Advanced Tutorial

Recommended for experienced users to explore more features.

Table of Contents

• Tree topology tests
• User-defined substitution models
• Consensus construction and bootstrap value assignment
• Computing Robinson-Foulds distance between trees
• Generating random trees

To get started, please read the Beginner's Tutorial first if not done so yet.

Tree topology tests

IQ-TREE can compute log-likelihoods of a set of trees passed via the -z option:

iqtree -s example.phy -z example.treels -m GTR+G

assuming that example.treels contains the trees in NEWICK format. IQ-TREE first reconstructs an ML tree. Then, it will compute the log-likelihood of the trees in example.treels based on the estimated parameters done for the ML tree. example.phy.iqtree will have a section called USER TREES that lists the tree IDs and the corresponding log-likelihoods. The trees with optimized branch lengths can be found in example.phy.treels.trees If you only want to evaluate the trees without reconstructing the ML tree, you can run:

iqtree -s example.phy -z example.treels -n 1

Here, IQ-TREE performs a very quick tree reconstruction using only 1 iteration and uses that tree to estimate the model parameters, which are normally accurate enough for our purpose.

IQ-TREE also supports several tree topology tests using the RELL approximation (Kishino et al., 1990). This includes bootstrap proportion (BP), Kishino-Hasegawa test (Kishino and Hasegawa, 1989), Shimodaira-Hasegawa test (Shimodaira and Hasegawa, 1999), expected likelihood weights (Strimmer and Rambaut, 2002), weighted-KH (WKH), and weighted-SH (WSH) tests. The trees are passed via -z option:

iqtree -s example.phy -z example.treels -n 1 -zb 1000

Here, -zb specifies the number of RELL replicates, where 1000 is the recommended minimum number. The USER TREES section of example.phy.iqtree will list the results of BP, KH, SH, and ELW methods. If you also want to perform the WKH and WSH, simply add -zw option:

iqtree -s example.phy -z example.treels -n 1 -zb 1000 -zw

Finally, note that IQ-TREE will automatically detect duplicated tree topologies and omit them during the evaluation.

User-defined substitution models

Users can specify any DNA model using a 6-letter code that defines which rates should be equal. For example, 010010 corresponds to the HKY model and 012345 to the GTR model. In fact, IQ-TREE uses this specification internally to simplify the coding. The 6-letter code is specified via the -m option, e.g.:

iqtree -s example.phy -m 010010+G

Moreover, with the -m option one can input a file which contains the 6 rates (A-C, A-G, A-T, C-G, C-T, G-T) and 4 base frequencies (A, C, G, T). For example:

iqtree -s example.phy -m mymodel+G

where mymodel is a file containing the 10 entries described above, in the correct order. The entries can be seperated by either empty space(s) or newline character. One can even specify the rates within -m option by e.g.:

iqtree -s example.phy -m 'TN{2.0,3.0}+G8{0.5}+I{0.15}'

That means, we use Tamura-Nei model with fixed transition-transversion rate ratio of 2.0 and purine/pyrimidine rate ratio of 3.0. Moreover, we use 8-category Gamma-distributed site rates with the shape parameter (alpha) equal to 0.5 and a proportion of invariable sites p-inv=0.15.

By default IQ-TREE computes empirical state frequencies from the alignment by counting, but one can also estimate the frequencies by maximum-likelihood with +Fo in the model name:

iqtree -s example.phy -m GTR+G+Fo

For amino-acid alignments, IQ-TREE use the empirical frequencies specified in the model. If you want frequencies as counted from the alignment, use +F, for example:

iqtree -s myprotein_alignment -m WAG+G+F

Note that all model specifications above can be used in the partition model NEXUS file.

Consensus construction and bootstrap value assignment

IQ-TREE can construct an extended majority-rule consensus tree from a set of trees written in NEWICK or NEXUS format (e.g., produced by MrBayes):

iqtree -con mytrees

To build a majority-rule consensus tree, simply set the minimum support threshold to 0.5:

iqtree -con mytrees -t 0.5

If you want to specify a burn-in (the number of beginning trees to ignore from the trees file), use -bi option:

iqtree -con mytrees -t 0.5 -bi 100

to skip the first 100 trees in the file.

IQ-TREE can also compute a consensus network and print it into a NEXUS file by:

iqtree -net mytrees

Finally, a useful feature is to read in an input tree and a set of trees, then IQ-TREE can assign the support value onto the input tree (number of times each branch in the input tree occurs in the set of trees). This option is useful if you want to compute the support values for an ML tree based on alternative topologies.

iqtree -sup input_tree set_of_trees

Computing Robinson-Foulds distance between trees

IQ-TREE implements a very fast Robinson-Foulds (RF) distance computation using hash table, which is a lot faster than PHYLIP package. For example, you can run:

iqtree -rf tree_set1 tree_set2

to compute the pairwise RF distances between 2 sets of trees. If you want to compute the all-to-all RF distances of a set of trees, use:

iqtree -rf_all tree_set

Generating random trees

IQ-TREE provides several random tree generation models. For example, to generate a 100-taxon random tree into the file 100.tree under the Yule Harding model, use the following command:

iqtree -r 100 100.tree 

Here, the branch lengths follow an exponential distribution with mean of 0.1. If you want to change the branch length distribution, run e.g:

iqtree -r 100 -rlen 0.05 0.2 0.3 100.tree 

to set the minimum, mean, and maximum branch lengths as 0.05, 0.2, and 0.3, respectively. If you want to generate trees under uniform model instead, use -ru option:

iqtree -ru 100 100.tree 

If you want to generate a random tree for your alignment, simply add the -s <alignment> option to the command line:

iqtree -s example.phy -r 44 example.random.tree 

Note that, you still need to specify the -r option with the correct number of taxa that is contained in the alignment.