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')