This document discusses using ontologies to integrate data across multiple model organism databases to enable cross-species queries of phenotypes and genotypes. It describes efforts to develop Uberon, a multi-species anatomy ontology, and phenotype ontologies to describe phenotypes in a standardized way. By semantically integrating these resources, researchers can search for phenotypes of interest and discover potential gene function and disease gene candidates by finding similar phenotypes across species. This will help enable more comprehensive knowledge discovery across model systems.

1. Removing roadblocks: leveraging
ontologies for data aggregation and
computation
NCBO Seminar series
March 6th, 2013
Melissa Haendel
On behalf of very many team members

2. Topics for today
 The Research Symbiosis
 Some Integration Projects Leveraging
Ontologies
 A more complete research profile – integrating
research resources and person information
 Improving query across multiple biospecimen
repositories
 Identifying disease candidates by leveraging
cross-species anatomy and phenotype queries

3. Consult
Databases
Share
Resources/
Data
Publish
papers
Contribute
to
Databases
The Research Symbiosis
Get funding
Do Experiments
The Web

4. We’ve all been here before:
Ontologies can help us do better.
OMIM Query # of records
“large bone” 1032
"enlarged bone" 207
"big bones" 22
"huge bones" 4
"massive bones" 39
"hyperplastic bones" 12
"hyperplastic bone" 44
"bone hyperplasia" 173
"increased bone growth" 836

5. Why not just map to ontology terms?
Class A Class B Mapped? Useful?
FMA: extensor
retinaculum of wrist
MouseAnatomy: retina Yes No
Vivo: legal decision Cognitive Atlas: decision Yes No
PlantOntology: Pith MouseAnatomy: medulla Yes No
TaxRank: domain NCI: protein domain Yes No
ZfishAnat: hypophysis MouseAnatomy: pituitary No Yes
FMA: tibia FlyAnatomy: tibia Yes No
FMA: colon GAZ: Colón, Panama Yes No
Quality: male Chebi: maleate 2(-) Yes No
Mapping requires manual work to perform and maintain; string
matching for mapping can lead to spurious results; semantics of
mappings and provenance are not always clear

6. Topics for today
 The Research Symbiosis
 Some Integration Projects Leveraging
Ontologies
 A more complete research profile – integrating
research resources and person information
 Improving query across multiple biospecimen
repositories
 Identifying disease candidates by leveraging
cross-species anatomy and phenotype queries

7. CTSAconnect:
A Linked Open Data
approach to represent
clinical and research
expertise, activities, and
resources
CTSA 10-001: 100928SB23
PROJECT #: 00921-0001

8. About eagle-i:
inventories “invisible” resources
Ontology-system for
collecting and querying
research resources
eagle-i.net net w o r k

9. About VIVO
 Primarily focused on people, activities, and
outcomes typically associated with research
networking
 Eager to represent more diverse components of
expertise, across domains
 e.g., exhibits, performances, specifics about research
 Had worked with core facilities at Cornell to
represent labs, equipment, and services
 Started collaborating with eagle-i to go further
with research resources

10. At the intersection of Vivo and eagle-i

11. www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.
And then was born the
“CTSAconnect” project
Ok, so it is perhaps not a very informative name for an effort to
consolidate researcher, research activities, and research
resource representation, but what else are we going to call it?
ARG! The Agents, Resources, and Grants ontology

12. ISF Content and modularization
eagle-I
Research resources
VIVO
Person profiling
ShareCenter
Discussions, requests,
share documents
ISF
Contact Organizations
Affiliations
Services Events
Clinical
Expertise
Reagents
Organisms
Credentials

13. ISF Modularization
Constraints
• Different ontology modeling principles
• Active ongoing development of eagle-i and VIVO applications
• Investments in existing RDF datasets and the need for stable
targets
Benefits
• Flexibility in what modules to populate at a given site
• Extensibility as needs and feedback influence future evolution

14.  Annotation view with approved or pending approval.
 Module view shows pending axiom changes per module and has ability to save the
changes with a log comment, and generate the spreadsheet summary
Protégé refactoring plugin

15. ISF Merging

16. Relating ICD9 to MeSH in support of
clinical expertise

17. Clinical expertise data visualization

18. Building translational teams
 We want to assemble teams of scientists to
examine, for example, specific drugs released
for repurposing
 Hard to identify and connect complementary
basic and clinical expertise across disciplines

19. Bringing together clinical expertise
and basic science expertise
Representation of a clinician expertise extracted
From ICD-9 codes for
Basic Researcher with Similar
Expertise based on MeSH Terms
Resources
a resource related to Autoimmune disease

20. Relating researchers across disciplines

21. Topics for today
 The Research Symbiosis
 Some Integration Projects Leveraging
Ontologies
 A more complete research profile – integrating
research resources and person information
 Improving query across multiple biospecimen
repositories
 Identifying disease candidates by leveraging
cross-species anatomy and phenotype queries

