Detecting the correlated mutations based on selection pressure with CorMut

In genetics, the Ka/Ks ratio is the ratio of the number of non-synonymous substitutions per non-synonymous site (Ka) to the number of synonymous substitutions per synonymous site (Ks)[1]. Ka/Ks ratio was used as an indicator of selective pressure acting on a protein-coding gene. A Ka/Ks ratio of 1 indicates neutral selection, i.e., the observed ratio of non-synonymous mutations versus synonymous mutations exactly matches the ratio expected under a random mutation model. Thus, amino acid changes are neither being selected for nor against. A Ka/Ks value of <1 indicates negative selection pressure. That is to say most amino acid changes are deleterious and are selected against, producing an imbalance in the observed mutations that favors synonymous mutations. In the condition of Ka/Ks>1 , it indicates that amino acid changes are favored, i.e., they increase the organism’s fitness. This unusual condition may reflect a change in the function of a gene or a change in environmental conditions that forces the organism to adapt. For example, highly variable viruses mutations which confer resistance to new antiviral drugs might be expected to undergo positive selection in a patient population treated with these drugs, such as HIV and HCV. The concept of correlated mutations is one of the basic ideas in evolutionary biology. The amino acid substitution rates are expected to be limited by functional constraints. Given the functional constraints operating on gene, a mutation in one position can be compensated by an additional mutation. Then mutation patterns can be formed by correlated mutations responsible for specific conditions. Here, we developed an R/Bioconductor package to detect the correlated mutations among positive selection sites by combining ka/ks ratio and correlated mutations analysis. The definition of Ka/Ks is based on Chen et al[2]. CorMut provides functions for computing kaks for individual site or specific amino acids and detecting correlated mutations among them. Two methods are provided for detecting correlated mutations,