1. Importing linked life science databases into
Neo4j
Simon Jupp
Sample Phenotypes and Ontologies Team
European Bioinformatics Institute
jupp@ebi.ac.uk

2. Purpose of the workshop
• Introduce two alternate graph models
• RDF graphs
• Property graph
• Demonstrate a simple data integration use-case
• Show how Neo4j data import tools can be used to rapidly
import life science data from public APIs
• Example Cypher for querying biological data
• Introduction to Neo4j sandboxes, Apoc procedures and
tips for creating your own Neo4j guide

3. Some biological questions
“Differentially expressed genes in adult mice, bred in oxygen rich vs
oxygen poor environments? Of this set, which biological processes
(GO) are enriched?”
“Where are genes with antigen binding function differentially
expressed, which disease and which associated pathways?”
“Get metformin associated pathways with differentially expressed
genes, find any proteins that are targets for known diabetes drugs”
How do you go about answering these kinds of questions?...

4. … you go to the data
Literature & ontologies
•Experimental Factor Ontology
•Gene Ontology
•BioStudies
•Europe PMC
Chemical biology
•ChEBI
•ChEMBL
•SureChEMBL
Molecular structures
•Protein Data Bank in Europe
•Electron Microscopy Data Bank
Gene, protein & metabolite expression
•Expression Atlas
•Metabolights
•PRIDE
•RNA Central
Protein sequences,
families & motifs
•InterPro
•Pfam
•UniProt
Genes, genomes & variation
•Ensembl
•Ensembl Genomes
•GWAS Catalog
•Metagenomics portal
Systems
•BioModels
•BioSamples
•Enzyme Portal
•IntAct
•Reactome
Molecular Archives
•European Nucleotide Archive
•European Variation Archive
•European Genome-phenome Archive
•ArrayExpress

5. Data integration challenges
• Heterogeneous formats and identifiers
• We invest heavily in mapping and cross-linking
resources, but it’s still hard to integrate and query across
internal/external resources.
• Lots of effort doing mapping, each groups duplicate these
efforts

6. Standardise data publishing
• What is we could standardise the way we publish data?
• Global identification systems (so we can identify the things
in our data)
• Common semantics (talking about the same things)
• A common query language to the data

7. Original vision of the Web
Information Management: A Proposal, Tim Berners-Lee, CERN, March 1989, May 1990,
http://www.w3.org/History/1989/proposal.html
Relations
“Things”
Vocabularies
Early Web

8. Semantic Web ( or Linked Data)
"The Semantic Web is a webby way to
link data"
“Turning the web into a global API”
“The existing web links documents, the
semantic web links data”
“Shared meaning through ontologies”
The Linking Open Data cloud 2017
http://lod-cloud.net

9. RDF is for describing graphs
• 1995-2004 W3C develop specification for a vocabulary
for Web meta-data called Resource Description
Framework (RDF)
http://en.wikipedia.org/wiki/Barack_Obama
Web
Document
Structured
dataPublishing data as a graph
dbpedia:Barack_Obama
Human President of the United States
Honolulu
1961-08-04
birthplace
birthdate
position_held
type

10. Anatomy of a triple statement
• All triples are composed of a subject, predicate and an
object
Barack Obama
Honolulu
birth place
Subject
Predicate
Object

11. Identify things on the web
• Build on existing Web technology
• global identifiers for resources (things) using URIs
• URIs should resolve
http://dbpedia.org/page/Barack_Obama
http://dbpedia.org/page/Honolulu
http://dbpedia.org/property/birthPlace
Subject
Predicate
Object

12. Turning relational data to RDF –
EBI Gene Expression Atlas database
Relational Data to RDF graph conversion
•Give “things” URIs
•Type “things” with ontologies
•Link “things” to other related “things”

13. Stardog
Apache Jena
SesameVirtuoso
Allegrograph
OWLIM
Storing and querying RDF
• Optimized databases for RDF data
• SPARQL query language

14. Querying RDF with SPARQL
• W3C standard query language for querying RDF data
• Query language for matching graph patterns in RDF
• SPARQL endpoints – common API to query RDF data
• ”Get all presidents of the united states?” from
https://query.wikidata.org/
PREFIX position_held: http://www.wikidata.org/prop/direct/P39
PREFIX potus: http://www.wikidata.org/entity/Q11696
SELECT ?label WHERE {
?subject position_held: potus: .
?subject rdfs:label ?label .
filter (lang(?label) = "en")
}

15. RDF and the Property graph
RDF graphs
dbpedia:Barack_Obama
Human President of the United States
Honolulu
“1961-08-04”xsd:datetime
birthplace
birthdate
position_held
type
Every statement adds a new edge to the graph
All nodes are resources (with URIs) or literals (with types)
Property graphs (Neo4j)
“Barack_Obama”xsd:string
name
dbpedia:Barack_Obam
a
{
name: “Barack Obama”
Type: “Human”
}
Nodes and edges have internal structure
Honolulu
Birthplace
{ Birthdate: “1961-08-04” }

16. Working with RDF and Neo4j
• RDF great for publishing data
• SPARQL gives flexible access to query data
• But…
• RDF schemas are often (necessarily) complex
• Expose the full underlying data semantics
• RDF comes with baggage that can be turn off for newomers
• Neo4j is for graphs
• Easier to grasp for begniners
• Powerful query language (Cypher)
• Excellent third-party tools, community and developer
integrations

17. Working with RDF and Neo
• In this tutorial we will harness the publishing power of
RDF
• Combine with the simplicity and querying power of Neo4j
• Use Neo4j data import tools to rapidly import data from
public SPARQL endpoints
• Simplify the graph schema to fit a specific use case

18. Use-case
• Build a simple graph of gene-disease and drug-disease
associations
• Data from public resources (Ensembl, GWAS, ChEMBL)

19. Setup for workshop
• Sandbox Neo4j instance from https://neo4j.com/sandbox-
v2/
• Optionally run your own local installation, but you’ll need
Apoc procedures installed to run
• Run the Neo4j guide
:play https://guides.neo4j.com/life-science-import