https://github.com/cgoliver/rnaglib
Deep learning datasets for RNA 3D and 2.5D structures.
https://github.com/cgoliver/rnaglib
bioinformatics deep-learning ml rna structural-biology
Last synced: 3 months ago
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Deep learning datasets for RNA 3D and 2.5D structures.
- Host: GitHub
- URL: https://github.com/cgoliver/rnaglib
- Owner: cgoliver
- License: bsd-3-clause
- Created: 2022-10-21T13:53:50.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2024-11-12T15:45:19.000Z (11 months ago)
- Last Synced: 2024-11-12T16:36:22.380Z (11 months ago)
- Topics: bioinformatics, deep-learning, ml, rna, structural-biology
- Language: Python
- Homepage: https://rnaglib.org
- Size: 135 MB
- Stars: 15
- Watchers: 2
- Forks: 3
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# RNA Geometric Library (`rnaglib`)
[](https://pypi.org/project/rnaglib/)
[](https://rnaglib.readthedocs.io/en/latest/?badge=latest)
[](https://codecov.io/gh/cgoliver/rnaglib)
`RNAglib` is a Python package for studying RNA 2.5D and 3D structures. Functionality includes automated data loading,
analysis, visualization, ML model building and benchmarking.
A web-based documentation is available at [**rnaglib.org**](https://rnaglib.org).
We host RNAs annotated with molecule, base pair, and nucleotide level attributes. These include, but are not limited to:
* Secondary structure and 3D coordinates
* Leontis-Westhof base pair geometry classification
* Protein binding, small molecule binding, chemical modifications...
To install the tool, follow the steps in [INSTALL.md](INSTALL.md).
## What can you do with `rnaglib`?
A quickstart and tutorials are available in our online documentation: [**rnaglib.org**](https://rnaglib.org).
In this readme we briefly review the functionality of rnaglib:
- [Benchmark ML models](#benchmark-ml-models-on-rna-3d-structures-new)
- [Get annotated RNA 3D structures](#get-annotated-rna-3d-structures)
- [Fetch and browse annotated RNA 3D structures](#fetch-and-browse-annotated-rna-3D-structures)
- [Dowloading whole RNA structure databases](#Dowloading-whole-RNA-structure-databases)
- [Annotate your own structures](#Annotate-your-own-structures)
- [Additional functionalities](#Additional-functionalities)
- [Quick visualization of 2.5D graphs](#Quick-visualization-of-2.5D-graphs)
- [2.5D graph comparison and alignment](#2.5D-graph-comparison-and-alignment)
- [Citing the tool](#citation)
- [Around RNAglib](#Around-RNAglib)
## Benchmark ML models on RNA 3D structures (**new**)
We now provide datasets of RNA 3D structures ready-to-use for machine learning model benchmarking in seven
biologically relevant tasks.
Moreover, we provide many tools to create your own new tasks.
A more detailed description is provided in the [Tasks' README ](src/rnaglib/tasks/README.md) and in the
[documentation](https://rnaglib.org/en/latest/tutorials/tuto_tasks.html).
Everything you need to train and evaluate a model is built on 3 basic ingredients:
1. A ``rnaglib.Task`` object with holds all the relevant data, splits and functionality.
2. A ``rnaglib.Representation`` object which converts raw RNAs to tensor formats.
3. A model of your choosing, though we provide a basic one to get started ``rnaglib.learning.PyGmodel``
```python
from rnaglib.tasks import ChemicalModification
from rnaglib.transforms import GraphRepresentation
from rnaglib.learning.task_models import PygModel
# Load task, representation, and get loaders
task = ChemicalModification(root="my_root")
model = PygModel.from_task(task)
pyg_rep = GraphRepresentation(framework="pyg")
task.add_representation(pyg_rep)
train_loader, val_loader, test_loader = task.get_split_loaders(batch_size=8)
for batch in train_loader:
batch = batch['graph'].to(model.device)
output = model(batch)
test_metrics = model.evaluate(task, split='test')
```
## Get annotated RNA 3D structures
### Fetch and browse annotated RNA 3D structures
Current release contains annotations generated by x3dna-dssr as well as some additional ones that we added for all
available PDBs at the time of release.
Each RNA is stored as a networkx graph where nodes are residues and edges are backbone and base pairing edges.
The networkx graph object has graph-level, node-level and edge-level attributes.
[Here](https://rnaglib.org/en/latest/rna_ref.html) is a reference for all the annotations currently
available.
