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- Introduced a comprehensive RNA velocity analysis pipeline using scVelo, including data loading, preprocessing, velocity estimation, and visualization. - Added a script for running RNA velocity analysis with customizable parameters and output options. - Created detailed documentation for IQ-TREE 2 phylogenetic inference, covering command syntax, model selection, bootstrapping methods, and output interpretation. - Included references for velocity models and their mathematical framework, along with a comparison of different models. - Enhanced the scVelo skill documentation with installation instructions, use cases, and best practices for RNA velocity analysis.
5.3 KiB
5.3 KiB
IQ-TREE 2 Phylogenetic Inference Reference
Basic Command Syntax
iqtree2 -s alignment.fasta --prefix output -m TEST -B 1000 -T AUTO --redo
Key Parameters
| Flag | Description | Default |
|---|---|---|
-s |
Input alignment file | Required |
--prefix |
Output file prefix | alignment name |
-m |
Substitution model (or TEST) | GTR+G |
-B |
Ultrafast bootstrap replicates | Off |
-b |
Standard bootstrap replicates (slow) | Off |
-T |
Number of threads (or AUTO) | 1 |
-o |
Outgroup taxa name(s) | None (unrooted) |
--redo |
Overwrite existing results | Off |
-alrt |
SH-aLRT test replicates | Off |
Model Selection
# Full model testing (automatically selects best model)
iqtree2 -s alignment.fasta -m TEST --prefix test_run -B 1000 -T 4
# Specify model explicitly
iqtree2 -s alignment.fasta -m GTR+G4 --prefix gtr_run -B 1000
# Protein sequences
iqtree2 -s protein.fasta -m TEST --prefix prot_tree -B 1000
# Codon-based analysis
iqtree2 -s codon.fasta -m GY --prefix codon_tree -B 1000
Bootstrapping Methods
Ultrafast Bootstrap (UFBoot, recommended)
iqtree2 -s alignment.fasta -B 1000 # 1000 replicates
# Values ≥95 are reliable
# ~10× faster than standard bootstrap
Standard Bootstrap
iqtree2 -s alignment.fasta -b 100 # 100 replicates (very slow)
SH-aLRT Test (fast alternative)
iqtree2 -s alignment.fasta -alrt 1000 -B 1000 # Both SH-aLRT and UFBoot
# SH-aLRT ≥80 AND UFBoot ≥95 = well-supported branch
Branch Support Interpretation
| Bootstrap Value | Interpretation |
|---|---|
| ≥ 95 | Well-supported (strongly supported) |
| 70–94 | Moderately supported |
| 50–69 | Weakly supported |
| < 50 | Unreliable (not supported) |
Output Files
| File | Description |
|---|---|
{prefix}.treefile |
Best ML tree in Newick format |
{prefix}.iqtree |
Full analysis report |
{prefix}.log |
Computation log |
{prefix}.contree |
Consensus tree from bootstrap |
{prefix}.splits.nex |
Network splits |
{prefix}.bionj |
BioNJ starting tree |
{prefix}.model.gz |
Saved model parameters |
Advanced Analyses
Molecular Clock (Dating)
# Temporal analysis with sampling dates
iqtree2 -s alignment.fasta -m GTR+G \
--date dates.tsv \ # Tab-separated: taxon_name YYYY-MM-DD
--clock-test \ # Test for clock-like evolution
--date-CI 95 \ # 95% CI for node dates
--prefix dated_tree
Concordance Factors
# Gene concordance factor (gCF) - requires multiple gene alignments
iqtree2 --gcf gene_trees.nwk \
--tree main_tree.treefile \
--cf-verbose \
--prefix cf_analysis
Ancestral Sequence Reconstruction
iqtree2 -s alignment.fasta -m LG+G4 \
-asr \ # Marginal ancestral state reconstruction
--prefix anc_tree
# Output: {prefix}.state (ancestral sequences per node)
Partition Model (Multi-Gene)
# Create partition file (partitions.txt):
# DNA, gene1 = 1-500
# DNA, gene2 = 501-1000
iqtree2 -s concat_alignment.fasta \
-p partitions.txt \
-m TEST \
-B 1000 \
--prefix partition_tree
IQ-TREE Log Parsing
def parse_iqtree_log(log_file: str) -> dict:
"""Extract key results from IQ-TREE log file."""
results = {}
with open(log_file) as f:
for line in f:
if "Best-fit model" in line:
results["best_model"] = line.split(":")[1].strip()
elif "Log-likelihood of the tree:" in line:
results["log_likelihood"] = float(line.split(":")[1].strip())
elif "Number of free parameters" in line:
results["free_params"] = int(line.split(":")[1].strip())
elif "Akaike information criterion" in line:
results["AIC"] = float(line.split(":")[1].strip())
elif "Bayesian information criterion" in line:
results["BIC"] = float(line.split(":")[1].strip())
elif "Total CPU time used" in line:
results["cpu_time"] = line.split(":")[1].strip()
return results
# Example:
# results = parse_iqtree_log("output.log")
# print(f"Best model: {results['best_model']}")
# print(f"Log-likelihood: {results['log_likelihood']:.2f}")
Common Issues and Solutions
| Issue | Likely Cause | Solution |
|---|---|---|
| All bootstrap values = 0 | Too few taxa | Need ≥4 taxa for bootstrap |
| Very long branches | Alignment artifacts | Re-trim alignment; check for outliers |
| Memory error | Too many sequences | Use FastTree; or reduce -T to 1 |
| Poor model fit | Wrong alphabet | Check nucleotide vs. protein specification |
| Identical sequences | Duplicate sequences | Remove duplicates before alignment |
MAFFT Alignment Guide
# Accurate (< 200 sequences)
mafft --localpair --maxiterate 1000 input.fasta > aligned.fasta
# Medium (200-1000 sequences)
mafft --auto input.fasta > aligned.fasta
# Fast (> 1000 sequences)
mafft --fftns input.fasta > aligned.fasta
# Very large (> 10000 sequences)
mafft --retree 1 input.fasta > aligned.fasta
# Using multiple threads
mafft --thread 8 --auto input.fasta > aligned.fasta