Metadata-Version: 2.4
Name: alphadia
Version: 2.0.0
Summary: A novel proteomics search engine for DIA data based on end-to-end transfer learning.
Author-email: Mann Labs <wallmann@biochem.mpg.de>
License:                                  Apache License
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Project-URL: repository, https://github.com/MannLabs/alphadia
Project-URL: issues, https://github.com/MannLabs/alphadia/issues
Project-URL: download, https://github.com/MannLabs/alphadia/releases
Project-URL: homepage, https://www.alphapept.org
Project-URL: Mann Labs Homepage, https://www.biochem.mpg.de/mann
Project-URL: publication, https://doi.org/10.1038/s41587-025-02791-w
Project-URL: documentation, https://alphadia.readthedocs.io/en/latest/
Keywords: mass spectrometry,proteomics,search engine,DIA,data-independent acquisition,bioinformatics,AlphaPept,AlphaPept ecosystem,alphapept.org
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.10.0
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<!-- PROJECT LOGO -->
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  <a href="https://github.com/othneildrew/Best-README-Template">
    <img src="release/logos/alphadia.png" alt="Logo" width="80" height="80">
  </a>

  <h3 align="center">alphaDIA</h3>

  <p align="center">
    <a href="https://doi.org/10.1038/s41587-025-02791-w">Publication</a>
    ·
    <a href="https://github.com/Mannlabs/alphadia/releases/latest">Download</a>
    ·
    <a href="https://github.com/MannLabs/alphadia/discussions">Discussion</a>
    ·
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    ·
    <a href="https://alphapept.org">alphapept.org</a>
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![preview](assets/preview.gif)

## Features
- Empirical library and fully predicted library search
- End-to-end transfer learning for custom RT, mobility, and MS2 models
- Label free quantification
- DIA multiplexing

We support the following vendor and processing modes:

| Platform              | Empirical lib | Predicted lib |
| :---------------- | :------: | :----: |
| Thermo .raw |   ✅   | ✅ |
| Sciex .wiff |   ✅   | ✅ |
| Bruker .d |  ✅   | ⚠️ |
> ⚠️ **Predicted libraries with Bruker .d data**<br>Alltough search is possible, alphaDIA's feature-free search takes a long time with fully predicted libraries. We are still evaluating how to better support fully predicted libraries.



alphaDIA is part of the [alphapept ecosystem](https://alphapept.org):
- [alphaBase](https://github.com/MannLabs/alphabase)
- [alphaRaw](https://github.com/MannLabs/alpharaw)
- [alphaTims](https://github.com/MannLabs/alphatims)
- [alphaPept](https://github.com/MannLabs/alphapept)
- [alphaPeptDeep](https://github.com/MannLabs/alphapeptdeep)
- [directLFQ](https://github.com/MannLabs/directlfq)
- [alphaQuant](https://github.com/MannLabs/alphaquant)

## Installation

AlphaDIA can be installed on Windows, macOS and Linux. Please choose the preferred installation:

* [**One-click GUI installation:**](#one-click-gui-installation) Choose this installation if you only want the GUI and/or keep things as simple as possible.

* [**Pip installation**](#pip-installation)This version allows you to use alphaDIA in your python environment (provided e.g. by conda). You will only have access to the search engine backend and the command line but not the GUI.

* [**Developer installation:**](docs/installation.md#developer-installation) This installation allows to modify alphaDIA's source code directly. Generally, the developer version of alphaDIA outperforms the precompiled versions which makes this the installation of choice for high-throughput experiments.

* [**Docker installation:**](docs/installation.md#use-the-dockerized-version) Choose this for running alphaDIA in a Docker container, which is useful if you want to run it in a cloud environment.

* [**Slurm installation:**](docs/installation.md#slurm-cluster-installation) Choose this for running alphaDIA on a research cluster with Slurm.

### One-click GUI installation
You can download the latest release of alphaDIA [here](https://github.com/Mannlabs/alphadia/releases/latest).


#### Windows
Download the latest `alphadia-X.Y.Z-windows-amd64.exe` build and double click it to install. If you receive a warning during installation click *Run anyway*.
Important note: always install AlphaDIA into a new folder, as the installer will not properly overwrite existing installations.

#### Linux
If you want to search `.raw` files from Thermo instruments or `.wiff` files from Sciex instruments, [AlphaRaw](https://github.com/MannLabs/alpharaw) is required, which depends on Mono.
A detailed guide to installing AlphaRaw with mono can be found [here](https://github.com/MannLabs/alpharaw#installation).

Download the latest `alphadia-X.Y.Z-linux-x64.deb` build and install it via `dpkg -i alphadia-X.Y.Z-linux-x64.deb`.

#### MacOS
If you want to search `.raw` files from Thermo instruments or `.wiff` files from Sciex instruments, [AlphaRaw](https://github.com/MannLabs/alpharaw) is required, which depends on Mono.
A detailed guide to installing AlphaRaw with mono can be found [here](https://github.com/MannLabs/alpharaw#installation).

Download the latest build suitable for your chip architecture
(can be looked up by clicking on the Apple Symbol > *About this Mac* > *Chip* ("M1", "M2", "M3" -> `arm64`, "Intel" -> `x64`),
`alphadia-X.Y.Z-macos-darwin-arm64.pkg` or `alphadia-X.Y.Z-macos-darwin-x64.pkg`. Open the parent folder of the downloaded file in Finder,
right-click and select *open*. If you receive a warning during installation click *Open*.

In newer MacOS versions, additional steps are required to enable installation of unverified software.
This is indicated by a dialog telling you `“alphadia. ... .pkg” Not Opened`.
1. Close this dialog by clicking `Done`.
2. Choose `Apple menu` > `System Settings`, then `Privacy & Security` in the sidebar. (You may need to scroll down.)
3. Go to `Security`, locate the line "alphadia.pkg was blocked to protect your Mac" then click `Open Anyway`.
4. In the dialog windows, click `Open Anyway`.


