Skip to main content
SpringerLink
Log in

Tree and Network Building

  • Chapter
  • First Online:
Introduction to Evolutionary Genomics

Part of the book series: Computational Biology ((COBO,volume 17))

  • 3336 Accesses

Abstract

Construction of phylogenetic trees from nucleotide or amino acid sequence data is one of the important areas of evolutionary genomics. We start from classification of tree-building methods, both by type of data and by type of tree search algorithm. Various distance matrix methods including UPGMA, minimum deviation methods, minimum evolution methods, transformed distance methods, and neighbor-joining method are explained. Among character-state methods, maximum parsimony methods, maximum likelihood methods, and Bayesian method are explained. These many phylogenetic tree-making methods were compared mainly based on computer simulation studies. Phylogenetic network constructions from distance matrix and from multiply aligned sequences are also discussed as well as phylogeny construction without multiple alignments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Saitou, N., & Nei, M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4, 406–425.

    Google Scholar 

  2. Ohtsuka, H., Oyanagi, M., Mafune, Y., Miyashita, N., Shiroishi, T., Moriwaki, K., Kominami, R., & Saitou, N. (1996). The presence/absence polymorphism and evolution of p53 pseudogene within the genus Mus. Molecular Phylogenetics and Evolution, 5, 548–556.

    Article  Google Scholar 

  3. Kimura, M. (1980). A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution, 16, 111–120.

    Article  Google Scholar 

  4. Saitou, N. (1996). Reconstruction of gene trees from sequence data. In R. Doolittle (Ed.), Methods in enzymology, 266: Computer methods for macromolecular sequence analysis (pp. 427–449). San Diego: Academic Press.

    Chapter  Google Scholar 

  5. Saitou, N. (2007). Genomu Shinkagaku Nyumon. Tokyo: Kyoritsu-Shuppan (in Japanese).

    Google Scholar 

  6. Saitou, N., & Imanishi, T. (1989). Relative efficiencies of the Fitch-Margoliash, maximum-parsimony, maximum-likelihood, minimum-evolution, and neighbor-joining methods of phylogenetic tree construction in obtaining the correct tree. Molecular Biology and Evolution, 6, 514–525.

    Google Scholar 

  7. Saitou, N., & Nei, M. (1986). The number of nucleotides required to determine the branching order of three species, with special reference to the human-chimpanzee-gorilla divergence. Journal of Molecular Evolution, 24, 189–204.

    Article  Google Scholar 

  8. Tamura, K., Nei, M., & Kumar, S. (2004). Prospects for inferring very large phylogenies by using the neighbor-joining method. Proceedings of National Academy of Sciences, USA, 101, 11030–11035.

    Article  Google Scholar 

  9. Sneath, P. H. P., & Sokal, R. (1973). Numerical taxonomy. San Francisco: W. H. Freeman.

    MATH  Google Scholar 

  10. Sokal, R., & Sneath, P. H. P. (1968) Principles of numerical taxonomy.

    Google Scholar 

  11. Sokal, R., & Michener, C. D. (1958). A statistical method for evaluating systematic relationship. University of Kansas Science Bulletin, 38, 1409–1438.

    Google Scholar 

  12. Nei, M. (1975). Molecular population genetics and evolution. Amsterdam: North-Holland.

    Google Scholar 

  13. Chakraborty, R. (1977). Estimation of the time of divergence from phylogenetic studies. Canadian Journal of Genetics and Cytology, 19, 217–223.

    Google Scholar 

  14. Fitch, W. M., & Margoliash, E. (1967). Construction of phylogenetic trees. Science, 155, 279–284.

    Article  Google Scholar 

  15. Tateno, Y., Nei, M., & Tajima, F. (1982). Accuracy of estimated phylogenetic trees from molecular data. I. Distantly related species. Journal of Molecular Evolution, 18, 387–404.

    Article  Google Scholar 

  16. Cavalli-Sforza, L. L., & Edwards, A. W. F. (1967). Phylogenetic analysis: Models and estimation procedures. American Journal of Human Genetics, 19, 233–257.

    Google Scholar 

  17. Rzhetsky, A., & Nei, M. (1992). Statistical properties of the ordinary least-squares, generalized least-squares, and minimum-evolution methods of phylogenetic inference. Journal of Molecular Evolution, 35, 367–375.

