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Tutorials in Mathematical Biosciences IV pp 39–76Cite as

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  2. Tutorials in Mathematical Biosciences IV
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Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases

Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases

  • D. Janies5 &
  • D. Pol5,6 
  • Chapter
  • 1922 Accesses

Part of the Lecture Notes in Mathematics book series (LNMBIOS,volume 1922)

Microorganisms that cause infectious diseases present critical issues of national security, public health, and economic welfare. For example, in recent years, highly pathogenic strains of avian influenza have emerged in Asia, spread through Eastern Europe, and threaten to become pandemic. As demonstrated by the coordinated response to Severe Acute Respiratory Syndrome (SARS) and influenza, agents of infectious disease are being addressed via large-scale genomic sequencing. The goal of genomic sequencing projects are to rapidly put large amounts of data in the public domain to accelerate research on disease surveillance, treatment, and prevention. However, our ability to derive information from large comparative genomic datasets lags far behind acquisition. Here we review the computational challenges of comparative genomic analyses, specifically sequence alignment and reconstruction of phylogenetic trees. We present novel analytical results on two important infectious diseases, Severe Acute Respiratory Syndrome (SARS) and influenza.

SARS and influenza have similarities and important differences both as biological and comparative genomic analysis problems. Influenza viruses (Orthymxyoviridae) are RNA based. Current evidence indicates that influenza viruses originate in aquatic birds from wild populations. Influenza has been studied for decades via well-coordinated international efforts. These efforts center on surveillance via antibody characterization of the hemagglutinin (HA) and neuraminidase (N) proteins of the circulating strains to inform vaccine design. However, we still do not have a clear understanding of (1) various transmission pathways such as the role of intermediate hosts like swine and domestic birds and (2) the key mutation and genomic recombination events that underlie periodic pandemics of influenza. In the past 30 years, sequence data from HA and N loci has become an important data type. In the past year, full genomic data has become prominent. These data present exciting opportunities to address unanswered questions in influenza pandemics.

SARS is caused by a previously unrecognized lineage of coronavirus, SARS-CoV, which like influenza has an RNA based genome. Although SARS-CoV is widely believed to have originated in animals, there remains disagreement over the candidate animal source that lead to the original outbreak of SARS. In contrast to the long history of the study of influenza, SARS was only recognized in late 2002 and the virus that causes SARS has been documented primarily by genomic sequencing.

In the past, most studies of influenza were performed on a limited number of isolates and genes suited to a particular problem. Major goals in science today are to understand emerging diseases in broad geographic, environmental, societal, biological, and genomic contexts. Synthesizing diverse information brought together by various researchers is important to find out what can be done to prevent future outbreaks [JON03]. Thus comprehensive means to organize and analyze large amounts of diverse information are critical. For example, the relationships of isolates and patterns of genomic change observed in large datasets might not be consistent with hypotheses formed on partial data. Moreover when researchers rely on partial datasets, they restrict the range of possible discoveries.

Phylogenetics is well suited to the complex task of understanding emerging infectious disease. Phylogenetic analyses can test many hypotheses by comparing diverse isolates collected from various hosts, environments, and points in time and organizing these data into various evolutionary scenarios. The products of a phylogenetic analysis are a graphical tree of ancestor–descendent relationships and an inferred summary of mutations, recombination events, host shifts, geographic, and temporal spread of the viruses. However, this synthesis comes at a price. The cost of computation of phylogenetic analysis expands combinatorially as the number of isolates considered increases. Thus, large datasets like those currently produced are commonly considered intractable. We address this problem with synergistic development of heuristics tree search strategies and parallel computing.

Keywords

  • Severe Acute Respiratory Syndrome
  • Tree Search
  • Tree Length
  • Severe Acute Respiratory Syndrome
  • Host Shift

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. J. Antonovics, M.E. Hood, and C.H. Baker, Molecular virology: was the 1918 flu avian in origin? Arising from: J.K. Taubenberger et al. Nature 437, 889–893, Nature 440 (2006) E9.

    Google Scholar 

  2. Aristotle, Historia Animalium, 343 BC.

    Google Scholar 

  3. M.J. Brauer, M.T. Holder, L.A. Dries, D.J. Zwickl, P.O. Lewis, and D.M. Hillis, Genetic Algorithms and Parallel Processing in Maximum-Likelihood Phylogeny Inference, Mol. Biol. Evol. 19 (2002) 1717–1726.

