Metadata-Version: 2.1
Name: rnaglib
Version: 0.0.1
Summary: RNAglib: Tools for learning on the structure of RNA using 2.5D graph representations
Home-page: https://jwgitlab.cs.mcgill.ca/cgoliver/rnaglib
Author: Vincent Mallet, Carlos Oliver, Jonathan Broadbent, William L. Hamilton and Jérome Waldispuhl
Author-email: vincent.mallet96@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: dgl
Requires-Dist: networkx
Requires-Dist: numpy
Requires-Dist: seaborn
Requires-Dist: sklearn
Requires-Dist: tqdm

# RNAGlib

`RNAglib` is a Python package for studying RNA 2.5D structures. Functionality includes automated data loading, analysis,
visualization, ML model building and benchmarking.

* What are RNA 2.5D structures?

RNA 2.5D structures are discrete graph-based representations of atomic coordinates derived from techniues such as X-ray
crystallography and NMR. This type of representation encodes all possible base pairing interactions which are known to
be crucial for understanding RNA function.

* Why use RNA 2.5D data?

The benefit is twofold. When dealing with RNA 3D data, a representation centered on base pairing is a very natural prior
which has been shown to carry important signals for complex interactions, and can be directly interpreted. Second,
adopting graph representations lets us take advantage of many powerful algorithmic tools such as graph neural networks
and graph kernels.

* What type of functional data is included?

The graphs are annotated with graph, node, and edge-level attributes. These include, but are not limited to:

* Secondary structure (graph-level)
* Protein binding (node-level)
* Small molecule binding (node-level)
* Chemical modifications (node-level)
* 3-d coordinates(node-level)
* Leontis-westhof base pair geometry classification (edge-level)

## Package Structure

* `/RNAGlib/prepare_data/`: processes raw PDB structures and builds a database of 2.5D graphs with full structural
  annotation
* `/RNAGlib/data_loading`: custom PyTorch dataloader implementations
* `/RNAGlib/models`: pre-built GCN models.
* `/RNAGlib/learning`: learning routines for the easiest use of the package.
* `/RNAGlib/drawing`: utilities for visualizing 2.5D graphs
* `/RNAGlib/ged`: custom graph similarity functions
* `/RNAGlib/kernels`: custom local neighbourhood similarity functions

## Data scheme

A more detailed description of the data is presented in `/RNAGlib/prepare_data/README`. It comes along with instructions
on how to produce the data from public databases. However since this construction is computationally expensive, we offer
a pre-built database.

We provide a visualization of what the graphs in this database contain :

![Example graph](https://github.com/Jonbroad15/RNAGlib/blob/main/images/Fig1.png)
## Installation

### Code

The package can be cloned and the source code used directly. We also deploy it as a pip package and recommend using this
install in conda environments.

If one wants to use GPU support, one should install [Pytorch](https://pytorch.org/get-started/locally/)
and [DGL](https://www.dgl.ai/pages/start.html) with the appropriate options. Otherwise you can just skip this step and
the pip installs of Pytorch and DGL will be used.

Then, one just needs to run :

```
pip install rnaglib
```

Then one can start using the packages functionalities by importing them in one's python script.

### Data

To perform machine learning one needs RNA data. The instructions to produce this data are presented in prepare_data. We
however offer two possibilities to directly access pre-built databases :

* Direct download at the address : http://rnaglib.cs.mcgill.ca/static/datasets/iguana
* In code download : if one instantiates a dataloader and the data cannot be found, the corresponding data will be
  automatically downloaded and cached

Because of this second option, after installing our tool with pip, one can start learning on RNA data without extra
steps.

## Example usage

To provide the user with a hands on tutorial, we offer two example learning pipelines in `RNAGlib/examples`.

If one has run the pip installation, just run :

```
rnaglib_first
rnaglib_second
```

Otherwise, after cloning the repository, run :

```
cd examples
python first_example.py
python second_example.py
```

You should see data getting downloaded and networks being trained. The first example is a basic supervised model
training to predict protein binding nucleotides. The second one starts by an unsupervised phase that pretrains the
network and then performs this supervised training in a principled way, suitable for benchmarking its performance. This
simple code was used to produce the benchmark values presented in the paper.

## Utils

## Visualization

To visualize the 2.5D graphs in the format described above, we have implemented a drawing toolbox with several
functions. The easiest way to use it in your application is to call
`rnaglib.drawing.draw(graph, show=True)`. A functioning installation of Latex is needed to plot the graphs. If one
encounters troubles, this [tutorial](https://matplotlib.org/stable/tutorials/text/usetex.html)
explains how to use matplotlib with Latex.

## Ged

GED is the gold standard of graph comparisons. We have put our ged implementation as a part of networkx, and offer in
`rnaglib/ged` the weighting scheme we propose to compare 2.5D graphs. One can call `rnaglib.ged.ged()` on two graphs to
compare them. However, due to the exponential complexity of the comparison, the maximum size of the graphs should be
around ten nodes, making it more suited for comparing graphlets or subgraphs.

## Associated Repositories:

[VeRNAl](https://github.com/cgoliver/vernal)

[RNAMigos](https://github.com/cgoliver/RNAmigos)

## 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



