Bio ontologies and semantic technologies[2]

Introduction to Bio Ontologies
and The Semantic Web
M. Devisscher
Biological Databases

Overview
• Bio ontologies
• Semantic technologies
• SPARQL in practice

Introduction
• Ontologies: what are ontologies ?
• Ontologies in the bio domain: OBO Foundry
• Ontologies in the semantic web
• OBO
• RDF, IRI, TTL, SPARQL, OWL

What is an ontology ?
• Ontology = a specification of a
conceptualization (Gruber 1993)
• In practice: controlled vocabularies
– Disambiguation (e.g. Bank, Running)
– Language/species independence
• Very useful in biology – complex hierarchies of
terms

Ontologies in the bio Domain
• OBO Foundry - open Biological and
Biomedical Ontologies
• Common principles
• List of ontologies at
http://www.obofoundry.org
• OBO is also a data format .obo

SideTrack – The Gene Ontology
• The mother of bio-ontologies: the GO
– Oldest bio – ontology
– Many practical applications:
• Cross species studies
• Overrepresentation studies (RNASeq)
• GO is an OBO ontology

• Collection of terms

• Relationships between terms:
– Subsumption: is_a
– Partonomic: part_of
• These terms are transitive
• Terms form a DAG (directed, acyclic graph)
• Some information can be inferred

• Know more: www.geneontology.org
• AMIGO : the GO browser

Gene Ontology Annotation
• Gene ontology annotations GOA = entities
labeled with GO terms
– E.g. Uniprot-GOA

Semantic Technologies
• The semantic web: Tim Berners Lee et al,
Scientific American 2001

• W3C: a set of specifications
http://www.w3.org/standards/semanticweb/
• A mature toolset
– Dedicated data formats
– Storage
– Query language

Resource Description Framework
• A standard model for data interchange on the
(semantic) web
• Basic data element = a Triple
– A mini sentence
– Contains three Terms:
• Subject Predicate Object

• Representation of triples
– Basic data format: RDF/XML
– All data expressed in RDF (Resource Description
Framework)
– Several compatible syntaxes: TTL (Terse Triple
Language) most human readable
Resource Description Framework

The Turtle Syntax
• Basic Triple
<http://bioinformatics.be/entities#martijn>
<http://bioinformatics.be/relations#has_favorite_beer>
<http://bioinformatics.be/entities#karmeliet>.

The Turtle Syntax
• Prefix
@prefix b4x: <http:bioinformatics.be/terms#>
b4x:martijn b4x:has_favorite_beer b4x:karmeliet.

The Turtle Syntax
• Predicate lists
@prefix b4x: <http:bioinformatics.be/terms#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
b4x:martijn b4x:has_favorite_beer b4x:karmeliet;
foaf:name “Martijn Devisscher”.

The Turtle Syntax
• Object lists
b4x:martijn b4x:has_favorite_beer b4x:karmeliet,
b4x:chimay_blauw;
foaf:name “Martijn Devisscher”.

IRI’s and Literals
• Terms can be either IRI’s, Literals or blank nodes
• IRI = Internationalized Resource Identifier
• Unique id – a virtual URI
– Example: <http://bioinformatics.be/terms#martijn>
– There is no requirement for resolving
– Now: Open Data initiatives: please do use resolvable
URI’s http://linkeddata.org
– Unique identifiers can be registered on
http://identifiers.org

Introduction
• Literals: can be typed, allowed types from the
XSD namespace:
– E.g. “This is a string example”^^xsd:string
– E.g. “5”^^xsd:integer
• IRI’s are used for entities and attributes
• Literals are used for attribute values that
aren’t entities

The Turtle Syntax
• Typed literals
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
b4x:chimay_blauw;
b4x:length “184”^^xsd:integer;
foaf:name “Martijn Devisscher”^^xsd:string.

The Turtle Syntax
• Blank nodes
b4x:chimay_blauw;
b4x:length “184”^^xsd:integer;
foaf:name “Martijn Devisscher”^^xsd:string;
b4x:owns_cat [ b4x:color “Gray” ].

Classes and Individuals
• rdf:type
b4x:martijn rdf:type foaf:Person.

