Inferring Species Phylogenies: A Microarray Approach

  • Xiaoxu Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


The incongruence between the gene trees and species remains a challenge in molecular phylogenetics. In this work, we propose a novel microarray approach to resolve this problem based on our previously proposed phylogenomic mining method. In our microarray approach, we first selected 28 genes from a set of statistically significant housekeeping genes from the S. cerevisiae cell cycle time series microarray data. Then we employed the BLAST and synteny criteria to identify homologs and orthologys of the selected genes among the genomes of other species. Finally, we applied the phylogenomic mining method for the aligned genes to infer the species phylogeny. The phylogenetic mining method used the self-organizing map mining, hierarchical clustering and entropy measure to concatenate the phylogenomically informative genes to infer species phylogenies. Compared with the original gene concatenation approach, our method not only overcome the ad-hoc mechanism and prohibitive phylogenetic computing problem of the species inference for the large number of taxa but also first integrated the microarray techniques in the species phylogeny inference.


Gene Tree Entropy Gene Yeast Genome Informative Gene Microarray Approach 
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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  1. 1.
    Nei, M., Kumar, S.: Molecular Evolution and Phylogenetics, 2nd edn. Oxford University Press, Oxford (2000)Google Scholar
  2. 2.
    Page, R., Charleston, M.: From Gene to Organismal Phylogeny: Reconciled Trees and the GeneTree/Species Tree Problem. Molecular Phylogenetics and Evolution 7, 231–240 (1997)CrossRefGoogle Scholar
  3. 3.
    Ma, B., Li, M., Zhang, L.: From Gene Trees to Species Trees. SIAM Journal on Computing 30, 729–752 (2000)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Huelsenbeck, J.P.: Performance of Phylogenetic Methods in Simulation. Systematic Biology 44, 17–48 (1995)Google Scholar
  5. 5.
    Delsuc, F., Brinkmann, H., Philippe, H.: Phylogenomics and the Reconstruction of the Tree of Life. Nature Review Genetics 6, 361–375 (2005)CrossRefGoogle Scholar
  6. 6.
    Moret, B., Tang, J., Warnow., T.: Reconstructing Phylogenies from Gene-Content and Gene-order Data. In: Mathematics of Evolution and Phylogeny, pp. 321–352. Oxford Univ. Press, Oxford (2005)Google Scholar
  7. 7.
    Rokas, A., Williams, B., King, N., Carroll, S.: Genome-scale Approaches to Resolving Incongruence in Molecular Phylogenies. Nature 425, 798–804 (2003)CrossRefGoogle Scholar
  8. 8.
    Huelsenbeck, J., Ronquist, F.: MRBAYES: Bayesian Inference of Phylogenetic Trees. Bioinformatics 17, 754–755 (2001)CrossRefGoogle Scholar
  9. 9.
    Felsentein, J.: Inferring Phylogenies. Sinauer Associates, Inc. (2004)Google Scholar
  10. 10.
    Shimodaira, H., Hasegawa, M.: Multiple Comparisons of Log-likelihoods with Applications to Phylogenetic Inference. Molecular Biology and Evolution 16, 1114–1116 (1999)Google Scholar
  11. 11.
    Han, X.: Resolve the Gene Tree and Species Tree Problem by Phylogenetic Mining. In: Proceedings of 4th Asia-Pacific Bioinformatics Conference (APBC), pp. 287–296. Imperial College Press, London (2006)CrossRefGoogle Scholar
  12. 12.
    Han, X.: A Machine Learning Approach to Resolving Incongruence in Molecular Phylogenies and Visualization Analysis. In: Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 346–353 (2005)Google Scholar
  13. 13.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)MATHGoogle Scholar
  14. 14.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)MATHGoogle Scholar
  15. 15.
    Nikkila, J., Toronen, P., Kaski, S., Venna, J., Castren, E., Wong, G.: Analysis and Visualization of Gene Expression Data using Self-organizing Maps. Neural Networks, 15. Special issue on New Developments on Self-Organizing Maps, 9530–9636 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaoxu Han
    • 1
  1. 1.Department of Mathematics and Bioinformatics ProgramEastern Michigan UniversityYpsilantiUSA

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