alphapept 0.5.3
A modular, python-based framework for mass spectrometry.
License: MIT License
Requires Python: >=3.6
Keywords: MS, mass spectrometry, python, numba

AlphaPept
================

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![](https://i.imgur.com/xkFtDff.jpg)

**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

![](https://i.imgur.com/UO64YPx.jpg)

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.

![](https://i.imgur.com/SQikLHQ.jpg)

### 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