AlphaPept
================
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
[](https://github.com/MannLabs/alphapept/actions/workflows/nbdev.yaml)
[](https://renkulab.io/projects/renku-stories/alphapept-gui-streamlit)
[](https://doi.org/10.1038/s41467-024-46485-4)
**AlphaPept: a modern and open framework for MS-based proteomics**
[Nature Communications](https://doi.org/10.1038/s41467-024-46485-4).
Be sure to check out other packages of our ecosystem:
- [alphatims](https://github.com/MannLabs/alphatims): Fast access to
TimsTOF data.
- [alphamap](https://github.com/MannLabs/alphamap): Peptide level MS
data exploration.
- [alphapeptdeep](https://github.com/MannLabs/alphapeptdeep): Predicting
properties from peptides.
- [alphapeptstats](https://github.com/MannLabs/alphapeptstats):
Downstream analysis of MS data
- [alphaviz](https://github.com/MannLabs/alphaviz): Vizualization of MS
data.
## Windows Quickstart
1. Download the latest installer
[here](https://github.com/MannLabs/alphapept/releases/latest),
install and click the shortcut on the desktop. A browser window with
the AlphaPept interface should open. In the case of Windows Firewall
asking for network access for AlphaPept, please allow.
2. In the `New Experiment`, select a folder with raw files and FASTA
files.
3. Specify additional settings such as modifications with `Settings`.
4. Click `Start` and run the analysis.
See also below for more detailed instructions.
## Current functionality
| Feature | Implemented |
|-----------------|----------------|
| Type | DDA |
| Filetypes | Bruker, Thermo |
| Quantification | LFQ |
| Isobaric labels | None |
| Platform | Windows |
Linux and macOS should, in principle, work but are not heavily tested
and might require additional work to set up (see detailed instructions
below). To read Thermo files, we use Mono, which can be used on Mac and
Linux. For Bruker files, we can use Linux but not yet macOS.
## Python Installation Instructions
### Requirements
We highly recommend the [Anaconda](https://www.anaconda.com) or
[Miniconda](https://docs.conda.io/en/latest/miniconda.html) Python
distribution, which comes with a powerful package manager. See below for
additional instructions for Linux and Mac as they require additional
installation of Mono to use the RawFileReader.
AlphaPept can be used as an application as a whole or as a Python
Package where individual modules are called. Depending on the use case,
AlphaPept will need different requirements, and you might not want to
install all of them.
Currently, we have the default `requirements.txt`, additional
requirements to run the GUI `gui` and packages used for developing
`develop`.
Therefore, you can install AlphaPept in multiple ways:
- The default `alphapept`
- With GUI-packages `alphapept[gui]`
- With pacakges for development `alphapept[develop]`
(`alphapept[develop,gui]`) respectively
The requirements typically contain pinned versions and will be
automatically upgraded and tested with `dependabot`. This `stable`
version allows having a reproducible workflow. However, in order to
avoid conflicts with package versions that are too strict, the
requirements are not pinned when being installed. To use the strict
version use the `-stable`-flag, e.g. `alphapept[stable]`.
For end-users that want to set up a processing environment in Python,
the `"alphapept[stable,gui-stable]"` is the `batteries-included`-version
that you want to use.
### Python
It is strongly recommended to install AlphaPept in its own
environment. 1. Open the console and create a new conda environment:
`conda create --name alphapept python=3.8` 2. Activate the environment:
`conda activate alphapept` 3. Install AlphaPept via pip:
`pip install "alphapept[stable,gui-stable]"`. If you want to use
AlphaPept as a package without the GUI dependencies and without strict
version dependencies, use `pip install alphapept`.
If AlphaPept is installed correctly, you should be able to import
AlphaPept as a package within the environment; see below.
------------------------------------------------------------------------
#### Linux
1. Install the build-essentials:
`sudo apt-get install build-essential`.
2. Install AlphaPept via pip:
`pip install "alphapept[stable,gui-stable]"`. If you want to use
AlphaPept as a package withouth the GUI dependencies and strict
version dependencies use `pip install alphapept`.