22. OHSU’s Biolibrary and Search Engine
 Data aggregated from two repositories:
– Department of Pathology repository (600K)
– Knight Cancer Institute repository (16K)
 A web-based search engine over de-identified
data
 Our group is applying semantic informatics to
improve
– Data format and quality
– Data integration across the two repositories
– Search capabilities
Funded by Medical Research
Foundation of Oregon

23. Opportunities for improving the
Biolibrary data
Limited anatomical data
– Cancer registry table has 300+
anatomical entities
– Pathology table only 86
– 99% of pathology reports (600K)
have no anatomical codes
– No anatomical relationships
– Coded sites are not as specific as
descriptions in the pathology
reports

24. Current Search Interface
Two separate search
interfaces
Multiple forms

25. Biolibrary Text Search
Syntactic free text
search

26. Coded Syntactic Search
Search through
anatomy and
histology lists

27. Extracting ontology concepts
 Pathology reports were the main focus
– Main source of data in the current system
– Contain richer information
 NLP tools were used to identify concepts
 Existing ontology resources were used to
add semantics

28. Developing a Biospecimen ontology
Phenotypes
(PATO)
Information
Ontology (IAO)
•HPO
•SNOMED
•NCI Thesaurus
•ICDO/ICD9
•GO
•CHEBI
•Cell
Anatomy
(FMA, Uberon)
Medicine
(OGMS)
Classes, Types,
Vocabulary
Data, Instances
Pathology Catalog
Pathology Inventory
Pathology
Report
Instance #123 Instance #456
Instantiates Classifies asUses

29. Structured data vs. pathology report
(about 7K cases)
However, pathology report also includes:
• Low grade pancreatic intraepithelial neoplasia
• Extensive perineural invasion
• Acute and chronic cholecystitis
• Bile duct tissue with chronic inflammation
• Chronic pancreatitis
• Acute gastric serositis
Available structured data from one case:

30. Adding Logical Relationships
 About 400 anatomical entities were mapped
to the Foundational Model of Anatomy
 An additional 300 to SNOMED
 Used the is_a and part_of relations
 Re-represented this in a semantic and
computable format
 Allows for semantic queries

31. Considerations
 Concept mapping helps with document retrieval
 Does not necessarily imply a fact
– Negation
– Differential diagnosis
– Past case history
 Researchers will likely need aggregated facts
from multiple sources to support real research
queries
 Information extraction options are being
explored as part of this work

32. Topics for today
 The Research Symbiosis
 Some Integration Projects Leveraging
Ontologies
 A more complete research profile – integrating
research resources and person information
 Improving query across multiple biospecimen
repositories
 Identifying disease candidates by leveraging
cross-species anatomy and phenotype queries

33. Vertebrata
Ascidians
Arthropoda
Annelida
Mollusca
Echinodermata
tetrapod limbs
ampullae
tube feet
parapodia
We want to understand gene
function across taxa

34. Databasing phenotypes
is hard
• Free text descriptions
• Clinical note
• Models
• Atlases
• Images
• Controlled terms
• Multiple file formats
• Measurements
• …
ATTCGGATTACCGTATTA…
genes, regulatory elements, …
sequence
Sequence data

35. Databases proliferate
ATTCGGATTACCGTATTA…
genes, regulatory elements, …
sequence
Sequence data

36. Ontologies as a tool for unification
Disease-
Phenotype
databases
Disease
phenotype
ontology
Expression
data
Gene function
data
Cell and tissue
ontology
GO
annotations
ontologies
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., et al. (2000). Gene ontology: tool
for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25(1). doi:10.1038/75556

37. Yet problems remains
Incomplete
data
Not connected
ontology
Missing & incorrect
annotations
Multiple
Overlapping
Ontologies
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
ontology
Annotations
miss the important
biology

38. Ontologies built for one species will
not work for others
http://fme.biostr.washington.edu:8080/FME/index.html
http://ccm.ucdavis.edu/bcancercd/22/mouse_figure.html

39. Uberon: a multi-species anatomy
ontology
• Contents:
– Over 8,000 classes (terms)
– Multiple relationships, including subclass, part-of and
develops-from
• Scope: metazoa (animals)
– Current focus is chordates
– Federated approach for other taxa
• Uberon classes are generic / species neutral
– ‘mammary gland’: you can use this class for any mammal!
– ‘lung’: you can use this class for any vertebrate (that has
lungs)
Mungall, C. J., Torniai, C., Gkoutos, G. V., Lewis, S. E., & Haendel, M. A. (2012). Uberon, an integrative
multi-species anatomy ontology. Genome Biology, 13(1), R5. doi:10.1186/gb-2012-13-1-r5
http://genomebiology.com/2012/13/1/R5

40. Bridging anatomy ontologies
ZFA
MA FMA
EHDAA2EMAPA
Uberon
CJ Mungall, C Torniai, GV Gkoutos, SE Lewis, MA Haendel.
Uberon, an integrative multi-species anatomy ontology. Genome biology 13 (1), R5
SNOMED
NCIt
GO
CL

