| Name | graphein JSON |
| Version |
1.7.7
JSON |
| download |
| home_page | https://github.com/a-r-j/graphein |
| Summary | Protein & Interactomic Graph Construction for Machine Learning |
| upload_time | 2024-09-18 04:23:02 |
| maintainer | None |
| docs_url | None |
| author | Arian Jamasb |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
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| VCS |
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| requirements |
No requirements were recorded.
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[Documentation](http://www.graphein.ai) | [Paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/ade039c1db0391106a3375bd2feb310a-Abstract-Conference.html) | [Tutorials](http://graphein.ai/notebooks_index.html) | [Installation](#installation)
Protein & Interactomic Graph Library
This package provides functionality for producing geometric representations of protein and RNA structures, and biological interaction networks. We provide compatibility with standard PyData formats, as well as graph objects designed for ease of use with popular deep learning libraries.
## What's New?
| | | |
|---|---|---|
| 1.7.0 | [FoldComp Datasets](http://graphein.ai/notebooks/foldcomp.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/foldcomp.ipynb) |
| 1.7.0 | [Creating Datasets from the PDB](http://graphein.ai/notebooks/creating_datasets_from_the_pdb.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/creating_datasets_from_the_pdb.ipynb) |
| 1.6.0 | [Protein Tensor Module](http://graphein.ai/notebooks/protein_tensors.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/protein_tensors.ipynb) |
| 1.5.0 | [Protein Graph Creation from AlphaFold2!](http://graphein.ai/notebooks/alphafold_protein_graph_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) |
| 1.5.0 | [RNA Graph Construction from Dotbracket notation](http://graphein.ai/modules/graphein.rna.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/rna_graph_tutorial.ipynb) |
| 1.4.0 | [Constructing molecular graphs](http://graphein.ai/notebooks/molecule_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/molecule_tutorial.ipynb) |
| 1.3.0 | [Ready-to-go Dataloaders for PyTorch Geometric](http://graphein.ai/notebooks/dataloader_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/dataloader_tutorial.ipynb) |
| 1.2.0 | [Extracting subgraphs from protein graphs](http://graphein.ai/notebooks/subgraphing_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/subgraphing_tutorial.ipynb) |
| 1.2.0 | [Protein Graph Analytics](http://graphein.ai/notebooks/protein_graph_analytics.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/protein_graph_analytics.ipynb) |
| 1.2.0 | [Graphein CLI](http://graphein.ai/getting_started/usage.html) | |
| 1.2.0 |[Protein Graph Visualisation!](http://graphein.ai/notebooks/interactive_plotly_example.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/interactive_plotly_example.ipynb)
| 1.1.0 | [Protein - Protein Interaction Network Support & Structural Interactomics (Using AlphaFold2!)](http://graphein.ai/notebooks/ppi_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/ppi_graph.ipynb) |
| 1.0.0 | [High and Low-level API for massive flexibility - create your own bespoke workflows!](http://graphein.ai/notebooks/residue_graphs.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) |
## Example usage
Graphein provides both a programmatic API and a command-line interface for constructing graphs.
### CLI
Graphein configs can be specified as `.yaml` files to batch process graphs from the commandline.
