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What is RAxML-NG?
RAxML-NG infers phylogenetic trees from an existing multiple sequence alignment using maximum likelihood. It is the current-generation rewrite of RAxML and ExaML, built for faster likelihood calculations, more stable optimization, and large alignments where branch support still matters.
The practical point of RAxML-NG is not just tree building. It is tree building under explicit evolutionary models, with branch lengths, likelihood scores, and bootstrap support that can stand up in downstream comparative analyses. That makes it a better fit than approximate methods when the topology will be interpreted biologically or used in a manuscript.
This tool expects an alignment, not raw sequences. If the sequences are not already aligned, start with MAFFT, MUSCLE5, or Clustal Omega before running tree inference.
How does RAxML-NG work?
RAxML-NG searches tree space for the topology and branch lengths that maximize the likelihood of the observed alignment under a substitution model. In practice, that means comparing many candidate trees, optimizing branch lengths and model parameters, and keeping the trees that improve the score.
The rewrite improved several pieces of the original RAxML search procedure. The published implementation fixes missed topological moves from earlier versions, improves optimization for models such as LG4X, adds transfer bootstrap expectation (TBE), and reports terraces in tree space when the data structure implies many equally scoring topologies. It also incorporates site-repeat optimizations and parallelization improvements that matter on taxon-rich datasets.
One consequence of maximum likelihood inference is that alignment quality dominates the final result. RAxML-NG can optimize a tree very efficiently, but it cannot rescue a poor alignment, mixed paralogs, or a nucleotide alignment forced into a protein model.
How to use RAxML-NG online
RAxML-NG runs on ProteinIQ from a pre-aligned protein or DNA dataset in FASTA or PHYLIP format. Submit the alignment, choose whether to run ML search, bootstrapping, or both, and the job returns Newick tree files, bootstrap outputs, support-annotated trees, model information, and the execution log produced by RAxML-NG.
Inputs
| Input | Description |
|---|---|
Alignment | One aligned protein or DNA dataset in FASTA, PHYLIP, or plain text containing one of those formats. At least 2 sequences are required. |
Job name | Optional label for the submitted job in the ProteinIQ interface. |
Settings
| Setting | Description |
|---|---|
Analysis mode | ML tree search, Bootstrapping only, or ML search + bootstrapping. The combined mode is the default. |
Sequence type | Auto-detect from alignment, Protein, or DNA. Auto-detection works when the alignment alphabet is unambiguous. Short nucleotide alignments made only of A, C, G, and N may need manual selection. |
Model mode | Automatic (AA or DNA) uses the upstream model family shorthand, AA or DNA. Custom model string passes an explicit RAxML-NG model such as GTR+G or LG+G8+F. |
Custom model | Required only when Model mode is set to custom. Useful when a manuscript or lab workflow needs a specific model string instead of the family default. |
Starting tree | auto, pars{N}, or rand{N}. pars{10} starts from 10 parsimony trees, rand{10} starts from 10 random trees. This only affects ML search workflows. |
Random seed | Positive integer used for reproducibility. The same alignment and same settings should reproduce the same random starting conditions. |
CPU threads | Number of CPU threads used by the hosted run. More threads can reduce wall-clock time on larger alignments. |
Bootstrap replicates | Number of bootstrap trees when bootstrap analysis is enabled. 100 is often enough for exploratory work. Publication workflows often use several hundred to one thousand replicates. |
Bootstrap support metric | FBP, TBE, or RBS. FBP is the classic bootstrap proportion. TBE is often more stable on large trees with a few unstable taxa. |
Output prefix | Prefix used for downloadable filenames such as raxml.bestTree.newick. |
Outputs
| Output file | When it appears | Meaning |
|---|---|---|
prefix.bestTree.newick | ML search or combined mode | Best maximum-likelihood tree in Newick format. |
prefix.bestTreeCollapsed.newick | ML search or combined mode | Best tree with near-zero branches collapsed, useful when tiny branches clutter interpretation. |
prefix.bootstraps.newick | Bootstrap or combined mode | All bootstrap replicate trees in one Newick file. |
prefix.support.newick or metric-specific support trees | Bootstrap or combined mode | Support-annotated tree. Depending on the chosen metric, the wrapper can return files such as supportFBP, supportTBE, or supportRBS. |
prefix.bestModel.txt | When emitted by RAxML-NG | Optimized model parameters and final model details. |
prefix.mlTrees.newick | ML search or combined mode | Candidate ML trees from the starting-tree searches. Useful for auditing the search rather than for final reporting. |
prefix.raxml.log | Always when the run succeeds | Full RAxML-NG log, including command details and summary statistics such as final log-likelihood, AIC, and BIC when available. |
Understanding the results
RAxML-NG results are easiest to interpret in three layers: topology, branch lengths, and branch support.
Topology
The branching pattern is the inferred evolutionary hypothesis. Internal nodes define clades. A change in topology is biologically meaningful, so any region of the tree with weak support should be treated as unresolved rather than overinterpreted.
Branch lengths
Branch lengths are reported in substitutions per site. Longer branches indicate more inferred evolutionary change. Extremely long terminal branches often signal problematic sequences, alignment issues, contamination, or highly divergent taxa.
Support values
Support values only appear when bootstrap analysis is run. Lower values indicate that small changes in the resampled alignment often alter that split.
| Support value | Interpretation |
|---|---|
>= 95 | Strong support for that clade in most datasets |
80-94 | Reasonable support, often usable but worth checking against alignment quality |
70-79 | Weak to moderate support, commonly treated with caution |
< 70 | Unstable split, usually not strong evidence for that relationship |
Those cutoffs are conventions, not laws. Deep trees with uneven taxon sampling can show lower classical bootstrap support even when the overall signal is meaningful. That is one reason TBE exists.
FBP vs TBE vs RBS
| Metric | What it emphasizes | When it is useful |
|---|---|---|
FBP | Exact recovery of the same bipartition across replicates | Standard reporting and direct comparison with older phylogenetics literature |
TBE | Similarity between splits rather than exact matching | Large trees where a few unstable taxa would otherwise deflate support for deep branches |
RBS | Rapid bootstrap workflow | Faster support estimation when runtime matters more than strict comparability with classical bootstrap |
When to use RAxML-NG vs alternatives
RAxML-NG occupies the middle ground between very fast approximate tree builders and feature-rich ML packages with their own model-selection ecosystems.
| Tool | Best use case | Tradeoff |
|---|---|---|
| RAxML-NG | Maximum-likelihood inference when a solid ML search and standard bootstrap workflow are the priority | Requires a pre-aligned dataset and is slower than approximate methods |
| IQ-TREE | Analyses where built-in model selection and ultrafast bootstrap are central | Different search heuristics and support framework, often more feature-focused on model testing |
| FastTree | Very large alignments where speed matters more than exact ML optimization | Much faster, but approximate |
RAxML-NG is a strong default when the alignment is already prepared and the goal is a conventional ML tree with explicit bootstrap support. IQ-TREE is often the better choice when systematic model testing is part of the analysis plan. FastTree is better for exploratory work on very large datasets where approximate topology is good enough.
Related tools
- MAFFT: Builds multiple sequence alignments before phylogenetic inference.
- MUSCLE5: Alternative MSA method for protein or nucleotide datasets.
- Clustal Omega: Scalable alignment tool that fits naturally before tree building.
- IQ-TREE: Another maximum-likelihood phylogeny tool with strong model-selection features.
- FastTree: Approximate maximum-likelihood trees for very large alignments.
Based on the RAxML-NG paper by Kozlov et al. (2019) in Bioinformatics and the official RAxML-NG GitHub repository.