Biological data models in InterMine + RDF-vs-Property Graph Alternative Facts

This month we'll have two talks by Daniela Butano from Intermine and Neo4j's Jesus Barrasa. Daniela will explain how Intermine use graph databases to explore biological data models and Jesus will debunk some RDF-vs-Property Graph fake news.

Please make sure you sign up on the Skillsmatter page as well (https://skillsmatter.com/meetups/9734-neo4j-june-meetup)for quick entry into the venue.

Exploring graph databases for biological data models in InterMine
Daniela Butano, Software Engineer at University of Cambridge

InterMine is an open source data warehouse built for the integration and analysis of large-scale biological datasets.

Developed at the University of Cambridge since 2002, InterMine currently has dozens of instances around the world covering a broad range of biomedically-relevant organisms, bacteria, and plant life.

InterMine provides parsers for integrating data from many common biological data sources and formats, an attractive and user-friendly web interface enabling researchers to answer sophisticated biological questions, and a public RESTful web-service API to allow programmatic access to the data.InterMine is based on the open source RDBMS PostgreSQL, which forces all data to be modelled in tables; graph databases seem more suited to naturally modelling the network shape of biological data.

We have imported FlyMine, an integrated database for Drosophila and Anopheles genomics, into Neo4j before starting benchmarking, prompting us to realize the importance of re-thinking our data model; for instance some associative tables have disappeared, replaced by Neo4j relationships, and multiple label support has significantly reduced the amount of data stored.

We really liked how our model could be represented as a graph structure in an intuitive way that is easy to browse.

Debunking some RDF-vs-PropertyGraph Alternative Facts
Jesus Barrasa, Developer Relations Engineer at Neo4j

Is RDF for unstructured data while property graphs are for highly structured data?

Will the RDF model discover new knowledge for me? Is RDF AI? Does RDF exclusively live in triple stores?

All of these are statements have been published by analysts and vendors, building a wall of misconceptions between the two worlds that are not helpful for your new graph project. In this talk, we will dig deeper into the similarities and differences between the two main approaches to modelling graph data, focusing on debunking some of the ‘alternative facts’ built over the years.