Skip to content

Latest commit

 

History

History
86 lines (69 loc) · 1.61 KB

README.md

File metadata and controls

86 lines (69 loc) · 1.61 KB

Graph Data Generation and Load into Neo4J

Pre-requisites

  • JDK 8

    • Install JDK 1.8 from Oracle
    • Set JAVA_HOME On MacOS, you can do: 
       export JAVA_HOME="$(/usr/libexec/java_home -v 1.8)"

  • Python 3

    • On MacOS install with 
       brew install python3

  • Install Neo4J

    • On MacOS install with

       brew install neo4j

    • Setup NEO4J_HOME Point NEO4J_HOME to the root of the NEO4J directory.

       export NEO4J_HOME=/usr/local/Cellar/neo4j/3.2.2/libexec

Generate Data

  • Clone the repository

     git clone https://github.com/nmdp-bioinformatics/grimm`
 cd grimm

  • Setup Python3 virtual environment Make sure virtualenv is installed.

    pip3 install virtualenv

    Create Virtual Environment

    virtualenv -p python3 venv
source venv/bin/activate

    Install pandas library

    pip3 install pandas

  • Download and prepare wmda data. Python script downloads reference wmda data and untars it in wmda directory

     cd graph_generator/data
 python wmda_download.py

  • Generate nodes/edges/toplinks from the reference wmda data. The freqs file is converted to HPF format first.

     cd ..
 python wmda_to_hpf_csv.py
 python generate_neo4j_wmda_hpf.py

  • Generated nodes and edges files are in the output/csv directory.

     output
 └── csv
     ├── edges.csv
     ├── nodes.csv
     └── top_links.csv

  • Load the nodes/graph into Neo4J database

     ./bulk_load_neo4j.sh

  • Login to Neo4j http://localhost:7474/

    • Default username/password is neo4j/ontological