This document discusses using ontologies and automated reasoning to classify proteins and cell types. It describes how protein phosphatases and cell types were classified by defining classes in terms of their functional domains/features. Reasoning over the ontologies inferred new subclass relationships and allowed automated classification of instances based on their described features. Normalizing tangled ontologies into separate modules for different classification axes was shown to produce cleaner models that enabled more flexible querying.

1. Making Semantics do Some
Work
Robert Stevens
BioHealth Informatics Group
School of Computer Science
University of Manchester
Robert.Stevens@manchester.ac.uk

2. Introduction
• What’s the use of highly axiomatised
ontological descriptions?
• Two use cases:
• Classifying instances based on features:
New discoveries;
• Building a complex terminology.
• Cost and benefit.
• Conclusions

3. Protein Classification
• Proteins divided into broad functional classes
“Protein Families”
• Families sub-divided to give family
classifications
• Class membership can be determined by
“protein features”, such as domains, etc.
• Resources exist for feature detection via
primary sequence– but not class membership
• Current Limitation of Automated Tools
• Needs human knowledge to recognise class
membership

4. Finding Domains on a
Sequence
A search of the linear sequence of protein tyrosine
phosphatase type K – identified 9 functional
domains
>uniprot|Q15262|PTPK_HUMAN Receptor-type protein-tyrosine phosphatase kappa precursor (EC
3.1.3.48) (R-PTP-kappa).
MDTTAAAALPAFVALLLLSPWPLLGSAQGQFSAGGCTFDDGPGACDYHQDLYDDFEWVHV
SAQEPHYLPPEMPQGSYMIVDSSDHDPGEKARLQLPTMKENDTHCIDFSYLLYSQKGLNP
GTLNILVRVNKGPLANPIWNVTGFTGRDWLRAELAVSSFWPNEYQVIFEAEVSGGRSGYI
AIDDIQVLSYPCDKSPHFLRLGDVEVNAGQNATFQCIATGRDAVHNKLWLQRRNGEDIPV………..

5. Why Classify?
• Classification and curation of a genome is the
first step in understanding the processes and
functions happening in an organism
• Classification enables comparative genomic
studies - what is already known in other
organisms
• The similarities and differences between
processes and functions in related organisms
often provide the greatest insight into the
biology
• In silico characterisation is the current
bottleneck

6. Phosphatase Classification
• Diagnostic phosphatase domains/motifs – sufficient for
membership of the protein phosphatase superfamily
• Any protein having a phosphatase domain is a
member of the phosphatase super-family
• Other motifs determine a protein’s place within the
family
• Usually needs human to recognise that features
detected imply class membership
• Can these be captured in an ontology?

7. OWL represents
classes of
instances
A
B
C

8. Necessity and Sufficiency
• An R2A phosphatase must have a fibronectin domain
• Having a fibronectin domain does not a phosphatase
make
• Necessity -- what must a class instance have?
• Any protein that has a phosphatase catalytic domain is a
phosphatase enzyme
• All phosphatase enzymes have a catalytic domain
• Sufficiency – how is an instance recognised to be a
member of a class?

9. Definition of Tyrosine
Phosphatase
Class:
TyrosineReceptorProteinPhosphatase
EquivalentTo:
Protein That
- contains atLeast 1
ProteinTyrosinePhosphataseDomain
and
- contains 1 TransmembraneDomain

10. …there are known knowns; there are things
we know we know. We also know there are
known unknowns; that is to say we know there
are some things we do not know. But there
are also unknown unknowns -- the ones we
don't know we don't know.

11. Definition for R2A Phosphatase
Class: R2A
EquivalentTO: Protein That
- contains 2 ProteinTyrosinePhosphataseDomain and
- contains 1 TransmembraneDomain and
- contains 4 FibronectinDomains and
- contains 1 ImmunoglobulinDomain and
- contains 1 MAMDomain and
- contains 1 Cadherin-LikeDomain and
- contains only TyrosinePhosphataseDomain or
TransmembraneDomain or FibronectinDomain or
ImnunoglobulinDomain or Clathrin-LikeDomain or
ManDomain

12. Automated Reasoning
• An OWL-DL ontology mapped to its DL form
as a collection of axioms
• An automated reasoner checks for
satisfiability – throws out the inconsistent
and infers subsumption
• Defined classes (where there are necessary
and sufficient restrictions) enable a reasoner
to infer subclass axioms
• Also infer to which class an individual
belongs