    Google Scholar 

  4. R.M. Bush, W.M. Fitch, C.A. Bender, and N.J. Cox, Positive Selection on the H3 Hemagglutinin Gene of Human Influenza Virus A., Mol. Biol. Evol 16 (1999) 1457–1465.

    Google Scholar 

  5. B. Budowle, M.W. Allard, M.R. Wilson, and R. Chakraborty, Forensics and mitochondrial DNA: Applications, Debates, and Foundations, Annu. Rev. Genomics Hum. Genet. 4 (2003) 119–141.

    CrossRef  Google Scholar 

  6. R.M. Bush, Influenza as a model system for studying the crossspecies transfer and evolution of the SARS coronavirus, Phil. Trans. R. Soc. Lond. B. 359 (2004) 1067–1073.

    CrossRef  Google Scholar 

  7. R.M. Bush, C.A. Bender, K. Subbarao, N.J. Cox, and W.M. Fitch, Predicting the Evolution of Human Influenza A., Science 286 (1999) 1921–1925.

    CrossRef  Google Scholar 

  8. J.P. Carulli, D.M. Chen, W.S. Stark, and D.L. Hartl, D. Phylogeny and physiology of Drosophila opsins., J. Mol. Evol. 38 (1994) 25–62.

    CrossRef  Google Scholar 

  9. C. Ceron, J. Dopazo, E. Zapata, J. Carazo, and O. Trelles, Parallel implementation of DNAml program on message-passing architectures, Parallel Computing 24 (1998) 701–716.

    CrossRef  MATH  MathSciNet  Google Scholar 

  10. B. Chang, and M. Donoghue, Recreating ancestral proteins, Trends Ecol. Evol. 15 (2004) 109–114.

    CrossRef  Google Scholar 

  11. M.A Charleston, Hitch-Hiking: A Parallel Heuristic Search Strategy, Applied to the Phylogeny Problem, J. Comput. Biol. 8 (2001) 79–91.

    CrossRef  Google Scholar 

  12. The Chinese SARS Molecular Epidemiology Consortium: Molecular Evolution of the SARS Coronavirus During the Course of the SARS Epidemic in China, Science 303 (2004) 1666–1669.

    Google Scholar 

  13. G. Dutton, Preparing for a Potential Pandemic. Therapeutic and Vaccine Manufacturers Working to Combat the Avian Flu, Genetic Engineering News 25 (2005) 15:1.

    Google Scholar 

  14. D. Earn, J. Dushoff, and S. Levin, Ecology and evolution of the flu, T.R.E.E 17 (2002) 334–340.

    Google Scholar 

  15. T. Fanning, R. Slemons, A. Reid, T. Janczewski, J. Dean, and J. Taubenberger, 1917 Avian influenza Virus Sequences Suggest that the 1918 Pandemic Virus Did Not Acquire Its Hemagglutinin Directly from Birds, Journal of Virology 76 (2002) 7860–7862.

    CrossRef  Google Scholar 

  16. J.S. Farris, Methods for Computing Wagner trees, Syst. Zool. 19 (1970) 83–92.

    CrossRef  Google Scholar 

  17. J.S. Farris, The logical basis of phylogenetic analysis. In: Platnick, N.I., Funk, V.A. (eds), Advances in Cladistics, Columbia University Press, New York, 1983.

    Google Scholar 

  18. J.S. Farris, V.A. Albert, M. Kallersjo, D. Lipscomb, and A.G. Kluge, Parsimony Jackknifing Outperforms Neighbor-Joining, Cladistics 12 (1996) 99–124.

    CrossRef  Google Scholar 

  19. J. Felsenstein, Maximum Likelihood and Minimum-Step Methods for Estimating Trees from Data on Discrete Characters, Syst. Zool. 22 (1973) 240–249.

    CrossRef  Google Scholar 

  20. J. Felsenstein, The Number of Evolutionary Trees, Systematic Zoology 27 (1978) 27–33.

    CrossRef  Google Scholar 

  21. J. Felsenstein, Evolutionary trees from dna sequences: A maximum likelihood approach, J. Mol. Evol. 17 (1981) 368–376.

