Plotkin et al. (2004), introduced a concept to detect positive and purifying selections of genes using a single genome. In this paper, the authors have used the widely used non-synonymous to synonymous substitution rate and Plotkin's method based on codon volatility for detecting natural selection. This study shows that Plotkin's method can efficiently detect purifying selection. This can be very useful in genomic research as well as targeted drug design.

An established method to detect selective pressure on genes at the molecular level relies on the comparison between nucleotide sequences (; Hudson et al., 1987; Tajima, 1989; McDonald and Kreitman, 1991; and Goldman and Yang, 1994). Plotkin et al. (2004), proposed an exceptional method for the study of selection on the basis of a single genome sequence. They present a method for rapidly detecting differential selective pressures on genes by inspecting a single genome sequence for a footprint of non-synonymous substitutions. Their method rests on a simple observation: if a protein-coding region of a nucleotide sequence has undergone an excess number of amino acid substitutions, then the region will, on average, contain an overabundance of `volatile codons', compared with the genome as a whole. For each of the 61 sense codons, they define its volatility as the proportion of its point mutation neighbors that encode different amino acids. The volatility of a codon will be used to quantify the chance that the most recent nucleotide mutation to that codon caused an amino acid substitution and they scanned an entire genome of M. tuberculosis and P. falciparum to find genes that show significantly more or less pressure for amino acid substitutions than the genome as a whole. If a gene contains many residues under pressure for amino acid replacements, then the resulting codons in that gene will on an average, exhibit elevated volatility, because its ancestor codons encoded different amino acids from those encoded by the current codons. Similarly, if a gene is under strong purifying selection not to change its amino acids, then the resulting sequence will, on average, exhibit lower volatility (Van Nimwegen et al., 1999).

They assess the statistical significance of each gene's observed volatility by comparing it with a bootstrap distribution of alternative synonymous sequences, drawn according to the background codon usage in the genome. This randomization procedure controls for the gene's length and amino acid composition. As a result of this procedure, they obtained two-sided `volatility p value' for each gene, indicating that, irrespective of whether the gene is more or less volatile than the genome as a whole. A p value near to zero indicates significantly elevated volatility, whereas p value near 1 indicates significantly depressed volatility. The potential of this method is out of question because, if the method predicts the nature of selection correctly (Friedman and Hughes, 2004), research in the field of genomics will be tremendously benefited.

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