Koninga J., Mustapha I., Kargbo K., Foday M., Yillah M., Kanneh F., Robert W., Massally J.L., Chapman S.B., Bochicchio J., Murphy C., Nusbaum C., Young S., Birren B.W., Grant D.S., Scheiffelin J.S., Lander E.S., Happi C., Gevao S.M., Gnirke A., Rambaut A., Garry R.F., Khan S.H., Sabeti P.C. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science, 2014, vol. 345, no. 6202, pp. 1369-1372. doi: 10.1126/science.1259657
  36. Griffiths R.C., Tavare S. Sampling theory for neutral alleles in a varying environment. Philos. Trans. R. Soc. London Ser. B. Biol. Sci., 1994, vol. 344, no. 1310, pp. 403-410. doi: 10.1098/rstb.1994.0079
  37. Higgins D.G., Sharp P.M. CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene, 1988, vol. 73, no. 1, pp. 237-244. doi: 10.1016/0378-1119(88)90330-7
  38. Hill V., Baele G. Bayesian estimation of past population dynamics in BEAST 1.10 using the skygrid coalescent model. Mol. Biol. Evol., 2019, vol. 36, no. 11, pp. 2620-2628. doi: 10.1093/molbev/msz172
  39. Ho S.Y.W., Duchene S. Molecular-clock methods for estimating evolutionary rates and timescales. Mol. Ecol., 2014, vol. 23, no. 24, pp. 5947-5965. doi: 10.1111/mec.12953
  40. Jeffreys H. Some tests of significance, treated by the theory of probability. Math Proc. Cambridge Philos. Soc., 1935, vol. 31, no. 2, pp. 203-222. doi: 10.1017/S030500410001330X
  41. Jorba J., Campagnoli R., De L., Kew O. Calibration of multiple poliovirus molecular clocks covering an extended evolutionary range. J. Virol., 2008, vol. 82, no. 9, pp. 4429-4440. doi: 10.1128/JVI.02354-07
  42. Jukes T., Cantor C. Evolution of protein molecules. In: Mammalian protein metabolism. New York: Academic Press, 1969, pp. 21-132.
  43. Kainer D., Lanfear R. The effects of partitioning on phylogenetic inference. Mol. Biol. Evol., 2015, vol. 32, no. 6, pp. 1611-1627. doi: 10.1093/molbev/msv026
  44. Kass R., Raftery A. Bayes factors. J. Am. Stat. Assoc., 1995, vol. 90, pp. 773-795. doi: 10.2307/2291091
  45. Katoh K., Standley D.M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol., 2013, vol. 30, no. 4, pp. 772-780. doi: 10.1093/molbev/mst010
  46. Keane T.M., Creevey C.J., Pentony M.M., Al E. Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified. BMC Evol. Biol., vol. 6: 29. doi: 10.1186/1471-2148-6-29
  47. Keeling M.J., Rohani P. Modeling infectious diseases in humans and animals. New Jersey: Princeton University Press, 2007. 408 p.
  48. Kingman J.F.C. The coalescent. Stoch. Process. Their Appl., 1982, vol. 13, no. 3, pp. 235-248. doi: 10.1016/0304-4149(82)90011-4
  49. Koonin E.V., Dolja V.V., Krupovic M. Origins and evolution of viruses of eukaryotes: the ultimate modularity. Virology, 2015, vol. 479-480, pp. 2-25. doi: 10.1016/j.virol.2015.02.039
  50. Kuhner M.K., Felsenstein J. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol., 1994, vol. 11, no. 3, pp. 459-468. doi: 10.1093/oxfordjournals.molbev.a040126
  51. Lanfear R., Frandsen P.B., Wright A.M., Senfeld T., Calcott B. PartitionFinder 2: new methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol., 2017, vol. 34, no. 3, pp. 772-773. doi: 10.1093/molbev/msw260
  52. Lartillot N., Philippe H. Computing bayes factors using thermodynamic integration. Syst. Biol., 2006, vol. 55, no. 2, pp. 195-207. doi: 10.1080/10635150500433722
  53. Lemey P., Rambaut A., Drummond A.J., Suchard M.A. Bayesian phylogeography finds its roots. PLoS Comput. Biol., 2009, vol. 5, no. 9: e1000520. doi: 10.1371/journal.pcbi.1000520
  54. Lemey P., Rambaut A., Welch J.J., Suchard M.A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol., 2010, vol. 27, no. 8, pp. 1877-1885. doi: 10.1093/molbev/msq067
  55. Maio De N., Wu C.H., O’Reilly K.M., Wilson D. New routes to phylogeography: a Bayesian structured coalescent approximation. PLOS Genet., 2015, vol. 11, no. 8: e1005421. doi: 10.1371/journal.pgen.1005421
  56. Margoliash E. Primary structure and evolution of cytochrome C. Proc. Natl. Acad. Sci., 1963, vol. 50, no. 4, pp. 672-679. doi: 10.1073/pnas.50.4.672