[Docs](http://graphein.ai/getting_started/usage.html)
```bash
graphein -c config.yaml -p path/to/pdbs -o path/to/output
```
### Creating a Protein Graph
| | | |
|---|---|---|
[Tutorial (Residue-level)](http://graphein.ai/notebooks/residue_graphs.html) | [Tutorial (Atomic)](http://graphein.ai/notebooks/atom_graph_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.graphs)
| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/atom_graph_tutorial.ipynb) | |
```python
from graphein.protein.config import ProteinGraphConfig
from graphein.protein.graphs import construct_graph
config = ProteinGraphConfig()
g = construct_graph(config=config, pdb_code="3eiy")
```
### Creating a Protein Graph from the AlphaFold Protein Structure Database
| | |
|---|---|
| [Tutorial](http://graphein.ai/notebooks/alphafold_protein_graph_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.graphs) |
| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/alphafold_protein_graph_tutorial.ipynb)|
```python
from graphein.protein.config import ProteinGraphConfig
from graphein.protein.graphs import construct_graph
from graphein.protein.utils import download_alphafold_structure
config = ProteinGraphConfig()
fp = download_alphafold_structure("Q5VSL9", aligned_score=False)
g = construct_graph(config=config, path=fp)
```
### Creating a Protein Mesh
| | |
|---|---|
| [Tutorial](http://graphein.ai/notebooks/protein_mesh_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.meshes) |
| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/protein_mesh_tutorial.ipynb) | |
```python
from graphein.protein.config import ProteinMeshConfig
from graphein.protein.meshes import create_mesh
verts, faces, aux = create_mesh(pdb_code="3eiy", config=config)
```
### Creating Molecular Graphs
Graphein can create molecular graphs from smiles strings as well as `.sdf`, `.mol2`, and `.pdb` files
| | |
|---|---|
| [Tutorial](http://graphein.ai/notebooks/molecule_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.molecule.html) |
| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/molecule_tutorial.ipynb) | |
```python
from graphein.molecule.config import MoleculeGraphConfig
from graphein.molecule.graphs import construct_graph
g = create_graph(smiles="CC(=O)OC1=CC=CC=C1C(=O)O", config=config)
```
### Creating an RNA Graph
| | |
|---|---|
|[Tutorial](http://graphein.ai/notebooks/rna_notebooks.html) | [Docs](http://graphein.ai/modules/graphein.rna.html) |
|[](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/rna_graph_tutorial.ipynb) | |
```python
from graphein.rna.graphs import construct_rna_graph
# Build the graph from a dotbracket & optional sequence
rna = construct_rna_graph(dotbracket='..(((((..(((...)))..)))))...',
sequence='UUGGAGUACACAACCUGUACACUCUUUC')
```
### Creating a Protein-Protein Interaction Graph
| | |
|---|---|
| [Tutorial](http://graphein.ai/notebooks/ppi_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.ppi.html) |
| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/ppi_graph.ipynb)|
```python
from graphein.ppi.config import PPIGraphConfig
from graphein.ppi.graphs import compute_ppi_graph
from graphein.ppi.edges import add_string_edges, add_biogrid_edges
config = PPIGraphConfig()
protein_list = ["CDC42", "CDK1", "KIF23", "PLK1", "RAC2", "RACGAP1", "RHOA", "RHOB"]
g = compute_ppi_graph(config=config,
protein_list=protein_list,
edge_construction_funcs=[add_string_edges, add_biogrid_edges]
)
```
### Creating a Gene Regulatory Network Graph
| | |
|---|---|
|[Tutorial](http://graphein.ai/notebooks/grn_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.grn.html) |
| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/grn_tutorial.ipynb) |
```python
from graphein.grn.config import GRNGraphConfig
from graphein.grn.graphs import compute_grn_graph
from graphein.grn.edges import add_regnetwork_edges, add_trrust_edges
config = GRNGraphConfig()
gene_list = ["AATF", "MYC", "USF1", "SP1", "TP53", "DUSP1"]
g = compute_grn_graph(
gene_list=gene_list,
edge_construction_funcs=[
partial(add_trrust_edges, trrust_filtering_funcs=config.trrust_config.filtering_functions),
partial(add_regnetwork_edges, regnetwork_filtering_funcs=config.regnetwork_config.filtering_functions),
],
)
```
## Installation
### Pip
The simplest install is via pip. *N.B this does not install ML/DL libraries which are required for conversion to their data formats and for generating protein structure meshes with PyTorch 3D.* [Further details](http://graphein.ai//getting_started/installation.html)
```bash
pip install graphein # For base install
pip install graphein[extras] # For additional featurisation dependencies
pip install graphein[dev] # For dev dependencies
pip install graphein[all] # To get the lot
```
However, there are a number of (optional) utilities ([DSSP](https://anaconda.org/salilab/dssp), [PyMol](https://pymol.org/2/), [GetContacts](https://getcontacts.github.io/)) that are not available via PyPI:
```
conda install -c salilab dssp # Required for computing secondary structural features
conda install -c schrodinger pymol # Required for PyMol visualisations & mesh generation
# GetContacts - used as an alternative way to compute intramolecular interactions
conda install -c conda-forge vmd-python
git clone https://github.com/getcontacts/getcontacts
# Add folder to PATH
echo "export PATH=\$PATH:`pwd`/getcontacts" >> ~/.bashrc
source ~/.bashrc
To test the installation, run:
cd getcontacts/example/5xnd
get_dynamic_contacts.py --topology 5xnd_topology.pdb \
--trajectory 5xnd_trajectory.dcd \
--itypes hb \
--output 5xnd_hbonds.tsv
```
### Conda environment
The dev environment includes GPU Builds (CUDA 11.1) for each of the deep learning libraries integrated into graphein.
