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Concordance Factor
layout: userdoc title: "Concordance Factor" author: AUTHOR date: DATE docid: 5 icon: info-circle doctype: tutorial tags:
- tutorial
description: "Gene and site concordance factor computation."
sections:
- name: Inferring species tree url: inferring-species-tree
- name: Inferring gene/locus trees url: inferring-genelocus-trees
- name: Gene concordance factor (gCF) url: gene-concordance-factor-gcf
- name: Site concordance factor (sCF) url: site-concordance-factor-scf
- name: Putting it all together url: putting-it-all-together
Since IQ-TREE 2, we provide two measures for quantifying genealogical concordance in phylogenomic datasets: the gene concordance factor (gCF) and the site concordance factor (sCF). For every branch of a reference tree, gCF is defined as the percentage of “decisive” gene trees containing that branch. gCF is already in wide usage, but here we allow to calculate gCF while correctly accounting for variable taxon coverage among the gene trees. sCF is defined as the percentage of decisive alignment sites supporting a branch in the reference tree. sCF is a novel measure that is particularly useful when individual gene alignments are relatively uninformative, such that gene trees are uncertain. gCF and sCF complement classical measures of branch support (e.g. bootstrap) in phylogenetics by providing a full description of underlying disagreement among loci and sites.
If you use this feature please cite:
Minh B.Q., Hahn M.W., Lanfear R. (2020) New methods to calculate concordance factors for phylogenomic datasets. Molecular Biology and Evolution, in press. https://doi.org/10.1093/molbev/msaa106
NOTE: For this feature please use IQ-TREE version 2!
See very nice tips on how to use and interpret concordance factors by Rob Lanfear. {: .tip}
First, you need to infer a reference tree (e.g. a species tree), on which the concordance factors will be annotated. The species tree can be reconstructed by a concatenation/supermatrix approach or a coalescent/reconciliation/supertree approach. Here, we will use the concatenation approach in IQ-TREE.
As an example, you can apply an edge-linked proportional partition model with ultrafast bootstrap (1000 replicates; for comparison with concordance factors):
iqtree -s ALN_FILE -p PARTITION_FILE --prefix concat -B 1000 -T AUTO
where ALN_FILE
and PARTITION_FILE
are your input files. -T AUTO
is to detect the best number of CPU cores. Here we use a prefix concat
, so that all output files (concat.*
) do not interfere with analyses below. If --prefix
is omitted, all output files will be PARTITION_FILE.*
.
Moreover, IQ-TREE 2 provides a new convenient feature: if you have a directory with many (locus) alignments, you can specify this directory directly with -p
option:
iqtree -p ALN_DIR --prefix concat -B 1000 -T AUTO
IQ-TREE detects if -p
argument is a directory and automatically load all alignment files and concatenate them into a supermatrix for the partition analysis.
We now construct a set of gene/locus trees. One can manually do a for-loop, but IQ-TREE 2 provides a new convenient option -S
to compute individual locus trees given a partition file or a directory:
iqtree -s ALN_FILE -S PARTITION_FILE --prefix loci -T AUTO
# or
iqtree -S ALN_DIR --prefix loci -T AUTO
In the second case, IQ-TREE automatically detects that ALN_DIR
is a directory and will load all alignment files within the directory. So -S
takes the same argument as -p
except that it performs model selection (ModelFinder) and tree inference separately for each partition/alignment. The output files are similar to those from a partitioned analysis, except that loci.treefile
now contains a set of trees.
Given the species tree concat.treefile
and the set of locus trees loci.treefile
computed above, you can calculate gCF for each branch of the species tree as the fraction of decisive gene trees concordant with this branch:
iqtree -t concat.treefile --gcf loci.treefile --prefix concord
Note that -t
accepts any reference tree (e.g., by coalescent/reconciliation approach) and --gcf
accepts any set of trees (e.g. locus trees and bootstrap trees), which may contain a subset of taxa from the reference tree. IQ-Tree will write three files:
-
concord.cf.tree
: Newick tree with gCF assigned for each internal branch of the reference tree. If the reference tree already has some branch label (such as bootstrap support in this case), gCF will be appended to the existing label separated by a/
. -
concord.cf.branch
: Newick tree with internal branch IDs. -
concord.cf.stat
: A tab-separated table with gCF and gDF (gene discordance factor) for every internal branch (rows of the table). The ID column can be linked withconcord.cf.branch
file. This file can be read in R to do some plot (see below).
If you omit --prefix
, all output files will be written to concat.treefile.*
.
Given the species tree concat.treefile
and the alignment, you can calculate sCF for each branch of the species tree as the fraction of decisive alignment sites supporting that branch:
iqtree -t concat.treefile -s ALN_FILE --scf 100 --prefix concord -T 10
--scf
specifies the number of quartets (randomly sampled around each internal branch) for computing sCF. We recommend at least 100 quartets for stable sCF values. Note that running this command several times may lead to slightly different sCF due to randomness. To make it reproducible, you need to use -seed
option to provide a random number generator seed.
Instead of -s
, you can alternatively provide a directory or a partition file. IQ-Tree then computes sCF for the concatenated alignment:
iqtree -t concat.treefile -p ALN_DIR --scf 100 --prefix concord -T 10
Finally, you can combine gCF and sCF within a single run:
iqtree -t concat.treefile --gcf loci.treefile -p ALN_DIR --scf 100 --prefix concord -T 10
Here, each branch of concord.cf.tree
will be assigned (or appended) with gCF/sCF
values and concord.cf.stat
will be written with both gCF and sCF values.
If you have separate alignments for each locus in a folder, then perform the following commands:
# infer a concatenation-based species tree with 1000 ultrafast bootstrap and an edge-linked partition model
iqtree -p ALN_DIR --prefix concat -B 1000 -T AUTO
# infer the locus trees
iqtree -S ALN_DIR --prefix loci -T AUTO
# compute concordance factors
iqtree -t concat.treefile --gcf loci.treefile -p ALN_DIR --scf 100 --prefix concord -T 10
If you have a single concatenated alignment with a partition file that defines loci:
# infer a concatenation-based species tree with 1000 ultrafast bootstrap and an edge-linked partition model
iqtree -s ALN_FILE -p PARTITION_FILE --prefix concat -B 1000 -T AUTO
# infer the locus trees
iqtree -s ALN_FILE -S PARTITION_FILE --prefix loci -T AUTO
# compute concordance factors
iqtree -t concat.treefile --gcf loci.treefile -s ALN_FILE --scf 100 --prefix concord -T 10
Note that you can adjust -T 10
if you have fewer/larger CPU cores.
Copyright (c) 2010-2016 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
- DNA models
- Protein 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