3. Install libgomp.1 with `sudo apt-get install libgomp1`.
##### Bruker Support
4. Copy-paste the Bruker library for feature finding to your /usr/lib
folder with
`sudo cp alphapept/ext/bruker/FF/linux64/alphapeptlibtbb.so.2 /usr/lib/libtbb.so.2`.
##### Thermo Support
5. Install Mono from mono-project website [Mono
Linux](https://www.mono-project.com/download/stable/#download-lin).
NOTE, the installed mono version should be at least 6.10, which
requires you to add the ppa to your trusted sources!
6. Install pythonnet with `pip install pythonnet>=2.5.2`
------------------------------------------------------------------------
#### Mac
1. Install AlphaPept via pip:
`pip install "alphapept[stable,gui-stable]"`. If you want to use
AlphaPept as a package withouth the GUI dependencies and strict
version dependencies use `pip install alphapept`.
##### Bruker Support
> Only supported for preprocessed files.
##### Thermo Support
2. Install [brew](https://brew.sh) and pkg-config:
`brew install pkg-config`
3. Install Mono from mono-project website [Mono
Mac](https://www.mono-project.com/download/stable/)
4. Register the Mono-Path to your system: For macOS Catalina, open the
configuration of zsh via the terminal:
- Type in `cd` to navigate to the home directory.
- Type `nano ~/.zshrc` to open the configuration of the terminal
- Add the path to your mono installation:
`export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:/usr/lib/pkgconfig:/Library/Frameworks/Mono.framework/Versions/Current/lib/pkgconfig:$PKG_CONFIG_PATH`.
Make sure that the Path matches to your version (Here 6.12.0)
- Save everything and execute `. ~/.zshrc`
4. Install pythonnet with `pip install pythonnet>=2.5.2`
------------------------------------------------------------------------
#### Developer
1. Redirect to the folder of choice and clone the repository:
`git clone https://github.com/MannLabs/alphapept.git`
2. Navigate to the alphapept folder with `cd alphapept` and install the
package with `pip install .` (default users) or with
`pip install -e .` to enable developers mode. Note that you can use
the different requirements here aswell
(e.g. `pip install ".[gui-stable]"`)
#### GPU Support
Some functionality of AlphaPept is GPU optimized that uses Nvidia’s
CUDA. To enable this, additional packages need to be installed.
1. Make sure to have a working [CUDA
toolkit](https://developer.nvidia.com/cuda-toolkit) installation
that is compatible with CuPy. To check type `nvcc --version` in your
terminal.
2. Install [cupy](https://cupy.dev). Make sure to install the cupy
version matching your CUDA toolkit (e.g. `pip install cupy-cuda110`
for CUDA toolkit 11.0.
### Additional Notes
> To access Thermo files, we have integrated
> [RawFileReader](https://planetorbitrap.com/rawfilereader) into
> AlphaPept. We rely on [Mono](https://www.mono-project.com/) for
> Linux/Mac systems.
> To access Bruker files, we rely on the `timsdata`-library. Currently,
> only Windows is supported. For feature finding, we use the Bruker
> Feature Finder, which can be found in the `ext` folder of this
> repository.
#### Notes for NBDEV
- For developing with the notebooks, install the nbdev package (see the
development requirements)
- To facilitate navigating the notebooks, use jupyter notebook
extensions. They can be called from a running jupyter instance like
so: `http://localhost:8888/nbextensions`. The extensions
`collapsible headings` and `toc2` are very beneficial.
## Standalone Windows Installer
To use AlphaPept as a stand-alone program for end-users, it can be
installed on Windows machines via a one-click installer. Download the
latest version
[here](https://github.com/MannLabs/alphapept/releases/latest).
## Docker
It is possible to run AlphaPept in a docker container. For this, we
provide two Dockerfiles: `Dockerfile_thermo` and `Dockerfile_bruker`,
depending on which filetypes you want to analyse. They are split because
of drastically different requirements.
