Abstract
In this chapter, we give a not-so-long and self-contained introduction to computational molecular evolution. In particular, we present the emergence of the use of likelihood-based methods, review the standard DNA substitution models, and introduce how model choice operates. We also present recent developments in inferring absolute divergence times and rates on a phylogeny, before showing how state-of-the-art models take inspiration from diffusion theory to link population genetics, which traditionally focuses at a taxonomic level below that of the species, and molecular evolution. Although this is not a cookbook chapter, we try and point to popular programs and implementations along the way.
Highlights
Many books [1–7] and review papers [8–10] have been published in recent years on the topic of computational molecular evolution, so that updating our previous primer on the very same topic [11] may seem redundant
(model), so that we argue that the frequentist vs. Bayesian controversy is sterile, and we advocate a more pragmatic approach, that often results in the mixing of both approaches [81, 82]
Most of the initial applications of likelihood-based methods were motivated by the shortcomings of parsimony, they have become well accepted as they constitute principled inference approaches that rely on probabilistic logic
Summary
Many books [1–7] and review papers [8–10] have been published in recent years on the topic of computational molecular evolution, so that updating our previous primer on the very same topic [11] may seem redundant. The field is continuously undergoing changes, as both models and algorithms become even more sophisticated, efficient, robust, and accurate. 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. 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. Genomic-scale data is briefly touched upon, but the details are left to other chapters
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