Advances in Data Analysis and Classification

, Volume 3, Issue 2, pp 95–108 | Cite as

Comparison of alignment free string distances for complete genome phylogeny

  • Frédéric GuyonEmail author
  • Céline Brochier-Armanet
  • Alain Guénoche
Regular Article


In this paper, we compare the accuracy of four string distances on complete genomes to reconstruct phylogenies using simulated and real biological data. These distances are based on common words shared by raw genomic sequences and do not require preliminary processing steps such as gene identification or sequence alignment. Moreover, they are computable in linear time. The first distance is based on Maximum Significant Matches (MSM). The second is computed from the frequencies of all the words of length k (KW). The third distance is based on the Average length of maximum Common Substrings at any position (ACS). The last one is based on the Ziv–Lempel compression algorithm (ZL). We describe a simulation process of evolution to generate a set of sequences having evolved according to a random tree topology T. This process allows both base substitution and fragment insertion/deletion, including horizontal transfers. The distances between the generated sequences are computed using the four formulas and the corresponding trees T′ are reconstructed using Neighbor-Joining. T and T′ are compared according to topological criteria. These comparisons show that the MSM distance outperforms the others whatever the parameters used to generate sequences. Finally, we test the MSM and KW distances on real biological data (i.e. prokaryotic complete genomes) and we compare the NJ trees to a Maximum Likelihood 16S + 23S RNA tree. We show that the MSM distance provides accurate results to study intra-phylum relationships, much better than those given by KW.


Phylogeny String distances Complete bacterial genomes 

Mathematics Subject Classification (2000)

05C05 68R15 90C27 92B10 


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  1. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403–410Google Scholar
  2. Amir A, Keselman D (1997) Maximum agreement subtree in a set of evolutionary trees: metric and efficient algorithms. SIAM J Comput 26: 1656–1669zbMATHCrossRefMathSciNetGoogle Scholar
  3. Deschavanne PJ, Giron A (1999) Genomic signature: characterization and classification of species assessed by chaos game representation of sequences. Mol Biol Evol 16(10): 1391–1399Google Scholar
  4. Edgar RC (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity . BMC Bioinformatics 5: 113CrossRefGoogle Scholar
  5. Estabrook GF, McMorris FR, Meacham CA (1985) Comparison of undirected phylogenetic trees based on subtrees of four evolutionary units. Syst Zool 34: 193–200CrossRefGoogle Scholar
  6. Guindon S, Gascuel O (2003) A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52: 696–704CrossRefGoogle Scholar
  7. Guyon F, Guénoche A (2009) An evolutionary distance based on maximal unique matches. Commun Stat (in press)Google Scholar
  8. Hao BI, Qi J, Wang B (2003) Prokaryotic phylogeny based on complete genomes without sequence alignment. Modern Phys Lett B 17(2): 1–4MathSciNetGoogle Scholar
  9. Henz SR, Huson DH, Auch AF, Nieselt-Struwe K, Schuster SC (2005) Whole-genome prokaryotic phylogeny. Bioinformatics 15;21(10): 2329–2335CrossRefGoogle Scholar
  10. Jeffrey HJ (1990) Chaos game representation of gene structure. Nucleic Acids Res 18(8): 2163–2170CrossRefGoogle Scholar
  11. Karlin S, Taylor H (1981) A second course in stochastic processes. Academic Press, New YorkzbMATHGoogle Scholar
  12. Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol 16: 111–120CrossRefGoogle Scholar
  13. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, Salzberg SL (2004) Versatile and open software for comparing large genomes. Genome Biol 5: R12CrossRefGoogle Scholar
  14. Otu HH, Sayood K (2003) A new sequence distance measure for phylogenetic tree construction. Bioinformatics 19(16): 2122–2130CrossRefGoogle Scholar
  15. Robinson DF, Foulds LR (1981) Comparison of phylogenetic trees. Math Biosci 53: 131–147zbMATHCrossRefMathSciNetGoogle Scholar
  16. Saitou N, Nei M (1987) The Neighbor-Joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4: 406–425Google Scholar
  17. Snel B, Huynen MA, Dutilh BE (2005) Genome trees and the nature of genome evolution. Annu Rev Microbiol 59: 191–209CrossRefGoogle Scholar
  18. Ulitsky I, Burnstein D, Tuller T, Chor B (2006) The average common substring approach to phylogenomic reconstruction. J Comput Biol 13: 336–350CrossRefMathSciNetGoogle Scholar
  19. Ziv J, Lempel A (1977) A universal algorithm for sequential data compression. IEEE Trans Inform Theory 23: 337–343zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Frédéric Guyon
    • 1
    Email author
  • Céline Brochier-Armanet
    • 2
  • Alain Guénoche
    • 3
  1. 1.MTI, INSERM-Université Denis DiderotParisFrance
  2. 2.IBSM-LCB, CNRS-Université de ProvenceMarseilleFrance
  3. 3.IML, CNRS-Université de la MéditerranéeMarseilleFrance

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