Last week I attended a workshop organized by Hélène Morlon, Tiago Quental, and Charles Marshall on integrating data from the fossil record into phylogenetic methods. This three-day workshop was sponsored by the the France-Berkeley Fund, a cool program that provides seed grants to build partnerships between UC Berkeley researchers and French collaborators. All of the events took place at the UCMP on the UC Berkeley campus.
Hélène, Charles, and Tiago recognized the increasing interest in methods and analyses that incorporate data from fossil taxa; and since there are several of us working in this area–particularly in methods development–the need for building a collaborative network is critical. Furthermore, as methods become more and more reliant on data from the fossil record, connections between neontologists and paleontologists must be formed. Notably, a similar working group – organized by Sam Price and Lars Schmitz – was held at NESCent this past spring and was made up of an overlapping set of researchers. One result of the NESCent catalysis meeting will be a SSE Symposium at Evolution 2014 on “Reuniting fossil and extant approaches to macroevolution”.
Many participants of the molecular evolution workshops I attend are very interested in methods for estimating the evolutionary dynamics of serially-sampled pathogens. Recent versions of BEAST and BEAST2 have some of the most exciting and cutting-edge models for understanding evolutionary processes in these data. Because of this, I wanted to call your attention to a new tutorial on this subject:
Trevor Bedford has posted a tutorial entitled: Inferring spatiotemporal dynamics of the H1N1 influenza pandemic from sequence data.
He provides several detailed exercises that will surely help anyone new to these methods understand how to analyze their own data in BEAST. Interestingly, Trevor is hosting his tutorial on github, which I think is a great idea.
I really enjoy teaching and participating in phylogenetics workshops. Currently, I’m preparing my teaching materials for the Wellcome Trust-EMBL-EBI Advanced Course on Computational Molecular Evolution, where I have the awesome opportunity to teach a section on divergence time estimation with Jeff Thorne. Since I’ve made some minor updates to the BEAST tutorial that I’ve given at recent workshops, I wanted to create a more permanent page to host the document and data files. So, for those interested, you can find the updated tutorial here. I will try to keep this tutorial as up-to-date as possible.
Last week I was hosted by Mike Palopoli and the Bowdoin College Biology Department, where I gave a departmental seminar on my current work on Bayesian divergence time estimation methods. Bowdoin College is a 4-year liberal arts college with some very bright undergraduates. Several of the honors biology majors attended my talk and after the seminar Mike and I led a discussion of my work and computational evolutionary biology, in general.
During this discussion I got stumped by a question from one of the students. He asked if I could recommend a basic and gentle primer on Bayesian statistics for someone with very little statistics training. (This particular student is currently involved in a population genetics project using Bayesian analysis.) I recommended online teaching materials from the various phylogenetics/molecular evolution workshops, including Bodega and this blog, but I couldn’t point him toward a book that I felt would be suitable for someone without a strong understanding of probability theory. As a graduate student, I took Bill Jefferys’ Bayesian Inference course at the University of Texas, where we used Bayesian Data Analysis by Gelman et al. This book is a great introduction (as was Bill’s awesome course), but might be too advanced even for a bright and motivated undergraduate biology major.
So my goal for my first post on the Treethinkers’ blog is to seek out suggestions from readers: What is a good, introductory primer on Bayesian inference that is suitable for the undergraduate level?
Please add your suggestions in the comments below.