22 papers · ranked by Valyu relevance
Cheng Zhang, Frederick A. Matsen
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms. This hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper, we present an alternative approach: a variational framework for Bayesian…
Matthew Macaulay, Aaron E. Darling, Mathieu Fourment
Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. It faces the challenging problem of moving throughout the high-dimensional space of trees. However, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences…
Alexander A. Fisher, Gabriel W. Hassler, Xiang Ji, Guy Baele + 2 more
'Marc A. Suchard' 'Philippe Lemey'] Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a…
Lars Berling, Jonathan Klawitter, Remco Bouckaert, Dong Xie + 2 more
Bayesian phylogenetic analysis with MCMC algorithms generates an estimate of the posterior distribution of phylogenetic trees in the form of a sample of phylogenetic trees and related parameters. The high dimensionality and non-Euclidean nature of tree space complicates summarizing the central tendency and variance of…
Lars Berling, Jonathan Klawitter, Remco Bouckaert, Dong Xie + 3 more
'Alex Gavryushkin' 'Alexei J Drummond' 'Natalia L. Komarova'] Bayesian phylogenetic analysis with MCMC algorithms generates an estimate of the posterior distribution of phylogenetic trees in the form of a sample of phylogenetic trees and related parameters. The high dimensionality and non-Euclidean nature of tree space…
Ivan Specht, Julia A. Palacios
Bayesian phylogenetic inference from molecular sequences can provide key insights into the evolutionary history of populations. Existing tools, however, often scale poorly with sample size. We present inPhynite, a highly-efficient Bayesian phylogenetics algorithm for genomic datasets compatible with the infinite sites…
Jakub Truszkowski, Allison Perrigo, David Broman, Fredrik Ronquist + 2 more
'Alexandre Antonelli' 'Simon Ho'] Title: Abstract Bayesian phylogenetics is now facing a critical point. Over the last 20 years, Bayesian methods have reshaped phylogenetic inference and gained widespread popularity due to their high accuracy, the ability to quantify the uncertainty of inferences and the possibility of…
Joëlle Barido-Sottani, Orlando Schwery, Rachel C. M. Warnock, Chi Zhang + 1 more
'Chi Zhang' 'April Marie Wright'] Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model…
Adam Siepel, Rebecca Hassett, Stephen J. Staklinski
Bayesian phylogenetic inference is now widely used but remains heavily reliant on Markov chain Monte Carlo (MCMC) sampling, which is computationally intensive and requires careful convergence monitoring. Variational inference (VI) is an appealing alternative that approximates posterior distributions without sampling…
Matthew Macaulay, Aaron Darling, Mathieu Fourment, William Stafford Noble
'William Stafford Noble'] Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. However, Bayesian phylogenetics faces the challenging computational problem of moving throughout the high-dimensional space of trees. Fortunately, hyperbolic space offers a low dimensional…
Patrick Varilly, Mark Schifferli, Katherine Yang, Tim Burcham + 21 more
Pathogen genomic analysis is central to tracking, understanding, and containing outbreaks, but complexity and high costs of state-of-the-art (SOTA) phylogenetic tools limit global access and impact. We introduce Delphy, an exact reformulation of Bayesian phylogenetics designed to transform its speed, scalability and…
Michael D. Karcher, Cheng Zhang, Frederic A. Matsen IV
Bayesian phylogenetic inference is powerful but computationally intensive. Researchers may find themselves with two phylogenetic posteriors on overlapping data sets and may wish to approximate a combined result without having to re-run potentially expensive Markov chains on the combined data set. This raises the…
Alex Chen, Philipe Chlenski, Kenneth Munyuza, Antonio Khalil Moretti + 2 more
Phylogenetics in Hyperbolic Space Authors: ['Alex Chen' 'Philipe Chlenski' 'Kenneth Munyuza' 'Antonio Khalil Moretti' 'Christian A. Naesseth' 'Itsik Pe’er'] Hyperbolic space naturally encodes hierarchical structures such as phylogenies (binary trees), where inward-bending geodesics reflect paths through least common…
Xiyun Jiao, Thomas Flouris, Ziheng Yang
Trans-model Markov chain Monte Carlo (MCMC) algorithms are widely used in Bayesian inference, and are particularly important in Bayesian phylogenetics where phylogenetic trees represent different statistical models. While the algorithm allows great flexibility, its mixing efficiency can vary hugely, and is poorly…
Frédéric Lemoine, Olivier Gascuel
Felsenstein’s bootstrap is the most commonly used method to measure branch support in phylogenetics. Current sequencing technologies can result in massive sampling of taxa (e.g. SARS-CoV-2). In this case, the sequences are very close, the trees are short, and the branches correspond to a small number of mutations…
Matthew J. Penn, Neil Scheidwasser-Clow, Mark P. Khurana, Christl A. Donnelly + 2 more
'Christl A. Donnelly' 'David A. Duchêne' 'Samir Bhatt'] As whole genomes become widely available, maximum likelihood and Bayesian phylogenetic methods are demonstrating their limits in meeting the escalating computational demands. Conversely, distance-based phylogenetic methods are efficient, but are rarely favoured…
Benjamin Teo, Paul Bastide, Cécile Ané
The evolution of molecular and phenotypic traits is commonly modelled using Markov processes along a rooted phylogeny. This phylogeny can be a tree, or a network if it includes reticulations, representing events such as hybridization or admixture. Computing the likelihood of data observed at the leaves is costly as the…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jérôme Eberhardt, Markus Lill, Torsten Schwede
This study introduces a novel Bayesian Optimization (BO) method to support the design and optimization of bioactive peptide sequences in the context of a fully automated closed-loop Design-Make-Test (DMT) pipeline. Using the major histocompatibility complex class I receptor system as test case, we showed that BO is…
Xinqiang Ding, John Drohan
A common approach for computing free energy differences among multiple states is to build a perturbation graph connecting the states and compute free energy differences on all edges of the graph. Such perturbation graphs are often designed to have cycles. Because free energy is a function of states, the free energy…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…