In addition to the R package phylter available on CRAN (https://CRAN.R-project.org/package=phylter) and on GitHub (https://github.com/damiendevienne/phylter), containerized versions of phylter (docker and singularity) are also proposed.
This may ease the use of phylter on some computing infrastructures (clusters) or for users reluctant to the R language.
The containers host python3 scrips allowing to easily run phylter with the same options than with the R package, but also perform additional tasks such as removing (pruning) outliers from input trees and/or filtering out outlier sequences from (aligned) sequence files (fasta format).
Using phylter from the container simply consists in
phylter.py function, specifying various options
such as the directory containing the gene trees (with -t), the job name
(with -j), etc.
The containers also contain a toy dataset of 255 Carnivora genes
trees and alignments from Allio et al. (2021) that will allow you to
test both the correct installation of the container(s), and the use of
phylter.py function and its options.
PhylteR is available as a Docker container: https://hub.docker.com/r/treecoutheo/phylter_docker.
Here are the steps needed to use the docker version of phylter:
Warning: you may need administrator rights to use docker!
$PWDspecifies that you work in your local Present Working Directory. This should not be changed.
-jspecifies the job name for this phylter run (and thus the name of the output folder)
-tspecifies the folder containing the input gene trees in Newick format (one tree per file)
The command above creates the directory
that will contain:
PhylteR_all_tree_named: a single file containing all the trees with the gene ID preceding the newick.
phylter.out: the phylter output file containing the log of the run and the list of identified outliers.
You may want to run phylter and to subsequently
remove the identified outliers from both the gene trees and the
sequences files. For this to be performed, the sequence files must
contain the same name as the corresponding tree, minus the extension if
any. For example, a sequence file named
ENSG00000274286_ADRA2B_final_align_NT.aln will be matched
automatically to a tree file named
will identify the gene ID as being
-p TRUEspecifies that the input trees should be pruned by removing the outliers identified by phylter. A new directorry containing the pruned trees is created (see after).
-sspecifies the directory containing the sequences (see above for specifications regarding naming conventions). If this directory is specified, the input sequences will be filtered by removing the outliers identified by phylter. A new directorry containing the filtered sequences is created (see after).
-g TRUEspecifies that a full report (in pdf) should be produced (in addition to the default
The command above generates, in addition to the two files described in the previous example:
Carnivora_docker/trees_PhylteR/: a directory
containing the trees pruned from their outliers. Note that the number of
trees inside the directory can be lower than the number of trees used as
input in case all species from a gene tree have been identified as
Carnivora_docker/seqs_PhylteR/: a directory
containing sequences with outlier sequences filtered out. The number of
sequences files inside the directory can be lower than the number of
sequences files used as input. Again, the number of sequence files
inside the directory can be lower than the number of trees sequence
files in the input directory in case all species from a gene tree have
been identified as outliers.
report.pdf: a PDF report containing a summary of the
Instead of performing the phylter analysis and the filtering of outliers at the same time, you can do it in multiple steps. here is how, on the example dataset:
The output file
phylter.out will be used for performing
the pruning and/or the sequence filtering (see below).
phylter.py allows specifying all the options available
in the R package. To see this list of options, simply use the
PhylteR is also available as a singularity container : (https://cloud.sylabs.io/library/theo.treecou/tool/phylter_singularity). Here are instructions to install (or build) and run it:
Alternatively, you can build a singularity image from the Docker Hub repository:
2.a Run phylter on the carnivora example dataset:
Note: For more options Please, refer to the description of the docker container to see how to use all the options available with the
2.b Alternatively, you can open a console in the singularity container as follows and use R in that console:
For comments, suggestions and bug reports, please open an issue on this GitHub repository.