Modern ML Tree Inference

Build maximum likelihood phylogenetic trees with automatic model selection and ultrafast bootstrap.

IQ-TREE2 Basic Usage

bash

# Simple ML tree with automatic model selection
iqtree2 -s alignment.fasta -m MFP -B 1000 -T AUTO

# -s: input alignment
# -m MFP: ModelFinder Plus (automatic model selection + tree inference)
# -B 1000: 1000 ultrafast bootstrap replicates (minimum recommended for publication)
# -T AUTO: automatic thread detection

IQ-TREE2 Output Files

File	Description
`.treefile`	Best ML tree in Newick format
`.iqtree`	Full analysis report with model parameters
`.log`	Run log
`.contree`	Consensus tree with bootstrap support
`.splits.nex`	Bootstrap splits in Nexus format
`.model.gz`	Model parameters
`.bionj`	Initial BIONJ tree
`.mldist`	ML distance matrix
`.ckp.gz`	Checkpoint file for resuming

Model Selection

bash

# ModelFinder only (no tree inference)
iqtree2 -s alignment.fasta -m MF

# Use specific model
iqtree2 -s alignment.fasta -m GTR+G4 -B 1000

# Test only specific models
iqtree2 -s alignment.fasta -m MF -mset GTR,HKY,K2P

# Protein models
iqtree2 -s protein.fasta -m MFP -B 1000 -st AA

Common DNA Substitution Models

Model	Parameters	Use Case
JC	Equal rates	Very simple, rarely appropriate
K2P/K80	Ti/Tv ratio	Simple, some rate variation
HKY	Ti/Tv + base freq	Moderate complexity
GTR	6 rates + base freq	Most general, recommended default
+G4	Gamma rate variation	4 discrete rate categories
+I	Invariant sites	Sites that never change
+R4	FreeRate model	More flexible than Gamma

Ultrafast Bootstrap

bash

# Standard ultrafast bootstrap (UFBoot2)
# B>=1000: Minimum for publication. Use 10000 for final analyses.
iqtree2 -s alignment.fasta -m GTR+G4 -B 1000

# Standard bootstrap (slower but more accurate for small datasets)
iqtree2 -s alignment.fasta -m GTR+G4 -b 100

# SH-aLRT test (fast approximate likelihood ratio test)
iqtree2 -s alignment.fasta -m GTR+G4 -alrt 1000

# Both UFBoot and SH-aLRT
iqtree2 -s alignment.fasta -m GTR+G4 -B 1000 -alrt 1000

Interpreting Bootstrap Values

UFBoot	SH-aLRT	Interpretation
>= 95	>= 80	Strong support
80-94	70-79	Moderate support
< 80	< 70	Weak support

Partitioned Analysis

For multi-gene datasets with different evolutionary rates:

bash

# Create partition file (partitions.nex)
cat > partitions.nex << 'EOF'
#nexus
begin sets;
    charset gene1 = 1-500;
    charset gene2 = 501-1200;
    charset gene3 = 1201-1800;
    charpartition mine = HKY:gene1, GTR:gene2, GTR+G:gene3;
end;
EOF

# Run partitioned analysis
iqtree2 -s concat.fasta -p partitions.nex -m MFP -B 1000

# Edge-linked partition model (proportional branch lengths)
iqtree2 -s concat.fasta -q partitions.nex -m MFP -B 1000

# Edge-unlinked (independent branch lengths per partition)
iqtree2 -s concat.fasta -Q partitions.nex -m MFP -B 1000

RAxML-ng Basic Usage

bash

# Simple ML tree with GTR+G
raxml-ng --all --msa alignment.fasta --model GTR+G --bs-trees 100

# --all: ML search + bootstrapping
# --msa: input alignment
# --model: substitution model
# --bs-trees: number of bootstrap replicates

RAxML-ng Model Specification

bash

# DNA models
raxml-ng --msa alignment.fasta --model GTR+G4+I

# Protein models (automatic detection)
raxml-ng --msa protein.fasta --model LG+G8+F

# Check alignment and determine model
raxml-ng --check --msa alignment.fasta --model GTR+G

RAxML-ng Output Files

File	Description
`.raxml.bestTree`	Best ML tree
`.raxml.support`	Tree with bootstrap support values
`.raxml.bootstraps`	All bootstrap trees
`.raxml.mlTrees`	All ML trees from search
`.raxml.log`	Analysis log
`.raxml.rba`	Binary alignment (for restart)

RAxML-ng Advanced Options

bash

# Multiple ML searches (find global optimum)
# --tree pars{10}: 10 starting parsimony trees recommended for thorough search
raxml-ng --msa alignment.fasta --model GTR+G --tree pars{10} --prefix ml_search

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

# Site likelihoods for topology tests
raxml-ng --sitelh --msa alignment.fasta --model GTR+G --tree candidate.tre

Comparing IQ-TREE2 vs RAxML-ng

Feature	IQ-TREE2	RAxML-ng
Model selection	Built-in ModelFinder	External (ModelTest-NG)
Ultrafast bootstrap	Yes (UFBoot2)	No
Standard bootstrap	Yes	Yes
Partition models	Extensive	Good
Speed	Faster for UFBoot	Faster for standard BS
Memory	Lower	Higher
Checkpointing	Yes	Yes

Large Dataset Strategies

bash

# IQ-TREE2 with reduced memory
iqtree2 -s large.fasta -m GTR+G -B 1000 -T 4 -mem 8G

# Use approximate NNI search
iqtree2 -s large.fasta -m GTR+G -B 1000 -fast

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

Tree Topology Tests

bash

# IQ-TREE2: AU test comparing trees
iqtree2 -s alignment.fasta -m GTR+G -z trees.nwk -n 0 -zb 10000 -au

# Output interpretation:
# p-AU < 0.05: Reject tree
# p-AU >= 0.05: Cannot reject tree

Constrained Analysis

bash

# IQ-TREE2: Enforce monophyly constraint
iqtree2 -s alignment.fasta -m GTR+G -g constraint.tre -B 1000

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

Complete Workflow Example

bash

# 1. Check alignment
iqtree2 -s alignment.fasta -m GTR+G -n 0

# 2. Find best model
iqtree2 -s alignment.fasta -m MF -T AUTO

# 3. Full analysis with best model
iqtree2 -s alignment.fasta -m GTR+I+G4 -B 1000 -alrt 1000 -T AUTO

# 4. Visualize result
cat alignment.fasta.treefile

Resuming Interrupted Runs

bash

# IQ-TREE2: Resume from checkpoint
iqtree2 -s alignment.fasta -m GTR+G -B 1000 --redo-tree

# RAxML-ng: Resume
raxml-ng --msa alignment.fasta --model GTR+G --redo

Reproducibility

bash

# Set random seed for reproducible results
# seed=12345: Any fixed seed ensures reproducibility across runs
iqtree2 -s alignment.fasta -m GTR+G -B 1000 --seed 12345

raxml-ng --msa alignment.fasta --model GTR+G --seed 12345 --bs-trees 100

Related Skills

•tree-io - Read and convert output tree files
•tree-visualization - Visualize trees with bootstrap support
•distance-calculations - Compare with distance-based methods
•alignment/alignment-io - Prepare alignments for tree inference