    CrossRef  Google Scholar 

  22. N.M. Ferguson and R. Anderson, Predicting evolutionary change in the influenza A virus, Nat Med. 8 (2002) 562–3.

    CrossRef  Google Scholar 

  23. N.M. Ferguson, A.P. Galvani, and R.M. Bush, Ecological and immunological determinants of influenza evolution, Nature 422 (2003) 428–433.

    CrossRef  Google Scholar 

  24. L.R. Foulds and R.L. Graham, The Steiner problem in phylogeny is NP-complete, Adv. Appl. Math. 3 (1982) 43–49.

    CrossRef  MATH  MathSciNet  Google Scholar 

  25. W.M. Fitch, Towards defining the course of evolution: Minimum change for a specific tree topology, Syst. Zool. 20 (1971) 406–416.

    CrossRef  Google Scholar 

  26. W.M. Fitch, R.M. Bush, C.A. Bender, and N.J. Cox, Long term trends in the evolution of H(3) HA1 human influenza type A, Proc. Natl. Acad. Sci. USA 94 (1997) 7712–7718.

    CrossRef  Google Scholar 

  27. W.M. Fitch and T. Smith, Optimal sequence alignments, Proc. Natl. Acad. Sci. USA 80 (1983) 1382–1386.

    CrossRef  Google Scholar 

  28. R. Fleissner, D. Metzler, and A.V. Haeseler, Simultaneous Statistical Alignment and Phylogeny Reconstruction, Syst. Biol. 54 (2005) 548–561.

    CrossRef  Google Scholar 

  29. D. Franz, The potential bioweaponization of zoonotic diseases. pp. 15–17 in The Emergence of Zoonotic Diseases: Understanding the Impact on Animal and Human Health. T. Burroughs, S. Knobler, and J. Lederberg, eds., Forum on Emerging Infections Board on Global Health (BGH), Institute of Medicine (IOM). National Academy of Sciences Press, Washington D.C., USA, 2002.

    Google Scholar 

  30. M. Gammelin, A. Altmller, U. Reinhardt, J. Mandler, V.R. Harley, P.J. Hudson, W.M. Fitch, and C. Scholtissek, Phylogenetic analysis of nucleoproteins suggests that human influenza A viruses emerged from a 19th century avian ancestor, Mol. Biol. Evol. 7 (1990) 194–200.

    Google Scholar 

  31. J. Gerberding, Pandemic Planning and Preparedness, http://www.cdc.gov/Washington/testimony/in05262005.htm (2005)

  32. E. Ghedin, N. Sengamalay, M. Shumway, J. Zaborsky, T. Feldblyum and 14 others, Large-scale sequencing of human influenza reveals the dynamic nature of viral genome evolution, Nature 437 (2005) 1162–1166.

    CrossRef  Google Scholar 

  33. M. Gibbs and A. Gibbs, Was the 1918 pandemic caused by a bird flu? Arising from: J.K. Taubenberger et al. Nature 437, 889–893, Nature (2005) 440:E8.

    CrossRef  Google Scholar 

  34. P.A. Goloboff, W.C. Wheeler, and D. Pol, Parallel searches of large datasets, Cladistics 19 (2002) 151.

    Google Scholar 

  35. P.A. Goloboff, S.J. Farris, and K.C. Nixon, TNT: Tree Analysis Using New Technologies, Software package distributed by the authors and available at: http://www.zmuc.dk/public/phylogeny/TNT (2003)

  36. P.A. Goloboff, Analyzing Large Datasets in Reasonable Times: Solutions for Composite Optima, Cladistics 15 (1999) 415–428.

    CrossRef  Google Scholar 

  37. T. Grant and A. Kluge, Data exploration in phylogenetic inference: Scientific, heuristic, or neither, Cladistics 19 (2003) 379–418.

    CrossRef  Google Scholar 

  38. B. Grenfell, O. Pybus, J. Gog, J. Wood, J. Daly, J. Mumford, and E. Holmes, Unifying the epidemiological and evolutionary dynamics of pathogens, Science 303 (2004) 327–332.