```bash
git clone https://www.github.com/a-r-j/graphein
cd graphein
conda env create -f environment-dev.yml
pip install -e .
```
A lighter install can be performed with:
```bash
git clone https://www.github.com/a-r-j/graphein
cd graphein
conda env create -f environment.yml
pip install -e .
```
### Dockerfile
We provide two `docker-compose` files for CPU (`docker-compose.cpu.yml`) and GPU usage (`docker-compose.yml`) locally. For GPU usage please ensure that you have [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed. Ensure that you install the locally mounted volume after entering the container (`pip install -e .`). This will also setup the dev environment locally.
To build (GPU) run:
```
docker-compose up -d --build # start the container
docker-compose down # stop the container
```
## Citing Graphein
Please consider citing graphein if it proves useful in your work.
```bibtex
@inproceedings{jamasb2022graphein,
title={Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks},
author={Arian Rokkum Jamasb and Ramon Vi{\~n}as Torn{\'e} and Eric J Ma and Yuanqi Du and Charles Harris and Kexin Huang and Dominic Hall and Pietro Lio and Tom Leon Blundell},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=9xRZlV6GfOX}
}
```
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We provide compatibility with standard PyData formats, as well as graph objects designed for ease of use with popular deep learning libraries.\n\n## What's New?\n\n| | | |\n|---|---|---|\n| 1.7.0 | [FoldComp Datasets](http://graphein.ai/notebooks/foldcomp.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/foldcomp.ipynb) |\n| 1.7.0 | [Creating Datasets from the PDB](http://graphein.ai/notebooks/creating_datasets_from_the_pdb.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/creating_datasets_from_the_pdb.ipynb) |\n| 1.6.0 | [Protein Tensor Module](http://graphein.ai/notebooks/protein_tensors.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/protein_tensors.ipynb) |\n| 1.5.0 | [Protein Graph Creation from AlphaFold2!](http://graphein.ai/notebooks/alphafold_protein_graph_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) |\n| 1.5.0 | [RNA Graph Construction from Dotbracket notation](http://graphein.ai/modules/graphein.rna.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/rna_graph_tutorial.ipynb) |\n| 1.4.0 | [Constructing molecular graphs](http://graphein.ai/notebooks/molecule_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/molecule_tutorial.ipynb) |\n| 1.3.0 | [Ready-to-go Dataloaders for PyTorch Geometric](http://graphein.ai/notebooks/dataloader_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/dataloader_tutorial.ipynb) |\n| 1.2.0 | [Extracting subgraphs from protein graphs](http://graphein.ai/notebooks/subgraphing_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/subgraphing_tutorial.ipynb) |\n| 1.2.0 | [Protein Graph Analytics](http://graphein.ai/notebooks/protein_graph_analytics.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/protein_graph_analytics.ipynb) |\n| 1.2.0 | [Graphein CLI](http://graphein.ai/getting_started/usage.html) | |\n| 1.2.0 |[Protein Graph Visualisation!](http://graphein.ai/notebooks/interactive_plotly_example.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/interactive_plotly_example.ipynb)\n| 1.1.0 | [Protein - Protein Interaction Network Support & Structural Interactomics (Using AlphaFold2!)](http://graphein.ai/notebooks/ppi_tutorial.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/ppi_graph.ipynb) |\n| 1.0.0 | [High and Low-level API for massive flexibility - create your own bespoke workflows!](http://graphein.ai/notebooks/residue_graphs.html) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) |\n\n## Example usage\n\nGraphein provides both a programmatic API and a command-line interface for constructing graphs.