| Name | alphaDIA JSON |
| Version |
1.7.2
JSON |
| download |
| home_page | None |
| Summary | A novel proteomics search engine for DIA data based on end-to-end transfer learning. |
| upload_time | 2024-07-15 13:36:43 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10.0 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2020 MannLabs Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
mass spectrometry
proteomics
search engine
dia
data-independent acquisition
bioinformatics
alphapept
alphapept ecosystem
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- PROJECT LOGO -->
<br />
<div align="center">
<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://www.biorxiv.org/content/10.1101/2024.05.28.596182v1">Preprint</a>
·
<a href="https://github.com/Mannlabs/alphadia/releases/latest">Download</a>
·
<a href="https://github.com/MannLabs/alphadia/discussions">Discussion</a>
·
<a href="https://alphadia.readthedocs.io/en/latest/">Documentation</a>
</p>
</div>
## Features
- Empirical library and fully predicted library search
- 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.
## Manuscript
**Please cite:**
> **AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics**<br>
> Georg Wallmann, Patricia Skowronek, Vincenth Brennsteiner, Mikhail Lebedev, Marvin Thielert, Sophia Steigerwald, Mohamed Kotb, Tim Heymann, Xie-Xuan Zhou, Magnus Schwörer, Maximilian T. Strauss, Constantin Ammar, Sander Willems, Wen-Feng Zeng, Matthias Mann<br>
> [biorxiv.org](https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1)
alphaDIA is part of the alphaX ecosystem:
- [alphaPept](https://github.com/MannLabs/alphapept)
- [alphaTims](https://github.com/MannLabs/alphatims)
- [alphaPeptDeep](https://github.com/MannLabs/alphapeptdeep)
- [alphaRaw](https://github.com/MannLabs/alpharaw)
- [alphaBase](https://github.com/MannLabs/alphabase)
- [directLFQ](https://github.com/MannLabs/directlfq)
## 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
Currently available for **MacOS** and **Windows**.
You can download the latest release of alphaDIA [here](https://github.com/Mannlabs/alphadia/releases/latest).
* **Windows:** Download the latest `win-x64` build. Save it and double click it to install. If you receive a warning during installation click *Run anyway*.
* **MacOS:** Download the latest `darwin-arm64` build. Please note that alphaDIA currently requires an ARM based M1/2/3 processor for the one-click installer. Save the installer and open the parent folder in Finder. Right-click and select *open*. If you receive a warning during installation click *Open*. 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).
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 dependancy 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 -v` 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.
---
## Citations
A manuscript has been submitted to bioRxiv.
---
## 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 [HISTORY.md](HISTORY.md) for a full overview of the changes made in each version.
---
## About
An open-source Python package of the AlphaPept ecosystem from the [Mann Labs at the Max Planck Institute of Biochemistry](https://www.biochem.mpg.de/mann).
---
## License
AlphaDIA was 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.
---
Raw data
{
"_id": null,
"home_page": null,
"name": "alphaDIA",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10.0",
"maintainer_email": null,
"keywords": "mass spectrometry, proteomics, search engine, DIA, data-independent acquisition, bioinformatics, AlphaPept, AlphaPept ecosystem",
"author": null,
"author_email": "Mann Labs <wallmann@biochem.mpg.de>",
"download_url": "https://files.pythonhosted.org/packages/c3/9a/15ff5dc196299a3248588e5e3606c03b24d940690af1a2220917e5d4d1fd/alphadia-1.7.2.tar.gz",
"platform": null,
"description": "\n\n\n\n\n\n\n\n\n\n<!-- PROJECT LOGO -->\n<br />\n<div align=\"center\">\n <a href=\"https://github.com/othneildrew/Best-README-Template\">\n <img src=\"release/logos/alphadia.png\" alt=\"Logo\" width=\"80\" height=\"80\">\n </a>\n\n <h3 align=\"center\">alphaDIA</h3>\n\n <p align=\"center\">\n <a href=\"https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1\">Preprint</a>\n \u00b7\n <a href=\"https://github.com/Mannlabs/alphadia/releases/latest\">Download</a>\n \u00b7\n <a href=\"https://github.com/MannLabs/alphadia/discussions\">Discussion</a>\n \u00b7\n <a href=\"https://alphadia.readthedocs.io/en/latest/\">Documentation</a>\n </p>\n</div>\n\n\n\n## Features\n- Empirical library and fully predicted library search\n- Label free quantification\n- DIA multiplexing\n\nWe support the following vendor and processing modes:\n\n| Platform | Empirical lib | Predicted lib |\n| :---------------- | :------: | :----: |\n| Thermo .raw | \u2705 | \u2705 |\n| Sciex .wiff | \u2705 | \u2705 |\n| Bruker .d | \u2705 | \u26a0\ufe0f |\n> \u26a0\ufe0f **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.