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Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7.

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TLDR
The software package Tracer is presented, for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference, which provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more.
Abstract
Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and analyzing the MCMC-generated samples from the posterior distribution is a key step in any non-trivial Bayesian inference. We present the software package Tracer (version 1.7) for visualizing and analyzing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualization, demographic trajectory reconstruction, conditional posterior distribution summary, and more. Tracer is open-source and available at http://beast.community/tracer.

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Detection of a SARS-CoV-2 variant of concern in South Africa.

TL;DR: A newly arisen lineage of SARS-CoV-2 (designated 501Y.V2) was identified in South Africa after the first wave of the epidemic in a severely affected metropolitan area (Nelson Mandela Bay) that is located on the coast of the Eastern Cape province.
Journal ArticleDOI

Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil.

Nuno R. Faria, +74 more
- 21 May 2021 - 
TL;DR: In this article, the authors used a two-category dynamical model that integrates genomic and mortality data to estimate that P.1 may be 1.7-to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.
Posted ContentDOI

Emergence and rapid spread of a new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) lineage with multiple spike mutations in South Africa

Houriiyah Tegally, +60 more
- 22 Dec 2020 - 
TL;DR: In this paper, the authors describe a new SARS-CoV-2 lineage (501Y.V2) characterised by eight lineage-defining mutations in the spike protein, including three at important residues in the receptor-binding domain (K417N, E484K and N501Y).
Journal ArticleDOI

Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

TL;DR: The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function as discussed by the authors , highlighting the rapid spread in regions with high levels of population immunity.
References
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Journal ArticleDOI

MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice across a Large Model Space

TL;DR: The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly, and provides more output options than previously, including samples of ancestral states, site rates, site dN/dS rations, branch rates, and node dates.
Journal ArticleDOI

Inference from Iterative Simulation Using Multiple Sequences

TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Journal ArticleDOI

Bayesian Phylogenetics with BEAUti and the BEAST 1.7

TL;DR: The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7 is presented, which implements a family of Markov chain Monte Carlo algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses.
Journal ArticleDOI

BEAST 2: A Software Platform for Bayesian Evolutionary Analysis

TL;DR: BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform.

CODA: convergence diagnosis and output analysis for MCMC

TL;DR: Bayesian inference with Markov Chain Monte Carlo with coda package for R contains a set of functions designed to help the user answer questions about how many samples are required to accurately estimate posterior quantities of interest.
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