DeepRank2
DeepRank2: Mining 3D Protein Structures with Geometric Deep Learning - Published in JOSS (2024)
Science Score: 100.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in JOSS metadata -
○Academic publication links
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✓Committers with academic emails
1 of 35 committers (2.9%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Repository
An open-source deep learning framework for data mining of protein-protein interfaces or single-residue variants.
Basic Info
- Host: GitHub
- Owner: DeepRank
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://deeprank2.readthedocs.io/en/latest/?badge=latest
- Size: 107 MB
Statistics
- Stars: 54
- Watchers: 6
- Forks: 13
- Open Issues: 29
- Releases: 15
Topics
Metadata Files
README.dev.md
deeprank2 developer documentation
If you're looking for user documentation, go here.
Code editor
We use Visual Studio Code (VS Code) as code editor. The VS Code settings for this project can be found in .vscode. The settings will be automatically loaded and applied when you open the project with VS Code. See the guide for more info about workspace settings of VS Code.
Package setup
After having followed the installation instructions and installed all the dependencies of the package, the repository can be cloned and its editable version can be installed:
bash
git clone https://github.com/DeepRank/deeprank2
cd deeprank2
pip install -e .'[test]'
Running the tests
You can check that all components were installed correctly, using pytest. The quick test should be sufficient to ensure that the software works, while the full test (a few minutes) will cover a much broader range of settings to ensure everything is correct.
Run pytest tests/test_integration.py for the quick test or just pytest for the full test (expect a few minutes to run).
Test coverage
In addition to just running the tests to see if they pass, they can be used for coverage statistics, i.e. to determine how much of the package's code is actually executed during tests. In an activated conda environment with the development tools installed, inside the package directory, run:
bash
coverage run -m pytest
This runs tests and stores the result in a .coverage file. To see the results on the command line, run:
bash
coverage report
coverage can also generate output in HTML and other formats; see coverage help for more information.
Linting and Formatting
We use ruff for linting, sorting imports and formatting of python (notebook) files. The configurations of ruff are set in pyproject.toml file.
If you are using VS code, please install and activate the Ruff extension to automatically format and check linting.
Otherwise, please ensure check both linting (ruff check .) and formatting (ruff format .) before requesting a review.
We use prettier for formatting most other files. If you are editing or adding non-python files and using VS code, the Prettier extension can be installed to auto-format these files as well.
Versioning
Bumping the version across all files is done before creating a new package release, running bump2version [part] from command line after having installed bump2version on your local environment. Instead of [part], type the part of the version to increase, e.g. minor. The settings in .bumpversion.cfg will take care of updating all the files containing version strings.
Branching workflow
We use a Git Flow-inspired branching workflow for development. DeepRank2's repository is based on two main branches with infinite lifetime:
main— this branch contains production (stable) code. All development code is merged intomainin sometime.dev— this branch contains pre-production code. When the features are finished then they are merged intodev.
During the development cycle, three main supporting branches are used:
- Feature branches - Branches that branch off from
devand must merge intodev: used to develop new features for the upcoming releases. - Hotfix branches - Branches that branch off from
mainand must merge intomainanddev: necessary to act immediately upon an undesired status ofmain. - Release branches - Branches that branch off from
devand must merge intomainanddev: support preparation of a new production release. They allow many minor bug to be fixed and preparation of meta-data for a release.
Development conventions
- Branching
- When creating a new branch, please use the following convention:
<issue_number>_<description>_<author_name>. - Always branch from
devbranch, unless there is the need to fix an undesired status ofmain. See above for more details about the branching workflow adopted.
- When creating a new branch, please use the following convention:
- Pull Requests
- When creating a pull request, please use the following convention:
<type>: <description>. Example types arefix:,feat:,build:,chore:,ci:,docs:,style:,refactor:,perf:,test:, and others based on the Angular convention.