To run, navigate to the AlphaPept repository and rename the dockerfile
you want to use, e.g. `Dockerfile_thermo` to `Dockerfile`.
- Build the image with: `docker build -t docker-alphapept:latest .`
- To run use
`docker run -p 8505:8505 -v /Users/username/Desktop/docker:/home/alphapept/ docker-alphapept:latest alphapept gui`
(Note that -v maps a local folder for convient file transfer)
- Access the AlphaPept GUI via `localhost:8505` in your browser.
- Note 1: The Thermo Dockerfile is built on a Jupyter image, so you can
also start a jupyter instance:
`docker run -p 8888:8888 -v /Users/username/Desktop/docker:/home/jovyan/ docker-alphapept:latest jupyter notebook --allow-root`
### Docker Troubleshooting on M1-Mac
- The Thermo dockerfile was tested on an M1-Mac. Resources were set to
18GB RAM and 2 CPUs, 200 GB disk
- It was possible to build the Bruker dockerfile with the platform tag
`--platform linux/amd64`. However, it was very slow and the Bruker
file is not recommended for an M1-Mac. Windows worked nicely.
## Additional Documentation
The documentation is automatically built based on the jupyter notebooks
(nbs/index.ipynb) and can be found
[here](https://mannlabs.github.io/alphapept/):
## Version Performance
An overview of the performance of different versions can be found
[here](https://charts.mongodb.com/charts-alphapept-itfxv/public/dashboards/5f671dcf-bcd6-4d90-8494-8c7f724b727b).
We re-run multiple tests on datasets for different versions so that
users can assess what changes from version to version. Feel free to
[suggest](https://github.com/MannLabs/alphapept/discussions) a test set
in case.
## How to use
AlphaPept is meant to be a framework to implement and test new ideas
quickly but also to serve as a performant processing pipeline. In
principle, there are three use-cases:
- GUI: Use the graphical user interface to select settings and process
files manually.
- CMD: Use the command-line interface to process files. Useful when
building automatic pipelines.
- Python: Use python modules to build individual workflows. Useful when
building customized pipelines and using Python as a scripting language
or when implementing new ideas.
### Windows Standalone Installation
For the [windows
installation](https://github.com/MannLabs/alphapept/releases/latest),
simply click on the shortcut after installation. The windows
installation also installs the command-line tool so that you can call
alphapept via `alphapept` in the command line.
### Python Package
Once AlphaPept is correctly installed, you can use it like any other
python module.
``` python
from alphapept.fasta import get_frag_dict, parse
from alphapept import constants
peptide = 'PEPT'
get_frag_dict(parse(peptide), constants.mass_dict)
```
{'b1': 98.06004032687,
'b2': 227.10263342687,
'b3': 324.15539728686997,
'y1': 120.06551965033,
'y2': 217.11828351033,
'y3': 346.16087661033}
### Using as a tool
If alphapept is installed an a conda or virtual environment, launch this
environment first.
To launch the command line interface use: \* `alphapept`
This allows us to select different modules. To start the GUI use: \*
`alphapept gui`
To run a workflow, use: \* `alphapept workflow your_own_workflow.yaml`
An example workflow is easily generated by running the GUI once and
saving the settings which can be modified on a per-project basis.
### CMD / Python
1. Create a settings-file. This can be done by changing the
`default_settings.yaml` in the repository or using the GUI.
