Benchmark existing placement software and compare placement accuracy on different reference trees.
- Please cite:
PEWO: a collection of workflows to benchmark phylogenetic placement
Benjamin Linard, Nikolai Romashchenko, Fabio Pardi, Eric Rivals
Bioinformatics, btaa657, 22 July 2020
PEWO compiles a set of workflows dedicated to the evaluation of phylogenetic placement algorithms and their software implementation. It focuses on reporting placement accuracy under different conditions and associated computational costs.
It is built on Snakemake and Miniconda3 and relies extensively on the Bioconda and Conda-forge repositories, making it portable to most OS supporting Python 3. A specific phylogenetic placement software can be evaluated by PEWO as long as it can be installed on said OS via conda.
Main purposes of PEWO:
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Evaluate phylogenetic placement accuracy, given a particular reference species tree and corresponding reference alignment. For instance, one can run PEWO on different trees built for different taxonomic marker genes and explore which markers produce better placements.
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Given a particular software/algorithm, empirically explore which sets of parameters produces the most accurate placements, and at which computational costs. This can be particularly useful when a huge volume of sequences are to be placed and one may need to consider a balance between accuracy and scalability (CPU/RAM limitations).
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For developers, provide a basis to standardize phylogenetic placement evaluation and the establishment of benchmarks. PEWO aims to remove the hassle of re-implementing evaluation protocols that were described in anterior studies. In this regard, any phylogenetic placement developer is welcome to pull request new modules in the PEWO repository or contact us for future support of their new productions.
An complete documentation, including a tutorial for each workflow, is available in the wiki section of this github repository.
Before installation, the following packages should be available on your system must be installed on your system:
- Python >=3.5
- Miniconda3. Please choose the installer corresponding to your OS: Miniconda dowloads
- GIT
PEWO will look for the commands git and conda. Not finding these commands will cancel the PEWO installation.
Below are debian commands to rapidly install them:
sudo apt-get install git
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod u+x Miniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.sh
# when installation ask if you want to run conda init, answer yes
# after installation ends, reload bash so that conda belongs to your PATH
bash
Download PEWO:
git clone --recursive https://github.com/phylo42/PEWO.git
cd PEWOExecute installation script:
chmod u+x INSTALL.sh
./INSTALL.shAfter installation, load environment:
conda activate PEWOYou can launch a dry-run, if no error is throwed, PEWO is correctly installed:
snakemake -np \
--snakefile eval_accuracy.smk \
--config workdir=`pwd`/examples/1_fast_test_of_accuracy_procedure/run \
--configfile examples/1_fast_test_of_accuracy_procedure/config.yamlYou can launch a 20 minutes test, using 2 CPU cores.
snakemake -p --cores 2 \
--snakefile eval_accuracy.smk \
--config workdir=`pwd`/examples/1_fast_test_of_accuracy_procedure/run \
--configfile examples/1_fast_test_of_accuracy_procedure/config.yamlIf the test is successful, you should produce the following statistics and image files in the examples/1_fast_test_of_accuracy_procedure/run directory:
results.csvsummary_plot_eND_epang_h1.svgsummary_plot_eND_pplacer.svgsummary_plot_eND_rappas.svg
The content and interpretation of these files are detailed in the wiki documentation. Please read the dedicated wiki page.
Software currently supported by PEWO.
- EPA(RAxML) (Berger et al, 2010)
- PPlacer (Matsen et al, 2011)
- EPA-ng (Barbera et al, 2019)
- RAPPAS (Linard et al, 2019)
- APPLES (Balaban et al, 2019)
- App-SpaM (Blanke et al, preprint)
PEWO can easily be extended to integrate new tools for phylogenetic placement, and new tools are welcome. As of November 2020, these tools are the main software for phylogenetic placement. To the best of your knowledge, no other implementation of phylogenetic placement algorithms are available (with a conda package).
If you implement a new method, you are welcome to contact us for requesting future support. You can also implement a new snakemake module and contribute to PEWO via pull requests (see the documentation for contribution guidelines).
1. Activate PEWO environment:
conda activate PEWOBy default, the latest version of every phylogenetic placement software is installed in PEWO environment. If you intend to evaluate anterior versions, you need to manually downgrade the corresponding package.