    Article  Google Scholar 

  18. Edwards, A. W. F., & Cavalli-Sforza, L. L. (1964). A method for cluster analysis. Biometrics, 21, 362–375.

    Article  Google Scholar 

  19. Courant, R., Robbins, H., & Stewart, I. (1996). What is mathematics? Second edition: Oxford University Press.

    Google Scholar 

  20. Rzhetsky, A., & Nei, M. (1992). A simple method for estimating and testing minimum-evolution trees. Molecular Biology and Evolution, 9, 945–967.

    Google Scholar 

  21. Robinson, D. F., & Foulds, L. R. (1981). Comparison of phylogenetic trees. Mathematical Bioscience, 53, 131–147.

    Article  MATH  MathSciNet  Google Scholar 

  22. Nei, M., Kumar, S., & Takahashi, K. (1998). The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small. Proceedings of National Academy of Sciences, USA, 95, 12390–12397.

    Article  Google Scholar 

  23. Pauplin, Y. (2000). Direct calculation of a tree length using a distance matrix. Journal of Molecular Evolution, 51, 41–47.

    Google Scholar 

  24. Semple, C., & Steel, M. (2004). Cyclic permutations and evolutionary trees. Advances in Applied Mathematics, 32, 669–680.

    Article  MATH  MathSciNet  Google Scholar 

  25. Gascuel, O., & Steel, M. (2006). Neighbor-joining revealed. Molecular Biology and Evolution, 23, 1997–2000.

    Article  Google Scholar 

  26. Mihaescu, R., & Pachter, L. (2008). Combinatorics of least-squares trees. Proceedings of the National Academy of Sciences of the United States of America, 105, 13206–13211.

    Article  MATH  MathSciNet  Google Scholar 

  27. Price, M., Dehal, P. S., & Arkin, A. P. (2009). FastTree: Computing large minimum evolution trees with profiles instead of a distance matrix. Molecular Biology and Evolution, 26, 1641–1650.

    Article  Google Scholar 

  28. Farris, J. S. (1972). Estimating phylogenetic trees from distance matrices. American Naturalist, 106, 645–668.

    Article  Google Scholar 

  29. Faith, D. P. (1985). Distance methods and the approximation of most-parsimonious trees. Systematic Zoology, 34, 312–325.

    Article  Google Scholar 

  30. Farris, J. S., Kluge, A. G., & Exkardt, M. J. (1970). A numerical approach to phylogenetic systematics. Systematic Zoology, 19, 172–191.

    Article  Google Scholar 

  31. Klotz, L. C., & Blanken, R. L. (1981). A practical method for calculating evolutionary trees from sequence data. Journal of Theoretical Biology, 91, 261–272.

    Article  Google Scholar 

  32. Li, W.-H. (1981). Simple method for constructing phylogenetic trees from distance matrices. Proceedings of National Academy of Sciences, USA, 78, 1085–1089.

    Article  MATH  Google Scholar 

  33. OOta, S. (1998). ThreeTree: A new method to reconstruct phylogenetic trees. Genome Informatics, 9, 340–341.

    Google Scholar 

  34. OOta S. (1998b). Ph.D. dissertation.

    Google Scholar 

  35. Buneman, P. (1971). The recovery of trees from measurements of dissimilarity. In F. R. Hodson, D. G. Kendall, & P. Tautu (Eds.), Mathematics in the archeological and historical sciences (pp. 387–395). Edinburgh: Edinburgh University Press.

    Google Scholar 

  36. Fitch, W. M. (1981). A non-sequential method for constructing trees and hierarchical classifications. Journal of Molecular Evolution, 18, 30–37.

    Article  Google Scholar 

  37. Sattath, S., & Tversky, A. (1977). Additive similarity trees. Psychometrika, 42, 319–345.

    Article  Google Scholar 

  38. Saitou N. (1986). Theoretical studies on the methods of reconstructing phylogenetic trees from DNA sequence data. Ph.D. dissertation. Graduate University of Biomedical Sciences, University of Texas Health Science Center at Houston.

    Google Scholar 

  39. Studier, J. A., & Keppler, K. J. (1988). A note on the neighbor-joining algorithm of Saitou and Nei. Molecular Biology and Evolution, 5, 729–731.