\n\n### CLI\n\nGraphein configs can be specified as `.yaml` files to batch process graphs from the commandline.\n\n[Docs](http://graphein.ai/getting_started/usage.html)\n\n```bash\ngraphein -c config.yaml -p path/to/pdbs -o path/to/output\n```\n\n### Creating a Protein Graph\n\n| | | |\n|---|---|---|\n[Tutorial (Residue-level)](http://graphein.ai/notebooks/residue_graphs.html) | [Tutorial (Atomic)](http://graphein.ai/notebooks/atom_graph_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.graphs)\n| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) | [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/atom_graph_tutorial.ipynb) | |\n\n```python\nfrom graphein.protein.config import ProteinGraphConfig\nfrom graphein.protein.graphs import construct_graph\n\nconfig = ProteinGraphConfig()\ng = construct_graph(config=config, pdb_code=\"3eiy\")\n```\n\n### Creating a Protein Graph from the AlphaFold Protein Structure Database\n\n| | |\n|---|---|\n| [Tutorial](http://graphein.ai/notebooks/alphafold_protein_graph_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.graphs) |\n| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/alphafold_protein_graph_tutorial.ipynb)|\n\n```python\nfrom graphein.protein.config import ProteinGraphConfig\nfrom graphein.protein.graphs import construct_graph\nfrom graphein.protein.utils import download_alphafold_structure\n\nconfig = ProteinGraphConfig()\nfp = download_alphafold_structure(\"Q5VSL9\", aligned_score=False)\ng = construct_graph(config=config, path=fp)\n```\n\n### Creating a Protein Mesh\n\n| | |\n|---|---|\n| [Tutorial](http://graphein.ai/notebooks/protein_mesh_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.meshes) |\n| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/protein_mesh_tutorial.ipynb) | |\n\n```python\nfrom graphein.protein.config import ProteinMeshConfig\nfrom graphein.protein.meshes import create_mesh\n\nverts, faces, aux = create_mesh(pdb_code=\"3eiy\", config=config)\n```\n\n### Creating Molecular Graphs\n\nGraphein can create molecular graphs from smiles strings as well as `.sdf`, `.mol2`, and `.pdb` files\n\n| | |\n|---|---|\n| [Tutorial](http://graphein.ai/notebooks/molecule_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.molecule.html) |\n| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/molecule_tutorial.ipynb) | |\n\n```python\nfrom graphein.molecule.config import MoleculeGraphConfig\nfrom graphein.molecule.graphs import construct_graph\n\ng = create_graph(smiles=\"CC(=O)OC1=CC=CC=C1C(=O)O\", config=config)\n\n```\n\n### Creating an RNA Graph\n\n| | |\n|---|---|\n|[Tutorial](http://graphein.ai/notebooks/rna_notebooks.html) | [Docs](http://graphein.ai/modules/graphein.rna.html) |\n|[](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/rna_graph_tutorial.ipynb) | |\n\n```python\nfrom graphein.rna.graphs import construct_rna_graph\n# Build the graph from a dotbracket & optional sequence\nrna = construct_rna_graph(dotbracket='..(((((..(((...)))..)))))...',\n sequence='UUGGAGUACACAACCUGUACACUCUUUC')\n```\n\n### Creating a Protein-Protein Interaction Graph\n\n| | |\n|---|---|\n| [Tutorial](http://graphein.ai/notebooks/ppi_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.ppi.html) |\n| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/ppi_graph.ipynb)|\n\n```python\nfrom graphein.ppi.config import PPIGraphConfig\nfrom graphein.ppi.graphs import compute_ppi_graph\nfrom graphein.ppi.edges import add_string_edges, add_biogrid_edges\n\nconfig = PPIGraphConfig()\nprotein_list = [\"CDC42\", \"CDK1\", \"KIF23\", \"PLK1\", \"RAC2\", \"RACGAP1\", \"RHOA\", \"RHOB\"]\n\ng = compute_ppi_graph(config=config,\n protein_list=protein_list,\n edge_construction_funcs=[add_string_edges, add_biogrid_edges]\n )\n```\n\n### Creating a Gene Regulatory Network