    CrossRef  Google Scholar 

  39. Y. Guan, B. Zheng, Y. He, X.L. Liu, Z.X. Zhuang, and 13 others, Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China, Science 302 (2003) 276–278.

    CrossRef  Google Scholar 

  40. W. Hennig, Phylogenetic Systematics, University of Illinois Press, Urbana, 1966.

    Google Scholar 

  41. M.D. Hendy and D. Penny, Branch and bound algorithms to determine minimal evolutionary trees, Math. Biosc. 59 (1982) 277–290.

    CrossRef  MATH  MathSciNet  Google Scholar 

  42. National Vaccine Program Office, Pandemics and Pandemic Scares in the 20th Century, http://www.hhs.gov/nvpo/pandemics/flu3.htm (2004)

  43. HHS Pandemic Influenza Plan, Part 2 Public Health Guidance for State and Local Partners, http://www.hhs.gov/pandemicflu/plan/pdf/S02.pdf (Date)

  44. D.M. Hillis, D.D. Pollock, J.A. McGuire, and D.J. Zwickl, Is Sparse Taxon Sampling a Problem for Phylogenetic Inference?, Syst. Biol. 52 (2003) 124–126.

    CrossRef  Google Scholar 

  45. D.M. Hillis, Inferring Complex Phylogenies, Nature 369 (1996) 130–131.

    CrossRef  Google Scholar 

  46. E. Holmes, E. Ghedin, N. Miller, J. Taylor, Y. Bao, and 6 others, Whole-Genome Analysis of Human Influenza A Virus Reveals Multiple Persistent Lineages and Reassortment among Recent H3N2 Viruses, PLoS Biol. 3 (2005) 1–11.

    CrossRef  Google Scholar 

  47. J. Huelsenbeck, B. Larget, R. Miller, and F. Ronquist, Potential applications and pitfalls of Bayesian inference of phylogeny, Syst. Biol. 51 (2002) 673–688.

    CrossRef  Google Scholar 

  48. D. Janies and W.C. Wheeler, Efficiency of parallel direct optimization. Cladistics, 17 (2001) S71–S82.

    CrossRef  Google Scholar 

  49. D. Janies and W.C. Wheeler, Theory and practice of parallel direct optimization. pp. 115–124 in R. Desalle, G. Giribet and W. Wheeler eds. Molecular Systematics and Evolution: Theory and Practice, Birkhuser Verlag, Basel Switzerland, 2002.

    Google Scholar 

  50. R. Johnston, Integrating Methodologists into Teams of Substantive Experts, Studies in Intelligence 47 (2003) No. 1.

    Google Scholar 

  51. J.A. Jones, K.A. Yelick, Parallelizing the Phylogeny Problem, Proc. 1995 ACM/IEEE Conf. Supercomp., 25 (1995)

    Google Scholar 

  52. M. Koopmans, B. Wilbrink, M. Conyn, G. Natrop, H. van der Nat, H. Vennema, A. Meijer, J. van Steenbergen, R. Fouchier, A. Osterhaus, and A. Bosman, Transmission of H7N7 avian influenza A virus to human beings during a large outbreak in commercial poultry farms in the Netherlands, The Lancet 363 (2004) 587–593s.

    CrossRef  Google Scholar 

  53. T. Ksiazek, D. Erdman, C. Goldsmith, S. Zaki, T. Peret and 22 others, A Novel Coronavirus Associated with Severe Acute Respiratory Syndrome, The New England Journal of Medicine 348 (2003) 1953–1966.

    CrossRef  Google Scholar 

  54. S. Lau, P. Woo, K. Li, Y. Huang, H. Tsoi, and 5 others, Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats, Proc. Natl. Acad. Sci. USA 102 (2005) 14040–14045.

    CrossRef  Google Scholar 

  55. G. Laver and E. Garman, The Origin and Control of Pandemic Influenza, Science 293 (2001) 1776–1777.

    CrossRef  Google Scholar 

  56. A.R. Lemmon and M.C. Milinkovitch, The metapopulation genetic algorithm: An efficient solution for the problem of large phylogeny estimation, Proc. Natl. Acad. Sci. USA 99 (2002) 10516–10521.

