how bio ontologies enable open science

How Bio-Ontologies enable Open Science

Nigam Shahnigam@stanford.edu

Ontologies

By Pedro Beltrão

Key Points

• Open science requires structured content.• Structured content acquisition runs into a

curation bottleneck.– And “controlled manual curation” will not scale

• For “open science” to really take off:– collaborative curation platforms are going to be

necessary and, – (semi-)automation of curation is going to be

necessary. • Researchers need to exactly identify what is

being mentioned/discussed.• NCBO provides services that support these needs

Currently, the main use of ontologies is for making sense of high throughput

data.

4

There are other uses of course, see Biomedical Ontologies: A functional perspective, Rubin et al, Briefings in Bioinformatics, Dec 2007, Vol 9:1 75-90

Ontologies and content acquisition

• First start naming ‘things’• Then name ‘relationships’• Then comes the ‘logic of combining simple

relationships’• … realization that all this “structure” is hard to

create manually and manual curation will not scale … lots of dead projects.– Leads to new found love for text-mining!

Emerging trends in content acquisition

• Increased Structure (in curation and annotations)

• Collaborative curation platforms– Knewco– SWAN– CBioC– …

• Integration of Text-mining in curation– Finding entities

• BioLit by Phil Bourne’s group– Finding relations … facts.

• Larry Hunter’s group• Biolink papers• EBI-MED

Increasing Structure

• Until now the predominant use of ontologies is as a vocabulary to describe data … minimal structure in the descriptions.

• Precise capture of biomedical knowledge in structured form is now considered essential– Hits the manual curation bottleneck.– WA Baumgartner Jr. et al, Manual curation is not sufficient for annotation of

genomic databases. Bioinformatics 2007 23(13):i41-i48. Presented at ISMB 2007

Knewco: Concept Web and Wikiprofesional

9

The SWAN discourse ontologyCiccarese P, Wu E, Clark T (2007) 'An Overview of the SWAN 1.0 Ontology of Scientific Discourse‘ at the 16th International World Wide Web Conference Banff, Canada. May 8-12, 2007.

Collaborative KB curation: SWAN Knowledge Workbench

Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital

Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital

Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital

The SWAN Team and papers

• Harvard/MGH: Paolo Ciccarese, Marco Ocana, Tim Clark

• Alzforum: Elizabeth Wu, Gwen Wong, June Kinoshita www.alzforum.org

Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital

Photo not

available

[1] Gao Y, Kinoshita J, Wu E, Miller E, Lee R, Seaborne A, Cayzer S, Clark T (2006) ‘SWAN: A Distributed Knowledge Infrastructure for Alzheimer Disease Research’. Journal of Web Semantics 4(3).

[2] Ciccarese P, Wu E, Clark T (2007) 'An Overview of the SWAN 1.0 Ontology of Scientific Discourse'. 16th International World Wide Web Conference (WWW2007). Banff, Canada. May 8-12, 2007.

[3] Clark T and Kinoshita J (2007) 'Alzforum and SWAN: The Present and Future of Scientific Web Communities'. Briefings in Bioinformatics 8(3).

[4] Ciccarese, P, Wu E, Kinoshita J, Wong G, Ocana M, Ruttenberg A and Clark T (submitted for publication 9/4/2007) 'The SWAN Ontology of Scientific Discourse'.

Integration of Text-mining + Curation

• Text mining works better if it uses appropriate ontologies.

• “Model” mismatch b/w needs of text mining and needs of KB builders.

• Text mining might work much better if:– It works in a loop with a

curator– It leverages the wisdom

of the masses

Integration of Text-mining + Curation

Quick recap

Use of ontologies in collaborative curation and content acquistion is not wide-spread; possibly because of:

1. Lack of a one stop shop for bio-ontologies2. Lack of tools to use ontologies for annotation

• Manual will not scale• Automatic can it be ‘good enough’?

3. Lack of a sustainable mechanism to create ontology based annotations

19

NCBO’s efforts

• The key ingredients needed for collaborative curation platforms to succeed: – Proper use of bioontologies (just enough

ontology!) – Appropriate use of Natural Language Processing in

the curation workflow.

