Markov chain Monte Carlo computation of confidence intervals for substitution-rate variation in proteins

We suggest a method implemented in a computer program, immodestly dubbed TSUNAMI, that allows us to compare two homologous protein subfamilies with respect to the distribution of substitution rates along sequences. This study furthers our earlier work on a wavelet model of rate variation (1). The current approach allows sensitive detection of subtle discordances in the selection patterns between two protein subfamilies. In addition to performing fast computation of the maximum posterior probability estimates of the relative substitution rates, the method can select the most appropriate number of wavelet parameters for a particular dataset. TSUNAMI is based on a Markov chain Monte Carlo technique, and appears to be more applicable to larger datasets than is the full likelihood-based approach.