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Rootstrap
IQ-TREE provides an option to calculate the rootstrap support values (Naser-Khdour et al 2021) for rooted and unrooted trees.
1.Using non-reversible models:
If you are using partitioned analysis, you first find the best partitioning scheme using the reversible model:
iqtree2 –s example.phy –p example.nex --prefix UNROOTED
This will infer the ML unrooted tree and print the result into UNROOTED.treefile
and the best partitioning scheme
and print the reult intoUNROOTED.best_scheme.nex.
. If you used the UFBoot option too, then the bootstrap trees will
be printed into UNROOTED.ufboot
file
You then can use the best partitioning scheme to find the ML rooted tree with the non-reversible model
For DNA, use the UNREST model:
iqtree2 –s example.phy –p UNROOTED.best_scheme.nex –t UNROOTED.treefile -m UNREST -B 1000 --prefix ROOTED
For amino acid, use the NONREV model:
iqtree2 –s example.phy –p UNROOTED.best_scheme.nex –t UNROOTED.treefile -m NONREV -B 1000 --prefix ROOTED
This will print the ML rooted tree with the rootstrap support values into the ROOTED.rootstrap.nex
file.
The tree file in Nexus format will look like this:
#NEXUS [ This file is best viewed in FigTree. ] begin trees; tree tree_1 = ((BOS_MUT:0.0035185508[&id="2",rootstrap="0"],BOS_TAU:0.0060681062[&id="3",rootstrap="0"]):0.0306288620[&id="1",rootstrap="23.8"],((CAPRA_HIR:0.0092827982[&id="6",rootstrap="0"],OVIS_ARI:0.0104285307[&id="7",rootstrap="0"]):0.0052665552[&id="5",rootstrap="33.5"],PANTH_HOD:0.0113457232[&id="8",rootstrap="42.7"]):0.0000022608[&id="4",rootstrap="23.8"]):0.0000000000[&id="0",rootstrap="23.8"]; end;
Note: If you are not using a partitioned analysis remove the -p UNROOTED.best_scheme.nex –t UNROOTED.treefile
option.
Note: It is recommended to compare AIC/BIC values of the reversible and the non-reversible models before making any conclusions about the root placement.
2.Using reversible models with outgroup:
First find the unrooted ML tree and the unrooted bootstrap trees and print them to files UNROOTED.treefile
and
UNROOTED.ufboot
respectively:
iqtree2 –s example.phy –p example.nex –wbt -B 1000 --prefix UNROOTED
You then can use the -o
option to root the ML tree and the bootstrap trees with outgroup taxons (e.g. taxon1,taxon2,taxon3)
and use --rootstrap
option to calculate the rootstrap support values
iqtree2 –t UNROOTED.treefile –-rootstrap UNROOTED.ufboot -o taxon1,taxon2,taxon3 --prefix ROOTED
This will print the ML rooted tree with the rootstrap support values into the ROOTED.rootstrap.nex
file. and will print the topology test
on rooting positions across all branches to the ROOTED.roottest.csv
.
3.Perform topology tests on rooting positions across all branches
To perform topology tests on rooting positions across all branches use --root-test –zb 1000 -au
option. This option will re-root the ML tree on all branches (including tips)
and will print the topology test table into ROOTED.roottest.csv
that looks like this:
ID,logL,deltaL,bp-RELL,p-KH,p-SH,c-ELW,p-AU 1,-1412910.023,0,0.251,0.695,1,0.3333548982,0.7135619126 8,-1412910.023,3.927876242e-05,0.338,0.305,0.837,0.3333420861,0.4428443979 5,-1412910.023,0.0001593173947,0.411,0.111,0.811,0.3333030158,0.1048951504 7,-1413575.029,665.0064102,0,0,0,3.129908119e-223,2.688384524e-44 6,-1413575.029,665.0064236,0,0,0,3.130318178e-223,2.809697961e-44 3,-1417753.19,4843.167287,0,0,0,0,0.0002968435628 2,-1417753.191,4843.167847,0,0,0,0,0.000296849965
The "id" columns in the ROOTED.roottest.csv
are identical to the "id" labels in the ROOTED.rootstrap.nex
file.
Note: This option can be combined with any of the options above to calculate the rootstrap support values and the AU test (and other topology tests) in one analysis.
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
- 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