    CrossRef  Google Scholar 

  57. P.O. Lewis, A Genetic Algorithm for Maximum-Likelihood Phylogeny Inference Using Nucleotide Sequence Data, Mol. Biol. Evol. 15 (1998) 277–283.

    Google Scholar 

  58. Y. Lin, M. Shaw, Y. Gregory, K. Cameron, W. Lim, and 8 others, Avian-to-human transmission of H9N2 subtype influenza A viruses: Relationship between H9N2 and H5N1 human isolates, Proc. Natl. Acad. Sci. USA 97 (2000) 9654–9658.

    CrossRef  Google Scholar 

  59. S. Li, D.K. Pearl, and H. Doss, Phylogenetic Tree Construction using Markov Chain Monte Carlo, J. Am. Stat. Assoc. 95 (2000) 493–508.

    CrossRef  Google Scholar 

  60. K. Li, ClustalW-MPI: a parallel implementation of Clustal-W, based on MPI Bioinformatics 19 (2003) 1585–1586.

    CrossRef  Google Scholar 

  61. K. Li, Y. Guan, J. Wang, G. Smith, K. Xu, and 17 others, Genesis of a highly pathogenic and potentially pandemic H5N1 influenza virus in eastern Asia, Nature 430 (2004) 209–213.

    CrossRef  Google Scholar 

  62. W. Li, Z. Shi, M. Yu, W. Ren, C. Smith, and 12 others, Bats are natural reservoirs of SARS-like coronaviruses, Science 310 (2005) 676–679.

    CrossRef  Google Scholar 

  63. Lipatov et al, Influenza Emergence and Control, J. Virol. (2004) 8951–8959.

    Google Scholar 

  64. M.A. Marra, S.J. Jones, C.R. Astell, R.A Holt RA, and 47 others, The Genome Sequence of the SARS-Associated Coronavirus, Science 300 (2003) 1399–404.

    CrossRef  Google Scholar 

  65. B.E. Martina, B.L. Haagmans, T. Kuiken, R.A. Fouchier, G,F. Rimmelzwaan and 5 others, SARS infection of cats and ferrets, Nature 425 (2003) 915.

    CrossRef  Google Scholar 

  66. M. Metzker, D. Mindell, X. Liu, R. Ptak, R. Gibbs, and D. Hillis, Molecular evidence of HIV-1 transmission in a criminal case, Proc. Natl. Acad. Sci. USA 99 (2002) 14292–14297.

    CrossRef  Google Scholar 

  67. A. Moilanen, Searching for most parsimonious trees with simulated evolutionary optimization, Cladistics 15 (1999) 39–50.

    CrossRef  Google Scholar 

  68. D. Morrison and J. Ellis, Some effects of nucleotide sequence alignment on phylogeny estimation, Molecular Biology and Evolution 14 (1997) 428–441.

    Google Scholar 

  69. S. Morse, Factors in the Emergence of Infectious Diseases, Emerging Infectious Diseases 1 (1995) 7–15.

    CrossRef  Google Scholar 

  70. K.C. Nixon and J.M. Carpenter, On outgroups, Cladistics 9 (1994) 413–426.

    CrossRef  Google Scholar 

  71. K.C. Nixon, The Parsimony Ratchet, a New Method for Rapid Parsimony Analysis, Cladistics 15 (1999) 407–414.

    CrossRef  Google Scholar 

  72. E. Obenauer, J. Denson, P. Mehta, X. Su, S. Mukatira, and 12 others, Large-Scale Sequence Analysis of Avian Influenza Isolates, Science, published online January 26 http://www.sciencemag.org/cgi/content/full/1121586/DC1 (2006)

  73. P. Palese, Making Better Influenza Virus Vaccines?, Emerging Infectious Diseases 12 (2006) 61–65.

    Google Scholar 

  74. A. Phillips, D. Janies, and W.C. Wheeler, Multiple sequence alignment in phylogenetic analysis, Mol. Phylogenet. Evol. 16 (2000) 317–330.

    CrossRef  Google Scholar 

  75. J. Plotkin, J. Dushoff, and S. Levin, Hemagglutinin sequence clusters and the antigenic evolution of influenza A virus, Proc. Natl. Acad. Sci. USA 99 (2002) 6263–68.

