Sage-Bionetworks/synapsegraphdb

Name: synapsegraphdb

Owner: Sage Bionetworks

Description: null

Created: 2016-07-21 16:21:38.0

Updated: 2017-05-30 17:23:27.0

Pushed: 2017-05-31 17:09:43.0

Homepage: null

Size: 101

Language: Python

GitHub Committers

UserMost Recent Commit# Commits

Other Committers

UserEmailMost Recent Commit# Commits

README

Introduction

Synapse provides a means of recording provenance as a graph, thus enabling a formal way of documenting work performed and the ability to assign credit to a specific Synapse user for performing it. However, the current implementation does not provide mechanisms to search or discover structures in the provenance graph across different activities.

This repository provides the mechanisms for loading Synapse provenance information into a graph database, which allows data to be organized such that relationships are prioritized. Those relationships can be exploited through queries that consider the nodes and the connections between them. By loading this information regarding into a graph database, users are empowered with a flexible means of tracking, searching, and visualizing provenance.

Here, we use the Neo4j graph database.

For all questions, suggestions, or inquiries, please open an issue.

Installation

This repository contains a Python requirements.txt file with a list of packages to be installed using pip. To install these dependencies, use pip install -r requirement.txt.

Download Neo4j for free from https://neo4j.com/download/, follow their online instructions to access the Neo4j browser.

Users must have Neo4j installed on a local or remote machine with their login information contained in a json file as follows:


"machine": ?your-machine?,
"username": ?your-username?,
"password": ?your-password?

Users must also have an active Synapse account.

Usage

The scripts in this repository are used to load data from any Synapse project to your graph database.

See examples of useful cypher queries here.


This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.