2. Run the analysis with the new settings file.
`alphapept run new_settings.yaml`
Within Python (i.e., Jupyter notebook) the following code would be
required)
from alphapept.settings import load_settings
import alphapept.interface
settings = load_settings('new_settings.yaml')
r = alphapept.interface.run_complete_workflow(settings)
This also allows you to break the workflow down in indiviudal steps,
e.g.:
settings = alphapept.interface.import_raw_data(settings)
settings = alphapept.interface.feature_finding(settings)
## Notebooks
Within the notebooks, we try to cover most aspects of a proteomics
workflow:
- Settings: General settings to define a workflow
- Chem: Chemistry related functions, e.g., for calculating isotope
distributions
- Input / Output: Everything related to importing and exporting and the
file formats used
- FASTA: Generating theoretical databases from FASTA files
- Feature Finding: How to extract MS1 features for quantification
- Search: Comparing theoretical databases to experimental spectra and
getting Peptide-Spectrum-Matches (PSMs)
- Score: Scoring PSMs
- Recalibration: Recalibration of data based on identified peptides
- Quantification: Functions for quantification, e.g., LFQ
- Matching: Functions for Match-between-runs
- Constants: A collection of constants
- Interface: Code that generates the command-line-interface (CLI) and
makes workflow steps callable
- Performance: Helper functions to speed up code with CPU / GPU
- Export: Helper functions to make exports compatbile to other Software
tools
- Label: Code for support isobaric label search
- Display: Code related to displaying in the streamlit gui
- Additional code: Overview of additional code not covered by the
notebooks
- How to contribute: Contribution guidelines
- AlphaPept workflow and files: Overview of the worfklow, files and
column names
## Contributing
If you have a feature request or a bug report, please post it either as
an idea in the
[discussions](https://github.com/MannLabs/alphapept/discussions) or as
an issue on the [GitHub issue
tracker](https://github.com/MannLabs/alphapept/issues). Upvoting
features in the discussions page will help to prioritize what to
implement next. If you want to contribute, put a PR for it. You can find
more guidelines for contributing and how to get started
[here](https://mannlabs.github.io/alphapept/contributing.html). We will
gladly guide you through the codebase and credit you accordingly.
Additionally, you can check out the Projects page on GitHub. You can
also contact us via opensource@alphapept.com.
If you like the project, consider starring it!
## Cite us
If you use this project in your research, please cite:
> Strauss, M.T., Bludau, I., Zeng, WF. et al. AlphaPept: a modern and
> open framework for MS-based proteomics. Nat Commun 15, 2168 (2024).
> https://doi.org/10.1038/s41467-024-46485-4
Raw data
{
"_id": null,
"home_page": "https://github.com/mannlabs/alphapept",
"name": "alphapept",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "MS, mass spectrometry, python, numba",
"author": "Maximilian T. Strauss",
"author_email": "straussmaximilian@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/95/eb/9c424463644291b74776a76344bad3ae175438f5a1696977119bbb130262/alphapept-0.5.3.tar.gz",
"platform": null,
"description": "AlphaPept\n================\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n[](https://github.com/MannLabs/alphapept/actions/workflows/nbdev.yaml)\n[](https://renkulab.io/projects/renku-stories/alphapept-gui-streamlit)\n[](https://doi.org/10.1038/s41467-024-46485-4)\n\n\n\n**AlphaPept: a modern and open framework for MS-based proteomics**\n\n[Nature Communications](https://doi.org/10.1038/s41467-024-46485-4).\n\nBe sure to check out other packages of our ecosystem:\n\n- [alphatims](https://github.com/MannLabs/alphatims): Fast access to\n TimsTOF data.\n- [alphamap](https://github.com/MannLabs/alphamap): Peptide level MS\n data exploration.\n- [alphapeptdeep](https://github.com/MannLabs/alphapeptdeep): Predicting\n properties from peptides.