    Google Scholar 

  40. Ishida, N., Oyunsuren, T., Mashima, S., Mukoyama, H., & Saitou, N. (1995). Mitochondrial DNA sequences of various species of the genus Equus with a special reference to the phylogenetic relationship between Przewalskii’s wild horse and domestic horse. Journal of Molecular Evolution, 41, 180–188.

    Article  Google Scholar 

  41. Gascuel, O. (1997). BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Molecular Biology and Evolution, 14, 685–695.

    Article  Google Scholar 

  42. Bruno, W. J., Socci, N. D., & Halpern, A. L. (2000). Weighted neighbor joining: A likelihood-based approach to distance-based phylogeny reconstruction. Molecular Biology and Evolution, 17, 189–197.

    Article  Google Scholar 

  43. Kumar, S. (1996). A stepwise algorithm for finding minimum evolution trees. Molecular Biology and Evolution, 13, 584–593.

    Article  Google Scholar 

  44. Pearson, W. R., Robins, G., & Zhang, T. (1999). Generalized neighbor-joining: More reliable phylogenetic tree reconstruction. Molecular Biology and Evolution, 16, 806–816.

    Article  Google Scholar 

  45. Dress, A. (1984). Trees, tight extensions of metric spaces, and the cohomological dimension of certain groups: A note on combinatorial properties of metric spaces. Advances in Mathematics, 53, 321–402.

    Article  MATH  MathSciNet  Google Scholar 

  46. Dress, A., Huber, K. H., Koolen, J., Moulton, V., & Spillner, A. (2012). Basic phylogenetic combinatorics. Cambridge: Cambridge University Press.

    Google Scholar 

  47. Bandelt, H. J., & Dress, A. W. (1992). Split decomposition: A new and useful approach to phylogenetic analysis of distance data. Molecular Phylogenetics and Evolution, 1, 242–252.

    Article  Google Scholar 

  48. Kitano, T., Noda, R., Takenaka, O., & Saitou, N. (2009). Relic of ancient recombinations in gibbon ABO blood group genes deciphered through phylogenetic network analysis. Molecular Phylogenetics and Evolution, 51, 465–471.

    Article  Google Scholar 

  49. Bryant, D., & Moulton, V. (2004). Neighbor-Net: An agglomerative method for the construction of phylogenetic networks. Molecular Biology and Evolution, 21, 255–265.

    Article  Google Scholar 

  50. Huson, D. H., & Bryant, D. (2006). Application of phylogenetic networks in evolutionary studies. Molecular Biology and Evolution, 23, 254–267.

    Article  Google Scholar 

  51. Camin, J. H., & Sokal, R. R. (1965). A method for deducing branching sequences in phylogeny. Evolution, 19, 311–326.

    Article  Google Scholar 

  52. Eck, R. V., & Dayhoff, M. (1966). Atlas of protein sequence and structure. Silver Spring: National Biomedical Research Foundation.

    Google Scholar 

  53. Felsenstein, J. (2004). Inferring phylogenies. Sunderland: Sinauer Associates.

    Google Scholar 

  54. Fitch, W. M. (1977). On the problem of discovering the most parsimonious tree. American Naturalist, 111, 223–257.

    Article  Google Scholar 

  55. Hartigan, J. A. (1973). Minimum mutation fits to a given tree. Biometrics, 29, 53–65.

    Article  Google Scholar 

  56. Zharkikh, A. A. (1977). Algorithm for constructing phylogenetic trees from amino acid sequences. In V. A. Ratner (Ed.), Mathematical models of evolution and selection (pp. 5–52). Novosibirsk: Institute of Cytology and Genetics (in Russian).

    Google Scholar 

  57. Zharkikh, A. A., & Ratner, V. A. (1996). Methods for studying the evolution of macromolecules. In V. A. Ratner et al. (Eds.), Molecular evolution (pp. 71–91). Berlin/New York: Springer-Verlag.

    Google Scholar 

  58. Saitou, N. (1998). Simultaneous sequence joining (SSJ): A new method for reconstruction of phylogenetic networks of closely related sequences (Abstract). Anthropological Science, 106, 141–142.

    Google Scholar 

  59. Tateno, Y. (1990). A method for molecular phylogeny construction by direct use of nucleotide sequence data. Journal of Molecular Evolution, 30, 85–93.