• NCBO has created web-services that allow use of ontologies in collaborative platforms http://bioontology.org/tools.html

NCBO ontology services

21

Description REST URL

List all ontologies ./ontologies

Find a specific ontology ./ontologies/{ontology version id}

Download ontology file ./ontologies/download/{ontology version id}

Get versions of an ontology ./ontologies/version/{ontology id}

Get concept ./concepts/{ontology version id}/{concept id}

Search for concepts ./search/concepts/{query}?ontologies={ids}

Get latest version of an ontology ./virtual/{ontology_id}

Get concept for latest ontology version

./virtual/{ontology id}/{concept id}

List all ontology categories ./categories

Base URL: http://rest.bioontology.org/restDocumentation: www.bioontology.org/wiki/index.php/NCBO_REST_services

NCBO annotation services

• Open Biomedical Annotator (OBA) web service – To automatically process textual metadata to recognize

relevant ontology concepts and return the terms as annotations

• Open Biomedical Resource (OBR) index– To index the contents of a few biomedical resources with

the biomedical concepts to which they relate … and allow programmatic access to the indexed data.

• URL: http://obs.bioontology.org22

ANNOTATOR SERVICEANNOTATOR SERVICE

Using Ontologies to Annotate Your Data

Annotator: The Basic IdeaProcess textual metadata to automatically tag text with as many ontology terms as possible.

24

Annotator: Usage

• Give your text as input

• Select your parameters (ontologies to use, semantic type to filter, semantic expansion…)

• Get your results… in text, tab-delimited, XML, or OWL

• Paper in AMIA STB 09

DATA SERVICEDATA SERVICE

Using Ontologies to Access and Analyze Public Data

Open Biomedical Resources index

• The index can be used for:• Search (next few slides)• Data mining (Paper in AMIA STB 08 on mining relationships

b/w drugs, diseases and genes from Medline) 27

Example

NCBO services

Ontology services(OBS)

Ontology services(OBS)

UMLS servicesUMLS services

BioPortal servicesBioPortal services

Data service(OBR)

Data service(OBR)

Annotation service(OBA)

Annotation service(OBA)

UsersUCSFLaboratreeCollabRxPharmGKB, JAXHGMD

UsersUCSFLaboratreeCollabRxPharmGKB, JAXHGMD

UsersBioPortal UIPDB/PLoSI2B2NextBioIO informatics

UsersBioPortal UIPDB/PLoSI2B2NextBioIO informatics

Users“Resources” tabKnewcoIO informatics

Users“Resources” tabKnewcoIO informatics

Uses of NCBO services

• For programmatic access to latest versions of ontologies

• For concept recognition from text– For annotation– For accelerating curation

• For data aggregation and summarization

33

BioLit web resource: automated recognition of ontology terms and database IDs after publication http://biolit.ucsd.edu

Automated recognition of ontology terms and database IDs before publication with manual curation by author Word 2007 add-in

End

Annotation: UCSF

• The task is to decide which trial is relevant for a particular patient.– Use the annotator service to map concepts in

eligibility rules to UMLS CUIs

– Use the annotations from the OBR index to create tag clouds in CTExplorer.

Annotation: Laboratree

Annotation: CollabRx

caTissue/TIES Specimen Banking

Specimen management is based on ontologies developed by NCI

Ontology-based integration to create a virtual specimen bank

Curation: JAX, UCHSC, PDB/PLoS

• JAX – Use concepts recognized in the abstracts of publications to triage papers for curation.

• UCSHC – Wrap our annotator as a UIMA component and compare performance on full text

• PDB/PLoS – BioLit and Word-plugin

Ontology Access: I2B2

• Needs a “source” for ontologies in their ontology cell

• Using our services, we export BioPortal Ontologies to the I2B2 format.

Ontology Access: IO-informatics

42

Ontology Access: NextBio“Our collaboration with NCBO on adopting public biomedical ontologies throughout NextBio enabled us to create a platform dealing with heterogeneous biological data. These ontology-based search capabilities have resulted in a rapid adoption of NextBio by over 100,000 researchers around the world since our public debut in May of 2008”.

1.CYP2C9,

2.VKORC1,

3.CYP2A6,

etc.1.Hemorrhage,

2.Venous Thrombosis,

etc.1.warfarin,

2.coumarin,

3.phenoprocoumon,

etc.

34 scored annotations:

5 scored annotations:

20 scored annotations:

Data Summarization: PharmGKB

Data Summarization: Knewco

Data Summarization: HGMD

• Use the disease hierarchy from SNOMED-CT to compute “enrichment” of mutation types in particular types of diseases

• … playing the GO-based microarray analysis game for disease mutations