Semantic Web Technologies for Analysis of Transcriptome

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• Semantic Web Technologies for Analysis of
  Transcriptome
• Rose Dieng-Kuntz1, Khaled Khelif1, Olivier
  Corby1 Pascal Barbry2
• 1INRIA - Sophia Antipolis ACACIA project,
  http//www.inria.fr/acaciahttp//www.inria.fr/ac
  acia/corese
• 2IPMC, Sophia Antipolishttp//www.ipmc.fr

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Outline

• Context Memory of Biochip Experiments
• The MEAT Project
• Semi-automatic generation of semantic
  annotations
• Conclusions Requirements for Semantic Web

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Context Biochip experiments

• DNA microarrays (gene chips, biochips) enable
  to simultaneously measure the expression level
  and transcription rate of various genes in an
  organism.

• Applications in biology, medicine, pharmacology

• Gene discovery
• Disease diagnosis or prognosis
• Drug discovery Pharmacogenomics
• Toxicological research Toxicogenomics

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Towards Biochip Experiment Memory
Experiment sheets
Biologist
Domain Ontologies
Experiment DB
Documents

• Need of Knowledge Management for a community of
  biologists ? Biochip Experiment memory

• Need of support to validation interpretation
  of results of biochip experiments

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The MEAT Project
MEDIANTE
MEAT-AnnotSearch
MEAT-Miner
MEAT-Onto
UMLS, Gene Onto
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Phases before experiment
Biologist checks validates probes available on
the biochip selects a subset
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Phases after experiment
Storage of the experiment description and of its
results in MEDIANTE, according to Array Express
format
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MEAT-AnnotSearch
ARRAY-EXPRESS - Experiment description - Result
description
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MEATAnnot Technical Choices

• NLP tools term extractor relation extractor

• Extraction of terms corresponding to UMLS
  Ontology concepts, from texts

• Extraction of relations between them, from texts

• Automatic generation of a semantic annotation and
  representation in RDF

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Relationship extraction
Test corpus
Syntex

• Syntex (Bourigault D. 2000) Corpus syntactic
  analyser

• Used to reveal  verb syntagms  usually used in
  the biochip domain

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Relationship extraction

• Choosing potential relationship revealed by
  Syntex

• Writing relationship extraction grammar using
  JAPE

Tag.lemme "play" SpaceToken (Token.string
"a" Token.string "an")? (SpaceToken)?
(Token.string "vital" Token.string
"important" Token.string "critical" Token
.string "some" Token.string
"unexpected" Token.string "multifaceted"
Token.string "major")? (SpaceToken)? Tag
.lemme "role"
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System architecture
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Example
 HGF plays an important role in lung
development 
The information extracted from this sentence are
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RDF Annotation Generated
ltrdfRDF xmlnsrdf'http//www.w3.org/1999/0
2/22-rdf-syntax-ns' xmlnsm'http//www.inria.
fr/acacia/meat' xmlnsrdfs'http//www.w3.org/
2000/01/rdf-schema'gt ltmAmino_Acid_Peptide_or_Pr
otein rdfabout'HGF'gt ltmplay_rolegt
ltmOrgan_or_Tissue_Function rdfabout'lung_
development'/gt lt/mplay_rolegt lt/mAmino_Ac
id_Peptide_or_Proteingt lt/rdfRDFgt
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CORESE Semantic search engine
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Ontology-based query
Annotation Base
UMLS
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Semantic Web requirements

• Adaptation of Corese semantic search engine to
  OWL

• Corese query language vs SPARQL

• Contextual annotations ? Need of expression of
  multiple contexts / viewpoints

• Temporal queries on the past biochip experiment
  base temporally evolving ontologies
  annotations

• Scalability of NLP tools articles stemming from
  scientific watch on the open (semantic) Web

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Many thanks to

• ACACIA team in particular Khaled Khelif,
  Laurent Alamarguy, Olivier Corby, Alain Giboin
• IPMC Pascal Barbry, Kevin Le Brigand, Hélène,
  Chimène, Yves
• Bayer Crop Science Rémi Bars
• Didier Bourigault (ERSS), developer of Syntex
• The developers of GATE (Sheffield Univ.)

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Support to health network
Medical Ontology
Semantic Annotations
Translator
Life Line
Coresesearch engine
Virtual Staff
Member of the health network
Nautilus DB
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Visual Staff Architecture