As of now, **Linux** users need follow the steps for the
[developer installation](docs/installation.md#developer-installation) in order to use the GUI.

### Pip installation
If you want to use alphaDIA as a python library (e.g. for importing it into Jupyter notebooks) or only use the command-line interface,
you can install alphaDIA via `pip`.

#### 1. Prerequisites
Please make sure you have a valid installation of conda or miniconda.
We recommend setting up miniconda as described on their [website](https://docs.conda.io/projects/miniconda/en/latest/).

If you want to use `.raw` files on Thermo instruments alphaRaw is required, which depends on Mono. A detailed guide to installing AlphaRaw with mono can be found [here](https://github.com/MannLabs/alpharaw#installation).

#### 2. Setting up the environment
For any Python package, it is highly recommended to use a separate, as otherwise dependency conflicts can occur with already existing packages. We generally recommend [miniconda](https://docs.anaconda.com/free/miniconda/).

Finally, alphaDIA and all its dependencies can be installed by
```bash
pip install "alphadia[stable]"
```
We strongly recommend using the `stable` version, which has all dependencies fixed,
for reasons of reproducibility and integrity.

Alternatively, use
`pip install alphadia`, which comes with less version constraints. This is not recommended, but may be useful to avoid
version clashes if alphaDIA is imported as a library into a defined python requirement.
Note however, that this "loose" version might be affected e.g. by breaking changes of third-party dependencies.

Finally, run `alphadia --check` to check if the installation was successful;
`alphadia -h` will give you a list of command-line options.

---
## Getting started

This guide will show you how to perform your first search using the One-click GUI.
### 1. Prerequisites
Make sure you have installed the GUI using the one-click installer. To verify your installation, open alphaDIA and make sure that the `BundledExecutionEngine` is selected.

<img src="assets/select_engine.gif" alt="Logo" width="400" height="auto">

### 2. Test data

For the first search we will be using a spectral library to search 60SPD bulk HeLa samples on the Orbitrap Astral.
Download the test samples and save them:
[HeLa library](https://datashare.biochem.mpg.de/s/Uw2yfNSbApfPpTk),
[RAW files](https://datashare.biochem.mpg.de/s/339jg5HtGrwLwDN)

### 3. Search settings

#### Input Files
Import the library `.hdf` file and select the thre `.raw` files. You can select a human `.fasta` file for reannotation but it's generally not recommended for empirical spectral libraries.

#### Method Settings
Although alphaDIA is highly customizable, we will only specify a limited number of settings here. Go to the *Search* settings and make the following changes:
* Number of candidates: 5
* MS1 Tolerance 4ppm
* MS2 Tolerance 7ppm

#### Output files
Select an output folder where the search progress and the final results should be saved.

### 4. Run the search
Click *Run Workflow* to start the search and see the progress.


---
## Troubleshooting

In case of issues, check out the following:

* [Issues](https://github.com/MannLabs/alphadia/issues): Try a few different search terms to find out if a similar problem has been encountered before
* [Discussions](https://github.com/MannLabs/alphadia/discussions): Check if your problem or feature requests has been discussed before.

### Problem: Error when downloading the peptdeep pretrained models
If on startup you receive an error like
`FileNotFoundError: [Errno 2] No such file or directory: '<some_directory>/peptdeep/pretrained_models'`,
it could be due to missing write permissions on `<some_directory>`.

#### Solution
For now, you need to create this directory yourself and manually download the file
```
mkdir -p <some_directory>/peptdeep/pretrained_models && cd <some_directory>/peptdeep/pretrained_models
wget https://github.com/MannLabs/alphapeptdeep/releases/download/pre-trained-models/pretrained_models.zip
```

---
## Citations

**Please cite:**
> **AlphaDIA enables DIA transfer learning for feature-free proteomics**<br>
> Georg Wallmann, Patricia Skowronek, Vincenth Brennsteiner, Mikhail Lebedev, Marvin Thielert, Sophia Steigerwald, Mohamed Kotb, Oscar Despard, Tim Heymann, Xie-Xuan Zhou, Maximilian T. Strauss, Constantin Ammar, Sander Willems, Magnus Schwörer, Wen-Feng Zeng & Matthias Mann
> [Nature Biotechnology (2025)](https://doi.org/10.1038/s41587-025-02791-w)

---
## How to contribute

If you like this software, you can give us a [star](https://github.com/MannLabs/alphadia/stargazers) to boost our visibility! All direct contributions are also welcome. Feel free to post a new [issue](https://github.com/MannLabs/alphadia/issues) or clone the repository and create a [pull request](https://github.com/MannLabs/alphadia/pulls) with a new branch. For an even more interactive participation, check out the [discussions](https://github.com/MannLabs/alphadia/discussions) and the [the Contributors License Agreement](misc/CLA.md).

Further information on the development process can be found [here](docs/installation.md).

---
## Changelog

See the [GitHub releases](https://github.com/MannLabs/alphadia/releases) for a full overview of the changes made in each version.

---
## About

An open-source Python package of the [AlphaPept ecosystem](https://alphapept.org) from the [Mann Labs at the Max Planck Institute of Biochemistry](https://www.biochem.mpg.de/mann).

---
## License

AlphaDIA is developed by the [Mann Labs at the Max Planck Institute of Biochemistry](https://www.biochem.mpg.de/mann) and is freely available with an [Apache License](LICENSE.txt). External Python packages (available in the [requirements](requirements) folder) have their own licenses, which can be consulted on their respective websites.

---
