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.
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Manna, S., Liou, CY. (2008). Pseudo-Reverse Approach in Genetic Evolution. In: Castillo, O., Xu, L., Ao, SI. (eds) Trends in Intelligent Systems and Computer Engineering. Lecture Notes in Electrical Engineering, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74935-8_17
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