- When creating a pull request, please use the following convention:
Making a release
Automated release workflow:
- IMP0RTANT: Create a PR for the release branch, targeting the
mainbranch. Ensure there are no conflicts and that all checks pass successfully. Release branches are typically: traditional release branches (these are created from thedevbranch), or hotfix branches (these are created directly from themainbranch).- if everything goes well, this PR will automatically be closed after the draft release is created.
- Navigate to Draft Github Release on the Actions tab.
- On the right hand side, you can select the level increase ("patch", "minor", or "major") and which branch to release from.
- Follow semantic versioning conventions to chose the level increase:
patch: when backward compatible bug fixes were mademinor: when functionality was added in a backward compatible mannermajor: when API-incompatible changes have been made
- Note that you cannot release from
main(the default shown) using the automated workflow. To release frommaindirectly, you must create the release manually.
- Follow semantic versioning conventions to chose the level increase:
- Visit Actions tab to check whether everything went as expected.
- NOTE: there are two separate jobs in the workflow: "draftrelease" and "tidyworkspace". The first creates the draft release on github, while the second merges changes into
devand closes the PR.- If "draftrelease" fails, then there are likely merge conflicts with
mainthat need to be resolved first. No release draft is created and the "tidyworkspace" job does not run. Coversely, if this action is succesfull, then the release branch (including a version bump) have been merged into the remotemainbranch. - If "draftrelease" is succesfull but "tidyworkspace" fails, then there are likely merge conflicts with
devthat are not conflicts withmain. In this case, the draft release is created (and changes were merged into the remotemain). Conflicts withdevneed to be resolved withdevby the user. - If both jobs succeed, then the draft release is created and the changes are merged into both remote
mainanddevwithout any problems and the associated PR is closed. Also, the release branch is deleted from the remote repository.
- If "draftrelease" fails, then there are likely merge conflicts with
- NOTE: there are two separate jobs in the workflow: "draftrelease" and "tidyworkspace". The first creates the draft release on github, while the second merges changes into
- Navigate to the Releases tab and click on the newest draft release that was just generated.
- Click on the edit (pencil) icon on the right side of the draft release.
- Check/adapt the release notes and make sure that everything is as expected.
- Check that "Set as the latest release is checked".
- Click green "Publish Release" button to convert the draft to a published release on GitHub.
- This will automatically trigger another GitHub workflow that will take care of publishing the package on PyPi.
Updating the token:
In order for the workflow above to be able to bypass the branch protection on main and dev, a token with admin priviliges for the current repo is required. Below are instructions on how to create such a token.
NOTE: the current token (associated to @DaniBodor) allowing to bypass branch protection will expire on 9 July 2025. To update the token do the following:
- Create a personal access token from a GitHub user account with admin priviliges for this repo.
- Check all the "repo" boxes and the "workflow" box, set an expiration date, and give the token a note.
- Click green "Generate token" button on the bottom
- Copy the token immediately, as it will not be visible again later.
- Navigate to the secrets settings.
- Edit the
GH_RELEASEkey giving your access token as the new value.
Manually create a release:
- Make sure you have all required developers tools installed
pip install -e .'[test]'. - Create a
release-branch frommain(if there has been an hotfix) ordev(regular new production release). - Prepare the branch for the release (e.g., removing the unnecessary dev files, fix minor bugs if necessary). Do this by ensuring all tests pass
pytest -vand that linting (ruff check) and formatting (ruff format --check) conventions are adhered to. - Bump the version using bump-my-version:
bump-my-version bump <level>where level must be one of the following (following semantic versioning conventions):major: when API-incompatible changes have been mademinor: when functionality was added in a backward compatible mannerpatch: when backward compatible bug fixes were made
- Merge the release branch into
mainanddev. - On the Releases page:
- Click "Draft a new release"
- By convention, use
v<version number>as both the release title and as a tag for the release. - Click "Generate release notes" to automatically load release notes from merged PRs since the last release.