13. Incremental Addition of Protein
Functional Domains
Phosphatase catalytic
Cadherin-like
Immunoglobulin
MAM domain Cellular retinaldehyde
Adhesion recognition Transmembrane
Fibronectin III Glycosylation

14. Classification of the Classical
Tyrosine Phosphatases

15. What is the Ontology Telling Us?
• Each class of phosphatase defined in terms of
domain composition
• We know the characteristics by which an
individual protein can be recognised to be a
member of a particular class of phosphatase
• We have this knowledge in a computational form
• If we had protein instances described in terms of
the ontology, we could classify those individual
proteins
• A catalogue of phosphatases

16. Description of an Instance of a
Protein
Individual: P21592
Types: Protein,
hasDomain 2
ProteinTyrosinePhosphataseDomain
hasdomain 1 TransmembraneDomain,,
hasdomain 4 FibronectinDomain,
hasDomain 1 ImmunoglobulinDomain,
hasdomain 1 MAMDomain,
hasdomain 1 Cadherin-LikeDomain

17. Instance: P21592
TypeOf: Protein That
Fact: hasDomain 2 ProteinTyrosinePhosphataseDomain
and
Fact: hasdomain 1 TransmembraneDomain and
Fact: hasdomain 4 FibronectinDomains and
Fact: hasDomain 1 ImmunoglobulinDomain and
Fact: hasdomain 1 MAMDomain and
Fact: hasdomain 1 Cadherin-LikeDomain
Tyrosine Phosphatase
(containsDomain some TransmembraneDomain) and
(containsDomain at least 1 ProteinTyrosinePhosphataseDomain)
tase
n some MAMDomain) and
n some ProteinTyrosineCatalyticDomain or ImmunoglobulinDomain) and
n some FibronectinDomain or FibronectinTypeIIIFoldDomain) and
n exactly 2 ProteinTyrosinePhosphataseDomain)

18. ClassifyingProteins
>uniprot|Q15262|PTPK_HUMAN Receptor-type protein-tyrosine
phosphatase kappa precursor (EC 3.1.3.48) (R-PTP-kappa).
MDTTAAAALPAFVALLLLSPWPLLGSAQGQFSAGGCTFDDGPGACDYHQDLYDDFEWVHV
SAQEPHYLPPEMPQGSYMIVDSSDHDPGEKARLQLPTMKENDTHCIDFSYLLYSQKGLNP
GTLNILVRVNKGPLANPIWNVTGFTGRDWLRAELAVSSFWPNEYQVIFEAEVSGGRSGYI
AIDDIQVLSYPCDKSPHFLRLGDVEVNAGQNATFQCIATGRDAVHNKLWLQRRNGEDIPV………..
InterPro
Instance Store
Reasoner
Translate
Codify

19. So Far…..
• Human phosphatases have been classified using the
system
• The ontology classification performed equally well as
expert classification
• The ontology system refined classification
- DUSC contains zinc finger domain
Characterised and conserved – but not in classification
- DUSA contains a disintegrin domain
previously uncharacterised – evolutionarily conserved
• A new kind of phosphatase?

20. Aspergillus fumigatus
• Phosphatase compliment very different from human
>100 human <50 A.fumigatus
• Whole subfamilies ‘missing’
Different fungi-specific phosphorylation pathways?
No requirement for tissue-specific variations?
• Novel serine/threonine phosphatase with homeobox
Conserved in aspergillus and closely related species,
but not in any other
Again, a new phosphatase?

21. Generic Technique
• Feature detection
• Categories defined in terms of those
features
• Produce catalogue of what you currently
know
• Highlight cases that don’t match current
knowledge

22. The Cell type Ontology
• Some 880 terms
• Describing cell function, lineage,
developmental stage, ploidy, secretion,
species,…
• Not explicitly classified according to anatomy
• Uses is-a and developsFrom
• Used to describe cell types used in
experiments

23. OBO Cell Type Ontology

24. Issues with Current CTO
• History: A need was seen and a few days
was spent “lashing” together an ontology
by hand
• Contains lots of knowledge
• Asserted multiple inheritance: Humans will
make slips and it is difficult
• Some biological mistakes
• All the knowledge is within the “is-a”
relationships and implicit in the cell names