\n\n## Manuscript\n**Please cite:**\n> **AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics**<br>\n> Georg Wallmann, Patricia Skowronek, Vincenth Brennsteiner, Mikhail Lebedev, Marvin Thielert, Sophia Steigerwald, Mohamed Kotb, Tim Heymann, Xie-Xuan Zhou, Magnus Schw\u00f6rer, Maximilian T. Strauss, Constantin Ammar, Sander Willems, Wen-Feng Zeng, Matthias Mann<br>\n> [biorxiv.org](https://www.biorxiv.org/content/10.1101/2024.05.28.596182v1)\n\nalphaDIA is part of the alphaX ecosystem:\n- [alphaPept](https://github.com/MannLabs/alphapept)\n- [alphaTims](https://github.com/MannLabs/alphatims)\n- [alphaPeptDeep](https://github.com/MannLabs/alphapeptdeep)\n- [alphaRaw](https://github.com/MannLabs/alpharaw)\n- [alphaBase](https://github.com/MannLabs/alphabase)\n- [directLFQ](https://github.com/MannLabs/directlfq)\n\n## Installation\n\nAlphaDIA can be installed on Windows, macOS and Linux. Please choose the preferred installation:\n\n* [**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.\n\n* [**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.\n\n* [**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.\n\n* [**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.\n\n* [**Slurm installation:**](docs/installation.md#slurm-cluster-installation) Choose this for running alphaDIA on a research cluster with Slurm.\n\n### One-click GUI installation\nCurrently available for **MacOS** and **Windows**.\nYou can download the latest release of alphaDIA [here](https://github.com/Mannlabs/alphadia/releases/latest).\n\n* **Windows:** Download the latest `win-x64` build. Save it and double click it to install. If you receive a warning during installation click *Run anyway*.\n* **MacOS:** Download the latest `darwin-arm64` build. Please note that alphaDIA currently requires an ARM based M1/2/3 processor for the one-click installer. Save the installer and open the parent folder in Finder. Right-click and select *open*. If you receive a warning during installation click *Open*. 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).\n\nAs of now, **Linux** users need follow the steps for the\n[developer installation](docs/installation.md#developer-installation) in order to use the GUI.\n\n### Pip installation\nIf you want to use alphaDIA as a python library (e.g. for importing it into Jupyter notebooks) or only use the command-line interface,\nyou can install alphaDIA via `pip`.\n\n#### 1. Prerequisites\nPlease make sure you have a valid installation of conda or miniconda.\nWe recommend setting up miniconda as described on their [website](https://docs.conda.io/projects/miniconda/en/latest/).\n\nIf 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).\n\n#### 2. Setting up the environment\nFor any Python package, it is highly recommended to use a separate, as otherwise dependancy conflicts can occur with already existing packages. We generally recommend [miniconda](https://docs.anaconda.com/free/miniconda/).\n\nFinally, alphaDIA and all its dependencies can be installed by\n```bash\npip install \"alphadia[stable]\"\n```\nWe strongly recommend using the `stable` version, which has all dependencies fixed,\nfor reasons of reproducibility and integrity.\n\nAlternatively, use\n`pip install alphadia`, which comes with less version constraints. This is not recommended, but may be useful to avoid\nversion clashes if alphaDIA is imported as a library into a defined python requirement.\nNote however, that this \"loose\" version might be affected e.g. by breaking changes of third-party dependencies.\n\nFinally, run `alphadia -v` to check if the installation was successful;\n`alphadia -h` will give you a list of command-line options.\n\n---\n## Getting started\n\nThis guide will show you how to perform your first search using the One-click GUI.\n### 1. Prerequisites\nMake 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.\n\n<img src=\"assets/select_engine.gif\" alt=\"Logo\" width=\"400\" height=\"auto\">\n\n### 2. Test data\n\nFor 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)\n\n### 3. Search settings\n\n#### Input Files\nImport 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.\n\n#### Method Settings\nAlthough alphaDIA is highly customizable, we will only specify a limited number of settings here. Go to the *Search* settings and make the following changes:\n* Number of candidates: 5\n* MS1 Tolerance 4ppm\n* MS2 Tolerance 7ppm\n\n#### Output files\nSelect an output folder where the search progress and the final results should be saved.\n\n### 4. Run the search\nClick *Run Workflow* to start the search and see the progress.\n\n\n---\n## Troubleshooting\n\nIn case of issues, check out the following:\n\n* [Issues](https://github.com/MannLabs/alphadia/issues): Try a few different search terms to find out if a similar problem has been encountered before\n* [Discussions](https://github.com/MannLabs/alphadia/discussions): Check if your problem or feature requests has been discussed before.\n\n---\n## Citations\n\nA manuscript has been submitted to bioRxiv.\n\n---\n## How to contribute\n\nIf 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).\n\nFurther information on the development process can be found [here](docs/installation.md).\n\n---\n## Changelog\n\nSee the [HISTORY.md](HISTORY.md) for a full overview of the changes made in each version.\n\n---\n## About\n\nAn open-source Python package of the AlphaPept ecosystem from the [Mann Labs at the Max Planck Institute of Biochemistry](https://www.biochem.mpg.de/mann).\n\n---\n## License\n\nAlphaDIA was 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.\n\n---\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. 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