Classes and Individuals
• Shorthand: a
b4x:martijn a foaf:Person;
foaf:knows b4x:geert.
b4x:geert a foaf:Person.

Example
<http://xmpl/entities#martijn>
<http://xmpl/relations#has_favorite_beer>
<http://xmpl/entities#karmeliet>.

• Sets of triples form a Graph

Graphs
• Triples are building blocks of Graphs
• Combining sets of triples allows the
construction of arbitrarily complex graphs
b4x:martijn b4x:karmeliethas_favorite_beer

Graph of graphs
• Combining RDF datasets can be considered a
slightly bigger graph, a knowledge cloud
• Key is interoperability: same format used for
disclosing information, independent of
backend

RDF dataset examples
• The LOD cloud http://lod-cloud.net : overview
of interlinked, RDF compatible datasources

RDF dataset examples
• Closer to home
https://data.stad.gent/devzone/docs/linked-
open-data

Add meaning !
• Reuse terms from existing, well defined
vocabularies – ontologies (foaf, dc, go, so)
• Describe new terms = Ontologies
• Contain
– A crisp human definition
– Some machine readable facts

Metadata
• Ontologies are also described in RDF
– RDFS: RDF - Schema
– OWL: Web Ontology Language
– Also expressed in RDF
• For clarity, file extension can be .rdfs or .owl

RDFS Essentials
• Descriptions
– rdfs:label
– rdfs:comment

RDFS
• Relationships between properties, classes
– rdfs:Class
– rdfs:subClassOf
– rdf:Property
– rdfs:subPropertyOf
– rdfs:range
– rdfs:domain

RDFS: Example
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
b4x:karmeliet a b4x:Tripel .
b4x:Beer a rdfs:Class .
b4x:Tripel a rdfs:Class .
b4x:Tripel rdfs:subClassOf b4x:Beer .
b4x:has_favorite_beer a rdf:Property ;
rdfs:domain foaf:Person ;
rdfs:range b4x:Beer .
b4x:Beer rdfs:subClassOf b4x:Drink .

Analogy
• RDF = database = data
• RDFS/OWL = schema = metadata
• Both are described in RDF, but have a different
scope

• Inference
– Enhance dataset using knowledge from metadata
(e.g. rdfs, owl)
• Types of inference engines
– RDFS inference
• RDFS entailment regime
– OWL inference
• Under active research
• Engines exist for specific subsets of OWL (OWL-DL)

RDFS: Inference
b4x:kevin b4x:has_favorite_beer b4x:stella
Q: What can we infer from this using RDFS
entailment ?

RDFS: Inference
b4x:kevin b4x:has_favorite_beer b4x:stella
Inferred triples:
b4x:kevin a foaf:Person [from domain]
b4x:stella a b4x:Beer [from range]
b4x:stella a b4x:Drink [from subClassOf]

DuckTyping
• Watch out with inference !
Example: You want to express that people can
have lengths
b4x:length a rdf:Property;
rdfs:domain foaf:Person;
rdfs:range xsd:integer.

DuckTyping
• Problem:
ex:VW_Transporter b4x:length “600”^xsd:integer.
• Would infer that VW_Transporter is a Person !
• This is called DuckTyping
If it looks like a duck, swims like a duck, and
quacks like a duck, then it probably is a duck

Task
• Find a solution: express in rdfs that people can
have lengths

Task
• Find a solution: express in rdfs that people can
have lengths
b4x:havingLenght a rdfs:Class.
b4x:length a rdf:Property;
rdfs:domain b4x:havingLength;
rdfs:range xsd:integer.
foaf:Person rdfs:subClassOf b4x:havingLength.

