22115 - Recent changes [en]

Bayesian phylogenetics: clock models

Gorm — Wed, 22 Apr 2026 13:43:44 GMT

Questions

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 == Overview ==
-In this exercise we will explore how to use the software tool BEAST2 to construct phylogenies based on molecular-clock models. In previous exercises we have worked with phylogenies where we did not have information about how fast sequences were evolving, and we therefore used the number of substitutions as branch lengths. When there ''is'' temporal information (e.g., fossils that can be used to date an internal node, or information about sampling-time for rapidly evolving sequences) we can instead use clock-based models. These models assume that sequences are evolving at a more or less constant rate, branch lengths are expressed in terms of time, and we can estimate times for internal nodes. Apart from being useful when the focus is on dating evolutionary events, time trees are also useful in that the clock model itself can lead to better inference of the phylogeny (essentially because it adds prior information to the problem, such that we dont have to infer all branch lengths only from limited amounts of sequence variation).
+In this exercise we will use the software package BEASTX to infer phylogenies under molecular-clock models.
-The main purpose with this exercise is to make you acquainted with BEAST2 and to learn how to fit clock-models using either fossil data (by setting a prior on the date for internal nodes) or using so-called heterochronous data, i.e., sequences where the individual leaves have been sampled at different, known times, and where evolution is sufficiently rapid that we can estimate the parameters in a clock-model by seeing how much change has happened over time.
+In previous exercises, branch lengths were measured only in expected numbers of substitutions per site. In a clock-based analysis, genetic change is instead related to calendar time through a model of evolutionary rates. If temporal information is available, for example in the form of known sampling times for rapidly evolving sequences, this can be used to estimate both the rate of evolution and the times of internal nodes in the tree.
-For these tutorials you only need to report minimally: make a small report with a handful of uncommented screendumps showing your progress through the exercise. The important thing is that you get to be a bit familiar with the use of the program, such that you can use it in the mini project later.
+In this exercise we will focus on so-called heterochronous data, i.e., sequence data where the individual sequences were sampled at different known times. When evolution is sufficiently rapid, the amount of sequence change observed over these sampling times contains information about the evolutionary rate and about the timing of common ancestors.
-:* In the exercises below, you should simply follow the instructions on the tutorial pages.
+The main purpose of the exercise is:
-:* Depending on your operating system and on how you installed the software, you can start required programs either from the command line or by double clicking an app. The names of the executables that you will need for this exercise are:
+:* to become familiar with the BEASTX workflow
 :** beauti
 :** beast
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 :** figtree
-== Introduction to BEAST2 ==
+== BEASTX tutorial ==
-:* Create a new directory for storing the results of this exercise:
+The tutorial then repeats the analysis using a relaxed lognormal clock. What is the difference between a strict clock and this relaxed-clock model? What extra biological possibility is the relaxed-clock model allowing for?
-== Prior selection and clock calibration using Influenza A data ==
+'''Question 8'''
-:* Open this link in a new tab: [https://taming-the-beast.org/tutorials/Prior-selection/ Prior selection and clock calibration using Influenza A data]
+Based on the relaxed-clock analysis, is there evidence for substantial rate variation among lineages? In your answer, state which parameter in Tracer you inspected to assess this, and explain what kind of result would indicate little versus substantial rate variation. Also comment on whether the main biological conclusion about introduction of yellow fever virus into the Americas changes or remains similar under the relaxed-clock model.

Bayesian phylogenetics: checking convergence

Gorm — Wed, 22 Apr 2026 09:31:17 GMT

Check convergence using Tracer

← Older revision		Revision as of 11:31, 22 April 2026
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	== Check convergence using Tracer ==		== Check convergence using Tracer ==

	In this exercise you will be briefly introduced to how to check if an MCMC run has converged using the program Tracer from the ~~BEAST2~~ package. You will do this by re-examining the output from the Bayesian analysis you did in the week 7 exercise.		In this exercise you will be briefly introduced to how to check if an MCMC run has converged using the program Tracer from the BEASTX package. You will do this by re-examining the output from the Bayesian analysis you did in the week 7 exercise.

