An open-source Python package of the AlphaPept ecosystem
Project description
msexpert
An open-source Python package of the AlphaPept ecosystem from the Mann Labs at the Max Planck Institute of Biochemistry and the University of Copenhagen. To enable all hyperlinks in this document, please view it at GitHub.
Quickstart
pip install msexpert
msexpert gui
About
An open-source Python package of the AlphaPept ecosystem from the Mann Labs at the Max Planck Institute of Biochemistry and the University of Copenhagen.
License
msexpert was developed by the Mann Labs at the Max Planck Institute of Biochemistry and the University of Copenhagen and is freely available with an Apache License. External Python packages (available in the requirements folder) have their own licenses, which can be consulted on their respective websites.
Installation
msexpert can be installed and used on all major operating systems (Windows, macOS and Linux). There are three different types of installation possible:
- One-click GUI installer: Choose this installation if you only want the GUI and/or keep things as simple as possible.
- Pip installer: Choose this installation if you want to use msexpert as a Python package in an existing Python 3.8 environment (e.g. a Jupyter notebook). If needed, the GUI and CLI can be installed with pip as well.
- Developer installer: Choose this installation if you are familiar with CLI tools, conda and Python. This installation allows access to all available features of msexpert and even allows to modify its source code directly. Generally, the developer version of msexpert outperforms the precompiled versions which makes this the installation of choice for high-throughput experiments.
One-click GUI
The GUI of msexpert is a completely stand-alone tool that requires no knowledge of Python or CLI tools. Click on one of the links below to download the latest release for:
Older releases remain available on the release page, but no backwards compatibility is guaranteed.
Pip
msexpert can be installed in an existing Python 3.8 environment with a single bash command. This bash command can also be run directly from within a Jupyter notebook by prepending it with a !:
pip install msexpert
Installing msexpert like this avoids conflicts when integrating it in other tools, as this does not enforce strict versioning of dependancies. However, if new versions of dependancies are released, they are not guaranteed to be fully compatible with msexpert. While this should only occur in rare cases where dependencies are not backwards compatible, you can always force msexpert to use dependancy versions which are known to be compatible with:
pip install "msexpert[stable]"
NOTE: You might need to run pip install pip==21.0 before installing msexpert like this. Also note the double quotes ".
For those who are really adventurous, it is also possible to directly install any branch (e.g. @development) with any extras (e.g. #egg=msexpert[stable,development-stable]) from GitHub with e.g.
pip install "git+https://github.com/MannLabs/msexpert.git@development#egg=msexpert[stable,development-stable]"
Developer
msexpert can also be installed in editable (i.e. developer) mode with a few bash commands. This allows to fully customize the software and even modify the source code to your specific needs. When an editable Python package is installed, its source code is stored in a transparent location of your choice. While optional, it is advised to first (create and) navigate to e.g. a general software folder:
mkdir ~/folder/where/to/install/software
cd ~/folder/where/to/install/software
The following commands assume you do not perform any additional cd commands anymore.
Next, download the msexpert repository from GitHub either directly or with a git command. This creates a new msexpert subfolder in your current directory.
git clone https://github.com/MannLabs/msexpert.git
For any Python package, it is highly recommended to use a separate conda virtual environment, as otherwise dependancy conflicts can occur with already existing packages.
conda create --name msexpert python=3.8 -y
conda activate msexpert
Finally, msexpert and all its dependancies need to be installed. To take advantage of all features and allow development (with the -e flag), this is best done by also installing the development dependencies instead of only the core dependencies:
pip install -e "./msexpert[development]"
By default this installs loose dependancies (no explicit versioning), although it is also possible to use stable dependencies (e.g. pip install -e "./msexpert[stable,development-stable]").
By using the editable flag -e, all modifications to the msexpert source code folder are directly reflected when running msexpert. Note that the msexpert folder cannot be moved and/or renamed if an editable version is installed. In case of confusion, you can always retrieve the location of any Python module with e.g. the command import module followed by module.__file__.
Usage
There are three ways to use msexpert:
NOTE: The first time you use a fresh installation of msexpert, it is often quite slow because some functions might still need compilation on your local operating system and architecture. Subsequent use should be a lot faster.
GUI
If the GUI was not installed through a one-click GUI installer, it can be activate with the following bash command:
msexpert gui
Note that this needs to be prepended with a ! when you want to run this from within a Jupyter notebook. When the command is run directly from the command-line, make sure you use the right environment (activate it with e.g. conda activate msexpert or set an alias to the binary executable (can be obtained with where msexpert or which msexpert)).
CLI
The CLI can be run with the following command (after activating the conda environment with conda activate msexpert or if an alias was set to the msexpert executable):
msexpert -h
It is possible to get help about each function and their (required) parameters by using the -h flag.
Python and Jupyter notebooks
msexpert can be imported as a Python package into any Python script or notebook with the command import msexpert.
A brief Jupyter notebook tutorial on how to use the API is also present in the nbs folder.
Troubleshooting
In case of issues, check out the following:
- Issues: Try a few different search terms to find out if a similar problem has been encountered before
- Discussions: Check if your problem or feature requests has been discussed before.
Citations
There are currently no plans to draft a manuscript.
How to contribute
If you like this software, you can give us a star to boost our visibility! All direct contributions are also welcome. Feel free to post a new issue or clone the repository and create a pull request with a new branch. For an even more interactive participation, check out the discussions and the the Contributors License Agreement.
Changelog
See the HISTORY.md for a full overview of the changes made in each version.
Release history Release notifications | RSS feed
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