FastTree builds approximately-maximum-likelihood phylogenetic trees from multiple sequence alignments. It can handle alignments with up to a million sequences while remaining computationally efficient—100 to 1,000 times faster than traditional maximum-likelihood methods like PhyML or RAxML.
Phylogenetic trees visualize evolutionary relationships between sequences. Branch lengths represent evolutionary distance (substitutions per site), while the tree topology shows which sequences share common ancestors. FastTree is particularly useful for large-scale studies where traditional ML methods would be prohibitively slow.
Important: FastTree requires pre-aligned sequences (a Multiple Sequence Alignment). All sequences must be the same length with homologous positions aligned in the same column. If your sequences aren't aligned, you'll need to align them first using tools like Clustal Omega or MUSCLE before using FastTree.
FastTree uses a three-phase approach to build phylogenetic trees efficiently.
FastTree first builds a rough starting tree using a heuristic variant of neighbor-joining. Instead of storing a full distance matrix (which would require memory), FastTree stores sequence profiles at internal nodes. This optimization allows it to handle much larger alignments than traditional methods.
Three heuristics speed up this phase: remembering the best join for each node, hill-climbing search for better joins, and the "top-hits" heuristic to avoid computing all pairwise distances.
The initial tree is improved using nearest-neighbor interchanges (NNI) and subtree-pruning-regrafting (SPR) moves under the minimum evolution criterion. FastTree performs rounds of NNIs and 2 rounds of SPRs by default.
SPR moves are computationally expensive ( possibilities), so FastTree treats them as chains of NNIs and only extends promising candidates.
Finally, FastTree performs maximum-likelihood NNI moves to optimize the tree topology. The CAT approximation assigns each alignment site to one of 20 rate categories, accounting for varying evolutionary rates across positions without the computational cost of full gamma-distributed rates.
FastTree supports different substitution models depending on your sequence type.
FastTree accepts sequences in FASTA format. All sequences must be aligned (same length) before submission.
Sequence names cannot contain the characters : , ( ) as these have special meaning in Newick tree format.
FastTree auto-detects whether your alignment contains protein or nucleotide sequences. You can override this if needed—select Nucleotide for DNA/RNA sequences or Protein for amino acid sequences.
Choose the amino acid substitution matrix. JTT works well for most cases. Try WAG or LG if you're working with specific protein families where these models have been shown to perform better.
For nucleotide sequences, the GTR model accounts for different substitution rates (e.g., transitions vs transversions). We recommend enabling this for DNA/RNA alignments.
Rescales branch lengths using the Gamma20 likelihood, which more accurately models rate variation across sites. This adds approximately 5% to computation time but provides more accurate branch length estimates.
Speeds up neighbor-joining approximately 4-fold. We recommend enabling this for alignments with more than 50,000 sequences.
By default, FastTree reports SH-like local support values. Setting bootstrap resamples > 0 performs traditional bootstrap analysis instead, which resamples alignment columns and rebuilds trees to assess branch support.
FastTree outputs a phylogenetic tree in Newick format, which our interactive viewer renders automatically.
Branch lengths represent evolutionary distance in substitutions per site. Longer branches indicate more sequence divergence. Closely related sequences (like proteins from the same species) will have short branches between them.
Numbers at internal nodes indicate statistical support for that split in the tree. FastTree reports SH-like local supports ranging from 0 to 1 by default. Values above 0.9 indicate well-supported branches; values below 0.7 suggest uncertainty in that part of the tree.
The branching pattern shows inferred evolutionary relationships. Sequences that cluster together share a more recent common ancestor than sequences on distant branches.
FastTree prioritizes speed over maximum accuracy. For datasets where topological accuracy is critical, consider using slower but more thorough methods like IQ-TREE or RAxML for final analysis.
The CAT approximation, while fast, is less accurate than full discrete gamma models for estimating rate variation. If you need precise branch lengths, enable Gamma optimization.
After building your tree, you might want to analyze the individual sequences:
For structure-based analysis of proteins in your tree:
Based on: Price MN, Dehal PS, Arkin AP. FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE 5(3): e9490. doi:10.1371/journal.pone.0009490
