Rapid Development of an Ontology of Coriell Cell Lines Chao Pang, Tomasz Adamusiak, Helen Parkinson and James Malone

malone@ebi.ac.uk

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Overview

• Motivation – our EBI use cases, big stuff in little time

• Methods – normalization, axiomatisation, automation

• Results – large flat ontology, adding structure

• Desiderata

• Future work

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Motivation

• Lot of large (often online) catalogues

• Infeasible to manually ontologise all of them (and cost is not easy to justify)

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High quality artefacts take time

33k classes 75k classes

FMA Gene Ontology

3k classes

OBI

30 person years

Since 1999

Since 2006

$money$

Funders

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Motivation

• Would like to include these in BioSample Database for curating data, querying, browsing

• Ontology use has proven to add value (e.g. GXA)

• RDF representations of data for integration

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Methods: A Model for Cell Lines

• Agreed with cell line ontology folk

• With addition of is_model_for, to reflect use of cell lines as models for particular diseases (they can be used as models for diseases other than the original disease borne by the subject)

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Methods: Programmatic Engineering

• Lexical concept recognition (2) - Map the terms to existing ontologies by either class label or synonyms in order to get identifiers (e.g. label – Homo sapiens and synonym – human)

• Produce a list of classes from reference ontologies (3) e.g.• Map organism terms: NCBI taxonomy ontology• Map disease terms: Disease ontology

• Map these to an upper ontology and axiomatic structure (4) cell line model on previous slide

Methods: Our “raw” data• Coriell database dump • Extract information for cell line, cell type, anatomy,

organism, sex and disease. (27002 cell lines)

Lexical Concept Recognition

• Our mapper tool was used, adapted the Levenshtein distance algorithm <http://www.ebi.ac.uk/efo/tools>

• Mapping result:• 93 organism - NCBI taxonomy ontology• 61 anatomy – MAT (species neutral), FMA and other anatomy• 23 cell type – cell ontology• 337 disease - Disease ontology (DO) (via OMIM)

• Check the mapping file manually• Inconsistency e.g. fibroma as anatomy• Unmapped terms• Unclear information e.g. buttock-thigh

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Using OWL-API to build ontology automatically

buildingOntology.java

Mapping files

Coriell cell line protocol

Reference ontologies Step one:Adding classes

Step two:Adding restriction

Coriell cell line Ontology

Spreadsheet.txt

Example: import class cervix from EFOIt has parent class organism part

It has class relations part_of some female reproductive system

Hela cell line

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Adding Structure to Normalized Hierarchy

Coriell cell line ontology

Query: human cancer cell line

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Explanation of query: human cancer cell line

Cancer

humanany cell type any organism part

Cell line

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Results In Brief

• Large (~28,000) cell line ontology, lots of axiomatisation for the axes mentioned (disease, organism, cell type, anatomy some extra bits like gender)

• Running code took ~5 minutes

• Flat asserted hierarchy under cell line; structure is all inferred using axioms as per Rector Normalization

• Makes more flexible to change and also querying more powerful

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BioPortal Analysis by Matt Horridge et al• http://bio-ontologies.knowledgeblog.org/135

• “the Coriell Cell Line Ontology, which contains over 45,000 non-trivial entailments, and an average of 4 justifications per entailment, peaking out at 65 justifications for one particular entailment“

• Coriell is quite rich axiomatically – this is what we would expect given the normalization method

• Suggestion rich ontologies can be created rapdily

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Discussion

• Good bits:• Adding new classes was simple once code was done• Re-running the code also simple if a part of pattern was changed• Can be reused for different ontology with similar sort of input• Axiomatisation gives richer querying and renders implicit knowledge

explicit

• Harder bits:• Some data clean up necessary before using the code (SQL dumps can

be messy)• Eliminating errors from concept matching (though expansion on

synonyms helped)• Textual definitions for all classes • Size of the ontology created a few issues, e.g. BioPortal not able to

browse, most reasoners can not complete on the ontology

Conclusions

• To build very big ontologies we need either• Lots of resource (i.e. money)• Lots of people (crowd-sourcing)• Good tools and methods

• Each having pros and cons• Lots of money requires, well, lots of money• Lots of people requires community buy in and ‘trust’ (so

audit trail crucial) – we’re not always good at that • Automation can introduce errors and require some curation

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Desiderata: Querying and External URIs • Easier to use interfaces, users like it simple

• One of top user feedbacks is “we’d like it to work like Google does”

• Users also like putting URIs into a webpage and getting something sensible back i.e.:

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Future Work• Creating text definitions

• Will follow up on work by Stevens R, Malone, J et al (2011)* for creating natural language definitions from OWL

• Text mining to extract missing disease information from textual descriptions from original catalogue

• Begin curating data for BioSample Database at EBI using ontology

• Integrate Coriell ontology with rest of process from sample to analysis, e.g. software ontology

http://theswo.sourceforge.net

*Robert Stevens, James Malone, Sandra Williams, Richard Power, Alan Third (2011) Automating generation of textual class definitions from OWL to English.

Journal of Biomedical Semantics, 2011 May 17, Vol. 2 Suppl 2:S5.

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Acknowledgements

• Thank to Chao Pang, Helen Parkinson,Tomasz Adamusiak, Paulo Silva and all people from functional genomics production team.

• Gen2phen• The Coriell Institute for Medical Research• Alan Ruttenberg and Science Commons for providing the

Coriell SQL dump• Lynn Schriml and colleagues from the Disease Ontology

for OMIM mappings • Sirarat Sarntivijai, Oliver He, Alexander Diehl and Terry

Meehan for discussions on the cell line model.