![]() ![]() With this in mind, our alignment can be recoded in the following manner. This recoding implies that columns, or sites, in our alignment evolve according to an identically and independently distributed (iid) process. In order to go further, we recode the data in terms of site patterns, which correspond to the patterns observed in each column of our alignment. The objective is to estimate which of the three fully resolved topologies in Fig. Likewise, the analysis of genomic-scale data is briefly touched upon, but the details are left to other chapters.ĪTGACCCCAATACGCAAAACTAACCCCCTAATAAAATTAATTAACCACTCCTTCĪTGACCCCAATACGGAAAACTAACCCCCAAATAAAATTAATTAACCACTCATTCĪTGACGCCAATACGCAAAACTAACCGCCTAATAAAATTAATTTACCACTCATTC Some of the details will be left out as they are dealt with by others in this volume. The aim of this chapter is still to provide readers with the essentials of computational molecular evolution, offering a brief overview of recent progress, both in terms of modeling and algorithm development. This increase in refinement has not been motivated by a desire to complicate existing models, but rather to make an old wish come true: that of having integrated methods that can take unaligned sequences as an input, and simultaneously output the alignment, the tree, and other estimates of interest, in a sound statistical framework justified by sound principles: those of population genetics. However, the field is continuously undergoing changes, as both models and algorithms become even more sophisticated, efficient, robust, and accurate. Many books and review papers have been published in recent years on the topic of computational molecular evolution, so that updating our previous primer on the very same topic may seem redundant. ![]()
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