41. UBERON
cerebellum
cerebellar
vermis
pp
cerebellum
cerebellar
vermis
cerebellum
vermis of
cereblleum
posterior
lobe of
cerebellum
pp
MA:mouse
FMA:human
GO/NIF: subcellular GO/NIF: subcellular
axon
CL:Purkinje cell
p
i i
CL:Purkinje cell
axon
i
i
i
i
dendrite dendrite
cerebellum
posterior
lobe
cerebellum
posterior
lobe
p
pp
Uberon enables
queries across
granularity

42. Niknejad, A., Comte, A., Parmentier, G., Roux, J., Frederic, B., & Robinson-rechavi, M. (2011).
vHOG , a multi-species vertebrate ontology of homologous or- gans groups. Ecology, 1-5.
http://bgee.unil.ch

43. Evo-devo applications
Dahdul, et al. 2010. Evolutionary characters, phenotypes and ontologies: curating data from the systematic biology literature. PLoS ONE 5(5):e10708.
doi:10.1371/journal.pone.0010708

44. The Monarch Initiative
The model systems research network
We are under construction
Goals are to:
 Aggregate model systems genotype
and phenotype information
 Integrate with network, genomic, and
functional data
 Leverage ontologies for phenotype
similarity matching
 Build knowledge exploration tools for
end users
 Build services for other applications
Funded by NIH # 1R24OD011883-01

45. Can we search by phenotype alone?
Washington NL, Haendel MA, Mungall CJ, Ashburner M, et al. (2009) Linking Human Diseases to Animal Models Using Ontology-Based
Phenotype Annotation. PLoS Biol 7(11): e1000247. doi:10.1371/journal.pbio.1000247
http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1000247

46. Integrating phenotypes using ontologies

47. Organism Genotype
zebrafish fgf8ati282a/+
; shhatq252/tq252
(AB)
mouse B6.Cg-Shhtm1(EGFP/cre)Cjt
/J
worm daf-2(e1370) III; fog-2(oz40) V
human* ATP1A3(NM_152296.3)
[c.946G>A, p.Gly316Ser]+[=]
But..different organisms record
genotypes differently
Phenotypes can be attached to full or partial
genotypes, alleles, or variants

48. Model systems
phenotype and
genotype data
Pulling it together
NIF DISCO
Data ingest Ontology annotation
OWLSIM
Enabling phenotype-based knowledge discovery tools
ONTOQUEST
Extensible Web resource
DISCOvery, registration and
interoperation framework
MONARCH tools and services

49. Phenotypic
qualities
Cells
Phenotypic
abnormalities
(Human Mouse
Zebrafish)
Molecular
function
Biological
process
Cellular
component
Anatomy
(Human Mouse Zebrafish)
Molecules
Chemicals Proteins
ZEBRAFISH-Term
"abnormally disrupted
pigmentation"MOUSE-Term
"abnormal ear
pigmentation"
HUMAN-Term
Abnormality of
pigmentation
Uberpheno
Ontologies
Semantic Integration
HP1 HP2 HP3 HP4 HP5
Human
Mouse
Zebrafish
ZP MP ZP
Phenome systems analysis
Phenogram
Genome systems analysis
T P73
tumor protein p73
GN B1
guanine nucleotide
binding protein
Cerebral cortical atrophy
GN B2L1
guanine nucleotide
binding protein (G protein),
Interactome
Orthology Annotation
CNV syndrome
Gene function
       
                
         
                
                
         
                  
             
         
                   
             
         
              
               
         
                     
               
         
         
             
         
                
             
         
            
                
         
              
               
Pheno-cluster
MP
Phenotype Gene

50. These integration projects…well,
integrate
CTSAconnect
Reveal Connections. Realize Potential.
OHSU Biolibrary
peopleResearch
resources
Clinical
encounters
Phenotypes
biospecimen
s
genes
variations

51. Conclusions
 Ontologies have provided us the capability to
integrate a variety of biomedical data, at
different levels of granularity, from different
applications, and across domains
 Describing biology works best with multiple
connected ontologies
 We need smart data, not just big data
 We need better tools to integrate multiple
ontologies
 We need better tools to make use of smarter
data structures (e.g. reasoning costs)

52. Monarch Initiative
CTSAconnect
Biospecimen Ontology
OHSU
Melissa Haendel
Carlo Torniai
Nicole Vasilevsky
Chris Kelleher
Shahim Essaid
Cornell University
Dean Krafft
Jon Corson-Rikert
Brian Lowe
University of Florida
Mike Conlon
Chris Barnes
Nicholas Rejack
OHSU
Melissa Haendel
Shahim Essaid
Carlo Torniai
OHSU
Melissa Haendel
Carlo Torniai
Shahim Essaid
Nicole Vasilevsky
Scott Hoffman
Matt Brush
LBNL
Chris Mungall
Suzi Lewis
Nicole Washington
UCSD/NIF
Maryann Martone
Anita Bandrowski
Jeff Grethe
Amarnath Gupta
Stony Brook University
Moises Eisenberg
Erich Bremer
Janos Hajagos
Harvard University
Daniela Bourges
Sophia Cheng
University at Buffalo
Barry Smith
Dagobert Soergel
Zaloni
Will Corbett
Ranjit Das
Ben Sharma
University of Pittsburgh
Harry Hochheiser
Chuck Borromeo