rnaglib 3.4.11

RNAglib: Tools for learning on the structure of RNA using 2.5D geometric representations

RNA Geometric Library (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.

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.

What can you do with rnaglib?

A quickstart and tutorials are available in our online documentation: rnaglib.org. In this readme we briefly review the functionality of rnaglib:

• Benchmark ML models
• Get annotated RNA 3D structures
  • Fetch and browse annotated RNA 3D structures
  • Dowloading whole RNA structure databases
  • Annotate your own structures
• Additional functionalities
  • Quick visualization of 2.5D graphs
  • 2.5D graph comparison and alignment
• Citing the tool
• 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 and in the documentation.

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

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 is a reference for all the annotations currently available.

>>> 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.

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 to support this functionality.

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 VARNAhttps://varna.lisn.upsaclay.fr/ or RNAscape for a full-featured visualizer.

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.

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
• Structure-and Function-Aware Substitution Matrices
• MultiModRLBP: A Deep Learning Approach for RNA-Small Molecule Ligand Binding Site Prediction using Multi-modal features
• VeRNAl
• RNAMigos

Resources

• Documentation
• Twitter
• Contact: [e-mail]

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