Storing RDF
• As an RDF file for download
• In a Triplestore
– Database optimised for storing triples
– Examples: BlazeGraph, Fuseki, Sesame

• Querying over RDF data: SPARQL
• Cool features:
– Distributed querying = actual distribution of data
and computing resources
– SPARQL/Update: modify data
• SPARQL endpoints: SPARQL over HTTP

SPARQL Query Syntax
• First example:
SELECT ?subject ?predicate ?object WHERE {
?subject ?predicate ?object.
}
(Generally not a good idea as it will pull down
the whole dataset)
Binding variables
Graph matching

?
SELECT ?person WHERE {
?person b4x:has_favorite_beer b4x:karmeliet
}

SPARQL Query Syntax
• Limit result size :
SELECT ?subject ?predicate ?object WHERE {
?subject ?predicate ?object.
} LIMIT 10

SPARQL Query Syntax
• Find all classes:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?class ?label WHERE {
?class a rdfs:Class.
?class rdfs:label ?label.
}
(This will only retrieve classes that have a label)

SPARQL Query Syntax
• Find all classes:
OPTIONAL {
}
}

SPARQL Query Syntax
• Find all classes that contain “duck” in the
label:
FILTER( CONTAINS (str(?label) , “duck” ) )
}

SPARQL Query Syntax
• Make it case insensitive:
FILTER( CONTAINS ( UCASE(str(?label)) , “DUCK” ) )
}

SPARQL Query Syntax
• Search in specific graph:
SELECT ?class ?label
FROM <http://example.org/animals>
WHERE {
}

SPARQL Query Syntax
• Search in specific graph:
GRAPH <http://example.org/animals> {
}
}

SPARQL Query Syntax
• Can also search for graphs :
SELECT ?g WHERE {
GRAPH ?g {
}
}

Summary: Querying RDF data
RDF Data
Inference
Engine
RDFS/OWL
RDF Data
Inferred
SPARQL
Endpoint

• Basic data element = a Triple
– A mini sentence
– Contains three Terms:
– Subject Predicate Object
• Example:
<http://xmpl/entities#martijn>
<http://xmpl/relations#has_favorite_beer>
<http://xmpl/entities#karmeliet>.
Take home Summary

• Combine triples to represent
knowledge

• Use terms from ONTOLOGIES
– COMMON VOCABULARIES
– POSSIBLE TO INFER
MEANING
• OMIABIS
• OBIB
• SNOMED/ICD
• MESH

?
• SPARQL searches for patterns

Interoperability between OBO and
• Originated from two separate academic worlds
• Computing applications of OBO mainly
consistency checking and overrepresentation
analysis
• Semantic Technologies: much broader toolset
• Interoperability ?
– Direct offering in both formats
– Automated mappings
• Migration towards semantic toolkits

Where to find ontologies
• OBO Foundry
• Bioportal; NCBO
• Biogateway
• Bio2RDF

Where to find RDF data
• Google for SPARQL endpoint
• => e.g. EBI databases
• Non biological: DBpedia

How about Tim Berners Lee’s vision
• We’re not there yet, but for bio data we’re
getting quite close
– The explicitome
– Crowd sourcing
– Nanopublications

SPARQL REFERENCE
http://www.w3.org/TR/sparql11-overview/
Recap:
SPARQL in 11 minutes

SPARQL : Recap
SELECT ?label
FROM <http://graphName> WHERE {
?x rdfs:label ?label.
FILTER ( CONTAINS(?label, “dimethylalinine”) )
} LIMIT 10 ORDER BY ?label

SPARQL : Recap
SELECT ?label
• FIND the pattern ?x rdfs:label ?label.

SPARQL : Recap
SELECT ?label
• BIND variables ?label, ?x

SPARQL : Recap
SELECT ?label
• RETRIEVE variable ?label

SPARQL : Recap
SELECT ?label
• PREFIX: replace rdfs:label by <http://www.w3.org/2000/01/rdf-schema#label>

SPARQL : Recap
SELECT ?label
• PREFIX: replace rdfs:label by <http://www.w3.org/2000/01/rdf-schema#>
• FILTER results to labels containing “dimethylalinine”

SPARQL : Recap
SELECT ?label
• PREFIX: replace rdfs:label by <http://www.w3.org/2000/01/rdf-schema#>
• FILTER results to labels containing “dimethylalinine”
• LIMIT results to first 10 matches ordered by label

SPARQL : Recap
DESCRIBE
<http://rdf.wikipathways.org/Pathway/WP1425_r74390/WP/Interaction/e077e>
• Useful short query to get direct links from/to a
given node