    CrossRef  Google Scholar 

  76. D. Pol and M.A. Norell, Comments on the Manhattan Stratigraphic Measure, Cladistics 17 (2001) 285–289.

    CrossRef  Google Scholar 

  77. S. Poe, The Effect of Taxonomic Sampling on Accuracy of Phylogeny Estimation, Test Case of a Known Phylogeny, Mol. Biol. Evol. 15 (1998)1086–1090.

    Google Scholar 

  78. B. Rannala and Z. Yang, Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference, J. Mol. Evol. 43 (1996) 304–311.

    CrossRef  Google Scholar 

  79. B. Rannala, J.P. Huelsenbeck, Z. Yang, and R. Nielsen, Taxon Sampling and the Accuracy of Large Phylogenies, Syst. Biol. 47 (1998) 702–710.

    CrossRef  Google Scholar 

  80. B.D. Redelings and M.A. Suchard, Joint Bayesian estimation of alignment and phylogeny, Syst. Biol. 54 (2005) 401–418.

    CrossRef  Google Scholar 

  81. K. Rice and T. Warnow, Parsimony is hard to beat, Computing and combinatorics, (Shanghai, 1997): 124–133 (1997)

    Google Scholar 

  82. R.S. Ross, S. Viazov, and M. Roggendorf, Phylogenetic analysis indicates transmission of hepatitis C virus from an infected orthopedic surgeon to a patient, J. Med. Virol. 66 (2002) 4617.

    CrossRef  Google Scholar 

  83. U. Roshan, T. Warnow, B.M.E. Moret, and T.L. Williams, Rec-IDCM3: A Fast Algorithmic Technique for Reconstructing Large Phylogenetic Trees, Proc. IEEE Comp. Syst. Bioinf. Conf. (2004)

    Google Scholar 

  84. P.A. Rota, M.S Oberste, S.S. Monroe, W.A. Nix, R. Campagnoli, and 30 others, Characterization of a novel coronavirus associated with severe acute respiratory syndrome, Science 300 (2003) 1394–9.

    CrossRef  Google Scholar 

  85. E.M. Rubin and A. Tall, Perspectives for vascular genomics, Nature 407 (2004) 265–269.

    CrossRef  Google Scholar 

  86. N. Saitou and M. Nei, The neighbor-joining method: a new method for reconstructing phylogenetic trees, Mol. Biol. Evol. 4 (1987) 406–425.

    Google Scholar 

  87. L.A. Salter and D.K. Pearl, Stochastic Search Strategy for Estimation of Maximum Likelihood Phylogenetic Trees, Syst. Biol. 50 (2001) 7–17.

    CrossRef  Google Scholar 

  88. D. Sankoff and R. Cedergren, Simultaneous comparison of three or more sequences related by a tree in D. Sankoff and J. B. Kruskal eds. Time Warps, String Edits, and Macromolecules: the Theory and Practise of Sequence Comparison. Addison-Wesley, Reading, MA. (1983) 253–264.

    Google Scholar 

  89. C. Scholtissek, Pigs as the mixing vessel for the creation of new pandemic influenza A viruses, Med. Principles Practice 2 (1990) 65–71.

    Google Scholar 

  90. D. Searls, Pharmacophylogenomics: Genes, evolution and drug targets, Nature Reviews Drug Discovery 2 (2003) 613–623.

    CrossRef  Google Scholar 

  91. D.L Swofford, G.J. Olsen, P.J. Waddell, and D.M. Hillis, Phylogenetic inference. In: Hillis, D.M., Moritz, C. Mable, B.K. (eds) Molecular Systematics, second edition. Sinauer Associates, Sunderland, 1996.

    Google Scholar 

  92. J. Silvertown, M. Franco, and J.L. Harper, Plant Life Histories Ecology, Phylogeny and Evolution, Cambridge University Press, Cambridge, 1997.

    Google Scholar 

  93. Q. Snell, M. Whiting, M. Clement, and D. McLaughlin, Parallel Phylogenetic Inference, Proc. 2000 ACM/IEEE Conf. Supercomp., 35 (2000)

    Google Scholar 

  94. P.H.A Sneath and R.R. Sokal, Numerical taxonomy The principles and practice of numerical classification, W. H. Freeman, San Francisco. xv + 573 p. (1973)

    Google Scholar 

  95. H. Song, C. Tu, G. Zhang, S. Wang, K. Zheng, and 21 others, Crosshost evolution of severe acute respiratory syndrome coronavirus in palm civet and human, Proc. Natl. Acad. Sci. USA 102 (2005) 2430–2435.

