Dynamic programming procedure for searching optimal models to estimate substitution rates based on the maximum-likelihood method

摘要

The substitution rate in a gene can provide valuable information for understanding its functionality and evolution. A widely used method to estimate substitution rates is the maximum-likelihood method implemented in the CODEML program in the PAML package. A limited number of branch models, chosen based on a priori information or an interest in a particular lineage(s), are tested, whereas a large number of potential models are neglected. A complementary approach is also needed to test all or a large number of possible models to search for the globally optional model(s) of maximum likelihood. However, the computational time for this search even in a small number of sequences becomes impractically long. Thus, it is desirable to explore the most probable spaces to search for the optimal models. Using dynamic programming techniques, we developed a simple computational method for searching the most probable optimal branch-specific models in a practically feasible computational time. We propose three search methods to find the optimal models, which explored O(n) (method 1) to O(n2) (method 2 and method 3) models when the given phytogeny has n branches. In addition, we derived a formula to calculate the number of all possible models, revealing the complexity of finding the optimal branch-specific model. We show that in a reanalysis of over 50 previously published studies, the vast majority obtained better models with significantly higher likelihoods than the conventional hypothesis model methods.