    Article  Google Scholar 

  60. Wilson, A. O. (1965). A consistency test for phylogenies based on contemporaneous species. Systematic Zoology, 14, 214–220.

    Article  Google Scholar 

  61. Le Quesne, W. J. (1969). A method of selection of characters in numerical taxonomy. Systematic Zoology, 18, 201–205.

    Article  Google Scholar 

  62. Saitou, N. (1989). A theoretical study of the underestimation of branch lengths by the maximum parsimony principle. Systematic Zoology, 38, 1–5.

    Article  Google Scholar 

  63. Felsenstein, J. (1978). Cases in which parsimony or compatibility methods will be positively misleading. Systematic Zoology, 27, 401–410.

    Article  Google Scholar 

  64. Zharkikh, A., & Li, W.-H. (1993). Inconsistency of the maximum parsimony method: The case of five taxa with a molecular clock. Systematic Biology, 42, 113–125.

    Google Scholar 

  65. Takezaki, N., & Nei, M. (1994). Inconsistency of the maximum parsimony method when the rate of nucleotide substitution is constant. Journal of Molecular Evolution, 39, 210–218.

    Google Scholar 

  66. Tamura, K., & Nei, M. (1993). Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution, 10, 512–526.

    Google Scholar 

  67. Saitou, N., & Ueda, S. (1994). Evolutionary rate of insertions and deletions in non-coding nucleotide sequences of primates. Molecular Biology and Evolution, 11, 504–512.

    Google Scholar 

  68. Bernstein, F. (1925). Zusammenfassende betrachtungen uber die erblichen blutstrukturen des menschen. Molecular and General Genetics, 37, 237–370.

    Google Scholar 

  69. Yasuda, N., & Kimura, M. (1968). A gene-counting method of maximum likelihood for estimating gene frequencies in ABO and ABO-like systems. Annals of Human Genetics, 31, 409–420.

    Article  Google Scholar 

  70. Neyman, J. (1971). Molecular studies of evolution: A source of novel statistical problems. In S. S. Gupta & J. Yackel (Eds.), Statistical decision theory and related topics (pp. 1–27). New York: Academic Press.

    Google Scholar 

  71. Felsenstein, J. (1973). Maximum-likelihood estimation of evolutionary trees from continuous characters. American Journal of Human Genetics, 25, 471–492.

    Google Scholar 

  72. Felsenstein, J. (1973). Maximum-likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Systematic Zoology, 22, 240–249.

    Article  Google Scholar 

  73. Kashap, R. L., & Subas, S. (1974). Statistical estimation of parameters in a phylogenetic tree using a dynamic model of the substitutional process. Journal of Theoretical Biology, 47, 75–101.

    Article  Google Scholar 

  74. Langley, C., & Fitch, W. M. (1974). An examination of the constancy of the rate of molecular evolution. Journal of Molecular Evolution, 3, 161–177.

    Article  Google Scholar 

  75. Thompson, E. A. (1975). Human evolutionary trees. Cambridge/New York: Cambridge University Press.

    Google Scholar 

  76. Felsenstein, J. (1981). Evolutionary trees from DNA sequences: A maximum likelihood approach. Journal of Molecular Evolution, 17, 368–376.

    Article  Google Scholar 

  77. Guindon, S., Dufayard, J. F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010). New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Systematic Biology, 59, 307–321.

    Article  Google Scholar 

  78. Saitou, N. (1988). Property and efficiency of the maximum likelihood method for molecular phylogeny. Journal of Molecular Evolution, 27, 261–273.

    Article  Google Scholar 

  79. Saitou, N. (1990). Maximum likelihood methods. Methods in Enzymology, 183, 584–598.

    Google Scholar 

  80. Hixson, J., & Brown, W. M. (1986). A comparison of the small ribosomal RNA genes from the mitochondrial DNA of the great apes and humans: Sequence, structure, evolution, and phylogenetic implications. Molecular Biology and Evolution, 3, 1–18.

    Google Scholar 

  81. Gouy, M., Guindon, S., & Gascuel, O. (2010). SeaView version 4: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Molecular Biology and Evolution, 27, 221–224.