- Adjust the notes as required.
- Ensure that "Set as latest release" is checked and that both other boxes are unchecked.
- Hit "Publish release".
- This will automatically trigger a GitHub workflow that will take care of publishing the package on PyPi.
UML
Code-base class diagrams updated on 02/11/2023, generated with https://www.gituml.com (save the images and open them in the browser for zooming).
- Data processing classes and functions:
- ML pipeline classes and functions:
Owner
- Name: DeepRank
- Login: DeepRank
- Kind: organization
- Repositories: 19
- Profile: https://github.com/DeepRank
JOSS Publication
DeepRank2: Mining 3D Protein Structures with Geometric Deep Learning
Authors
The Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
The Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
The Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
The Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands
Tags
PyTorch structural biology geometric deep learning 3D protein structures protein-protein interfaces single-residue variants graph neural networks convolutional neural networks DeepRankCitation (CITATION.cff)
cff-version: "1.2.0"
authors:
- family-names: Crocioni
given-names: Giulia
orcid: "https://orcid.org/0000-0002-0823-0121"
- family-names: Bodor
given-names: Dani L.
orcid: "https://orcid.org/0000-0003-2109-2349"
- family-names: Baakman
given-names: Coos
orcid: "https://orcid.org/0000-0003-4317-1566"
- family-names: Parizi
given-names: Farzaneh M.
orcid: "https://orcid.org/0000-0003-4230-7492"
- family-names: Rademaker
given-names: Daniel-T.
orcid: "https://orcid.org/0000-0003-1959-1317"
- family-names: Ramakrishnan
given-names: Gayatri
orcid: "https://orcid.org/0000-0001-8203-2783"
- family-names: Burg
given-names: Sven A.
name-particle: van der
orcid: "https://orcid.org/0000-0003-1250-6968"
- family-names: Marzella
given-names: Dario F.
orcid: "https://orcid.org/0000-0002-0043-3055"
- family-names: Teixeira
given-names: João M. C.
orcid: "https://orcid.org/0000-0002-9113-0622"
- family-names: Xue
given-names: Li C.
orcid: "https://orcid.org/0000-0002-2613-538X"
contact:
- family-names: Crocioni
given-names: Giulia
orcid: "https://orcid.org/0000-0002-0823-0121"
doi: 10.5281/zenodo.10566809
message: If you use this software, please cite our article in the
Journal of Open Source Software.
preferred-citation:
authors:
- family-names: Crocioni
given-names: Giulia
orcid: "https://orcid.org/0000-0002-0823-0121"
- family-names: Bodor
given-names: Dani L.
orcid: "https://orcid.org/0000-0003-2109-2349"
- family-names: Baakman
given-names: Coos
orcid: "https://orcid.org/0000-0003-4317-1566"
- family-names: Parizi
given-names: Farzaneh M.
orcid: "https://orcid.org/0000-0003-4230-7492"
- family-names: Rademaker
given-names: Daniel-T.
orcid: "https://orcid.org/0000-0003-1959-1317"
- family-names: Ramakrishnan
given-names: Gayatri
orcid: "https://orcid.org/0000-0001-8203-2783"
- family-names: Burg
given-names: Sven A.
name-particle: van der
orcid: "https://orcid.org/0000-0003-1250-6968"
- family-names: Marzella
given-names: Dario F.
orcid: "https://orcid.org/0000-0002-0043-3055"
- family-names: Teixeira
given-names: João M. C.
orcid: "https://orcid.org/0000-0002-9113-0622"
- family-names: Xue
given-names: Li C.
