Quickstart

Get the data

Once you have installed RNAglib, you can fetch a pre-built dataset 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 graph_from_pdbid()

>>> from rnaglib.utils import available_pdbids
>>> from rnaglib.utils import graph_from_pdbid

>>> pdbids = available_pdbids()
>>> rna = graph_from_pdbid(pdbids[0])
>>> rna
DiGraph with 69 nodes and 194 edges
>>> rna.graph
{'dbn': {'all_chains': {'num_nts': 143, 'num_chars': 144, 'bseq': 'GCCCGGAUAGCUCAGUCGGUAGAGCAGGGGAUUGAAAAUCCCCGUGUCCUUGGUUCGAUUCCGAGUCUGGGCAC&CGGAUAGCUCAGUCGGUAGAGCAGGGGAUUGAAAAUCCCCGUGUCCUUGGUUCGAUUCCGAGUCCGGGC', 'sstr': '(((((((..((((.....[..)))).(((((.......))))).....(((((..]....))))))))))))..&((((..((((.....[..)))).(((((.......))))).....(.(((..]....))).)))))...', 'form': 'AAAAAA...AA...A.......AAA.AAAA.......A.AAA......AAAAA..A....AAAAAAAAAAAA.-&.AA...AA...A.......AAA.AAAA.......A.AAA......AAAAA..A....A...AAAA.A.-'}...,

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.data_loading 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 torch.nn import BCELoss
from rnaglib.tasks import BindingSiteDetection
from rnaglib.transforms import GraphRepresentation

# Load the task data and annotations
ta = BindingSiteDetection("my_root")

# Select a data representation and framework (see docs for support of other data modalities and deep learning frameworks)

ta.dataset.add_representation(GraphRepresentation(framework="pyg"))

train_loader, val_loader, test_loader = ta.get_split_loaders()

# most tasks ship with a dummy model for debugging, gives random outputs of the right shape
model = ta.dummy_model

# Access the predefined splits
for batch in train_loader:
    pred = ta.dummy_model(batch["graph"])
    y = batch["graph"].y
    loss = BCELoss()(y, pred)