  57. Martin D.P., Murrell B., Golden M. Khoosal A., Muhire B. RDP4: detection and analysis of recombination patterns in virus genomes. Virus Evol., 2015, vol. 1, no. 1, pp. 1-5. doi: 10.1093/ve/vev003
  58. Nascimento F.F., dos Reis M., Yang Z. A biologist’s guide to Bayesian phylogenetic analysis. Nat. Ecol. Evol., 2017, vol. 1, no. 10, pp. 1446-1454. doi: 10.1038/s41559-017-0280-x
  59. Needleman S.B., Wunsch C.D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol., 1970, vol. 48, no. 3, pp. 443-453. doi: 10.1016/0022-2836(70)90057-4
  60. Notredame C., Higgins D.G., Heringa J. T-coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol., 2000, vol. 302, no. 1, pp. 205-217. doi: 10.1006/jmbi.2000.4042
  61. Parag K.V., Pybus O.G. Exact Bayesian inference for phylogenetic birth-death models. Bioinformatics, 2018, vol. 34, no. 21, pp. 3638-3645. doi: 10.1093/bioinformatics/bty337
  62. Pybus O.G. The epidemiology and iatrogenic transmission of hepatitis C virus in Egypt: a Bayesian coalescent approach. Mol. Biol. Evol., 2003, vol. 20, no. 3, pp. 381-387. doi: 10.1093/molbev/msg043
  63. Rambaut A., Lam T., Carvalho L., Pybus O. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol., 2016, vol. 2, no. 1: vew007. doi: 10.1093/ve/vew007
  64. Rannala B., Yang Z. Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference. J. Mol. Evol., 1996, vol. 43, no. 3, pp. 304-311. doi: 10.1007/PL00006090
  65. Rice P., Longden I., Bleasby A. EMBOSS: the European molecular biology open software suite. Trends Genet., 2000, vol. 16, no. 6, pp. 276-277. doi: 10.1016/S0168-9525(00)02024-2
  66. Russel P.M., Brewer B.J., Klaere S., Bouckaert R.R. Model selection and parameter inference in phylogenetics using nested sampling. Syst. Biol., 2019, vol. 68, no. 2, pp. 219-233. doi: 10.1093/sysbio/syy050
  67. Saitou N., Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol., 1987, vol. 4, no. 4, pp. 406-425. doi: 10.1093/oxfordjournals.molbev.a040454
  68. Schwarz G. Estimating the dimension of a model. Ann. Stat., 1978, vol. 6, no. 2, pp. 461-464. doi: 10.1214/aos/1176344136
  69. Shaman J., Kohn M. Absolute humidity modulates influenza survival, transmission, and seasonality. Proc. Natl. Acad. Sci. USA, 2009, vol. 106, no. 9, pp. 3243-3248. doi: 10.1073/pnas.0806852106
  70. Sinsheimer J.S., Lake J.A., Little R.J.A. Bayesian hypothesis testing of four-taxon topologies using molecular sequence data. Biometrics, 1996, vol. 52, no. 1: 193. doi: 10.2307/2533156
  71. Skilling J. Nested sampling for general Bayesian computation. Bayesian Anal., 2006, vol. 1, no. 4, pp. 833-860. doi: 10.1214/06-BA127
  72. Smith T.F., Waterman M.S. Identification of common molecular subsequences. J. Mol. Biol., 1981, vol. 147, no. 1, pp. 195-197. doi: 10.1016/0022-2836(81)90087-5
  73. Song W., Qin K. Human-infecting influenza A (H9N2) virus: a forgotten potential pandemic strain? Zoonoses Public Health, 2020, vol. 67, no. 3, pp. 203-212. doi: 10.1111/zph.12685
  74. Stadler T., Kuhnert D., Bonhoeffer S., Drummond A.J. Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc. Natl. Acad. Sci., 2013, vol. 110, no. 1, pp. 228-233. doi: 10.1073/pnas.1207965110
  75. Stadler T., Kouyos R., Wyl V. von, Yerly S., Boni J., Burgisser P., Klimkait T., Joos B., Rieder P., Xie D., Gunthard H.F., Drummond A.J. Estimating the basic reproductive number from viral sequence data. Mol. Biol. Evol., 2012, vol. 29, no. 1, pp. 347357. doi: 10.1093/molbev/msr217
  76. Stadler T., Vaughan T.G., Gavryushkin A., Guindon S., Kuhnert D., Leventhal G.E., Drummond A.J. How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics? Proc. R. Soc. B. Biol. Sci., 2015, vol. 282, no. 1806: 20150420. doi: 10.1098/rspb.2015.0420
  77. Stadler T., Yang Z. Dating phylogenies with sequentially sampled tips. Syst. Biol., 2013, vol. 62, no. 5, pp. 674-688. doi: 10.1093/sysbio/syt030
