Skip to main content
SpringerLink
Log in

Distance-Based Phylogeny Reconstruction (Fast-Converging)

2003; King, Zhang, Zhou

  • Reference work entry
Encyclopedia of Algorithms

  • 188 Accesses

Keywords and Synonyms

Learning an evolutionary tree      

Problem Definition

Introduction

From a mathematical point of view, a phylogeny defines a probability space for random sequences observed at the leaves of a binary tree T. The tree T represents the unknown hierarchy of common ancestors to the sequences. It is assumed that (unobserved) ancestral sequences are associated with the inner nodes. The tree along with the associated sequences models the evolution of a molecular sequence, such as the protein sequence of a gene. In the conceptually simplest case, each tree node corresponds to a species, and the gene evolves within the organismal lineages by vertical descent.

Phylogeny reconstruction consists of finding Tfrom observed sequences. The possibility of such reconstruction is implied by fundamental principles of molecular evolution, namely, that random mutations within individuals at the genetic level spreading to an entire mating population are not uncommon, since often they...

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 399.00
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

Recommended Reading

  1. Chang, J.T.: Full reconstruction of Markov models on evolutionary trees: identifiability and consistency. Math. Biosci. 137, 51–73 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  2. Csürös, M.: Fast recovery of evolutionary trees with thousands of nodes. J. Comput. Biol. 9(2), 277–297 (2002) Conference version at RECOMB 2001

    Article  Google Scholar 

  3. Csürös, M., Kao, M.-Y.: Provably fast and accurate recovery of evolutionary trees through Harmonic Greedy Triplets. SIAM J. Comput. 31(1), 306–322 (2001) Conference version at SODA (1999)

    Article  MathSciNet  Google Scholar 

  4. Daskalakis, C., Hill, C., Jaffe, A., Mihaescu, R., Mossel, E., Rao, S.: Maximal accurate forests from distance matrices. In: Proc. Research in Computational Biology (RECOMB), pp. 281–295 (2006)

    Google Scholar 

  5. Daskalakis, C., Mossel, E., Roch, S.: Optimal phylogenetic reconstruction. In: Proc. ACM Symposium on Theory of Computing (STOC), pp. 159–168 (2006)

    Google Scholar 

  6. Erdős, P.L., Steel, M.A., Székely, L.A., Warnow, T.J.: A few logs suffice to build (almost) all trees (I). Random Struct. Algorithm 14, 153–184 (1999) Preliminary version as DIMACS TR97-71

    Article  Google Scholar 

  7. Erdős, P.L., Steel, M.A., Székely, L. A., Warnow, T.J.: A few logs suffice to build (almost) all trees (II). Theor. Comput. Sci. 221, 77–118 (1999) Preliminary version as DIMACS TR97-72

    Article  Google Scholar 

  8. Felsenstein, J.: Inferring Pylogenies. Sinauer Associates, Sunderland, Massachusetts (2004)

    Google Scholar 

  9. Huson, D., Nettles, S., Warnow, T.: Disk-covering, a fast converging method of phylogenetic reconstruction. J. Comput. Biol. 6(3–4) 369–386 (1999) Conference version at RECOMB (1999)

    Article  Google Scholar 

  10. King, V., Zhang, L., Zhou, Y.: On the complexity of distance-based evolutionary tree reconstruction. In: Proc. ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 444–453 (2003)

    Google Scholar 

  11. Lagergren, J.: Combining polynomial running time and fast convergence for the disk-covering method. J. Comput. Syst. Sci. 65(3), 481–493 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  12. Lake, J.A.: Reconstructing evolutionary trees from DNA and protein sequences: paralinear distances. Proc. Natl. Acad. Sci. USA 91, 1455–1459 (1994)

    Article  Google Scholar 

  13. Lockhart, P.J., Steel, M.A., Hendy, M.D., Penny, D.: Recovering evolutionary trees under a more realistic model of sequence evolution. Mol. Biol. Evol. 11, 605–612 (1994)

    Google Scholar 

  14. Neyman, J.: Molecular studies of evolution: a source of novel statistical problems. In: Gupta, S.S., Yackel, J. (eds) Statistical Decision Theory and Related Topics, pp. 1–27. Academic Press, New York (1971)

    Google Scholar 

  15. Ohta, T.: Near-neutrality in evolution of genes and gene regulation. Proc. Natl. Acad. Sci. USA 99, 16134–16137 (2002)

    Article  Google Scholar 

  16. Steel, M.A.: Recovering a tree from the leaf colourations it generates under a Markov model. Appl. Math. Lett. 7, 19–24 (1994)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Montreal, Montreal, QC, Canada

    Miklós Csűrös

Authors
  1. Miklós Csűrös
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer ScienceMcCormick School of Engineering and Applied Science, Northwestern University, Evanston, IL, 60208, USA

    Ming-Yang Kao Professor of Computer Science

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag

About this entry

Cite this entry

Csűrös, M. (2008). Distance-Based Phylogeny Reconstruction (Fast-Converging). In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_114

Download citation

Publish with us

Policies and ethics

Search

Navigation