Pseudo-Reverse Approach in Genetic Evolution

  • Sukanya Manna
  • Cheng-Yuan Liou
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 6)

Important insights into evolutionary processes can be determined by the rates of substitution in protein-coding sequences. Increase in the availability of coding sequence data has enabled researchers to estimate more accurately the coding sequence divergence of pairs of organisms. The use of different data sources, alignment protocols, and methods to estimate the substitution rates leads to widely varying estimates of key parameters that define the coding sequence of orthologous genes.

The rates of molecular evolution generally vary among lineages. Different studies have predicted that the source of this variation has differential effects on the synonymous and nonsynonymous substitution rates [3]. Changes in generation length or mutation rates are likely to have an impact on both the synonymous and nonsynonymous substitution rates. Hence, the number of substitutions per site between nucleotide sequences has become one of the most fundamental quantities for molecular evolution studies. It provides a valuable means for characterizing the evolutionary divergence of homologues. Thus accurate quantification of genetic evolutionary distances in terms of number of nucleotide substitutions between two homologous DNA sequences is an essential goal in evolutionary genetics. When two coding regions are analyzed, it is important to distinguish between the numbers of synonymous and nonsynonymous nucleotide substitutions per site. Estimation of calculation of these rates is not very simple; several methods have been developed to obtain these estimates from a comparison of two sequences [4, 5]. The early methods have been improved or simplified by many authors [1, 6–9]. Those methods follow almost the same strategy. The numbers of synonymous (S) and nonsynonymous (N) sites in the sequence and the numbers of synonymous (Sd) and nonsynonymous (Nd) differences between the two sequences are counted. Corrections for multiple substitutions are then applied to calculate the numbers of synonymous (ds) and nonsynonymous substitutions per site (dn) between two sequences. These methods assume an equal base and codon frequencies.


Substitution Rate Pyruvate Carboxylase Species Pair Nonsynonymous Substitution Nucleotide Substitution Rate 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nei M, Gojobori T (1986) Molecular Biology and Evolution 3:418–426.Google Scholar
  2. 2.
    Jukes TH, Cantor CR (1969) Evolution of Protein Molecules. Mammalian Protein Metabolism, Academic Press, New York.Google Scholar
  3. 3.
    Seo TK, Kishino H, Thorne JL (2004) Molecular Biology and Evolution 21:1201–1213.CrossRefGoogle Scholar
  4. 4.
    Miyata T, Yasunaga T (1980) Journal of Molecular Evolution 16:23–36.CrossRefGoogle Scholar
  5. 5.
    Li WH, Wu CI, Luo CC (1985) Molecular Biology and Evolution 2:150–174.Google Scholar
  6. 6.
    Yorozu Y, Hirano M, Oka K, Tagawa Y (1982) IEEE Translation Journal on Magnetics in Japan 2:740–741.CrossRefGoogle Scholar
  7. 7.
    Li WH (1993) Journal of Molecular Evolution 36:96–99.CrossRefGoogle Scholar
  8. 8.
    Pamilo P, Bianchi NO (1993) Molecular Biology and Evolution 10:271–281.Google Scholar
  9. 9.
    Comeron JM (1995) Journal of Molecular Evolution 41:1152–1159.CrossRefGoogle Scholar
  10. 10.
    Waterston RH, et al. (2002) Nature 420:520–562.CrossRefGoogle Scholar
  11. 11.
    Lamder ES, et al. (2001) Nature 409:860–921.CrossRefGoogle Scholar
  12. 12.
    Li WH (1997) Molecular Evolution. Sinauer, Sunderland, MA.Google Scholar
  13. 13.
    Makalowski W, Boguski MS (1998) Proceedings of the National Academy of Sciences U.S.A 95:9407–9412.CrossRefGoogle Scholar
  14. 14.
    Nekrutenko A, Wu WY, Li WH (2003) Trends in Genetics 19:306–310.CrossRefGoogle Scholar
  15. 15.
    Yang Z (1997) Computer Applications in the Biosciences 13:555–556.Google Scholar
  16. 16.
    Graur D, Li WH (2000) Fundamentals of Molecular Evolution. 2nd edn. Sinauer, Sunderland, MA.Google Scholar
  17. 17.
    Liou CY, Wu JM (1996) Neural Networks 9:671–684.CrossRefGoogle Scholar
  18. 18.
    Liou CY, Yuan SK (1999) Biological Cybernetics 81:331–342.zbMATHCrossRefGoogle Scholar
  19. 19.
    Liou CY, Lin SL (2006) Natural Computing 5:15–42.zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Liou CY (2006) The 16th International Conference on Artificial Neural Networks, LNCS 4131:688–697, Springer, New York.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Sukanya Manna
    • 1
  • Cheng-Yuan Liou
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiRepublic of China

Personalised recommendations