Kristen Beast Archives

BEAST is a cross-platform program for Bayesian analysis of molecular sequences using MCMC. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models.

This tutorial can be easily adapted to display and summarize annotated summary BEAST trees and prepare publication-ready figures for other datasets. FigTree - this is an application for displaying and printing molecular phylogenies, in particular those obtained using BEAST. At the time of writing, the current version is v1.4.4.

Tracer (now at version 1.7.2) is a software package for visualising and analysing the MCMC trace files generated through Bayesian phylogenetic inference. Tracer provides kernel density estimation, multivariate visualisation, demographic trajectory reconstruction, conditional posterior distribution summary and more. Tracer v1.7.2 can read output files from MrBayes, BEAST, BEAST2, RevBayes ...

Kristen Beast Archives 3

Installing BEAST BEAST has been developed in Java, which allows the same code to run on any platform that has the Java software installed. We have also created packages for each of the common operating systems to provide a user-interface that is ‘native’ and familiar. Latest Version

BEAST | Bayesian Evolutionary Analysis Sampling Trees. This is the main program that takes a control file generated by BEAUti and performs the analysis.

Kristen Beast Archives 5

BEAST is a software package for phylogenetic analysis with an emphasis on time-scaled trees.

BEAST is an ongoing development project with new models and techniques being added on a regular basis. The BEAST website provides details of the mailing list that is used to announce new features and to discuss the use of the package.

Kristen Beast Archives 7

This realisation includes all the transitions (Markov jumps) between states along phylogenetic branches and the time (Markov rewards) spent in the states between two transitions. This tutorial discusses how to estimate such quantities using stochastic mapping techniques implemented in BEAST.