ontology databases: detecting inconsistencies in the gene ontology using not-gadgets

Ontology Databases:Detecting Inconsistencies in the Gene Ontology using

Not-gadgets

Paea LePenduUniversity of Oregon

Talk: National Center for Biomedical Ontology ◦ Stanford University ◦ September, 2009

General Interests

Programming Languages

Automated Reasoning

Databases

Logic

Outline

• Ontology-based Data Management– Background, Motivation– Theory– Benchmarking– Application Domain, Query Answering

• Inconsistency Detection– Theory– The serotonin example– GO plus ZFIN, MGI annotations

Ontology-based Database Integration:reducing database integration to ontology translation

Ontology-based Database Integration:reducing database integration to ontology translation

RelationAttributeDatatype

KeysConstraint

ViewTriggerTuple

Ontology-based Data Management

ClassPropertyDatatypeSubClass

RestrictionIndividual

Ontology-based Data Management

Ontology

RDBMS

User

RDBMSRDBMS

RDBMSRDBMS

Data Annotation Data Management

Data Access Layer

Example: sisters-siblings

All sisters are siblings.

Hilary and Lynn are sisters.

This is what we know :

This is what we want to know :

Who are siblings?

{ <Hilary, Lynn> }

Obviously, the answer should be :

Hilary and Lynn are siblings.

{ <x,y> | siblingOf(x,y) }

Example: sisters-siblings

Example: sisters-siblings

Example: The Gene OntologyGO_0003674

z01, z02, z03

GO_0005488e01,e02, e03

GO_0030528y01, y02, y03

GO_0003677x01, x02, x03

GO_0003700w01, w02, w03

GO_0003676c01, c02, c03

GO_0003723d01, d02, d03

GO_0008135a01, a02, a03

GO_0045182b01, b02, b03

Example: The Gene OntologyGO_0003674

z01, z02, z03

GO_0005488e01,e02, e03

GO_0030528y01, y02, y03

GO_0003677x01, x02, x03

GO_0003700w01, w02, w03

GO_0003676c01, c02, c03

GO_0003723d01, d02, d03

GO_0008135a01, a02, a03

GO_0045182b01, b02, b03

Ontology Databases:General Models for Database Designs

• Generality is important– Avoid rewriting

• Scalability of KB is important– Persistence, caching and indexing

• Major generic models– Horizontal Models– Vertical Models– Decomposition Storage Models

Ontology Databases: View-based Approach

CREATE VIEW v_Person(id) AS SELECT id FROM Person UNION SELECT id FROM v_Male UNION SELECT id FROM v_Female

Person

Female Male

[Pan & Heflin. DLDB: Extending Relational Databases to Support Semantic Web Queries. ISWC, 2003.]

v_Person

Person

P-0004

FemaleP-0002

MaleP-0001

P-0003

v_Female v_Male

Ontology Databases: Active Database Approach

Person

Female Male

[LePendu, et al. Ontology Database: a New Method for Semantic Modeling and an Application to Brainwave Data. SSDBM, 2008.]

Female Male

Person

ON INSERT into Male INSERT into Person

On INSERT into Female INSERT into Person

Ontology Databases: Active Database Approach

Person

Female Male Female MaleP-0001

Person

ON INSERT into Male INSERT into Person

On INSERT into Female INSERT into Person

Ontology Databases: Active Database Approach

Person

Female Male Female MaleP-0001

PersonP-0001

Ontology Databases: Active Database Approach

Person

Female Male FemaleP-0002

MaleP-0001

PersonP-0001

Ontology Databases: Active Database Approach

Person

Female Male FemaleP-0002

MaleP-0001

PersonP-0001P-0002

Ontology Databases: Active Database Approach

Person

Female Male FemaleP-0002

MaleP-0001P-0003

PersonP-0001P-0002

Ontology Databases: Active Database Approach

Person

Female Male FemaleP-0002

MaleP-0001P-0003

PersonP-0001P-0002P-0003

Ontology Databases: Active Database Approach

Person

Female Male FemaleP-0002

MaleP-0001P-0003

PersonP-0001P-0002P-0003P-0004

All sisters are siblings.