```python
>>> from rnaglib.dataset import rna_from_pdbid
>>> rna_dict = rna_from_pdbid('1fmn') # fetch from local database or RCSB if not found
>>> rna_dict['rna'].graph # display graph-level features
{'name': '1fmn', 'pdbid': '1fmn', 'ligand_to_smiles': {'FMN': 'Cc1cc2c(cc1C)N(C3=NC(=O)NC(=O)C3=N2)CC(C(C(COP(=O)(O)O)O)O)O'}, 'ss': {'A': '..(((((......(((....))).....)))))..'}, 'seq': {'A': 'GGCGUGUAGGAUAUGCUUCGGCAGAAGGACACGCC'}}
```
## Dowloading whole RNA structure databases
In addition to analysing RNA data, RNAglib also distributes available parsed RNA structures.
Databases of annotated structures can be downloaded directly from [Zenodo](https://zenodo.org/records/14625192).
| Version | Date | Total RNAs | Total Non-Redundant | Non-redundant version | `rnaglib` commit |
---------|----------|------------|---------------------|-----------------------|------------------|
2.0.2 | 25-02-25 | 8441 | 2921 | 3.375 | ac303c7 |
2.0.0 | 12-01-25 | 8305 | 2877 | 3.369 | 33a9e989 |
1.0.0 | 15-02-23 | 5759 | 1176 | 3.269 | 5446ae2c |
0.0.0 | 20-07-21 | 3739 | 899 | 3.186 | eb25dabd |
They can also be obtained through the provided command line utility, where you can specify the version and redundancy.
```
$ rnaglib_download -r all|nr
```
## Annotate your own structures
You can extract Leontis-Westhof interactions and convert 3D structures to 2.5D graphs.
We wrap a fork of [fr3d-python](https://github.com/cgoliver/fr3d-python) to support this functionality.
```python
from rnaglib.prepare_data import fr3d_to_graph
G = fr3d_to_graph("../data/structures/1fmn.cif")
```
Warning: this method currently does not support non-standard residues. Support coming soon. Up to version 1.0.0 of the
RNA database were created using x3dna-dssr which do contain non-standard residues.
## Additional functionalities
### Quick visualization of 2.5D graphs
We customize networkx graph drawing functionalities to give some convenient visualization of 2.5D base pairing networks.
This is not a dedicated visualization tool, it is only intended for quick debugging. We point you
to [VARNA]()https://varna.lisn.upsaclay.fr/ or [RNAscape](https://academic.oup.com/nar/article/52/W1/W354/7648766) for a
full-featured visualizer.
```python
from rnaglib.drawing import rna_draw
rna_draw(G, show=True, layout="spring")
```
### 2.5D graph comparison and alignment
When dealing with 3D structures as 2.5D graphs we support graph-level comparison through the graph edit distance.
```python
from rnaglib.algorithms import graph_edit_distance
from rnaglib.dataset import rna_from_pdbid
G = rna_from_pdbid("4nlf")["rna"]
print(graph_edit_distance(G, G)) # 0.0
```
## Citation
```
@article{mallet2022rnaglib,
title={RNAglib: a python package for RNA 2.5 D graphs},
author={Mallet, Vincent and Oliver, Carlos and Broadbent, Jonathan and Hamilton, William L and Waldisp{\"u}hl, J{\'e}r{\^o}me},
journal={Bioinformatics},
volume={38},
number={5},
pages={1458--1459},
year={2022},
publisher={Oxford University Press}
}
```
## Around RNAglib
### Projects using `rnaglib`
If you use rnaglib in one of your projects, please cite and feel free to make a pull request so we can list your project
here.
* [RNAMigos2](https://github.com/cgoliver/RNAmigos2)
* [Structure-and Function-Aware Substitution Matrices](https://github.com/BorgwardtLab/GraphMatchingSubstitutionMatrices)
* [MultiModRLBP: A Deep Learning Approach for RNA-Small Molecule Ligand Binding Site Prediction using Multi-modal features](https://github.com/lennylv/MultiModRLBP)
* [VeRNAl](https://github.com/cgoliver/vernal)
* [RNAMigos](https://github.com/cgoliver/RNAmigos)
### Resources
* [Documentation](https://rnaglib.readthedocs.io/en/latest/?badge=latest)
* [Twitter](https://twitter.com/rnaglib)
* Contact: `rnaglib@cs.mcgill.ca`
### References
1. Leontis, N. B., & Zirbel, C. L. (2012). Nonredundant 3D Structure Datasets for RNA Knowledge Extraction and
Benchmarking. In RNA 3D Structure Analysis and Prediction N. Leontis & E. Westhof (Eds.), (Vol. 27, pp. 281–298).
Springer Berlin Heidelberg. doi:10.1007/978-3-642-25740-7\_13