Version Compatibility

Reference examples tested with: IQ-TREE 2.2+, RAxML-NG 1.2+

Before using code patterns, verify installed versions match. If versions differ:

• CLI:

  iqtree2 --version

  then

  iqtree2 --help

  to confirm flags
• CLI:

  raxml-ng --version

  then

  raxml-ng --help

  to confirm flags

If commands fail, introspect the installed version and adapt flags rather than retrying.

Modern ML Tree Inference

"Infer a maximum likelihood tree from my alignment" -> Build publication-quality ML trees with automatic substitution model selection, ultrafast bootstrap support, concordance factors, and topology testing.

• CLI:

  iqtree2 -s alignment.fa -m MFP -B 1000 -alrt 1000 -bnni

  (IQ-TREE2)
• CLI:

  raxml-ng --all --msa alignment.fa --model GTR+G --bs-trees 100

  (RAxML-NG)

IQ-TREE2 vs RAxML-NG Decision

Default recommendation: IQ-TREE2 for most workflows (integrated model selection, UFBoot2, concordance factors). Use RAxML-NG when precise branch lengths matter, for very large trees, or when using transfer bootstrap for rogue-taxon-prone datasets.

Best practice for important results: Run both tools and compare topologies.

Model Selection

Use ModelFinder (

-m MFP

), Not

-m TEST

Goal: Select the substitution model that best describes the evolutionary process in the alignment.

Approach: ModelFinder (

-m MFP

-m TEST

# Recommended: ModelFinder Plus (includes FreeRate models)
iqtree2 -s alignment.fasta -m MFP -B 1000 -alrt 1000 -bnni -T AUTO

# Model selection only (no tree inference)
iqtree2 -s alignment.fasta -m MF -T AUTO

# Partition model with automatic merging
iqtree2 -s concat.fasta -p partitions.nex -m MFP+MERGE -B 1000 -bnni -T AUTO

Rate Heterogeneity Models

-m MFP

-m TEST

FreeRate models can absorb rate variation from long tails that discretized gamma cannot. For large datasets or datasets with complex rate variation, FreeRate often fits better.

BIC vs AIC for Model Selection

BIC is the IQ-TREE default and recommended for most analyses. BIC penalizes complexity more heavily than AIC, reducing overfitting risk. AIC tends to select overly complex models.

DNA Model Hierarchy

The real decision is usually not GTR vs HKY but which rate heterogeneity model (+G vs +I+G vs +R).

Protein Models

Let ModelFinder choose. For deep phylogenies, profile mixture models (C10-C60 in IQ-TREE, CAT in PhyloBayes) can outperform fixed-matrix models by capturing site-specific amino acid preferences.

# Protein with ModelFinder
iqtree2 -s protein.fasta -m MFP -B 1000 -bnni -st AA -T AUTO

# Protein with mixture model (for deep phylogenies)
iqtree2 -s protein.fasta -m LG+C60+F+G -B 1000 -bnni -st AA -T AUTO

Branch Support Assessment

Standard Analysis: UFBoot2 + SH-aLRT

# Recommended for most analyses
iqtree2 -s alignment.fasta -m MFP -B 1000 -alrt 1000 -bnni -T AUTO --seed 12345

The

-bnni

Interpreting Support Values

Critical nuance: UFBoot values are NOT comparable to standard bootstrap. UFBoot >= 95 corresponds roughly to standard bootstrap >= 70. Do not apply the traditional >= 70 threshold to UFBoot values.

When Low Support Matters

• Low support on backbone branches: genuine topological uncertainty. Investigate with concordance factors or coalescent methods
• Low support on recent divergences within a well-sampled clade: may reflect insufficient data rather than genuine uncertainty
• Low support throughout the tree: suspect rapid radiation, incomplete lineage sorting (ILS), hybridization, or inadequate data

Transfer Bootstrap (RAxML-NG)

For large trees (>1000 taxa), transfer bootstrap expectation (TBE) is less sensitive to rogue taxa than standard bootstrap:

raxml-ng --all --msa alignment.fasta --model GTR+G --bs-trees autoMRE --bs-metric tbe

Concordance Factors

Concordance factors quantify agreement among loci (gCF) and sites (sCF), complementing bootstrap:

# After obtaining gene trees and species tree
iqtree2 -t species.treefile --gcf gene_trees.treefile -s concat.fasta --scf 100

# Likelihood-based sCF (more accurate; requires recent IQ-TREE)
iqtree2 -t species.treefile --gcf gene_trees.treefile -s concat.fasta --scfl 100

For publication, report UFBoot + SH-aLRT at minimum; add concordance factors for phylogenomic datasets.

