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Command Reference
minh edited this page Nov 10, 2015
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iqtree -s <alignment> [OPTIONS]
Assuming that IQ-TREE can be run by simply entering iqtree. If not, please read the Quick start guide.
| Option | Usage and meaning |
|---|---|
| -h or -? | Print help usage. |
| -s | Specify input alignment file in PHYLIP, FASTA, NEXUS, CLUSTAL or MSF format. |
| -st | Specify sequence type: BIN (binary), DNA, AA (amino-acid), NT2AA (converting NT to AA), CODON or MORPH (morphology). By default IQ-TREE automatically detects the sequence type. |
| -q | Specify partition file (NEXUS or RAxML-style format) for edge-equal partition model. That means, all partitions share the same set of branch lengths (like -q option of RAxML). |
| -spp | Like -q but each partition has its own rate (edge-proportional partition model). |
| -sp | Specify partition file for edge-unlinked partition model. That means, each partition has its own set of branch lengths (like -M option of RAxML). |
| -t | Specify starting tree for tree search. By default, IQ-TREE starts from 100 parsimony trees and BIONJ tree. The special option -t BIONJ starts tree search from BIONJ tree and -t RANDOM starts tree search from random Yule-Harding tree. |
| -te | Like -t but fixing user tree. That means, no tree search is performed and IQ-TREE computes the log-likelihood of the fixed user tree. |
| -o | Specify an outgroup taxon name to root the tree. The output tree in .treefile will be rooted accordingly. |
| -pre | Specify a prefix for all output files. By default, the prefix is either the alignment file name (s) or the partition file name (-q, -spp or -sp). |
| -seed | Specify a random number seed to reproduce a previous run. This is normally used for debugging purpose. By default, IQ-TREE draws a random number seed from the current machine clock. |
| -v | Turn on verbose mode for printing more messages to screen. This is normally used for debugging purpose. |
Copyright (c) 2010-2022 IQ-TREE development team.
- First example
- Model selection
- New model selection
- Codon models
- Binary, Morphological, SNPs
- Ultrafast bootstrap
- Nonparametric bootstrap
- Single branch tests
- Partitioned analysis
- Partitioning with mixed data
- Partition scheme selection
- Bootstrapping partition model
- Utilizing multi-core CPUs
- Tree topology tests
- User-defined models
- Consensus construction and bootstrap value assignment
- Computing Robinson-Foulds distance
- Generating random trees
- Estimating amino acid substitution models
- DNA models
- Protein models
- 3Di and TEA models
- Codon models
- Binary, morphological models
- Ascertainment bias correction
- Rate heterogeneity
- Counts files
- First running example
- Substitution models
- Virtual population size
- Sampling method
- Bootstrap branch support
- Interpretation of branch lengths