I am looking for an understanding of how coalescent methods can be applied to tree-building (specifically the construction of a species tree). I know that programs like BEAST can do the analysis for me, but I have yet to find a simple explanation of how it works specifically in the context of tree building (and not in a population genetics context).
My current understanding is that it samples from various gene trees that I give it and does some manner of branch swapping/distance recalculations based on the topologies from the input trees to give me a species tree.
If someone could explain to me how coalescence works (either in general or preferably as it pertains to the aforementioned situation) it would be much appreciated. I don't need an implementation-grade algorithmic understanding as I won't be implementing it. Links to references would also be appreciated.
Lastly, I am not active on forums. If there is anything I can do to improve how I asked my question or if my question is not of appropriate scope for this forum, please let me know.
Thank you for your time and attention to this matter
There are several reviews and book chapters floating around, so I'd recommend one of those. I really recommend John Wakeley's book on the subject, Coalescent Theory: An Introduction. Its really comprehensive and goes over the derivations, too. It's only 'an introduction' in the sense that coalescent theory is quite broad and the book doesn't cover everything.
If you want an introduction to how coalescent approaches are specifically employed in a species-tree context, I recommend Laura Kubatko and Lacey Knowles' compilation, Estimating Species Trees: Practical and Theoretical Aspects. Other papers in the gene tree-species tree discordance literature are also helpful.
For how coalescent approaches are used in Bayesian phylogenetics, I recommend Bayesian Evolutionary Analysis with BEAST by Alexei Drummond and Remco Bouckaret, which also goes into the programming underlying BEAST. It's also a great resource for Bayesian phylogenetic approaches in general.
I'd recommend grabbing these from your library. Sorry for not providing a detailed explanation (the field is too large to provide anything beyond a cursory introduction here, and we'd move too far out of bioinformatics), but these resources will definitely get you what you want.
Thank you! I found these sources to be helpful. There is a lot of bloat in the literature about people using the models, but not explaining it.