Phyloseminar
http://phyloseminar.org/
phyloseminar -- a free online seminar about phylogenetics
Rosana Zenil-Ferguson: Understanding Diversification Complexity in Large Phylogenies with State-Dependent Speciation and Extinction Models, 2023-10-20 09:00 PDT
http://phyloseminar.org/
<p>Sixteen years have passed since Wayne Maddison, Peter Midford, and Sally Otto introduced the binary state speciation and extinction model (BiSSE). BiSSE revolutionized the field of phylogenetic comparative analysis by enabling us to investigate the influence of traits on the tempo of speciation and extinction. However, working with phylogenies presents challenges. The lack of independence between taxa complicates likelihood calculations, and as phylogenies grow, additional factors such as accelerated extinction and unmeasured variables can impact diversification rates.</p>
<p>In this presentation, I will explore recent advancements in state-dependent diversification models using the graphical modeling software RevBayes. I will demonstrate how to incorporate multiple traits and assess their respective contributions to the diversification process. Additionally, I will show how to integrate enhanced extinction histories and conclude by explaining how to measure the significance of diversification based on transition type and not state value within a cladogenetic framework.</p>2023-09-21T10:00:00-07:00134zenilFerguson
Sarah (Sally) P. Otto: Inferring the past for traits that alter speciation and extinction rates, 2023-09-21 09:00 PDT
http://phyloseminar.org/
<p>I introduce SSE methods, a likelihood-based approach to infer how speciation and extinction rates depend on the character state of a particular species. The phylogenetic tree of a group of species contains information about character transitions and about diversification: higher speciation rates, for example, give rise to shorter branch lengths. The likelihood method that we developed uses information contained in a phylogeny and integrates over all possible evolutionary histories to infer the speciation and extinction rates for species with different character states. The method can be used to provide more detailed information than previous methods, allowing us to disentangle whether a particular character state is rare because species in that state are prone to extinction, are unlikely to speciate, or tend to move out of that state faster than they move in. I also discuss the challenges facing the use of such approaches and open questions.</p>2023-09-10T10:00:00-07:00133otto
Samir Bhatt: Methods for phylogenetic inference using gradients, 2023-08-09 09:00 PDT
http://phyloseminar.org/
<p>In this talk, I will summarize the content of two recent preprints and a paper in preparation. I will introduce a novel representation of a phylogenetic tree called Phylo2Vec. Phylo2Vec is a bijection to tree space, meaning that every possible tree can be uniquely represented. It is a very simple representation. Phylo2Vec could be useful as an alternative to subtree-prune and regrafting, as well as a metric for specifying tree distance.</p>
<p>Next, I will discuss how Phylo2Vec can be used for gradient-based inference on topologies. I will also cover the possibility of root finding. I will show how gradient based topology searches can potentially be better than the state-of-the-art. Finally, I will touch on new approximations for maximum likelihood and Bayesian inference.</p>2023-07-12T10:00:00-07:00132bhatt
Ammon Thompson and Michael Landis: Deep learning approaches to viral phylogeography are fast and as robust as likelihood methods to model misspecification, 2023-07-12 09:00 PDT
http://phyloseminar.org/
<p>Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among five locations and found they achieve similar levels of accuracy to Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.</p>2023-05-03T10:00:00-07:00131thompsonLandis
Dana Azouri: The tree reconstruction game: phylogenetic reconstruction using reinforcement learning, 2023-06-21 09:00 PDT
http://phyloseminar.org/
<p>We propose a reinforcement-learning algorithm to tackle the challenge of reconstructing phylogenetic trees. The search for the tree that best describes the data is algorithmically challenging, thus, all current algorithms for phylogeny reconstruction use various heuristics to make it feasible. In this study, we demonstrate that reinforcement learning can be used to learn an optimal search strategy, thus providing a novel paradigm for predicting the maximum-likelihood tree. Our proposed method does not require likelihood calculation with every step, nor is it limited to greedy uphill moves in the likelihood space. We demonstrate the use of the developed deep-Q-learning agent on a set of unseen empirical data, namely, on unseen environments defined by nucleotide alignments of up to 20 sequences. Our results show that the likelihood scores of the inferred phylogenies are similar to those obtained from widely-used software. It thus establishes a proof-of-concept that it is beneficial to optimize a sequence of moves in the search-space, rather than optimizing the progress made in every single move only. This suggests that a reinforcement-learning based method provides a promising direction for phylogenetic reconstruction.</p>2023-06-12T10:00:00-07:00130azouri