\n- [alphapeptstats](https://github.com/MannLabs/alphapeptstats):\n Downstream analysis of MS data\n- [alphaviz](https://github.com/MannLabs/alphaviz): Vizualization of MS\n data.\n\n## Windows Quickstart\n\n\n\n1. Download the latest installer\n [here](https://github.com/MannLabs/alphapept/releases/latest),\n install and click the shortcut on the desktop. A browser window with\n the AlphaPept interface should open. In the case of Windows Firewall\n asking for network access for AlphaPept, please allow.\n2. In the `New Experiment`, select a folder with raw files and FASTA\n files.\n3. Specify additional settings such as modifications with `Settings`.\n4. Click `Start` and run the analysis.\n\nSee also below for more detailed instructions.\n\n## Current functionality\n\n| Feature | Implemented |\n|-----------------|----------------|\n| Type | DDA |\n| Filetypes | Bruker, Thermo |\n| Quantification | LFQ |\n| Isobaric labels | None |\n| Platform | Windows |\n\nLinux and macOS should, in principle, work but are not heavily tested\nand might require additional work to set up (see detailed instructions\nbelow). To read Thermo files, we use Mono, which can be used on Mac and\nLinux. For Bruker files, we can use Linux but not yet macOS.\n\n## Python Installation Instructions\n\n### Requirements\n\nWe highly recommend the [Anaconda](https://www.anaconda.com) or\n[Miniconda](https://docs.conda.io/en/latest/miniconda.html) Python\ndistribution, which comes with a powerful package manager. See below for\nadditional instructions for Linux and Mac as they require additional\ninstallation of Mono to use the RawFileReader.\n\nAlphaPept can be used as an application as a whole or as a Python\nPackage where individual modules are called. Depending on the use case,\nAlphaPept will need different requirements, and you might not want to\ninstall all of them.\n\nCurrently, we have the default `requirements.txt`, additional\nrequirements to run the GUI `gui` and packages used for developing\n`develop`.\n\nTherefore, you can install AlphaPept in multiple ways:\n\n- The default `alphapept`\n- With GUI-packages `alphapept[gui]`\n- With pacakges for development `alphapept[develop]`\n (`alphapept[develop,gui]`) respectively\n\nThe requirements typically contain pinned versions and will be\nautomatically upgraded and tested with `dependabot`. This `stable`\nversion allows having a reproducible workflow. However, in order to\navoid conflicts with package versions that are too strict, the\nrequirements are not pinned when being installed. To use the strict\nversion use the `-stable`-flag, e.g.\u00a0`alphapept[stable]`.\n\nFor end-users that want to set up a processing environment in Python,\nthe `\"alphapept[stable,gui-stable]\"` is the `batteries-included`-version\nthat you want to use.\n\n### Python\n\nIt is strongly recommended to install AlphaPept in its own\nenvironment. 1. Open the console and create a new conda environment:\n`conda create --name alphapept python=3.8` 2. Activate the environment:\n`conda activate alphapept` 3. Install AlphaPept via pip:\n`pip install \"alphapept[stable,gui-stable]\"`. If you want to use\nAlphaPept as a package without the GUI dependencies and without strict\nversion dependencies, use `pip install alphapept`.\n\nIf AlphaPept is installed correctly, you should be able to import\nAlphaPept as a package within the environment; see below.\n\n------------------------------------------------------------------------\n\n#### Linux\n\n1. Install the build-essentials:\n `sudo apt-get install build-essential`.\n2. Install AlphaPept via pip:\n `pip install \"alphapept[stable,gui-stable]\"`. If you want to use\n AlphaPept as a package withouth the GUI dependencies and strict\n version dependencies use `pip install alphapept`.\n3. Install libgomp.1 with `sudo apt-get install libgomp1`.\n\n##### Bruker Support\n\n4. Copy-paste the Bruker library for feature finding to your /usr/lib\n folder with\n `sudo cp alphapept/ext/bruker/FF/linux64/alphapeptlibtbb.so.2 /usr/lib/libtbb.so.2`.