Quickstart

Get the data

Once you have installed RNAglib, you can fetch a pre-built database of RNA structures using the command line:

rnaglib_download

By default you will get non-redundant RNA structures saved to ~/.rnaglib.

To obtain different versions or larger sets of RNAs have a look at the command line options rnaglib_download --help.

Load single RNA

Annotations for each RNA are accessed through networkx graph objects. You can load one RNA using rna_from_pdbid()

>>> from rnaglib.dataset import rna_from_pdbid

>>> rna = rna_from_pdbid("1fmn")
>>> rna['rna'].graph
{'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'}
}

See the data tutorial for more on the data.

Load an RNA Dataset

For machine learning purposes, we often want a collection of data objects in one place. For that we have the RNADataset object.:

from rnaglib.dataset import RNADataset

dataset = RNADataset()

This object holds the same objects as above but also supports ML functionalities such as converting the RNAs to different representations (graphs, point clouds, voxels) and to different frameworks (dgl, torch, pytorch geometric) See the ML tutorial for more on model training and tasks.

Train a model on an RNA Task

The rnaglib.tasks library contains all utilities necessary for loading predefined tasks with splits and evaluation functions.:

from rnaglib.tasks import get_task
from rnaglib.transforms import GraphRepresentation
from rnaglib.learning.task_models import PygModel

# Load task, representation, and get loaders task = get_task(root="my_root",
task_id="rna_cm")
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')