- recipe mpa-server
Independent platform for interpretation of proteomics identification results
- Homepage:
- License:
APACHE / Apache License 2.0
- Recipe:
MetaProteomeAnalyzer (MPA) is a scientific software for analyzing and visualizing metaproteomics (and also proteomics) data. The tool presents a MS/MS spectrum data processing application for protein identification in combination with a user-friendly interactive graphical interface. The metaproteomics data analysis software is developed in the Java programming language and provides various features for user-defined querying of the results.
- package mpa-server¶
- Versions:
3.4-3,3.4-2,3.4-1,3.4-0,3.3-1,3.3-0- Depends:
on mysql
5.*on openjdk
8.*on perl
on python
on requests
on tqdm
- Additional platforms:
Installation¶
You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).
Pixi¶
With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:
pixi global install mpa-server
to add into an existing workspace instead, run:
pixi add mpa-server
In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:
pixi workspace channel add conda-forge
pixi workspace channel add bioconda
Conda¶
With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:
conda install mpa-server
Alternatively, to install into a new environment, run:
conda create -n envname mpa-server
with envname being the name of the desired environment.
Container¶
Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:
docker pull quay.io/biocontainers/mpa-server:<tag>
(see mpa-server/tags for valid values for <tag>).
Integrated deployment¶
Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/mpa-server/README.html)