Graph\n\n| | |\n|---|---|\n|[Tutorial](http://graphein.ai/notebooks/grn_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.grn.html) |\n| [](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/grn_tutorial.ipynb) |\n\n```python\nfrom graphein.grn.config import GRNGraphConfig\nfrom graphein.grn.graphs import compute_grn_graph\nfrom graphein.grn.edges import add_regnetwork_edges, add_trrust_edges\n\nconfig = GRNGraphConfig()\ngene_list = [\"AATF\", \"MYC\", \"USF1\", \"SP1\", \"TP53\", \"DUSP1\"]\n\ng = compute_grn_graph(\n gene_list=gene_list,\n edge_construction_funcs=[\n partial(add_trrust_edges, trrust_filtering_funcs=config.trrust_config.filtering_functions),\n partial(add_regnetwork_edges, regnetwork_filtering_funcs=config.regnetwork_config.filtering_functions),\n ],\n)\n```\n\n## Installation\n\n### Pip\n\nThe simplest install is via pip. *N.B this does not install ML/DL libraries which are required for conversion to their data formats and for generating protein structure meshes with PyTorch 3D.* [Further details](http://graphein.ai//getting_started/installation.html)\n\n```bash\npip install graphein # For base install\npip install graphein[extras] # For additional featurisation dependencies\npip install graphein[dev] # For dev dependencies\npip install graphein[all] # To get the lot\n```\n\nHowever, there are a number of (optional) utilities ([DSSP](https://anaconda.org/salilab/dssp), [PyMol](https://pymol.org/2/), [GetContacts](https://getcontacts.github.io/)) that are not available via PyPI:\n\n```\nconda install -c salilab dssp # Required for computing secondary structural features\nconda install -c schrodinger pymol # Required for PyMol visualisations & mesh generation\n\n# GetContacts - used as an alternative way to compute intramolecular interactions\nconda install -c conda-forge vmd-python\ngit clone https://github.com/getcontacts/getcontacts\n\n# Add folder to PATH\necho \"export PATH=\\$PATH:`pwd`/getcontacts\" >> ~/.bashrc\nsource ~/.bashrc\nTo test the installation, run:\n\ncd getcontacts/example/5xnd\nget_dynamic_contacts.py --topology 5xnd_topology.pdb \\\n --trajectory 5xnd_trajectory.dcd \\\n --itypes hb \\\n --output 5xnd_hbonds.tsv\n```\n\n### Conda environment\n\nThe dev environment includes GPU Builds (CUDA 11.1) for each of the deep learning libraries integrated into graphein.\n\n```bash\ngit clone https://www.github.com/a-r-j/graphein\ncd graphein\nconda env create -f environment-dev.yml\npip install -e .\n```\n\nA lighter install can be performed with:\n\n```bash\ngit clone https://www.github.com/a-r-j/graphein\ncd graphein\nconda env create -f environment.yml\npip install -e .\n```\n\n### Dockerfile\n\nWe provide two `docker-compose` files for CPU (`docker-compose.cpu.yml`) and GPU usage (`docker-compose.yml`) locally. For GPU usage please ensure that you have [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed. Ensure that you install the locally mounted volume after entering the container (`pip install -e .`). This will also setup the dev environment locally.\n\nTo build (GPU) run:\n\n```\ndocker-compose up -d --build # start the container\ndocker-compose down # stop the container\n```\n\n## Citing Graphein\n\nPlease consider citing graphein if it proves useful in your work.\n\n```bibtex\n@inproceedings{jamasb2022graphein,\n title={Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks},\n author={Arian Rokkum Jamasb and Ramon Vi{\\~n}as Torn{\\'e} and Eric J Ma and Yuanqi Du and Charles Harris and Kexin Huang and Dominic Hall and Pietro Lio and Tom Leon Blundell},\n booktitle={Advances in Neural Information Processing Systems},\n editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},\n year={2022},\n url={https://openreview.net/forum?id=9xRZlV6GfOX}\n}\n\n```\n",
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