The Ontrez project at NCBO

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The Ontrez project at NCBO

• Nigam Shah
• nigam_at_stanford.edu

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Public data repositories

• Around 1100 databases in the NARs 2008 database
  issue.
• High throughput gene expression data in
  repositories such as GEO, SMD, Array Express
• Clinical Trial repositories such as caBIG,
  TrialBank, clinicaltrials.gov
• Guideline repositories such as www.guideline.gov
• Image repositories such as BIRN
• Observational studies such as Framingham, NHANES,
  AMCIS.

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Database annotation

• Ontology based annotation is not as wide-spread
  as desired
• Most annotation is still free-text
• Possible reasons
• Lack of a one stop shop for bio-ontologies
• Lack of tools to annotate experimental data
• Manual ? phenote
• Automatic ? ?
• Lack of a sustainable mechanism to create
  ontology based annotations

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Different kinds of annotations

• Expression profiling of cultured bladder smooth
  muscle cells subjected to repetitive mechanical
  stimulation for 4 hours. Chronic overdistension
  results in bladder wall thickening, associated
  with loss of muscle contractility. Results
  identify genes whose expression is altered by
  mechanical stimuli.

• ELMO1 expression is altered by mechanical stimuli
• Other experiments
• ELMO1 associated_with actin cytoskeleton
  organization and biogenesis

Low level result
metadata
summary result
annotation
Chronic Bladder Overdistension
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Annotations as assertions

• Annotation An assertion declaring a
  relationship b/w a biomedical entity and a type
  in an ontology.
• e.g. p53 cell death
• Annotations tell us what the biologists believe
  to be true (in particular or in general)
• Most annotations are based on particular
  observations and are generalized during
  interpretation by a biologist/curator.
• Semantics of annotations are not always declared
  apriori (e.g. associated_with, involves)

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Annotations as Meta-data

• Metadata The text description accompanying a
  dataset in a database.
• Metadata-annotations should be machine processed
  (and indexed using ontologies) because
• The volume is orders of magnitude more than the
  summary results
• These annotations are not stating any biological
  fact
• Hence dont need a curator to create them
• These annotations are to be used to LOCATE
  datasets accurately as soon as they are available
  in a public repository
• we can not afford to have a curation bottleneck

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High level goal

• Process the metadata annotations to automatically
  tag the elements in public repositories with as
  many ontology terms as possible.
• For example in case of the GEO dataset 906
• Expression profiling of cultured bladder smooth
  muscle cells subjected to repetitive mechanical
  stimulation for 4 hours. Chronic overdistension
  results in bladder wall thickening, associated
  with loss of muscle contractility. Results
  identify genes whose expression is altered by
  mechanical stimuli.
• Gets tagged with
• Expression, Expression of bladder, bladder,
  smooth, bladder muscle, muscle, smooth muscle,
  cells, mechanical, mechanical stimulation,
  stimulation, Chronic, results, bladder
  overdistension, associated, associated with,
  with, loss, genes, altered

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Tagging annotating with ontology terms
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Querying the annotation index
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What new science do we enable?
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New Science enabled

• Nature study on image features and gene
  expression
• Correlation b/w protein and gene expression for
  cancer classification
• Correlating gene expression and drug effect
  information for predicting drug efficacy
• Training and testing image processing algorithms

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Decoding global gene expression programs in liver
cancer by noninvasive imaging Eran Segal, Claude
B Sirlin, Clara Ooi, Adam S Adler, Jeremy Gollub,
Xin Chen, Bryan K Chan, George R Matcuk,
Christopher T Barry, Howard Y Chang Michael D
Kuo Nature Biotechnology 25, 675 - 680 (2007)
Published online 21 May 2007
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Correlation of protein and gene expression for
the stratification of breast cancer patients
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There are 20 other diseases for which this is
possible!
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TMAD incorporates the NCI Thesaurus ontology for
searching tissues in the cancer domain. Image
processing researchers can extract images and
scores for training and testing classification
algorithms.
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Current status of the prototype

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Ontrez Target resources
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Where can we go?

• Become a service for annotating biomedical
  text.
• People send us text, we send back recognized
  concepts (may be even relationships)
• Given a set of concepts we provide a similarity
  metric between them
• Both these services can be plugged into a variety
  of community and collaborative annotations tools
• Become the one stop shop for finding items
  across a wide variety of resources
• Integrate on the disease dimension. Gene cards
  exist, disease cards dont
• Focus on approx. 15 resources in the next year.
• PDB and PLoS are interested

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Research questions - 1
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Research questions - 2
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Credits and collaborations

• Clement Jonquet
• Nipun Bhatia
• Manhong Dai

• Fan Meng
• Brian Athey
• Mark Musen