Phylogenetics is the study of genetic relatedness of individuals of the same or different species, whether you're comparing individuals of the same species or tracking evolutionary changes across kingdoms. It’s a key tool in evolutionary biology, microbial genomics, and infectious disease research.
When you construct a phylogenetic tree, you’re building a visual model of those relationships. That tree may be rooted (if the common ancestor is known) or unrooted (if it’s not). The branches represent evolutionary paths, and the lengths often reflect evolutionary time or genetic distance. While every tree is ultimately an estimate, your choice of construction method can impact accuracy, speed, and interpretability.
Let’s take a quick look at the most common approaches, such as maximum likelihood and Bayesian inference, and how your lab setup can support better reproducibility.
4 common phylogenetic tree construction methods
Distance-matrix methods
Distance-matrix methods are some of the fastest ways to construct a phylogenetic tree. After aligning your sequences with multiple sequence alignment software, the method calculates genetic distances (i.e., mismatches) and organizes them into a matrix.
From this, a tree is generated in which closely related sequences cluster under the same internal node. Two common techniques are:
- Neighbor Joining (NJ) builds unrooted trees without assuming equal evolutionary rates.
- UPGMA builds rooted trees but assumes a constant rate of evolution across all lineages, making it less popular for most real-world datasets.
Maximum parsimony
This method looks for the tree that requires the fewest changes, essentially offering the simplest evolutionary explanation. It evaluates every possible tree and selects the one with the least homoplasy (convergent evolution).
Simple doesn’t always mean better, though. Maximum parsimony isn’t statistically consistent and can miss complex evolutionary patterns.
Maximum likelihood
Maximum likelihood is the gold standard in phylogenetics. It evaluates the probability of your observed sequences under different tree topologies and chooses the one with the highest overall likelihood.
The upside: It’s powerful and detailed. The downside: It’s computationally demanding, especially for larger datasets.
It assumes each site evolves independently, calculating likelihoods at every bifurcation point. When more than four sequences are analyzed, sequence order can introduce bias, which can be solved by randomizing the process and selecting a consensus tree.
Bayesian inference
Bayesian phylogenetics builds on likelihood models by adding prior probabilities. It produces a range of trees, each with a posterior probability, giving you a clear sense of uncertainty and variation in your dataset.
It’s great for nuanced analysis and supports complex evolutionary models. Tools like MrBayes, BEAST, and RevBayes are often used for this approach.
Quick comparison: Phylogenetic tree construction methods
|
Method |
Pros |
Cons |
|
Distance-Matrix |
Fast, scalable, simple to implement |
Less accurate for complex models; assumptions vary (NJ vs. UPGMA) |
|
Maximum Parsimony |
Conceptually simple; minimal evolutionary changes |
Not statistically consistent; may miss true tree |
|
Maximum Likelihood |
Statistically robust; widely used in research |
Computationally intensive; risk of bias with sequence order |
|
Bayesian Inference |
Accounts for uncertainty; supports complex evolutionary models |
Computationally heavy; requires priors and specialized software |
Getting the most out of your phylogenetic workflows
No matter which tree-building method you choose, success depends on more than just the algorithm. Reproducibility starts with consistent lab supplies, from sequencing enzymes to sample prep kits, and a dependable life sciences procurement process that helps you avoid backorders, delays, and inconsistent results.
ZAGENO’s biotech procurement solution brings together over 5,000 suppliers and 40 million products into a single cart. You get side-by-side comparisons, pricing, and real-time availability, all in one place.
Building trees is complex. Sourcing your supplies doesn’t have to be.
ZAGENO’s procurement platform helps research teams streamline how they find and order everything from sequencing kits to enzymes and consumables—so you can focus on analysis, not backorders.
Request a demo and see how ZAGENO supports better research workflows.
