biopax-explorer 1.1

A python library for BIOPAX manipulation

BIOPAX-Explorer

Features

A python library for BIOPAX manipulation

BIOPAX-Explorer is a Python package, designed to ease the manipulation of BIOPAX datasets. It exploits RDF and SPARQL, using a simple object-oriented, domain specific syntax. Biological Pathway Exchange (BioPAX) is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. BioPAX is defined in OWL and is represented in the RDF/XML format. using these specifications and formats, BIOPAX-Explorer provides a 100 % compatible object-model with advanced query features, enabling quick analysis of datasets with a low learning curve.

Documentation

The Python code documentation is available on https://fjrmoreews.github.io/biopax-explorer/

Installation

' pip install biopax-explorer '

For large files, use a triple store , do not use the 'direct file in memory' option . the processing time can be long and a lot of RAM is needed.

Prerequisites

The package relies mainly on rdfobj, RDFLib and NetworkX. Optionnaly you can install graph-tool for better performances on large graphs https://graph-tool.skewed.de/static/doc/quickstart.html.

Source repository

git repository

Docker installation

ready to use Docker container

We provide a easy to use Docker installation,tested with Docker on Linux and Singularity

The Biopax-Explorer Docker container is available at https://hub.docker.com/r/fjrmore/biopax_explorer

The container can be used to deploy a jupyter enviroment with tutorials and example notebooks

A easy way to test Biopax Explorer is to use docker compose :

# at the root of the repository
docker-compose up
#the triple store (fuseki) will be available on http://localhost:3030/  (login/password admin/admin)
#Jupyter will be available on http://localhost:8888/  (password : pass)

#contain of the docker-compose.yml file:

  biopax-explorer:

    image: fjrmore/biopax_explorer

    volumes:
      - ./script/:/work/script
      - ./input:/work/input
    
    links:
      - "db:tstore"
    ports:
      - "8888:8888"
    command: ["jupyter", "notebook", "--port=8888", "--no-browser", "--ip=0.0.0.0", "--allow-root","--NotebookApp.token='pass'" ]


  db:

    image: stain/jena-fuseki:4.0.0
 
    ports:
      - "3030:3030"
    volumes:
      - ./input/:/staging
      - ./db/fuseki:/fuseki
      - ./script/:/script
    environment: 
      - JVM_ARGS=-Xmx5024M
      - ADMIN_PASSWORD=admin

Linux and Mac installation (Ubuntu and Debian)

pip install biopax-explorer

Windows installation

Installation on windows is possible using at least 2 options :

Installation on Windows using Docker-desktop

This procedure has been tested on Windows 10 Enterprise. first install Docker-desktop : see https://docs.docker.com/docker-for-windows/install/

when Docker-desktop is installed, open a power-shell terminal, then entre the followig commands:

• Biopax-Explorer in Python scripts

$vol= (pwd).Path

docker run -p 81:8888 -p 6006:6006 -it  -v ${vol}:/home/user fjrmore/Biopax-Explorer  python3 -c "import Biopax-Explorer; print('package p2g OK')"

curl https://gitlab.inria.fr/fmoreews/Biopax-Explorer/-/raw/master/test/test.py -o test.py
docker run -p 81:8888 -p 6006:6006 -it   -v ${vol}:/home/user fjrmore/Biopax-Explorer   python3 user/test.py

#create your own script (mycript.py) , go in the directory where your script is located and execute it :.
cd script_dir
$vol= (pwd).Path
docker run -p 81:8888 -p 6006:6006 -it   -v ${vol}:/home/user fjrmore/Biopax-Explorer  python3 mycript.py

How to run power-shell : see this link

• Biopax-Explorer in jupyter lab

$vol= (pwd).Path
docker run -p 81:8888 -p 6006:6006 -it   -v ${vol}:/home/user -e SHOW_PWD=true -e JUPYTER_PWD=password fjrmore/Biopax-Explorer  jupyter.sh

then launch a browser and go to the following url : http://localhost:81/lab

and enter the password (password)

Installation on Windows using ubuntu terminal

See https://www.microsoft.com/en-us/p/ubuntu-2004-lts/9n6svws3rx71?activetab=pivot:overviewtab

After you're environment configuration, follow the Docker or Conda + pip installation process (see above).