For instance, downgrading to anterior versions of PPlacer can be done with:
conda uninstall pplacer
conda install pplacer=1.1.alpha172. Select a procedure:
PEWO proposes several procedures aiming to evaluate different aspects of phylogenetic placement. Each procedure is coding as a Snakemake workflow, which can be loaded via a dedicated Snakefile (PEWO_workflow/\*.smk).
Identify the Snakefile corresponding to your needs.
| Procedure | Snakefile | Description |
|---|---|---|
| Pruning-based Accuracy evaluation (PAC) | eval_accuracy.smk |
Given a reference tree/alignment, compute both the "Node Distance" and "expected Node Distance" for a set of software and a set of conditions. This procedure is based on a pruning approach and an important parameter is the number of prunings that is run (see documentation). |
| Ressources evaluation (RES) | eval_ressources.smk |
Given a reference tree/alignment and a set of query reads, measures CPU/RAM consumptions for a set of software and a set of conditions. An important parameter is the number of repeats from which mean consumptions will be deduced (see documentation). |
| Likelihood-based Accuracy evaluation (LAC) | eval_likelihood.smk |
Given a reference tree and alignment, compute tree likelihoods induced by placements under a set of conditions, with a lower likelihood reflecting better placements. |
3. Setup the workflow by editing config.yaml:
The file config.yaml is where you setup the workflow. It contains 4 sections:
- Workflow configuration The most important section: set the working directory, the input tree/alignment on which to evaluate placements, the number of pruning experiments or experiment repeats (see procedures).
- Per software configuration Select a panel of parameters and parameter values for each software. Measurements will be operated for every parameter combination.
- Options common to all software Mostly related to the formatting of the jplace outputs. Note that these options will impact expected Node Distances.
- Evolutionary model A very basic definition of the evolutionary model used in the procedures. Currently, only GTR+G (for nucleotides), JTT+G, WAG+G and LG+G (for amino acids) are supported.
Notes on memory usage for alignment-free methods: Alignment-free methods such as RAPPAS require to build/load a database in memory prior to the placement. This step can be memory consuming for large datasets. Correctly managing the memory is done as follows:
- Do a test with a single RAPPAS run on the tree/alignment you use as input dataset.Write down the peek memory. To measure peek memory you can use:
/usr/bin/time -v command
#Make sure to use the full path, it's not the default linux 'time' command.
And search for the line "Maximum resident set size".
- For instance, You conclude RAPPAS requires 8Gb per analysis. Then force RAPPAS to use 8Gb of RAM via the snakemake config file and the field:
config_rappas:
memory: 8- Finally choose the maximum amount of RAM that can be used by snakemake during its launch. For instance, if you have a machine with 32Gb of RAM :
snakemake -p --cores 8 --resources mem_mb=32000 \
--snakefile eval_accuracy.smk \
--config workdir=`pwd`/examples/2_placement_accuracy_for_a_bacterial_taxonomy/run \
--configfile examples/2_placement_accuracy_for_a_bacterial_taxonomy/config.yaml
With this setup, snakemake will wait for 8gb to be free before each RAPPAS launch and will run with a maximum of four RAPPAS launches in parallel (32/8=4).
4. Test your workflow:
It is strongly recommended to test if your configuration is valid and matches the analyses you intended. To do so, launch a dry run of the pipeline using the command:
snakemake --snakefile [snakefile].smk -npwhere \[snakefile\] is one of the sub-workflow snakefiles listed in the table above.
This will list the operations that will be run by the workflow. It is also recommended to export a graph detailing the different steps of the workflow (to avoid very large graphs in "Accuracy" sub-workflow, we force a single pruning).
to display the graph in a window
snakemake --snakefile [snakefile].smk --config pruning_count=1 --dag | dot | displayto produce an image of the graph
snakemake --snakefile [snakefile].smk --config pruning_count=1 --dag | dot -Tsvg > graph.svg5. Launch the analysis:
snakemake --snakefile [snakefile].smk -p --core [#cores] Note that the workflow can be launched on a grid environment such as SunGridEngine or SLURM (i.e., with qsub command).
Refer to the snakemake documentation to learn how to configure a workflow for such environments.
B Linard, N Romashchenko, F Pardi, E Rivals
MAB team (Methods and Algorithms in Bioinformatics), LIRMM, Montpellier, France.
PEWO is available under the MIT license.