metagenomix is a pipeline
creator,
monitor,
manager,
exporter
and
merger
that takes care of writing the command-lines for any shotgun metagenomics
software, either as bash scripts or
Slurm
/
Torque
jobs (incl. scratch space usage), based on user-defined
configurations
for databases, co-assemblies, strain foci, as well as software-specific or
computing resource parameters (incl. memory, scratch relocations, modules
and conda environments).
Outputs are scripts that the user needs to run sequentially, which typically
can be handled by packages such as
snakemake: this is not (yet)
used here as metagenomix is only meant to facilitate the creation,
monitoring, management and access of shotgun metagenomic analyses results for
personalized pipelines including any software.
Any software? Well, if not already in the softwares list, someone will need to add it to metagenomix, following the instructions to contribute code.
In a nutshell,
Please read the full documentation for more details, using the Wiki pages
pip install metagenomix
or
pip install --upgrade git+https://github.com/FranckLejzerowicz/metagenomix.git
While a container solution with all the softwares and conda environments is being develop, it currently is the responsibility of the user to install all the databases and softwares that the pipeline will allow you to prepare command-lines for. Some softwares necessitate to be present either as a single binary file or with an entire folder (e.g., pre-trained models or scripts). Since some softwares require the user to edit configurations after install, some level of manual installation/tuning will be needed before usage.
Usage: metagenomix [OPTIONS] COMMAND [ARGS]...
Metagenomix command line manager
Options:
--version Show the version and exit.
--help Show this message and exit.
Commands:
create Write jobs for your pipeline configuration.
export Prepare an archive for specific pipeline outputs.
manage Edit the contents of your pipeline output folder.
monitor Check IO/job status of your pipeline configuration.
merge Combine the per-sample outputs into feature tables.
Detailed explanations for each command at Running Wiki page