Polymorphism Aware Models

Polymorphism-aware models

Polymorphism-aware phylogenetic Models (PoMo) related documentation.

Table of Contents

• Counts files
• First running example
• Substitution models
• Virtual population size
• Sampling method
• Bootstrap branch support
• Interpretation of branch lengths

The Polymorphism-aware phylogenetic Model (PoMo) tries to use population data (site frequency data) to improve phylogenetic inference. Thereby it builds on top of DNA substitution models and naturally accounts for incomplete lineage sorting. In order to run PoMo, you need more sequences per species (ideally about ten chromosomes) so that information about the site frequency spectrum is available.

The binary of IQ-TREE with PoMo can be downloaded or built from source. Please confirm that your version of IQ-TREE supports PoMo.

iqtree

>> IQ-TREE PoMo version 1.4.3-pomo for Linux 64-bit built Jul  6 2016
>> ...

TIP: For a quick overview of all PoMo related options in IQ-TREE, run the command iqtree -h and scroll to the heading POLYMORPHISM AWARE MODELS (PoMo).

If you use PoMo, please cite Schrempf et al., 2016:

Dominik Schrempf, Bui Quang Minh, Nicola De Maio, Arndt von
Haeseler, and Carolin Kosiol (2016) Reversible polymorphism-aware
phylogenetic models and their application to tree inference.
J. Theor. Biol., 407, 362–370.
http://doi.org/10.1016/j.jtbi.2016.07.042.

Counts files

The input of PoMo is allele frequency data. Especially, when populations have many individuals it is preferable to count the number of bases at each position. This decreases file size and speeds up the parser.

Counts files contain:

• One headerline that specifies the file as counts file and states the number of populations as well as the number of sites (separated by white space).

• A second headerline with white space separated headers: CRHOM (chromosome), POS (position) and sequence names.

• Many lines with counts of A, C, G and T bases and their respective positions.

Comments:

• Lines starting with # before the first headerline are treated as comments.

A toy example:

COUNTSFILE  NPOP 5   NSITES N
CHROM  POS  Sheep    BlackSheep  RedSheep  Wolf     RedWolf
1      1    0,0,1,0  0,0,1,0     0,0,1,0   0,0,5,0  0,0,0,1
1      2    0,0,0,1  0,0,0,1     0,0,0,1   0,0,0,5  0,0,0,1
.
.
.
9      8373 0,0,0,1  1,0,0,0     0,1,0,0   0,1,4,0  0,0,1,0
.
.
.
Y      9999 0,0,0,1  0,1,0,0     0,1,0,0   0,5,0,0  0,0,1,0

The download also includes an example counts file called example.cf. This file is a subset of the great ape data that has been analyzed in one of our publications. It includes twelve great ape population with one to 23 inividuals each (two to 56 chromosomes).

Conversion scripts

If you do not want to create counts files with your own scripts, you can use one of the scripts that we provide. For detailed instructions, please refer to the GitHub repository of the counts file library cflib.

First running example

You can now start to reconstruct a maximum-likelihood tree from this alignment by entering (assuming that you are now in the same folder with example.cf):

iqtree -s example.cf

-s is the option to specify the name of the alignment file. At the end of the run IQ-TREE writes the same output files as in the standard version (see tutorial).

• example.cf.iqtree: the main report file that is self-readable. You should look at this file to see the computational results. It also contains a textual representation of the final tree.
• example.cf.treefile: the ML tree in NEWICK format, which can be visualized by any supported tree viewer programs like FigTree or iTOL.
• example.cf.log: log file of the entire run (also printed on the screen). To report bugs, please send this log file and the original alignment file to the authors.

The default prefix of all output files is the alignment file name. However, you can always change the prefix using the -pre option, e.g.:

iqtree -s example.cf -pre myprefix

This prevents output files to be overwritten when you perform multiple analyses on the same alignment within the same folder.

Substitution models

By default, PoMo runs with the HKY model. Different DNA substitution models can be selected with the -m option. E.g., to select the GTR model, run IQ-TREE with:

iqtree -s example.cf -m GTR

If a counts file is given as input file, the PoMo model will be automatically chosen. You can also explicitly specify to run the (reversible) PoMo model with:

iqtree -s example.cf -m GTR+rP

The frequency type can also be selected With -m. The default is to empirically estimate allele frequencies. To estimate the allele frequencies together with the rate parameters, use:

iqtree -s example.cf -m GTR+rP+FO

TIP: For a quick overview of all available models in IQ-TREE, run the command iqtree -h and scroll to the heading POLYMORPHISM AWARE MODELS (PoMo).

Virtual population size

PoMo models the evolution of populations by means of a virtual population of constant size N, which defaults to nine (for details, see Schrempf et al., 2016). The optimal choice of N depends on the data. If only very few chromosomes have been sequenced per population (e.g., two to four), N should be lowered to five. If enough data is available and calculations are not too time consuming, we advise to increase N up to a maximum of 19. This can be done with the sequence type option -st. You can choose odd values from three to 19 as well as two and ten. E.g., to set N to 19:

iqtree -s example.cf -st CF19

Odd values of N allows the usage of the fast AVX instruction set. This results in a considerable decrease of runtime.

Sampling method

For advanced users. PoMo offers two different methods to read in the data (Schrempf et al., 2016). Briefly, each species and site are treated as follows

1. Weighted (default): assign the likelihood of each PoMo state to its probability of leading to the observed data, assuming it is binomially sampled.

2. Sampled: randomly draw N samples with replacement from the given data and set the PoMo state to the chosen one;

Again, the sequence type option -st can be used to change the input method.

• To use the sampled input method (R for random):

    iqtree -s example.cf -st CR
  

• To use the weighted input method (default behavior; CF for counts file):

    iqtree -s example.cf -st CF
  

Bootstrap branch support

To overcome the computational burden required by the non-parametric bootstrap, IQ-TREE introduces an ultra fast bootstrap approximation (UFBoot) that is orders of magnitude faster than the standard procedure and provides relatively unbiased branch support values. To run UFBoot, use the option -bb, e.g., for 1000 replicates:

iqtree -s example.cf -bb 1000

The standard non-parametric bootstrap is invoked by the -b option, e.g., for 100 replicates:

iqtree -s example.cf -b 100

For a detailed description, please refer to the bootstrap tutorial.

Interpretation of branch lengths

PoMo estimates the branch length in number of mutations and frequency shifts (drift) per site. The number of drift events compared to the number of mutations becomes higher if the virtual population size is increased. To get the branch length measured in number of substitutions per site which enables a comparison to the branch length estimated by standard DNA substitution models, it has to be divided by N^2. PoMo also outputs the total tree length measured in number of substitution per site in example.cf.iqtree. An example of the relevant section:

NOTE: The branch lengths of PoMo measure mutations and frequency shifts.
To compare PoMo branch lengths to DNA substitution models use the tree length
measured in substitutions per site.

Total tree length (sum of branch lengths)
 - measured in number of mutations and frequency shifts per site: 0.71200751
 - measured in number of substitutions per site (divided by N^2): 0.00879022
Sum of internal branch lengths
- measured in mutations and frequency shifts per site: 0.01767814 (2.48285810% of tree length)
- measured in substitutions per site: 0.01767814 (2.48285810% of tree length)