Frequently Asked Questions

For common questions and answers.

Table of Contents

• How do I interpret ultrafast bootstrap (UFBoot) support values?
• How does IQ-TREE treat gap/missing/ambiguous characters?
• Can I mix DNA and protein data in a partitioned analysis?

How do I interpret ultrafast bootstrap (UFBoot) support values?

The ultrafast bootstrap (UFBoot) feature (-bb option) was published in (Minh et al., 2013). One of the main conclusions is, that UFBoot support values are more unbiased: 95% support correspond roughly to a probability of 95% that a clade is true. So this has a different meaning than the normal bootstrap supports (where you start to believe in the clade if it has >80% BS support). For UFBoot, you should only start to believe in a clade if its support is >= 95%. Thus, the interpretations are different and you should not compare BS% with UFBoot% directly.

Moreover, it is recommended to also perform the SH-aLRT test (Guindon et al., 2010) by adding -alrt 1000 into the IQ-TREE command line. Each branch will then be assigned with SH-aLRT and UFBoot supports. One would typically start to rely on the clade if its SH-aLRT >= 80% and UFboot >= 95%.

How does IQ-TREE treat gap/missing/ambiguous characters?

Gaps (-) and missing characters (? or N for DNA alignments) are treated in the same way as unknown characters, which represent no information. The same treatment holds for many other ML software (e.g., RAxML, PhyML). More explicitly, for a site (column) of an alignment containing AC-AG-A (i.e. A for sequence 1, C for sequence 2, - for sequence 3, and so on), the site-likelihood of a tree T is equal to the site-likelihood of the subtree of T restricted to those sequences containing non-gap characters (ACAGA).

Ambiguous characters that represent more than one character are also supported: each represented character will have equal likelihood. For DNA the following ambigous nucleotides are supported according to IUPAC nomenclature:

Nucleotide	Meaning
R	A or G (purine)
Y	C or T (pyrimidine)
W	A or T (weak)
S	G or C (strong)
M	A or C (amino)
K	G or T (keto)
B	C, G or T (next letter after A)
H	A, C or T (next letter after G)
D	A, G or T (next letter after C)
V	A, G or C (next letter after T)
?, -, ., ~, O, N, X	A, G, C or T (unknown; all 4 nucleotides are equally likely)

For protein the following ambiguous amino-acids are supported:

Amino-acid	Meaning
B	N or D
Z	Q or E
J	I or L
U	unknown AA (although it is the 21st AA)
?, -, ., ~, * or X	unknown AA (all 20 AAs are equally likely)

Can I mix DNA and protein data in a partitioned analysis?

Yes! You can specify this via a NEXUS partition file. In fact, you can mix any data types supported in IQ-TREE, including also codon, binary and morphological data. To do so, each data type should be stored in a separate alignment file (see also partition file format). As an example, assuming dna.phy is a DNA alignment and and prot.phy is a protein alignment. Then a partition file mixing two types of data can be specified as follows:

#nexus
begin sets;
    charset part1 = dna.phy: 1-100 201-300;
    charset part2 = dna.phy: 101-200;
    charset part3 = prot.phy: 1-150;
    charset part4 = prot.phy: 151-400;
    charpartition mine = HKY:part1, GTR+G:part2, WAG+I+G:part3, LG+G:part4;
end;

NOTE: The site count for each alignment should start from 1, and not continue from the last position of a previous alignment (e.g., see part3 and part4 declared above).

What is the interpretation of branch lengths when mixing codon and DNA data?

When mixing codon and DNA data in a partitioned analysis, the branch lengths are interpreted as the number of nucleotide substitutions per nucleotide site! This is different from having only codon data, where branch lengths are the number of nucleotide substitutions per codon site (thus typically 3 times longer than under DNA models).

Note that if you mix codon, DNA and protein data, the branch lengths are then the number of character substitutions per site, where character is either nucleotide or amino-acid.

What is the purpose of the composition test?

At the beginning of each run, IQ-TREE performs a composition chi-square test for every sequence in the alignment. The purpose is to test for homogeneity of character composition (e.g., nucleotide for DNA, amino-acid for protein sequences). A sequence is denoted failed if its character composition significantly deviates from the average composition of the alignment.

More specifically, for each sequence, compute:

chi2 = \sum_{i=1}^k (O_i - E_i)^2 / E_i

where k is the size of the alphabet (e.g. 4 for DNA, 20 for amino acids) and the values 1 to k correspond uniquely to one of the characters. O_i is the character frequency in the sequence tested. E_i is the overall character frequency from the entire alignment.

Whether the character composition deviates significantly from the overall composition is done by testing the chi2 value using the chi2-distribution with k-1 degrees of freedom (df=3 for DNA or df=19 for amino acids). By and large it is a normal Chi^2 test.

This test should be regarded as an explorative tool which might help to nail down problems in a dataset. One would typically not remove failing sequences by default. But if the tree shows unexpected topology the test might point in direction of the origin of the problem.

Furthermore, please keep in mind, this test is performed at the very beginning, where IQ-TREE does not know anything about partitions yet. That means, it might be more reasonable to test this separately for each partition in a partition analysis. Here, one might want to be able to decide whether some partitions should better be discarded if it is hard to find a composition representing the sequences in the partition. Or on the other hand if a sequence fails for many partitions and show very unexpected phylogenetic topologies, try without it.

In any case if one has sequences that fail, it is recommended to always check the alignment (something one should always do anyway), especially if they have been collected and produced automatically.