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	:::::[[File:Tracer lousy convergence.png\|800px]]		:::::[[File:Tracer lousy convergence.png\|800px]]

	'''Question: '''~~Take screen dumps of~~ the marginal posterior plots for ~~the following~~ parameters ~~and include them~~ in your report~~: m{1}~~ and ~~piA{all}~~		'''Question: '''Select both trace files in the “Trace Files” pane and inspect the Marginal Density plots for the parameters r(A<->G){all} and m{2}.

			Include the marginal posterior plots for these two parameters in your report. For each parameter, describe whether the posterior distributions from the two independent runs overlap closely, and explain what this indicates about convergence.

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	:* Related to this: The ESS column gives the "Effective Sample Size" for each parameter. As a rule of thumb we want this to be at least 200 (and Tracer flags smaller values by colouring the ESS values).		:* Related to this: The ESS column gives the "Effective Sample Size" for each parameter. As a rule of thumb we want this to be at least 200 (and Tracer flags smaller values by colouring the ESS values).
	:** Briefly, the problem here is that consecutive samples from MCMC are correlated (they are not independent). This is due to the use of a Markov chain for sampling: the new position in parameter space depends on the previous location (and the proposal distribution).		:** Briefly, the problem here is that consecutive samples from MCMC are correlated (they are not independent). This is due to the use of a Markov chain for sampling: the new position in parameter space depends on the previous location (and the proposal distribution).
	:** The degree of non-indepence can be quantified by the auto-correlation for different lags: The autocorrelation for lag k is found by computing the Pearson correlation between all samples, and the ~~samples~~ k ~~generations later~~.		:** The degree of non-indepence can be quantified by the auto-correlation for different lags: The autocorrelation for lag k is found by computing the Pearson correlation between the vector of all samples, and the values in the same vector shifted k positions.
	:** Based on computation of auto-correlation at different lags (<math>k = [1, 2, 3, ...]</math>) Tracer determines the Auto-Correlation Time (ACT), which is the number of generations in the MCMC chain that two samples have to be separated by for them to be uncorrelated. The ACT for a parameter can be seen in the Estimates tab in Tracer.		:** Based on computation of auto-correlation at different lags (<math>k = [1, 2, 3, ...]</math>) Tracer determines the Auto-Correlation Time (ACT), which is the number of generations in the MCMC chain that two samples have to be separated by for them to be uncorrelated. The ACT for a parameter can be seen in the Estimates tab in Tracer.
	:** Tracer also estimates the Effective Sample Size (ESS), which is the number of independent samples that the trace is equivalent to. This is essentially the chain length (excluding the burn-in) divided by the ACT.		:** Tracer also estimates the Effective Sample Size (ESS), which is the number of independent samples that the trace is equivalent to. This is essentially the chain length (excluding the burn-in) divided by the ACT.
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	:::::[[File:Tracer ugly caterpillar.png\|800px]]		:::::[[File:Tracer ugly caterpillar.png\|800px]]

	'''Question: '''~~Take screen dumps of~~ the ~~trace~~ plots for the ~~following~~ parameters and ~~include them~~ in your report: m{1} and ~~piA~~{~~all~~}. ~~What is~~ the ESS ~~for these parameters?~~		'''Question: '''Now inspect the Trace plots for the same two parameters: r(A<->G){all} and m{2}.

			Include the trace plots for these two parameters in your report, and state the ESS for each of them. For each parameter, describe whether the trace looks well mixed, and compare the two parameters in terms of sampling efficiency. Does one of them appear to have stronger autocorrelation than the other?

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			'''Question 3'''

			'''Question: '''Using both the marginal density plots and the trace plots/ESS values, assess whether r(A<->G){all} and m{2} appear to have converged. Explain what pattern in either the marginal densities or the trace/ESS values would have made you doubt convergence.