\n\n##### Thermo Support\n\n5. Install Mono from mono-project website [Mono\n Linux](https://www.mono-project.com/download/stable/#download-lin).\n NOTE, the installed mono version should be at least 6.10, which\n requires you to add the ppa to your trusted sources!\n6. Install pythonnet with `pip install pythonnet>=2.5.2`\n\n------------------------------------------------------------------------\n\n#### Mac\n\n1. Install AlphaPept via pip:\n `pip install \"alphapept[stable,gui-stable]\"`. If you want to use\n AlphaPept as a package withouth the GUI dependencies and strict\n version dependencies use `pip install alphapept`.\n\n##### Bruker Support\n\n> Only supported for preprocessed files.\n\n##### Thermo Support\n\n2. Install [brew](https://brew.sh) and pkg-config:\n `brew install pkg-config`\n3. Install Mono from mono-project website [Mono\n Mac](https://www.mono-project.com/download/stable/)\n4. Register the Mono-Path to your system: For macOS Catalina, open the\n configuration of zsh via the terminal:\n\n- Type in `cd` to navigate to the home directory.\n- Type `nano ~/.zshrc` to open the configuration of the terminal\n- Add the path to your mono installation:\n `export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:/usr/lib/pkgconfig:/Library/Frameworks/Mono.framework/Versions/Current/lib/pkgconfig:$PKG_CONFIG_PATH`.\n Make sure that the Path matches to your version (Here 6.12.0)\n- Save everything and execute `. ~/.zshrc`\n\n4. Install pythonnet with `pip install pythonnet>=2.5.2`\n\n------------------------------------------------------------------------\n\n#### Developer\n\n1. Redirect to the folder of choice and clone the repository:\n `git clone https://github.com/MannLabs/alphapept.git`\n2. Navigate to the alphapept folder with `cd alphapept` and install the\n package with `pip install .` (default users) or with\n `pip install -e .` to enable developers mode. Note that you can use\n the different requirements here aswell\n (e.g.\u00a0`pip install \".[gui-stable]\"`)\n\n#### GPU Support\n\nSome functionality of AlphaPept is GPU optimized that uses Nvidia\u2019s\nCUDA. To enable this, additional packages need to be installed.\n\n1. Make sure to have a working [CUDA\n toolkit](https://developer.nvidia.com/cuda-toolkit) installation\n that is compatible with CuPy. To check type `nvcc --version` in your\n terminal.\n2. Install [cupy](https://cupy.dev). Make sure to install the cupy\n version matching your CUDA toolkit (e.g.\u00a0`pip install cupy-cuda110`\n for CUDA toolkit 11.0.\n\n### Additional Notes\n\n> To access Thermo files, we have integrated\n> [RawFileReader](https://planetorbitrap.com/rawfilereader) into\n> AlphaPept. We rely on [Mono](https://www.mono-project.com/) for\n> Linux/Mac systems.\n\n> To access Bruker files, we rely on the `timsdata`-library. Currently,\n> only Windows is supported. For feature finding, we use the Bruker\n> Feature Finder, which can be found in the `ext` folder of this\n> repository.\n\n#### Notes for NBDEV\n\n- For developing with the notebooks, install the nbdev package (see the\n development requirements)\n- To facilitate navigating the notebooks, use jupyter notebook\n extensions. They can be called from a running jupyter instance like\n so: `http://localhost:8888/nbextensions`. The extensions\n `collapsible headings` and `toc2` are very beneficial.\n\n## Standalone Windows Installer\n\nTo use AlphaPept as a stand-alone program for end-users, it can be\ninstalled on Windows machines via a one-click installer. Download the\nlatest version\n[here](https://github.com/MannLabs/alphapept/releases/latest).\n\n## Docker\n\nIt is possible to run AlphaPept in a docker container. For this, we\nprovide two Dockerfiles: `Dockerfile_thermo` and `Dockerfile_bruker`,\ndepending on which filetypes you want to analyse. They are split because\nof drastically different requirements.\n\nTo run, navigate to the AlphaPept repository and rename the dockerfile\nyou want to use, e.g.\u00a0`Dockerfile_thermo` to `Dockerfile`.\n\n- Build the image with: `docker build -t docker-alphapept:latest .