25. CTO Axes of Classification
• Histology: What cells look like
• Lineage: Whence a given cell develops
• Ploidy: How sets of chromosomes in a cell
• Nucleation: How many nuclei
• Secretion & accumulation: What chemicals a
cells secretes or accumulates
• Function: What does the cell do
• Location: In anatomy
• Species: In what taxa does the cell exist
• And some others

26. Implicit Knowledge
• Anatomy: muscle cell; red blood cell
• Maturity: immature t-lymphocyte
• Cell surface protein: CD45 positive
lymphocyte
• Size
• Shape

27. Problems
• Tangles
• Hard to maintain
• Difficult to add a new cell
• Inflexible queries: What about hormone
secreting mesodermal cells?
• Information hidden inside term names

28. A Tangled Ontology of Cars

29. Describing a Big Blue Ford Car
Class: BigBlueFordCar
SubClassOf: Car
that hasColour some Blue
and hasSize some Big
and hasManufacturer some Ford

30. Modules
• Choose a primary axis: In this case
Vehicle
• Other axes are represented in separate
modules (Colour, Size (qualities) and
manufacturer)
• Represent other aspects of classes
through restrictions
• (Spot the ontological howler in this toy
example)

31. Definition of a Red Car
Class: RedCar
EquivalentTo: Car
that hasColour some Red
• Any car that has the colour red is
recognised to be a member of the
class RedCar
• The reasoner works it all out and
builds the hierarchy for you

32. Normalisation
• This technique of “pulling” apart tangled
ontologies is “normalisation”
• Makes for cleaner modelling
• Makes for re-usable components
• The reasoner builds the taxonomy
“completely”
• A new car (e.g., yellow Saab” is described
and it just appears in the right place

33. What We Did
• Examined CTO
• Chose primary axis of classification
• All other axes added as restrictions on
class membership
• Describe cells
• Build ontology
• Use reasoner

34. Ontologies Used
CTO
Ontolog
y
PATO
Ontology
GO
Biological Process
GO
Cellular Component
Species
Taxonomy
Anatom
y
Nucleation
Morphology
Size
Ploidy
Muscle Contraction
Secretion
Bacillus anthracis str. Ames
Chloroplast
Cell Membrane
Epithelium
Kidney

35. Mammalian Red Blood Cell
Class: RedBloodCell
SubclassOf: Cell
That hasNucleation some Anucleate
and participatesIn some
OxygenTransport
and existsIn some mammalia
and part_of some BloodTissue
and developsFrom some Reticulocyte

36. Mesodermal Lineage Cells
Class: MesodermalLineageCell
EquivalentTo: Cell
That developsFrom some
MesodermalCell
(developsFrom is transitive)

37. Spreadsheet

38. Workflow
Spreadsheet CVS OPPL
OWL
Ontology
Reasoned
Ontology

39. Secreting Cells
Class: EpinephrinSecretingCell
SubclassOf: Cell
That belongs_to_line some Somatic
and has_nucleation some mononucleate
and has_ploidy some diploid
and potentiality some TerminallyDifferentiated
and participates_in some EpinephrineBiosyntheticProcess
and participates_in EpinephrineSecretion
Class: ProlactinSecretingCell
SubclassOf: Cell
That belongs_to_line some Somatic
and has_nucleation some mononucleate
and has_ploidy some diploid
and potentiality some TerminallyDifferentiated
and participates_in some PeptideHormoneSecretion
and participates_in some ProlactinSecretion

40. Defined Cells
Class: SecretoryCell
EquivalentTo: Cell
that participates_in some (secretion or
(part_of some secrection)
Class: EndocrineCell
EquivalentTo: Cell
that participates_in some (EndocrineProcess or
(part_of some EndocrineProcess)

41. Asserted Hierarchy

42. Inferred Hierarchy

43. What We Found
• More subsumption relationships
• The “is-a” hierarchy is complete
• Explicitness made us ask questions
• Found bad structure
• Can just slip in a new cell
• Can make arbitrary queries based on any
of the types of axis

44. Conclusions
• Can use strict semantics and automated
reasoning to build structurally sound ontologies
• Can catalogue instances and make discoveries
• If an object can be recognised by its features
and features can be computationally generated
classification can be automated
• High cost and high benefit

45. Acknowledgements
• Katy Wolstencroft did the protein
phosphtase work as part of her Ph.D.
• The work on the cell type ontology was
udnertaken by members of the EPSRC
OntoGenesis Network
• All the ontoogy work at Manchester relies
on the support and input of the wider
BioHealth and Information Management
Groups