Partitioned Analysis

For multi-gene concatenated datasets where genes evolve at different rates:

# Edge-linked proportional (recommended default)
iqtree2 -s concat.fasta -p partitions.nex -m MFP -B 1000 -bnni -T AUTO

# Edge-unlinked (independent branch lengths per partition)
# Most general but parameter-rich; risk of overfitting with missing data
iqtree2 -s concat.fasta -Q partitions.nex -m MFP -B 1000 -bnni -T AUTO

# With automatic partition merging
iqtree2 -s concat.fasta -p partitions.nex -m MFP+MERGE -B 1000 -bnni -T AUTO

-q

-p

-Q

Topology Testing

AU Test (Approximately Unbiased)

Goal: Test whether alternative tree topologies are significantly worse than the best tree.

Approach: Compare candidate topologies using the AU test, which provides proper multiple-testing correction. Preferred over the overly conservative SH test.

# Compare candidate topologies
iqtree2 -s alignment.fasta -m GTR+F+R3 --trees candidates.treefile --test-au --test 10000 -n 0

• p-AU >= 0.05: Tree cannot be rejected
• p-AU < 0.05: Tree is significantly worse

Use topology tests for evaluating specific competing hypotheses, not for fishing through thousands of random topologies.

Long Branch Attraction (LBA) Awareness

LBA causes distantly related long-branched taxa to group together artifactually due to model misspecification. It affects ML and Bayesian methods, not just parsimony.

Detection signs:

• Two long-branched taxa group together when they should not
• Removing one long-branch taxon causes the other to move
• Switching to a more complex model changes the placement

Mitigation (in order of effectiveness):

1. Add taxa that break long branches (most effective)
2. Use site-heterogeneous models (C60 in IQ-TREE, CAT-GTR in PhyloBayes)
3. Remove saturated sites (3rd codon positions, hypervariable regions)
4. Use amino acids instead of nucleotides for coding regions
5. RY-coding or Dayhoff-6 recoding for proteins

When LBA is suspected, consider running PhyloBayes with CAT-GTR model (see bayesian-inference skill).

IQ-TREE2 Output Files

.treefile

.iqtree

.contree

.splits.nex

.model.gz

.log

.ckp.gz

RAxML-NG Usage

# ML search + bootstrap
raxml-ng --all --msa alignment.fasta --model GTR+G --bs-trees 100

# Thorough search with multiple starting trees
raxml-ng --msa alignment.fasta --model GTR+G --tree pars{10} --prefix ml_search

# Protein models
raxml-ng --msa protein.fasta --model LG+G8+F

# Constrained tree search
raxml-ng --msa alignment.fasta --model GTR+G --tree-constraint constraint.tre

# Check alignment before full run
raxml-ng --check --msa alignment.fasta --model GTR+G

RAxML-NG Output Files

.raxml.bestTree

.raxml.support

.raxml.bootstraps

.raxml.mlTrees

.raxml.log

Large Dataset Strategies

# IQ-TREE2 fast mode for >500 taxa
iqtree2 -s large.fasta -m GTR+G -B 1000 -bnni -T 4 -mem 8G -fast

# RAxML-NG with limited starting trees
raxml-ng --msa large.fasta --model GTR+G --tree pars{5} --threads 8

For very large trees (>1000 taxa), consider FastTree 2 for an initial exploratory tree, then refine with RAxML-NG.

Constrained Analysis

# Enforce monophyly constraint
iqtree2 -s alignment.fasta -m MFP -g constraint.tre -B 1000 -bnni

# Constraint file: Newick with taxa to constrain
# ((Human,Chimp),Gorilla);

Reproducibility

# Always set random seed for reproducible results
iqtree2 -s alignment.fasta -m MFP -B 1000 -bnni --seed 12345 -T AUTO
raxml-ng --msa alignment.fasta --model GTR+G --seed 12345 --bs-trees 100

Resuming Interrupted Runs

iqtree2 -s alignment.fasta -m MFP -B 1000 --redo-tree
raxml-ng --msa alignment.fasta --model GTR+G --redo

Related Skills

• bayesian-inference - Bayesian tree inference with MrBayes, BEAST2, convergence diagnostics
• species-trees - Coalescent methods (ASTRAL) when gene tree discordance is high
• divergence-dating - Molecular clock analysis and divergence time estimation
• tree-io - Read and convert output tree files
• tree-visualization - Visualize trees with support values
• distance-calculations - Compare with distance-based methods
• alignment/alignment-io - Prepare alignments for tree inference
• alignment/multiple-alignment - Alignment quality affects tree inference