    CrossRef  Google Scholar 

  96. A. Stamatakis, T. Ludwig, H. Meier, and M.J. Wolf, Accelerating Parallel Maximum Likelihood-based Phylogenetic Tree Calculations using Subtree Equality Vectors, Proc. 15 IEEE/ACM Supercomp. Conf.(2002)

    Google Scholar 

  97. T. Sterling, J. Salmon, D. Becker, and D. Savarese, How to Build a Beowulf. A Guide to the Implementation and Application of PC Clusters, MIT press, 2000.

    Google Scholar 

  98. Y. Suzuki and M. Nei, Origin and Evolution of Influenza Virus Hemagglutinin Genes, Mol. Biol. Evol. 19 (2003) 501–509.

    Google Scholar 

  99. D.L. Swofford, Paup: Phylogenetic Analysis using Parsimony (and other methods), Sinauer Associates, Sunderland, 2002.

    Google Scholar 

  100. D.L. Swofford, When are phylogency estimates from molecular and morphological data incongruent? In: Miyamoto, M.M., Cracraft, J. (eds) Phylogenetic analysis of DNA sequences, Oxford Univ. Press, Oxford, 1991.

    Google Scholar 

  101. J.K. Taubenberger, A.H. Ried, T.A. Janczeqski, and T.G. Fanning, Integrating historical, clinical and molecular genetic data in order to explain the origin and virulence of the 1918 Spanish influenza virus, Philos. Trans. R. Soc. Lond. B. 356 (2001) 1829–1839.

    CrossRef  Google Scholar 

  102. J.K. Taubenberger, A. Reid, A.E. Frafft, K.E. Bijwaard, and T. Fanning, Initial Genetic Characterization of the 1918 Spanish Influenza Virus, Science 275 (1997) 1793–1796.

    CrossRef  Google Scholar 

  103. J.K. Taubenberger, A. Reid, R. Lourens, R. Wang, G. Jin, and T. Fanning, Characterization of the 1918 influenza virus polymerase genes, Nature 437 (2005) 889–893.

    CrossRef  Google Scholar 

  104. J.K. Taubenberger and D. Morens, 1918 influenza: the mother of all pandemics, Emerg Infect Dis. 12 (2006) 15–22.

    Google Scholar 

  105. L.H. Taylor, S. M. Latham, and M. E. Woolhouse, Risk factors for human disease emergence, Philos. Trans. R. Soc. Lond. B. Biol. Sci. 356 (2001) 983–989.

    CrossRef  Google Scholar 

  106. A. Tehler, D.P. Little, J.S. Farris, The full-length phylogenetic tree from 1551 ribosomal sequences of chitinous fungi, Fungi. Mycol. Res. 107 (2003) 901–916.

    CrossRef  Google Scholar 

  107. J.D. Thompson, D.G. Higgins, and T.J. Gibson, CLUSTAL W: improving the sensitivity of progressive multiple sequence alignments through sequence weighting, position specific gap penalties and weight matrix choice, Nucl. Acids Res. 22 (1994) 4673–4680.

    CrossRef  Google Scholar 

  108. J. Thornton, Resurrecting ancient genes, Experimental analysis of extinct molecules, Nat. Rev. Genet. 5 (2004) 366–375.

    CrossRef  Google Scholar 

  109. J.L. Thorne, H. Kishino, and J. Felsenstein, Inching toward reality: An improved likelihood model of sequence evolution, J. Mol. Evol. 34 (1992) 3–16.

    CrossRef  Google Scholar 

  110. S. Tweed, D. Skowronski, S. David, A. Larder, M. Petric, and 10 others, Human illness from avian influenza H7N3, British Columbia. Emerg Infect Dis. http://www.cdc.gov/ncidod/EID/vol10no12/04-0961.htm (2004)

  111. K. Ungchusak, P. Auewarakul, S. Dowell, R. Kitphati, P. Wattana, and 10 others, Probable Person-to-Person Transmission of Avian Influenza A (H5N1), N Engl J Med 352 (2005) 333–340.