  78. Su S., Wong G., Shi W., Liu J., Lai A.C.K., Zhou J., Liu W., Bi Y., Gao G.F. Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol., 2016, vol. 24, no. 6, pp. 490-502. doi: 10.1016/j.tim.2016.03.003
  79. Suchard M., Lemey P., Baele G., Ayres D.L., Drummond A.J., Rambaut A. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol., 2018, vol. 4, no. 1: vey016. doi: doi: 10.1093/ve/vey016
  80. Suchard M.A., Weiss R.E., Sinsheimer J.S. Bayesian selection of continuous-time Markov chain evolutionary models. Mol. Biol. Evol., 2001, vol. 18, no. 6, pp. 1001-1013. doi: 10.1093/oxfordjournals.molbev.a003872
  81. Tateno Y., Takezaki N., Nei M. Relative efficiencies of the maximum-likelihood, neighbor-joining, and maximum-parsimony methods when substitution rate varies with site. Mol. Biol. Evol., 1994, vol. 11, no. 2, pp. 261-277. doi: 10.1093/oxfordjournals.molbev.a040108
  82. Vakulenko Yu., Deviatkin A., Lukashev A. The effect of sample bias and experimental artefacts on the statistical phylogenetic analysis of picornaviruses. Viruses, 2019, vol. 11, no. 11: 1032. doi: 10.3390/v11111032
  83. Vakulenko Yu., Deviatkin A., Lukashev A. Using statistical phylogenetics for investigation of enterovirus 71 genotype A reintroduction into circulation. Viruses, 2019, vol. 11, no. 10: 895. doi: 10.3390/v11100895
  84. Vaughan T.G., Leventhal G.E., Rasmussen D.A., Drummond A.J., Welch D., Stadler T. Estimating epidemic incidence and prevalence from genomic data. Mol. Biol. Evol., 2019, vol. 36, no. 8, pp. 1804-1816. doi: 10.1093/molbev/msz106
  85. Waterhouse A.M., Procter J.B., Martin D.M., Clamp M., Barton G.J. Jalview Version 2 — a multiple sequence alignment editor and analysis workbench. Bioinformatics, 2009, vol. 25, no. 9, pp. 1189-1191. doi: 10.1093/bioinformatics/btp033
  86. Welch J., Bromham L. Molecular dating when rates vary. Trends Ecol. Evol., 2005, vol. 20, no. 6, pp. 320-327. doi: 10.1016/j.tree.2005.02.007
  87. Worobey M., Han G.-Z., Rambaut A. A synchronized global sweep of the internal genes of modern avian influenza virus. Nature, 2014, vol. 508, no. 7495, pp. 254-257. doi: 10.1038/nature13016
  88. Worobey M., Watts T.D., McKay R.A., Suchard M.A., Granade T., Teuwen D.E., Koblin B.A., Heneine W., Lemey P., Jaffe H.W. 1970s and ‘Patient 0’ HIV-1 genomes illuminate early HIV/AIDS history in North America. Nature, 2016, vol. 539, no. 7627, pp. 98-101. doi: 10.1038/nature19827
  89. Xie W., Lewis P.O., Fan Y., Kuo L., Chen M.H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol., 2011, vol. 60, no. 2, pp. 150-160. doi: 10.1093/sysbio/syq085
  90. Yang B., Liu F., Liao Q., Wu P., Chang Z., Huang J., Long L., Luo L., Li Y., Leung G.M., Cowling B.J., Yu H. Epidemiology of hand, foot and mouth disease in China, 2008 to 2015 prior to the introduction of EV-A71 vaccine. Euro Surveill., 2017, vol. 22, no. 50: 16-00824. doi: 10.2807/1560-7917.ES.2017.22.50.16-00824
  91. Yang J., Xie D., Nie Z., Xu B., Drummond A.J. Inferring host roles in Bayesian phylodynamics of global avian influenza A virus H9N2. Virology, 2019, vol. 538, pp. 86-96. doi: 10.1016/i.virol.2019.09.011
  92. Yule G.U. Mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S. Philos. Trans. R., 1924, vol. B213, pp. 21-87.
  93. Zhu J., Luo Z., Wang J., Xu Z., Chen H., Fan D., Gao N., Ping G., Zhou Z., Zhang Y., An J. Phylogenetic analysis of enterovirus 71 circulating in Beijing, China from 2007 to 2009. PLoS One, 2013, vol. 8, no. 2: e56318. doi: 10.1371/journal.pone.0056318
  94. Zuckerkandl E., Pauling L. Molecular disease, evolution, and genic heterogeneity. In: Horizons in Biochemistry. New York: Academic Press, 1962, pp. 189-225.
  95. Zuckerkandl E., Pauling L. Molecules as documents of history. J. Theor. Biol., 1965, vol. 8, no. 2, pp. 357-366.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2020 Vakulenko Y.A., Lukashev A.N., Deviatkin A.A.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 64788 от 02.02.2016.


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies