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Exploiting semantic technologies to build an application ontology. James Malone PhD, Helen Parkinson PhD, Tomasz Adamusiak Phd, MD. Overview. Motivation Our use cases Annotating HTP experimental data Integrating clinical data Methodology for creating the ontology

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exploiting semantic technologies to build an application ontology

Exploiting semantic technologies to build an application ontology

James Malone PhD, Helen Parkinson PhD, Tomasz Adamusiak Phd, MD

overview
Overview
  • Motivation
    • Our use cases
    • Annotating HTP experimental data
    • Integrating clinical data
  • Methodology for creating the ontology
    • Semi-automated mapping and manual curation
  • Current ontology usage
  • Future use

Exploiting semantic technologies to build an application ontology

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our use cases
Our Use Cases
  • Query support (e.g, query for \'cancer\' and get also \'leukemia\')
  • Over-representation analysis in groups of samples (analogous to the use of GO terms in over-representation analysis in groups of genes)
  • Data visualisation – e.g., presenting an ontology tree to the user of what is in the database
  • Data integration by ontology terms – e.g., we assume that \'kidney\' in independent studies roughly means the same, so we can count how many kidney samples we have in the database
  • Intelligent template generation for different experiment types in submission or data presentation
  • Summary level data
  • Nonsense detection – e.g. telling us that something marked as cancer can not be marked as healthy

Exploiting semantic technologies to build an application ontology

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scope of experimental factor ontology efo
Scope of Experimental Factor Ontology (EFO)

Modelling all of the experimental factors that are currently present in the ArrayExpress repository

Experimental factors are variable aspects of an experiment design which can be used to describe an experiment

Scope is primarily determined by data currently held in ArrayExpress

clinical conditions level (e.g. disease)

developmental stage

sample level

species level

Exploiting semantic technologies to build an application ontology

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experimental factors
‘Experimental Factors’

Exploiting semantic technologies to build an application ontology

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Developing an Experimental Factor Ontology

annotating high throughput data
Annotating High Throughput Data

Public/Private

Public Only

Genes in

Expts

ATLAS

Experiment queries > 200 species

Gene level queries, 9 species

Re-annotate

Summarize

acquire

246,000

assays

Ranked gene/

condition queries

  • Text mining at data acquisition
  • Ontology driven queries
  • Data mining

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integrating clinical data
Integrating Clinical Data
  • Use cases include:
    • Homologizing clinical data for study designs (e.g. GWAS)

Exploiting semantic technologies to build an application ontology

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building the experimental factor ontology
Building the Experimental Factor Ontology

Position of EFO in the ‘bigger picture’

  • Key is orthogonal coverage, reuse of existing resources and shared frameworks

Chemical Entities of Biological Interest (ChEBI)

Relation Ontology

Cell Type Ontology

Text mining

Various Species Anatomy Ontologies

Anatomy Reference Ontology

Disease Ontology

EFO

Exploiting semantic technologies to build an application ontology

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semi automated mapping text to ontology
Semi-automated mapping text to ontology
  • Following an evaluation from Tim Rayner we selected Double Metaphone algorithm
  • Perform matching of our values in database to ontology class labels and definitions.
  • Also perform mappings from EFO to other ontologies, so that EFO: cancer = NCI: cancer, DO: cancer et al.
  • Sanity checking over mappings before adding to ontology

Exploiting semantic technologies to build an application ontology

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mapping using agent technology

User

Repositories

Bioontologies

Querying Mechanism

Search Engines

Mapping using Agent Technology

Component 2: ontology mappings

Component 3: ontology discovery

Component 1: MAS architecture

Exploiting semantic technologies to build an application ontology

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what does agent technology buy us
What does agent technology buy us?
  • Annotation consistency
  • EFO_1001214 is now inconsistent

because DO_15654 has new parent

  • Richer mappings (hence annotations)
  • EFO_1000156 can have new mappings

because new cancer class found in MIT ontology

  • New potentially relevant ontologies
  • New ontology found relating to molecular + pathways
  • Semantic web compatible (i.e. can be deployed as standards compliant service)

Exploiting semantic technologies to build an application ontology

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efo axes

EFO

EFO Axes

process

material

material property

information

site

Exploiting semantic technologies to build an application ontology

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process
Process

process

Exploiting semantic technologies to build an application ontology

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information
Information

information

Exploiting semantic technologies to build an application ontology

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material
Material

material

Exploiting semantic technologies to build an application ontology

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material property
Material Property

material property

Exploiting semantic technologies to build an application ontology

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using the ontology querying
Using the ontology: Querying

Exploiting semantic technologies to build an application ontology

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Public repository of gene expression data

Multiple sources – direct submissions, external databases

>200 species

8400 experiments, 246,000 assays

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slide18

Using the ontology: Atlas Querying

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using the ontology exposing data via external resources
Using the ontology:Exposing data via external resources

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NCBO Bioportal

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Developing an Experimental Factor Ontology

using the ontology detecting nonsense enforcing correctness
Using the Ontology:Detecting Nonsense: Enforcing correctness

species (human)

organism part (cervix)

cell line (Hela)

cell type (epithelial)

disease (cervical adenocarcinoma)

Exploiting semantic technologies to build an application ontology

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using the ontology detecting nonsense enforcing correctness1
Using the Ontology:Detecting Nonsense: Enforcing correctness

species (human)

organism part (hair follicle)

disease (cardiovascular disease)

Exploiting semantic technologies to build an application ontology

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using ontology integrating clinical data for study design
Using Ontology:Integrating Clinical data for Study Design

Exploiting semantic technologies to build an application ontology

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future work for efo
Future Work for EFO
  • Mapping in external ids on request – Snomed-CT, FMA, ChEBI, Brenda tissue ontology etc
  • API development for serving external ids from AE
  • Working with external ontologies to produce cross products
  • Extensions for clinical data capture Gen2Phen, Engage
  • Extensions for mouse model of human disease queries
  • Addressing ‘temporal dimension’
  • Addition of units
  • Improving query implementation in ArrayExpress Atlas – GUI changes
  • Addition of synonyms
  • Semantic clustering of experiments

Exploiting semantic technologies to build an application ontology

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conclusion
Conclusion
  • Ontology development for text mining, annotation, query Built with our needs in mind, however covers a wide range of experimental variables across a wide range of technologies, extensible, open source
  • Xref’d to existing ontology resources when possible
  • Text mining works, reduces the workload
  • 1.0 is released on April 1st 2009
  • 0.10 version currently available in OLS and NCBO bioportal
    • http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=EFO
    • http://www.ebi.ac.uk/microarray-srv/efo/

Exploiting semantic technologies to build an application ontology

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acknowledgments
Acknowledgments

Ontology creation:

James Malone, Helen Parkinson, Tomasz Adamusiak, Ele Holloway

Mapping tools and text mining evaluation:

Tim Rayner, Holly Zheng

External Specialist Review:

Trish Whetzel, Jonathan Bard

AE Team:

Anna Farne, Ele Holloway, Margus Lukk, Eleanor Williams, Tony Burdet, Alvis Brazma, Misha Kapushesky

EBI Rebholz Group (Whatizit text mining tool)

EC (Gen2Phen,FELICS,MUGEN, EMERALD, ENGAGE, SLING), EMBL, NIH

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