`\n- To run use\n `docker run -p 8505:8505 -v /Users/username/Desktop/docker:/home/alphapept/ docker-alphapept:latest alphapept gui`\n (Note that -v maps a local folder for convient file transfer)\n- Access the AlphaPept GUI via `localhost:8505` in your browser.\n- Note 1: The Thermo Dockerfile is built on a Jupyter image, so you can\n also start a jupyter instance:\n `docker run -p 8888:8888 -v /Users/username/Desktop/docker:/home/jovyan/ docker-alphapept:latest jupyter notebook --allow-root`\n\n### Docker Troubleshooting on M1-Mac\n\n- The Thermo dockerfile was tested on an M1-Mac. Resources were set to\n 18GB RAM and 2 CPUs, 200 GB disk\n- It was possible to build the Bruker dockerfile with the platform tag\n `--platform linux/amd64`. However, it was very slow and the Bruker\n file is not recommended for an M1-Mac. Windows worked nicely.\n\n## Additional Documentation\n\nThe documentation is automatically built based on the jupyter notebooks\n(nbs/index.ipynb) and can be found\n[here](https://mannlabs.github.io/alphapept/):\n\n## Version Performance\n\nAn overview of the performance of different versions can be found\n[here](https://charts.mongodb.com/charts-alphapept-itfxv/public/dashboards/5f671dcf-bcd6-4d90-8494-8c7f724b727b).\nWe re-run multiple tests on datasets for different versions so that\nusers can assess what changes from version to version. Feel free to\n[suggest](https://github.com/MannLabs/alphapept/discussions) a test set\nin case.\n\n## How to use\n\nAlphaPept is meant to be a framework to implement and test new ideas\nquickly but also to serve as a performant processing pipeline. In\nprinciple, there are three use-cases:\n\n- GUI: Use the graphical user interface to select settings and process\n files manually.\n- CMD: Use the command-line interface to process files. Useful when\n building automatic pipelines.\n- Python: Use python modules to build individual workflows. Useful when\n building customized pipelines and using Python as a scripting language\n or when implementing new ideas.\n\n### Windows Standalone Installation\n\nFor the [windows\ninstallation](https://github.com/MannLabs/alphapept/releases/latest),\nsimply click on the shortcut after installation. The windows\ninstallation also installs the command-line tool so that you can call\nalphapept via `alphapept` in the command line.\n\n\n\n### Python Package\n\nOnce AlphaPept is correctly installed, you can use it like any other\npython module.\n\n``` python\nfrom alphapept.fasta import get_frag_dict, parse\nfrom alphapept import constants\n\npeptide = 'PEPT'\n\nget_frag_dict(parse(peptide), constants.mass_dict)\n```\n\n {'b1': 98.06004032687,\n 'b2': 227.10263342687,\n 'b3': 324.15539728686997,\n 'y1': 120.06551965033,\n 'y2': 217.11828351033,\n 'y3': 346.16087661033}\n\n### Using as a tool\n\nIf alphapept is installed an a conda or virtual environment, launch this\nenvironment first.\n\nTo launch the command line interface use: \\* `alphapept`\n\nThis allows us to select different modules. To start the GUI use: \\*\n`alphapept gui`\n\nTo run a workflow, use: \\* `alphapept workflow your_own_workflow.yaml`\nAn example workflow is easily generated by running the GUI once and\nsaving the settings which can be modified on a per-project basis.\n\n### CMD / Python\n\n1. Create a settings-file. This can be done by changing the\n `default_settings.yaml` in the repository or using the GUI.\n2. Run the analysis with the new settings file.\n `alphapept run new_settings.yaml`\n\nWithin Python (i.e., Jupyter notebook) the following code would be\nrequired)\n\n from alphapept.settings import load_settings\n import alphapept.interface\n settings = load_settings('new_settings.yaml')\n r = alphapept.interface.run_complete_workflow(settings)\n\nThis also allows you to break the workflow down in indiviudal steps,\ne.g.:\n\n settings = alphapept.interface.import_raw_data(settings)\n settings = alphapept.interface.feature_finding(settings)\n\n## Notebooks\n\nWithin the notebooks, we try to cover most aspects of a proteomics\nworkflow:\n\n- Settings: General settings to define a workflow\n- Chem: Chemistry related functions, e.g., for calculating isotope\n distributions\n- Input / Output: Everything related to importing and exporting and the\n file formats used\n- FASTA: Generating theoretical databases from FASTA files\n- Feature Finding: How to extract MS1 features for quantification\n- Search: Comparing theoretical databases to experimental spectra and\n getting Peptide-Spectrum-Matches (PSMs)\n- Score: Scoring PSMs\n- Recalibration: Recalibration of data based on identified peptides\n- Quantification: Functions for quantification, e.g., LFQ\n- Matching: Functions for Match-between-runs\n- Constants: A collection of constants\n- Interface: Code that generates the command-line-interface (CLI) and\n makes workflow steps callable\n- Performance: Helper functions to speed up code with CPU / GPU\n- Export: Helper functions to make exports compatbile to other Software\n tools\n- Label: Code for support isobaric label search\n- Display: Code related to displaying in the streamlit gui\n- Additional code: Overview of additional code not covered by the\n notebooks\n- How to contribute: Contribution guidelines\n- AlphaPept workflow and files: Overview of the worfklow, files and\n column names\n\n## Contributing\n\nIf you have a feature request or a bug report, please post it either as\nan idea in the\n[discussions](https://github.com/MannLabs/alphapept/discussions) or as\nan issue on the [GitHub issue\ntracker](https://github.com/MannLabs/alphapept/issues). Upvoting\nfeatures in the discussions page will help to prioritize what to\nimplement next. If you want to contribute, put a PR for it. You can find\nmore guidelines for contributing and how to get started\n[here](https://mannlabs.github.io/alphapept/contributing.html). We will\ngladly guide you through the codebase and credit you accordingly.\nAdditionally, you can check out the Projects page on GitHub. You can\nalso contact us via opensource@alphapept.com.\n\nIf you like the project, consider starring it!\n\n## Cite us\n\nIf you use this project in your research, please cite:\n\n> Strauss, M.T., Bludau, I., Zeng, WF. et al.\u00a0AlphaPept: a modern and\n> open framework for MS-based proteomics. Nat Commun 15, 2168 (2024).\n> https://doi.org/10.1038/s41467-024-46485-4\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "A modular, python-based framework for mass spectrometry.",
"version": "0.5.3",
"project_urls": {
"Homepage": "https://github.com/mannlabs/alphapept"
},
"split_keywords": [
"ms",
" mass spectrometry",
" python",
" numba"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ecd23acdd5b5b192621d652a9c4287cb2ef5b354a49b555f75aea648967d4dde",
"md5": "0d7eb7da766c7e81dece7d6b400ec387",
"sha256": "f21ed88382d81706294f657b18ff97a06f4b5677ce96a8400860d575c61e92b3"
},
"downloads": -1,
"filename": "alphapept-0.5.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0d7eb7da766c7e81dece7d6b400ec387",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 64682025,
"upload_time": "2024-04-19T15:54:41",
"upload_time_iso_8601": "2024-04-19T15:54:41.692556Z",
"url": "https://files.pythonhosted.org/packages/ec/d2/3acdd5b5b192621d652a9c4287cb2ef5b354a49b555f75aea648967d4dde/alphapept-0.5.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "95eb9c424463644291b74776a76344bad3ae175438f5a1696977119bbb130262",
"md5": "d0de7df04c75a2ea55bb350415222879",
"sha256": "ba48703d1f61d836f4ae1c12064d9b0aa0eae74dcd9913cd85e9b845246ee652"
},
"downloads": -1,
"filename": "alphapept-0.5.3.tar.gz",
"has_sig": false,
"md5_digest": "d0de7df04c75a2ea55bb350415222879",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 64252711,
"upload_time": "2024-04-19T15:54:48",
"upload_time_iso_8601": "2024-04-19T15:54:48.830182Z",
"url": "https://files.pythonhosted.org/packages/95/eb/9c424463644291b74776a76344bad3ae175438f5a1696977119bbb130262/alphapept-0.5.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-19 15:54:48",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "mannlabs",
"github_project": "alphapept",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "alphapept"
}
JSON
