phyloGAN is a Generative Adversarial Network (GAN) that infers phylognetic relationships. phyloGAN takes as input a concatenated alignments, or a set of gene alignments, and then infers a phylogenetic tree either considering or ignoring gene tree heterogeneity.
phyloGAN requires several python packages, along with AliSim.
Handling trees and alignments: Biopython, dendropy, ete3
Machine learning: tensorflow
Miscellaneous utilities: copy, datetime, io, itertools, matplotlib, numpy, os, random, re, scipy, sys
phyloGAN was developed using the version of AliSim distributed with IQ-TREE v2.2.0 (Beta). A version of IQ-TREE with AliSim must be installed, and the user must provide the path to the executable.
To install phyloGAN, clone the GitHub repository:
git clone https://github.com/meganlsmith/phyloGAN.git
The original version of phyloGAN takes as input a concatenated alignment and infers a phylogenetic tree.
The concatenated alignment should be provided in phylip format. For an example see test_data/concatenated_test.phy.
The only other input to phyloGAN is the parameters file. For an example see test_data/params_concatenated.txt. In the example file, each line is described in a comment (following '#'). A few things to note:
- The temporary folder provided will be deleted during the run. DO NOT use an existing folder. This must be a new directory. If using an HPC, scratch spaces may be ideal because a lot of I/O to this directory will occur.
- The pseudoobserved setting is only recommended to be used in specific development contexts. Datasets are simulated from branch lengths drawn from an exponential distribution with mean lambda, and this is likely not desired in most simulation studies.
- It is recommended that users begin with a 'Random' start tree, rather than a 'NJ' start tree. Beginning with the 'NJ' tree seems to cause issues because the generated data seen early in training is too good.
To run phyloGAN:
python ./phyloGAN/scripts/phyloGAN.py ./test_data/params_concatenated.txt
To continue a run for which checkpoint files have previously been generated:
python ./phyloGAN/scripts/phyloGAN.py ./test_data/params_concatenated.txt checkpoint
For an example of phyloGAN (concatenation) output, see example_results.
- Lambdas from stage 1 are recorded in
Lambdas.txt. - Discriminator accuracies are recorded in
DiscriminatorRealAcc.txtandDiscriminatorFakeAcc.txt. - Generator accuracies are recorded in
GeneratorFakeAcc.txt. - Discriminator losses are recorded in
DiscriminatorLoss.txt. - Generator losses are recorded in
GeneratorLoss.txt. - The trees at each iteration are recorded in
Trees.txt. - Robinson–Foulds distances between each tree and the true tree (when provided) are recorded in
RFdistances.txt. - Various plots are provided in
.pngformat.
phyloGAN-ILS takes as input a folder with gene alignments and infers a species tree.
A directory containing single copy gene alignments should be provided to phyloGAN. If any species is missing from an alignment, it should be included with 'N's.
The params file is similar to that needed in the original version. For an example see test_data/params_coalescent.txt
Unpack test gene alignments:
cd test_data
tar -xzf gene_alignments.tar.gz
cd ../
To run phyloGAN:
python ./phyloGAN_ils/scripts/phyloGAN.py ./test_data/params_coalescent.txt
To continue a run for which checkpoint files have previously been generated:
python ./phyloGAN_ils/scripts/phyloGAN.py ./test_data/params_coalescent.txt checkpoint