    CrossRef  Google Scholar 

  112. E. Ukkonen, Algorithms for approximate string matching, Information and Control Archive 64 (1985) 100–118.

    CrossRef  MATH  MathSciNet  Google Scholar 

  113. L. Wang and T. Jiang, On the complexity of multiple sequence alignment, J. Comput. Biol. 4 (1994) 337–348.

    CrossRef  Google Scholar 

  114. Q. Wang, M. Han, J. Funk, G. Bowman, D. Janies, and L. Saif, Genetic Diversity and Recombination of Porcine Sapoviruses. Journal of Clinical Microbiology 43 (2005) 5963–5972.

    CrossRef  Google Scholar 

  115. L. Watrous and Q. Wheeler, The outgroup comparison method of character analysis, Syst. Zool. 30 (1981) 1–11.

    CrossRef  Google Scholar 

  116. R.G. Webster, W.J. Bean, O.T. Gorman, T.M. Chambers, and Y. Kawaoka, Evolution and ecology of influenza A viruses, Microbiol. Rev. 56 (1992) 152–179.

    Google Scholar 

  117. W. Wheeler and D. Gladstein, MALIGN: A multiple sequence alignment program, J. Hered. 85 (1994) 417–418.

    Google Scholar 

  118. W. Wheeler, Sequence alignment, parameter sensitivity, and the phylogenetic analysis of molecular data, Systematic Biology 44 (1995) 321–331.

    Google Scholar 

  119. W.C. Wheeler, Nucleic acid sequence phylogeny and random outgroups, Cladistics 6 (1990) 363–367.

    CrossRef  Google Scholar 

  120. W.C. Wheeler, D. Janies, and J. DeLaet, DNA sequence alignment and parallel processing, McGraw-Hill Yearbook of Science and Technolog, (2004)

    Google Scholar 

  121. W.C. Wheeler, A. Varon, D. Gladstein, and J. DeLaet, POY. Phylogeny program for optimization of nucleic acids and other data, American Museum of Natural History, http://research.amnh.org/scicomp/projects/poy.php (2005)

  122. W.C. Wheeler, Optimization Alignment: the end of multiple sequence alignment in phylogenetics?, Cladistics 12 (1996) 1–9.

    CrossRef  Google Scholar 

  123. K.P. White, Functional genomics and the study of development, variation and evolution, Nature Genetics 2 (2003) 528–537.

    Google Scholar 

  124. Report on Global Surveillance of Epidemic-prone Infectious Diseases - Influenza (2000)

    Google Scholar 

  125. H5N1 avian influenza: timeline of major events; 11 September (2007)

    Google Scholar 

  126. WHO. Cumulative Number of Confirmed Human Cases of Avian Influenza A/(H5N1) Reported to WHO; 10 September (2007)

    Google Scholar 

  127. Y. Yang, M.E. Halloran, J. Sugimoto, Jr I.M Longini, Detecting human-to-human transmission of avian influenza A (H5N1), Emerg Infect Dis. http://www.cdc.gov/EID/content/13/9/1348.htm (2007).

  128. Y. Guan, J.S.M. Peiris, A.S. Lipatov, T.M. Ellis, K.C. Dyrting, S. Krauss, L.J. Zhang, R.G. Webster, and K.F. Shortridge, Emergence of multiple genotypes of H5N1 avian influenza viruses in Hong Kong SAR, Proc. Natl. Acad. Sci. USA 99 (2002) 8950–8955.

    CrossRef  Google Scholar 

  129. D.J. Zwickl and D.M. Hillis, Increased Taxon Sampling Greatly Reduces Phylogenetic Error, Syst. Biol. 51 (2002) 588–598.

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA

    D. Janies & D. Pol

  2. Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, 43210, USA

    D. Pol

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  1. Mathematical Biosciences Institute, Ohio State University, 231 West 18th Avenue, Columbus, OH, 43210-1292, USA

    Avner Friedman

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Janies, D., Pol, D. (2008). Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases. In: Friedman, A. (eds) Tutorials in Mathematical Biosciences IV. Lecture Notes in Mathematics, vol 1922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74331-6_2

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