A massively parallel tool for model selection and tree inference on thousands of genes
ParGenes is for you if:
- you have several MSAs (typically gene alignments).
- you want to run maximum likelihood tree inference (RAxML) independently on each of them. For instance, to get one gene tree per gene alignment.
- you want to run these jobs in parallel, (single or multiple nodes).
In addition, ParGenes:
- can find the best-fit model with ModelTest and use this model in the RAxML calls.
- has a checkpoint mechanism.
- filters out and reports a list of the MSAs that RAxML can not process.
- handles multiple starting trees, bootstrap replicates, support value. ParGenes can run these searches simultaneously, and thus improves RAxML parallelization scheme.
- provides a (global or per-MSA) way to customize the modeltest and RAxML calls.
- can infer the optimal number of cores to assign to a given ParGenes call.
- gcc 5.0 or > (we did not try with clang yet)
- CMake 3.6 or >
- Either MPI or OpenMP. MPI for multiple nodes parallelization.
Please use git, and clone with --recursive!!!
git clone --recursive email@example.com:BenoitMorel/ParGenes.git
To build the sources:
See the wiki (https://github.com/BenoitMorel/ParGenes/wiki)
Documentation and Support
Documentation: in the github wiki.
Also please check the online help with
python3 pargenes/pargenes.py --help
A suggestion, a bug to report, a question? Please use the RAxML google group.