If you've just downloaded BEAST and are feeling a bit lost, you're not alone. Setting up a phylogenetic analysis can feel overwhelming, especially if this is your first time using software like BEAST or its companion tools like BEAUti and Tracer. So, let's start with something simple: what even is BEAST, and why do biologists in Singapore and around the world use it? At its core, BEAST is a powerful software package designed for Bayesian phylogenetic analysis — basically, it helps scientists understand how different species or genetic sequences are related over time.

For many researchers, especially those in Singapore's vibrant academic and research communities, BEAST is a go-to tool when studying evolutionary biology, molecular epidemiology, and even viral outbreaks. The country’s investment in life sciences and bioinformatics means more students and professionals are diving into tools like BEAST, often paired with programs like FigTree and Tracer to visualize and interpret results. So, if you’re in Singapore and trying to wrap your head around how to set up your first analysis, this guide is for you.

Whether you're a biology student at NUS, an NUS graduate researcher, or a bioinformatician working in a private lab, the process of using BEAST can feel a bit scattered at first. There's BEAUti for setting up the XML input files, BEAST to run the actual analysis, Tracer to check your results, and FigTree for visualizing the trees. If you're new to this, it's easy to feel like you're juggling too many tools at once. But once you get the hang of it, the whole workflow starts to make sense — and becomes a powerful part of your research toolkit.

• Tomselleck

Table of Contents

• What Is BEAST?
• Getting Started with BEAUti
• Running BEAST for the First Time
• Analyzing Results with Tracer and FigTree
• Combining Runs with LogCombiner
• Frequently Asked Questions

What Is BEAST?

BEAST — which stands for Bayesian Evolutionary Analysis Sampling Trees — is a software package widely used in evolutionary biology and molecular epidemiology. It's part of a suite of programs developed under the BEAST framework, which includes BEAUti, Tracer, FigTree, and LogCombiner. These tools work together to help researchers infer evolutionary trees from sequence data using Bayesian inference methods.

So, how does it work in practice? Well, BEAST uses a method called Markov Chain Monte Carlo (MCMC) to estimate the most likely evolutionary trees based on genetic sequence data. It's especially useful for analyzing time-stamped sequences, like those collected during viral outbreaks. That’s why researchers in Singapore, where public health and disease surveillance are critical, often turn to BEAST when studying infectious diseases like dengue or influenza.

But BEAST on its own isn't enough — it needs input files generated by BEAUti, and its results are best interpreted using Tracer and FigTree. Think of it like a chain of tools: BEAUti sets everything up, BEAST crunches the numbers, Tracer checks if the results make sense, and FigTree shows you the final tree. If you're just starting out, this might seem like a lot, but once you get the hang of each step, it becomes second nature.

• Seo Yeji

Getting Started with BEAUti

Before you can run BEAST, you need to create a configuration file using BEAUti — a graphical user interface that helps you set up all the necessary parameters. For example, you can import your sequence alignment, assign dates to each sample (if available), and choose which evolutionary model to use. One of the key features in BEAUti is the “Use Tip Dates” option, which lets you tell the software the sampling time of each sequence — something very useful for time-scaled phylogenetic analyses.

If you’re just starting out, here’s a quick list of steps to get you going in BEAUti:

1. Import your FASTA file containing sequence data
2. Check and format the taxa names properly
3. Set up tip dates under the “Tips” menu (especially useful for studies involving viruses or rapidly evolving organisms)
4. Choose the appropriate substitution model under the “Site Models” tab
5. Set the clock model (strict, relaxed, etc.) based on your research question
6. Specify the tree prior (e.g., coalescent or birth-death model)
7. Generate the XML file by clicking “Generate BEAST XML”

Once that's done, you're ready to run BEAST. But before you click “Run,” take a moment to double-check everything in BEAUti — even a small mistake can lead to hours of wasted analysis time later.

Running BEAST for the First Time

Once you've generated your XML file using BEAUti, it’s time to run BEAST. This part can feel like magic — you click a button, and suddenly your computer starts churning out data. But behind the scenes, BEAST is running a complex MCMC chain that samples from the posterior distribution of trees and model parameters.

When you first launch BEAST, it will ask you to select the XML file you created in BEAUti. After that, it starts logging the chain, saving the results into a log file and a tree file at regular intervals. You can set how often these files are written — typically every 1,000 to 10,000 steps — depending on the size of your dataset and how long you’re planning to run the analysis.

Here are a few tips to help you run BEAST smoothly:

• Make sure you have enough disk space — large datasets can generate huge log files
• Set a reasonable chain length (like 10 million steps) for better convergence
• Use a burn-in of around 10% to discard initial samples that may not be reliable
• Run multiple independent chains if possible, to check for consistency

Once BEAST finishes running (which could take hours or even days depending on your dataset), you’ll need to analyze the output — and that’s where Tracer comes in.

Analyzing Results with Tracer and FigTree

After running BEAST, the next step is to load your log file into Tracer. This tool helps you visualize the MCMC traces and check whether the chain has converged — meaning, the analysis has stabilized and the results are reliable. Tracer shows you the Effective Sample Size (ESS) for each parameter. A rule of thumb is that ESS values should be above 200 for the results to be trustworthy.

Once you’re confident your analysis has converged, it’s time to look at the trees. FigTree is the go-to tool for visualizing the maximum clade credibility (MCC) tree, which summarizes all the trees sampled during the BEAST run. You can color branches, add time scales, and even highlight specific clades based on metadata like geographic location or host species — which can be super helpful if you're studying how a virus spreads across different regions in Singapore.

Here’s how to make the most of Tracer and FigTree:

1. Open your BEAST log file in Tracer to check trace plots and ESS values
2. Use TreeAnnotator to generate the MCC tree from your BEAST output
3. Open the resulting tree file in FigTree to customize the appearance
4. Export your tree as a publication-ready image for reports or papers

If you're a student or researcher in Singapore working on a paper or project, getting your tree visuals just right can make a big difference in how your results are perceived.

Combining Runs with LogCombiner

If you ran multiple independent BEAST analyses (which is a good idea for checking consistency), you can use LogCombiner to merge the log and tree files. This helps you get a more robust estimate of your results by combining data from all runs. Once combined, you can analyze the merged log file in Tracer again to make sure everything looks good.

LogCombiner is pretty straightforward to use — you just select the files you want to combine, choose a burn-in, and let it do the rest. After that, you can load the combined log into Tracer and the combined tree file into FigTree for further analysis. It’s a small but powerful tool in the BEAST ecosystem that can really improve the reliability of your results.

Frequently Asked Questions

How do I set sampling dates in BEAUti?

To set sampling dates in BEAUti, go to the “Tips” menu and click “Use Tip Dates.” Then, you can either manually enter the dates or import them from a file. This is especially useful for time-scaled phylogenetic analyses.

Can I run BEAST on Windows, macOS, or Linux?

Yes, BEAST works on all three operating systems. You can download the appropriate version from the official BEAST website and follow the installation instructions for your platform.

What is the MCC tree in FigTree?

The MCC (Maximum Clade Credibility) tree is a summary tree that shows the most probable tree structure based on all the trees sampled during the BEAST analysis. It’s commonly used to visualize the main evolutionary relationships in your dataset.

If you're looking for more in-depth tutorials or documentation, you can visit the official BEAST 2 website for guides, forums, and example datasets.