    Article  Google Scholar 

  82. Horai, S., Hayasaka, K., Kondo, R., Tsugane, K., & Takahata, N. (1995). Recent African origin of modern humans revealed by complete sequences of hominoid mitochondrial DNAs. Proceedings of the National Academy of Sciences of the United States of America, 92, 532–536.

    Article  Google Scholar 

  83. Adachi, J., & Hasegawa, M. (1996). MOLPHY version 2.3: Programs for molecular phylogenetics based on maximum likelihood. Computer Science Monographs, 28, 1–150.

    Google Scholar 

  84. Yang, Z. (1997). PAML: A program package for phylogenetic analysis by maximum likelihood. CABIOS Applications Note, 13, 555–556.

    Google Scholar 

  85. Strimmer, K., & von Haeseler, A. (1996). Quartet puzzling: A quartet maximum-likelihood method for constructing phylogenetic trees. Molecular Biology and Evolution, 13, 1401–1409.

    Article  Google Scholar 

  86. Ota, S., & Li, W.-H. (2000). NJML: A hybrid algorithm for the neighbor-joining and maximum-likelihood methods. Molecular Biology and Evolution, 17, 1401–1409.

    Article  Google Scholar 

  87. Ota, S., & Li, W.-H. (2001). NJML+: An extension of the NJML method to handle protein sequence data and computer software implementation. Molecular Biology and Evolution, 18, 1983–1992.

    Article  Google Scholar 

  88. Yule, G. U. (1924). A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S (Philosophical transaction of royal society of London, series B, Vol. 213, pp. 21–87). London: Harrison and Sons.

    Google Scholar 

  89. Rannala, B., & Yang, Z. (1996). Probability distribution of molecular evolutionary trees: A new method of phylogenetic inference. Journal of Molecular Evolution, 17, 368–376.

    Google Scholar 

  90. Huelsenbeck, J. P., Ronquist, F., Nielsen, R., & Bollback, J. P. (2001). Bayesian inference of phylogenetic trees and its impact on evolutionary biology. Science, 294, 2310–2314.

    Article  Google Scholar 

  91. Nei, M. (1987). Molecular evolutionary genetics. New York: Columbia University Press.

    Google Scholar 

  92. Li, W.-H., & Guoy, M. (1991). Statistical methods for testing molecular phylogenies. In M. M. Miyamoto & J. Cracraft (Eds.), Phylogenetic analysis of DNA sequences (pp. 249–277). New York: Oxford University Press.

    Google Scholar 

  93. Yang, Z. H. (1996). Phylogenetic analysis using parsimony and likelihood methods. Journal of Molecular Evolution, 42, 294–307.

    Article  Google Scholar 

  94. Felsenstein, J. (1984). The statistical approach to inferring evolutionary trees and what it tells us about parsimony and compatibility. In T. Duncan & T. F. Steussy (Eds.), Cladistics: Perspectives on the reconstruction of evolutionary history (pp. 169–191). New York: Columbia University Press.

    Google Scholar 

  95. Bandelt, H. J., Forster, P., & Rohl, A. (1999). Median-joining networks for inferring intraspecific phylogenies. Molecular Biology and Evolution, 16, 37–48.

    Article  Google Scholar 

  96. Kruskal, J. B. (1956). On the shortest spanning subtree of the graph and the travelling salesman problem. Proceedings of the American Mathematical Society, 7, 48–57.

    Article  MATH  MathSciNet  Google Scholar 

  97. FarrisJ, S. (1970). Methods for computing Wagner trees. Systematic Zoology, 19, 83–92.

    Article  Google Scholar 

  98. Jinam, T. A., Hong, L. -C., Phipps, M. E., Stoneking, M., Ameen, M., Edo, J., HUGO Pan-Asian SNP Consortium, & Saitou, N. (2012). Evolutionary history of Continental Southeast Asians: “Early train” hypothesis based on genetic analysis of mitochondrial and autosomal DNA data. Molecular Biology and Evolution, 29, 3513–3527.

    Google Scholar 

  99. Kryukov, K., & Saitou, N. (2003). Netview: Application software for constructing and visually exploring phylogenetic networks. Genome Informatics, 14, 280–281.

    Google Scholar 

  100. Grunewald, S., Farslund, K., Dress, A., & Moulton, V. (2007). QNet: An agglomerative method for the construction of phylogenetic networks from weighted quartets. Molecular Biology and Evolution, 24, 532–538.