Hilary and Lynn are sisters.

This is what we know :

This is what we want to know :

Who are siblings?

Obviously, the answer should be :

Hilary and Lynn are siblings.

Example: sisters-siblings (revisited)

All sisters are siblings.

Hilary and Lynn are sisters.

This is what we know :

This is what we want to know :

Who are siblings?

Obviously, the answer should be :

Hilary and Lynn are siblings.

Example: sisters-siblings (revisited)SiblingOf

SisterOf

Hilary Lynn

All sisters are siblings.

Hilary and Lynn are sisters.

This is what we know :

This is what we want to know :

Who are siblings?

Obviously, the answer should be :

Hilary and Lynn are siblings.

Example: sisters-siblings (revisited)SiblingOf

Hilary Lynn

SisterOf

Hilary Lynn

All sisters are siblings.

Hilary and Lynn are sisters.

This is what we know :

This is what we want to know :

Who are siblings?

Obviously, the answer should be :

Hilary and Lynn are siblings.

Example: sisters-siblings (revisited)SiblingOf

Hilary Lynn

SisterOf

Hilary Lynn

{ <x,y> | siblingOf(x,y) }

All sisters are siblings.

Hilary and Lynn are sisters.

This is what we know :

This is what we want to know :

Who are siblings?

Obviously, the answer should be :

Hilary and Lynn are siblings.

Example: sisters-siblings (revisited)SiblingOf

Hilary Lynn

SisterOf

Hilary Lynn

{ <x,y> | siblingOf(x,y) }

Just look it up!

All sisters are siblings.

Hilary and Lynn are sisters.

This is what we know :

This is what we want to know :

Who are siblings?

Obviously, the answer should be :

Hilary and Lynn are siblings.

Example: sisters-siblings (revisited)SiblingOf

Hilary Lynn

SisterOf

Hilary Lynn

{ <x,y> | siblingOf(x,y) }

Just look it up!

{ <Hilary, Lynn> }

Lehigh University Benchmark (LUBM)Load Time and Query Time

(1.5 million facts)(10 Universities, 20 Departments)

[Guo, et al. LUBM: A Benchmark for OWL Knowledge Base Systems. J Web Semantics, 2005.]

Ontology-based Data Management

[Frishkoff, et al. Development of Neural Electromagnetic Ontologies (NEMO): Ontology-based Tools for Representation and Integration of Event-related Brain Potentials. ICBO, 2009]

Ontology-based Query AnsweringReturn all data instances that belong to ERP pattern classes which have a surface positivity over

frontal regions of interest and are earlier than the N400.

Which patterns have a region of interest that is left-occipital and manifests between 220 and 300ms?

What is the range of intensity mean for the region of interest for N100?

Show the region of interest for all ERP patterns that occur between 0 and 300ms.

Which PCA factor do P100 patterns most often appear in?

What is the range of intensity mean for the region of interest for N100 patterns?

Show the patterns whose region of interest is left occipital and occurs between 220 and 300ms.

Inconsistency Detection

• Background and Motivation– Expressiveness– From disjunctions to negations

• Theory– Not-gadgets

• Motivation– Serotonin example– ATP-gated cation channel activity

• Results from ZFIN and MGI Annotations

Not-gadgets

¬ →

¬

Example: inconsistency detection

"Annotations in this way sometimes point to errors in the type-type relationships described in the ontology. An example is the recent removal of the type serotonin secretion as an is_a child of neurotransmitter secretion from the GO Biological Process ontology. This modification was made as a result of an annotation from a paper showing that serotonin can be secreted by cells of the immune system where it does not act as a neurotransmitter.“

[Hill, et al. Gene Ontology annotations: what they mean and where they come from. BMC Bioinformatics, 2008]

gene-x

gene-x

not-gadgetfail!