Running SPARQL
• From a web interface

• From a web interface
• Using http
– HTTP GET
– HTTP POST : for larger query strings
– Headers determine response type (JSON, XML, HTML)
http://…/sparql?default-graph-uri=<http://graphName>&query=URLENCODEDQUERYSTRING
Running SPARQL

Access
• From the web interface !
• SPARQL endpoint: using API key; on request
• Running a local copy: download VM image; on
request

Exercises
• Find a term
• Find ontologies containing a term
• Browse some ontologies
• Check the NCBO annotator !

gene
transcript
exon
ordered part
sio:SIO_001261
obo:SO_0000147
obo:SO_0000234
obo:SO_transcribed_from
faldo:location
obo:SO_0001217
obo:SO_has_part
location
faldo:location
location
faldo:location
location
obo:SO_translates_to
sio:SIO_000300
rank
translation
id
id
id
id
synonym
skos:altLabelxref
Simplified

Exercise
• From uniprot find proteins that are annotated
with a given Gene Ontology term

PREFIX up:<http://purl.uniprot.org/core/>
PREFIX taxon:<http://purl.uniprot.org/taxonomy/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>
PREFIX obo:<http://purl.obolibrary.org/obo/>
SELECT * WHERE {
?protein up:classifiedWith obo:GO_0004499.
?protein up:organism taxon:9606.
}
http://sparql.uniprot.org

Exercise
• From Expression Atlas find proteins that are
differentially expressed (P < 1e-12) in Crohn’s
disease

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX dcterms: <http://purl.org/dc/terms/>
PREFIX obo: <http://purl.obolibrary.org/obo/>
PREFIX sio: <http://semanticscience.org/resource/>
PREFIX efo: <http://www.ebi.ac.uk/efo/>
PREFIX atlas: <http://rdf.ebi.ac.uk/resource/atlas/>
PREFIX atlasterms: <http://rdf.ebi.ac.uk/terms/atlas/>
PREFIX up:<http://purl.uniprot.org/core/>
PREFIX biopax3:<http://www.biopax.org/release/biopax-level3.owl#>
SELECT distinct ?protein ?expressionValue ?pvalue WHERE {
?factor rdf:type efo:EFO_0000384 .
?value atlasterms:hasFactorValue ?factor .
?value atlasterms:isMeasurementOf ?probe .
?value atlasterms:pValue ?pvalue .
?value rdfs:label ?expressionValue .
?probe atlasterms:dbXref ?protein .
FILTER ( ?pvalue < 1e-12 )
FILTER ( strstarts(str(?protein),"http://purl.uniprot.org/uniprot/") )
}ORDER BY ASC (?pvalue)
https://www.ebi.ac.uk/rdf/services/atlas/sparql

• Links pathways with genes, terms from
Pathway, Cell line and Disease ontology,
PubMed references
• Models individual Interactions
• Can be downloaded as RDF
• Has an experimental SPARQL endpoint
WikiPathways

• Define a query to find pathways linked to
TNFalpha gene
Exercise

PREFIX wp: <http://vocabularies.wikipathways.org/wp#>
PREFIX dc: <http://purl.org/dc/elements/1.1/>
SELECT DISTINCT ?PathwayName where {
?geneProduct a wp:GeneProduct .
?geneProduct dc:identifier ?GeneID .
?geneProduct dcterms:isPartOf ?pathway .
?geneProduct rdfs:label ?geneName .
?pathway dc:identifier ?pathwayid .
?pathway dc:title ?PathwayName .
FILTER(str(?geneName) = "TNFalpha" )
}
http://sparql.wikipathways.org

• Try this, or another query
– Using web interface
– Using http get
• Define a simple describe
• Use a web tool to URLEncode the query
• Submit query as a URL parameter
Exercise

Phenotype
GDA
gene
sio:SIO_001121 ncit:C7057
sio:SIO_010056
sio:SIO_000628
id
id
skos:exactMatch
Mesh
DiseaseClass
sio:SIO_000628
HPO
score
ncit:C25338
ncit:C16612
sio:SIO_000628
id
skos:exactMatch
Simplified