    Article  Google Scholar 

  101. Wooley, S., Posada, D., & Crandall, K. A. (2007). A comparison of phylogenetic network methods using computer simulation. PLoS One, 3, e1913.

    Article  Google Scholar 

  102. Takahashi, K., & Nei, M. (2000). Efficiencies of fast algorithms of phylogenetic inference under the criteria of maximum parsimony, minimum evolution, and maximum likelihood when a large number of sequences are used. Molecular Biology and Evolution, 17, 1251–1258.

    Article  Google Scholar 

  103. DeBry, R. W. (1992). The consistency of several phylogeny-inference methods under varying evolutionary rates. Molecular Biology and Evolution, 9, 537–551.

    Google Scholar 

  104. Nei, M., Tajima, F., & Tateno, Y. (1983). Accuracy of estimated phylogenetic trees from molecular data. II. Gene frequency data. Journal of Molecular Evolution, 19, 153–170.

    Article  Google Scholar 

  105. Tateno, Y., Takezaki, N., & Nei, M. (1994). Relative efficiencies of the maximum likelihood, neighbor-joining, and maximum parsimony methods when substitution rate varies with site. Molecular Biology and Evolution, 11, 261–277.

    Google Scholar 

  106. Kuhner, M. K., & Felsenstein, J. (1994). A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Molecular Biology and Evolution, 11, 459–468. Erratum in: Molecular Biology and Evolution, 12, p. 525.

    Google Scholar 

  107. Nei, M., Kumar, S., & Takahashi, K. (1998). The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small. Proceedings of the National Academy of Sciences of the United States of America, 95, 12390–12397.

    Article  Google Scholar 

  108. Russo, C., Takezaki, N., & Nei, M. (1996). Efficiencies of different genes and different tree-making methods in recovering a known vertebrate phylogeny. Molecular Biology and Evolution, 13, 525–536.

    Article  Google Scholar 

  109. Nguyen, M. A. H., Klaere, S., & von Haeseler, A. (2011). MISFITS: Evaluating the goodness of fit between a phylogenetic model and an alignment. Molecular Biology and Evolution, 28, 143–152.

    Article  Google Scholar 

  110. Nguyen, M. A. H., Gesell, T., & von Haeseler, A. (2012). ImOSM: Intermittent evolution and robustness of phylogenetic methods. Molecular Biology and Evolution, 29, 663–673.

    Article  Google Scholar 

  111. Karlin, S., & Ladunga, I. (1994). Comparisons of eukaryotic genomic sequences. Proceedings of the National Academy of Sciences of the United States of America, 91, 12832–12836.

    Article  Google Scholar 

  112. Nakashima, H., Nishikawa, K., & Ooi, T. (1997). Differences in dinucleotide frequencies of human, yeast, and Escherichia coli genes. DNA Research, 4, 185–192.

    Article  Google Scholar 

  113. Karlin, S., Mrazek, J., & Campbell, A. (1997). Compositional biases of bacterial genomes and evolutionary implications. Journal of Bacteriology, 179, 3899–3913.

    Google Scholar 

  114. Abe, T., et al. (2003). Informatics for unveiling hidden genome signatures. Genome Research, 13, 693–702.

    Article  Google Scholar 

  115. Pride, D. T., Meinersmann, R. J., Wassenaar, T. M., & Blaser, M. J. (2003). Evolutionary implications of microbial genome tetranucleotide frequency biases. Genome Research, 13, 145–155.

    Article  Google Scholar 

  116. Takahashi, M., Kryukov, K., & Saitou, N. (2009). Estimation of bacterial species phylogeny through oligonucleotide frequency distances. Genomics, 93, 525–533.

    Article  Google Scholar 

  117. Felsenstein, J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution, 39, 783–791.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Population Genetics, National Institute of Genetics (NIG), Mishima, Shizuoka, Japan

    Naruya Saitou

Authors
  1. Naruya Saitou
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Saitou, N. (2013). Tree and Network Building. In: Introduction to Evolutionary Genomics. Computational Biology, vol 17. Springer, London. https://doi.org/10.1007/978-1-4471-5304-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5304-7_16

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5303-0

  • Online ISBN: 978-1-4471-5304-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation