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Tutorials
Tutorials in running BEAST for various types of analyses
This document gives a brief guide to the practicalities of performing an MCMC analysis with the BEAST software package, by going through a number of examples in detail.
Self-contained practicals to download
These archives contain complete practicals to using BEAST, Tracer and FigTree complete with the required data sets.
Divergence Dating (Primates) v1.2a.zip (BEAST v1.6.x)
correct a mistake in v1.2: missing prior of node calibration (HomiCerco).
Divergence Dating (Primates) v1.1a.zip (BEAST v1.5.x)
Divergence Dating (Primates) v1.0.zip
Estimating a date of divergence using fossil calibration for primates.
Divergence Dating (Papillomaviruses) v1.0.zip
Estimating a date of divergence using a host co-divergence for feline papillomaviruses (similar to the primate practical but virus-orientated).
Estimating Rates in Viruses (RSVA) v1.0.zip
Estimating the rate of evolution from serially-sampled sequences (dated tips) using an RSVA (human respiratory syncytial virus subgroup A) data set.
Combined Practical (Viruses) v1.0.zip
Virus Practical.zip (BEAST v1.5.x)
Virus_Practical_1_6_1.zip (BEAST v1.6.x) new
A three-part virus practical that covers the same material as the two above but has an additional part on Bayesian Skyline Plots.
A two-part practical focused on human influenza virus A (A/H1N1pdm and A/H3N2).
Introductory tutorials by example
Using sequences sampled at different points in time to estimate rates in dengue virus
Describes the use of the BEAUti GUI application to create a BEAST XML file for analysing data where sequences have known dates of sampling.
Analysing BEAST output using Tracer
Describes the use of the Tracer GUI application to analyse the output of BEAST.
Summarizing BEAST trees using TreeAnnotator and FigTree
Describes the use of the TreeAnnotator utility and FigTree to summarize the trees produced by BEAST.
This tutorial has yet to be written.
Estimating the divergence time of a monophyletic group using a known mutation rate
This tutorial describes the use of BEAUti and BEAST to analyse some primate sequences and estimate the date of divergence of humans and chimps when the mutation rate is known.
How to increase the ESS of a parameter
A short list of techniques for increasing the effective sample size (ESS) for a BEAST analysis.
Advanced XML tutorials
The following tutorials describe how to edit BEAST XML format to create more advanced analyses. Many of these option can now be selected within BEAUti making these tutorials obselete. However, we have left these for reference purposes and to learn more about the XML format.
Describes how to create and log various statistics in order to estimate posterior distributions of particular hypotheses of interest.
Placing an upper limit on the root height
Describes using the BEAST XML format to place an upper limit on the age of the root of the tree.
Calibrating the date of an MRCA using a bounded range or parametric probability distribution
Describes how to specify a uniform bounded or parametrically distributed prior on the age of the MRCA of a subset of taxa.
Providing a user-specified starting tree
Describes editing the BEAST XML input file to provide an initial user-specified tree for the MCMC to start with.
Describes editing the BEAST XML input file to create a UPGMA tree for the MCMC to start with.
Constraining a group of taxa to be monophyletic
Describes editing the BEAST XML input file to constrain a particular group of taxa to be kept monophyletic throughout the analysis. This can be used to keep an outgroup as the outgroup.
Creating partitions of sites with different substitution parameters
Describes editing the BEAST XML to add an additional data partition with a different substitution model and rate.
Imposing a prior distribution on a parameter
Describes how to place a probability distribution as a prior on the age of the root of the tree or the substitution rate.
Describes how to incorporate multiple unlinked loci into a single analysis.
Creating your own general data type
Describes how to create a general data type for a custom type of discrete characters. (Updated on 22/01/2010)
Using a nucleotide substitution model other than HKY and GTR
Describes how to set up time-reversible nucleotide models other than HKY and GTR.
Running BEAST without data to sample from the Prior
Describes how to alter a BEAST XML file so that BEAST only samples from the Prior distribution.
Reconstructing ancestral states/sequences
Describes how to reconstruct an ancestral state or sequence at a particular node
Model testing and model selection
Describes how to perform model selection in BEAST using the harmonic mean estimator (HME), a posterior simulation-based analogue of Akaike's information criteration (AICM), path sampling (PS) and stepping-stone sampling (SS). This tutorial replaces the previous model comparison tutorial.
Epoch substitution models tutorial
This tutorial describes how to extend standard XML documents, such as generated by BEAUTi, to include analysis under time-heterogenous (epoch) models of substitution (see Bielejec et al., 2014).
Demographic model tutorials
Describes how to set up a demographic model involving two epochs and a transition time.
Bayesian skyline plot tutorials
Creating an upper limit on population size
Describes how to set up an upper limit on population size in a Bayesian Skyline Plot.
Extended Bayesian skyline plot (EBSP) tutorial and script
This short practical explains how to set up an Extended Bayesian Skyline Plot (EBSP) analysis in BEAST, and how to generate some EBSP plots.
Trait analysis tutorials
Continuous morphological characters
Continuous traits and the comparative method
This tutorial describes how to setup an analysis of continuous traits to look at the correlation between them in a phylogenetically corrected way (i.e., the comparative method). This examples uses some mitochondrial sequence data to sample trees whilst jointly estimating the coevolution of 5 continuous morphological traits.
Phylogeography tutorials
Discrete phylogeographic diffusion models
These tutorials describe how to set up the analyses described in Lemey, Rambaut, Drummond & Suchard (2009) PLoS Comput Biol.
Setting up a standard discrete phylogeographic analysis
Describes how to edit a BEAST XML file to set up a discrete phylogeographic analysis using a standard continuous-time Markov chain.
Describes how to modify a discrete phylogeographic model XML file to set up BSSVS.
Summarizing a discrete phylogeographic inference using an MCC tree and visualization in FigTree
Describes how to annotate an MCC tree using the modal location states.
Visualizing a location-annotated MCC tree in Google Earth
Describes how to convert a location-annotated MCC to KML for visualization in Google Earth.
Performing a Bayes factor test to identify well-supported rates
Describes how to identify rates that are frequently invoked to explain the diffusion process and how to visualize these in Google Earth.
Continuous phylogeographic diffusion models
These tutorials describe how to set up the analyses described in Lemey, Rambaut, Welch & Suchard (2010) MBE.
Setting up a standard continuous phylogeographic analysis
Describes how to edit a BEAST XML file to set up a phylogeographic analysis in continuous space using a standard random walk.
Setting up a continuous phylogeographic analysis using relaxed random walks
Describes how to modify an XML file that specifies a homogenous Brownian diffusion phylogeographic model to set up a relaxed random walk.
Hierarchical phylogenetic model (HPM) tutorials
These tutorials will describe how to set up HPMs in BEAST as described in Suchard, Kitchen, Sinsheimer & Weiss (2003) Syst Biol
Setting up a hierarchical phylogenetic model in BEAST
Describes how to edit a BEAST XML file to set up a hierarchical phylogenetic model across several evolutionary parameters.
*BEAST tutorial and example
The objective of this tutorial is to estimate the species tree that is most probably given the multi-individual multi-locus sequence data. The species tree has 9 taxa, whereas each gene tree has 26 taxa. *BEAST will co-estimate three gene trees embedded in a shared species tree (see Heled and Drummond, 2010 for details).