• Find diseases linked to BRCA1
Exercise

PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX void: <http://rdfs.org/ns/void#>
PREFIX ncit: <http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#>
PREFIX up: <http://purl.uniprot.org/core/>
SELECT DISTINCT ?disease WHERE {
?gda a sio:SIO_000983.
?gda sio:SIO_000628 ?disease.
?disease a ncit:C7057.
?gda sio:SIO_000628 ?gene.
?gene a ncit:C16612.
?gene skos:exactMatch <http://identifiers.org/hgnc.symbol/BRCA1>}
http://rdf.disgenet.org/lodestar/sparql

SELECT DISTINCT ?disease WHERE {
?gda a [(rdfs:subClassOf)* sio:SIO_000983].
?gda sio:SIO_000628 ?disease.
?disease a ncit:C7057.
?gda sio:SIO_000628 ?gene.
?gene a ncit:C16612.
?gene skos:exactMatch <http://identifiers.org/hgnc.symbol/BRCA1>}

• Inference cannot be assumed on a SPARQL
endpoint => take care with defining queries
Why ?

• Define a query to find genes with important
link to Crohn’s disease (score > 0.35)
Exercise

SELECT DISTINCT ?gene WHERE {
?gda sio:SIO_000628 ?gene,?disease .
?gene a ncit:C16612 .
?gene skos:exactMatch ?GeneID .
?disease a ncit:C7057 .
?disease dcterms:title ?DiseaseName .
?gda sio:SIO_000216 ?scoreIRI .
?scoreIRI sio:SIO_000300 ?score .
FILTER (?score > "0.35"^^xsd:decimal)
FILTER (contains(str(?DiseaseName),"Crohn"))
}

• Define a query to find proteins related with
Cardio diseases
• Define a query to find the genomic location of
gene “TP53”
Exercise

select distinct ?id where {
?entry skos:exactMatch ?id.
?entry :isoform ?isoform.
?isoform :medical ?medical_annotation.
?medical_annotation :term ?term.
?term :related ?disease.
?disease a :MeshCv.
?disease rdfs:label ?label.
FILTER(CONTAINS(?label,"Cardio")).
}
https://snorql.nextprot.org/

select ?chrom ?start ?end where
{
?gene rdf:type :Gene.
?gene :name ?name.
?gene :chromosome ?chrom.
?gene :begin ?start.
?gene :end ?end.
FILTER (str(?name) = "TP53")
}
https://snorql.nextprot.org/

• Federated querying: include data from
another endpoint using the SERVICE keyword
• Example: find pathways (from wikipathways)
involving gene linked to Crohn’s disease (from
disgenet)
SPARQL and federated queries

PREFIX wp: <http://vocabularies.wikipathways.org/wp#>
PREFIX dc: <http://purl.org/dc/elements/1.1/>
http://rdf.disgenet.org/lodestar

SELECT DISTINCT ?PathwayName WHERE {
?gda sio:SIO_000628 ?gene, ?disease .
?gene a ncit:C16612 .
?disease a ncit:C7057 .
?disease dcterms:title ?DiseaseName .
?gda sio:SIO_000216 ?scoreIRI .
?scoreIRI sio:SIO_000300 ?score .
FILTER (?score > "0.35"^^xsd:decimal)
FILTER (contains(str(?DiseaseName),"Crohn"))
SERVICE <http://sparql.wikipathways.org/> {
?geneProduct a wp:GeneProduct .
?geneProduct dc:identifier ?gene .
?geneProduct dcterms:isPartOf ?pathway .
?pathway dc:identifier ?pathwayid .
?pathway dc:title ?PathwayName .
}
}

Application: BOINQ
• Framework for managing sequencing data
using semantic technologies
• Find it here :
https://github.com/mr-tijn/boinq2

Functionalities
• Uploader/converter
– Upload BED/GFF/VCF files
– Automatically translated into triples
– Stored in triplestore

Functionalities
• Query builder
– Visually build SPARQL queries that query your data
along with public data
– Store results as new graphs or download as CSV

Demo: query for finding first exons of
genes related to colon cancer

Bio ontologies and semantic technologies[2]

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