Example: serotonin secretion

Example: GO:0004931

ATP-gated cation channel activity (as of 3/09):[Term]id: GO:0004931name: ATP-gated cation channel activitynamespace: molecular_functiondef: "Catalysis of the transmembrane transfer of an ion by a channel that opens when extracellular ATP has been bound by the channel complex or one of its constituent parts." [GOC:mah, PMID:9755289]comment: Note that this term refers to an activity and not a gene product. Consider also annotating to the molecular function term 'purinergic nucleotide receptor activity ; GO:0001614'.synonym: "P2X activity" RELATED []synonym: "purinoceptor" BROAD []synonym: "purinoreceptor" BROAD []is_a: GO:0005231 ! excitatory extracellular ligand-gated ion channel activityis_a: GO:0005261 ! cation channel activity

Example: GO:0004931

GO:0004391 sub-graph (using Jambalaya):

Example: GO:0004931

What is so interesting about GO:0004391?

ZFIN ZDB-GENE-030319-2 p2rx2 NOT GO:0004931ZFIN:ZDB-PUB-031031-8|PMID:14580944 IDA F

purinergic receptor P2X, ligand-gated ion channel, 2gene taxon:7955 20071005 ZFIN

ZFIN ZDB-GENE-030319-2 p2rx2 GO:0004931ZFIN:ZDB-PUB-031031-8|PMID:14580944 IGI ZFIN:ZDB-GENE-000427-3 F purinergic receptor P2X, ligand-gated ion channel, 2 gene taxon:7955 20071005

ZFIN

Source: [1/13/2009] http://www.geneontology.org/gene-associations/

Example: GO:0004931

The not-gadget will raise a logical inconsistency. p2rx2 NOTGO:0004931 p2rx2 GO:0004931

GO_0004931

* Tables starting with an '_' are negations.

not-gadgetfail!_GO_0004931

p2rx2

Example: GO:0004931

GO:0004391 sub-graph (using Jambalaya):

Example: GO:0004931

GO:0004391 sub-graph (using Jambalaya):

Example: GO:0004931

GO:0004391 sub-graph (using Jambalaya):

ZFIN

ZFIN

MGI

MGI

MGI

ZFIN - MGI

ZFIN

Outcome: suspect IEA annotations

GO Online SQL Environment (GOOSE)

Source: [1/13/2009] http://www.geneontology.org/GO.database.shtml#diagram

pos,IEA(graph_path x association) x neg(grapth_path x association)

What do logical inconsistencies mean?

• Several possibilities:– Incorrect annotation (e.g., suspect IEA annotations)– Incorrect relationship (e.g., serotonin secretion)– Incomplete model:

Recall:ZFIN ZDB-GENE-030319-2 p2rx2 GO:0004931

ZFIN:ZDB-PUB-031031-8|PMID:14580944 IGIZFIN:ZDB-GENE-000427-3 F purinergic receptor P2X, ligand-gated ion channel, 2 gene taxon:795520071005 ZFIN

– Perfectly admissible!

Next Directions

• Explanation and proof-reconstruction ✓• Deep (data) annotation tools• Distributed network of Ontology Databases

Data Annotation:Neural ElectroMagnetic Ontologies

LFRON

RFRON

frontocentral

[Frishkoff, et al. ERP measures of partial semantic knowledge: Left temporal indices of skill differences and lexical quality. Biological Psychology, 2009.]

Network of Ontology Databases

[Thorisson, Muilu and Brookes. Genotype–phenotype databases: challenges and solutions for the post-genomic era. Nature Reviews, 2009.]

Thank you

Questions?

Andrea’s Example

Is John supervised by a TopManager who is a friend of an AreaManager?

{ {Mary/y, Andrea/z}, {Andrea/y, Paul/z} }[Franconi. Ontologies and databases: myths and challenges. VLDB, 2008.]

Raymond Reiter

[Reiter. Deductive Question-Answering on Relational Data Bases. Logic and Data Bases, 1977]

Raymond Reiter

Raymond Reiter

Raymond Reiter

Benchmarking Suite

Origins

CIS @ UO

CIS @ UO• Research Areas in Computer Science:

– software engineering– programming languages– human-computer interaction– parallel and distributed computing– networking and graph theory– scientific computation/visualization– information integration and mining

• Affiliates:– Neurosciences Institute– Computational Science Institute– Zebrafish Information Network

Ontology-based Data Access

[Rodriguez-Muro , et al. Realizing Ontology Based Data Access: A plug-in for protégé. ICDEW, 2008.]