Metadata-Version: 2.1
Name: graphein
Version: 1.0.6
Summary: Protein & Interactomic Graph Construction for Machine Learning
Home-page: https://github.com/a-r-j/graphein
Author: Arian Jamasb
Author-email: arian@jamasb.io
License: MIT
Description: 
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        <br></br>
        
        [Documentation](http://www.graphein.ai) | [Paper](https://www.biorxiv.org/content/10.1101/2020.07.15.204701v1) | [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?
        * [Protein Graph Creation from AlphaFold2!](http://graphein.ai/notebooks/alphafold_protein_graph_tutorial.html)
        * [Protein Graph Visualisation!](http://graphein.ai/notebooks/protein_mesh_tutorial.html)
        * [RNA Graph Construction from Dotbracket notation](http://graphein.ai/modules/graphein.rna.html)
        * [Protein - Protein Interaction Network Support & Structural Interactomics (Using AlphaFold2!)](http://graphein.ai/notebooks/ppi_tutorial.html)
        * [High and Low-level API for massive flexibility - create your own bespoke workflows!](http://graphein.ai/notebooks/residue_graphs.html)
        
        ## Example usage
        ### Creating a Protein Graph
        [Tutorial (Residue-level)](http://graphein.ai/notebooks/residue_graphs.html) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) | [Tutorial - Atomic](http://graphein.ai/notebooks/atom_graph_tutorial.html) [![Open In Colab(https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/atom_graph_tutorial.ipynb) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.graphs)
        
        ```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
         [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/residue_graphs.ipynb) [Tutorial](http://graphein.ai/notebooks/alphafold_protein_graph_tutorial.html) | [Docs](http://graphein.ai/modules/graphein.protein.html#module-graphein.protein.graphs)
        ```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, pdb_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)
        ```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 an RNA Graph
        Tutorial | [Docs](http://graphein.ai/modules/graphein.rna.html)
        ```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)
        ```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)
        ```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]
        ```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 also provide a [Dockerfile](https://github.com/a-r-j/graphein/pull/69)
        
        ## Citing Graphein
        
        Please consider citing graphein if it proves useful in your work.
        
        ```
        @article{Jamasb2020,
          doi = {10.1101/2020.07.15.204701},
          url = {https://doi.org/10.1101/2020.07.15.204701},
          year = {2020},
          month = jul,
          publisher = {Cold Spring Harbor Laboratory},
          author = {Arian Rokkum Jamasb and Pietro Lio and Tom Blundell},
          title = {Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures}
        }
        ```
Platform: any
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: extras
Provides-Extra: all