orcid: "https://orcid.org/0000-0002-2613-538X"
date-published: 2024-02-27
doi: 10.21105/joss.05983
issn: 2475-9066
issue: 94
journal: Journal of Open Source Software
publisher:
name: Open Journals
start: 5983
title: "DeepRank2: Mining 3D Protein Structures with Geometric Deep
Learning"
type: article
url: "https://joss.theoj.org/papers/10.21105/joss.05983"
volume: 9
title: "DeepRank2: Mining 3D Protein Structures with Geometric Deep
Learning"
version: "3.1.0"
GitHub Events
Total
- Issues event: 1
- Watch event: 14
- Issue comment event: 6
- Pull request review event: 1
- Pull request event: 2
- Fork event: 3
- Create event: 1
Last Year
- Issues event: 1
- Watch event: 14
- Issue comment event: 6
- Pull request review event: 1
- Pull request event: 2
- Fork event: 3
- Create event: 1
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| gcroci2 | c****a@g****m | 1,020 |
| DaniBodor | d****r@e****l | 850 |
| manonreau | m****u@g****m | 285 |
| Coos Baakman | c****n@r****l | 256 |
| Nicolas Renaud | n****d@g****m | 169 |
| Joyce | j****6@g****m | 93 |
| cbaakman | c****n@u****l | 52 |
| Sven van der Burg | s****g@e****l | 44 |
| Chia Yu Lin | c****n@i****l | 30 |
| Giulia Crocioni | c****n@i****l | 30 |
| Chia Yu Lin | c****n@i****l | 22 |
| Giulia Crocioni | c****n@i****l | 21 |
| Daniel Rademaker | 4****r | 18 |
| Manon F. Réau | m****u@i****x | 15 |
| Dani Bodor | d****r@e****l | 13 |
| Chia Yu Lin | c****n@s****l | 13 |
| Giulia Crocioni | c****n@i****l | 12 |
| Chia Yu Lin | c****n@i****l | 12 |
| Giulia Crocioni | c****n@i****l | 11 |
| Manon Réau | m****u@u****l | 10 |
| rgayatri | r****i | 10 |
| Giulia Crocioni | c****n@i****l | 8 |
| Manon F. Réau | m****u@i****x | 4 |
| Giulia Crocioni | c****n@i****l | 4 |
| Farzaneh Parizi | 5****i | 4 |
| Coos Baakman | c****n@r****l | 3 |
| Jennifer Wei | 9****i | 2 |
| Alexandre Bonvin | a****n@g****m | 2 |
| Coos | c****n@g****m | 2 |
| root | r****t@D****n | 1 |
| and 5 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 120
- Total pull requests: 100
- Average time to close issues: 6 months
- Average time to close pull requests: 9 days
- Total issue authors: 17
- Total pull request authors: 9
- Average comments per issue: 2.28
- Average comments per pull request: 1.52
- Merged pull requests: 79
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 5
- Pull requests: 7
- Average time to close issues: 2 days
- Average time to close pull requests: 3 days
- Issue authors: 2
- Pull request authors: 3
- Average comments per issue: 0.6
- Average comments per pull request: 0.71
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- gcroci2 (67)
- DaniBodor (43)
- DTRademaker (3)
- Tizzzzy (3)
- rubenalv (3)
- DanLep97 (2)
- lloydtripp (2)
- Lsz-20 (2)
- svenvanderburg (1)
- KevinVG207 (1)
- cbaakman (1)
- elboyran (1)
- aminzia (1)
- amjjbonvin (1)
- Max1461 (1)
Pull Request Authors
- gcroci2 (94)
- DaniBodor (46)
- DarioMarzella (2)
- dependabot[bot] (2)
- FriederikeBiermann (2)
- cbaakman (1)
- DanLep97 (1)
- jnwei (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 43 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 10
- Total maintainers: 1
pypi.org: deeprank2
DeepRank2 is an open-source deep learning framework for data mining of protein-protein interfaces or single-residue missense variants.
- Documentation: https://deeprank2.readthedocs.io/en/latest/?badge=latest
- License: Apache-2.0 license
-
Latest release: 3.1.0
published over 1 year ago
Rankings
Maintainers (1)
Dependencies
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