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PATO An Ontology of Phenotypic Qualities. George Gkoutos University of Cambridge. Phenotype Information. Literature Qualitative descriptions Experimental data Qualitative descriptions Quantitative descriptions Various representation methodologies Complex phenotype data Need for :

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pato an ontology of phenotypic qualities

PATOAn Ontology of Phenotypic Qualities

George Gkoutos

University of Cambridge

phenotype information
Phenotype Information
  • Literature
    • Qualitative descriptions
  • Experimental data
    • Qualitative descriptions
    • Quantitative descriptions
  • Various representation methodologies
  • Complex phenotype data
  • Need for :

“A platform for facilitating mutual understanding and interoperability of phenotype information across species and domains of knowledge amongst people and machines” …..

slide4

Assay Controlled Vocabulary

  • Abnormality
  • Relative_to
  • Ranges of values
  • Allows the schema to be dynamic
  • Definition of qualities and their relations
  • Explicit differences (between laboratories)
  • Allows labs around the world to “plug-in” their
  • assays to the schema

Phenotypic

Character

Assay

Phenotypic

Character

Phenotypic

Character

phenotypic character representation methodologies
Phenotypic character representation methodologies
  • Pre-composition
    • Examples:
      • MGI Mouse genotype-phenotype annotation (Mammalian Phenotype)
      • Gramene trait annotation (Plant trait ontology)
      • etc.
    • Pre-composition often follows the compositional structure occasionally adopted by GO terms.
      • Positive/negative regulation of mitosis  positive/negative + regulation of mitosis (GO:0045839)
      • Increased/decreased angiogenesis  increased/decreased + angiogenesis (GO:0001525)
    • Advantages
      • Easy for annotation
      • Control
      • Complex phenotypic information
    • Disadvantages
      • Lack of rigidity
      • Ontology management
      • Expansion
      • Quantitative data
methodologies cont
Methodologies (cont.)
  • post-composition
    • The post-composition methodology takes advantage of the ability to describe phenotypes by describing the particular affected entity (bearer), which could be an anatomical structure, a biological process, a particular function etc. , and the qualities that this entity possess, which can be described either in qualitative or quantitative terms.  
    • Advantages
      • Ontology management
      • Rigidity
      • expansion
      • Quantitative data
      • Advanced queries
    • Disadvantages
      • Complex phenotypic information
      • More difficult for annotation
      • Need for constraints for ensuring meaningful annotations
phenotype and trait ontology pato
Phenotype And Trait Ontology (PATO)
  • An ontology of phenotypic qualities, which can be shared across different species and domains of knowledge.
  • Qualities are the basic entities that we can perceive and/or measure:
    • colors, sizes, masses, lengths etc.
  • Qualities inhere to entities: every entity comes with certain qualities, which exist as long as the entity exist.
  • Qualities belong in a finite set of quality types (i.e. color, size etc) and inhere in specific individuals. No two individuals can have the same quality, and each quality is specifically constantly dependent on the entity it inheres in.
phenotypic character
Phenotypic Character

Core Ontologies

(e.g. anatomy, behaviour, pathology)

PATO

Species Independent

PATO

Species Independent

Entity (E)

Quality (Q)

EQ

Phenotype Description

EQ

Phenotype Description

simple phenotype descriptions

entity + quality

Phenotypic Character

(mouse anatomy: body + PATO: weight)

(mouse body weight)

(Drosophila anatomy: eye + PATO: colour)

(eye colour)

(glucose concentration)

(ChEBI: glucose + PATO: concentration)

Simple phenotype descriptions

increased size hepatocellular carcinoma

hepatocellular carcinoma (MPATH:357) has_qualityincreased size (PATO:0000586)

phenotype annotation model
Phenotype annotation model

Genetic

Environment

Evidence

Qualifier

Assertion

Source

Entity

Quality

relationship

Attribution

Properties

Units

Who makes the assertion

When, what organization

annotation phenotypes in literature
Annotation:Phenotypes in literature

Evidence:

light microscopy

Source:

PMID:8431945

Assertion

eya1

E=eye disc

(FBbt:00001768)

Q=condensed

(PATO:0001485)

influences

appears

Date: 10/26/2007

Organization: FlyBase

Version: 1

M. Ashburner

quantitative data
Quantitative Data
  • PATO – part of a representation of qualitative phenotypic information
  • More often than not it is important to record quantitative information that results from a specific measurement of a quality
  • Measurements involve units (Phenotypic Character + Unit)

The tail of my mouse is 2.1 cm

pato measurements
PATO & measurements
  • UO – an ontology of unit
    • UO’s top-level division is between primary base units of a particular measure and units that are derived from base units
    • mapping between the various scalar qualities (such as weight, height, concentration etc.) and the corresponding units used to measure those qualities
  • UO includes 264 terms, all of which are defined
  • email list (http://sourceforge.net/mailarchive/forum.php?forum_id=50613)
linking quantitative data to qualitative descriptions
Linking quantitative data to qualitative descriptions
  • Measurement  qualitative description
  • Assay
    • range
    • normality
    • necessary & sufficient conditions
  • EQ descriptor  high level annotation marking phenodeviance (e.g. MP)
multiple phenotypic characters to describe complex phenotypes
Multiple phenotypic characters to describe complex phenotypes

SHH-/+

SHH-/-

shh-/+

shh-/-

slide17

Phenotype

(character) = entity + quality

slide18

Phenotype

(character) = entity + quality

P1 = eye + hypoteloric

slide19

Phenotype

(character) = entity + quality

P1 = eye + hypoteloric

P2 = midface + hypoplastic

slide20

Phenotype

(character) = entity + quality

P1 = eye + hypoteloric

P2 = midface + hypoplastic

P3 = kidney + hypertrophied

slide21

Phenotype

(character) = entity + quality

P1 = eye + hypoteloric

P2 = midface + hypoplastic

P3 = kidney + hypertrophied

PATO:

hypoteloric

hypoplastic

hypertrophied

ZFIN:

eye

midface

kidney

+

slide22

Phenotype

(character) = entity + quality

P1 = eye + hypoteloric

P2 = midface + hypoplastic

P3 = kidney + hypertrophied

Phenotype = P1 + P2 + P3

(phenotypic profile) = holoprosencephaly

assays for complex phenotype data quantitative data
Assays for complex phenotype data & quantitative data

Phenotypic

Character

Assay

Phenotypic

Character

Phenotypic

Character

  • necessary
  • necessary & sufficient
  • phenodeviance
linking qualitative descriptions across species
Linking qualitative descriptions across species
  • Decomposition of precomposed phenotype ontologies by providing logical definitions based on PATO
  • Link annotations across different knowledge domains and species
  • Link phenotypic descriptions of human diseases to animal models
reconciling pre and post composed annotations
Reconciling pre and post composed annotations
  • Retrospective PATO definitions of pre-coordinated terms in phenotype ontology
  • Precomposed Ontologies
    • Mammalian Phenotype
    • Plant trait
    • Worm phenotype
    • etc.
  • OMIM
eq definitions
EQ definitions

Aristotelian definitions (genus-differentia)

A <Q> *which* inheres_in an <E>

[Term]

id: MP:0001262

name: decreased body weight

namespace:mammalian_phenotype_xp

Synonym:low body weight

Synonym: reduced body weight

def: "lower than normal average weight “[]

is_a: MP:0001259  ! abnormal body weight

intersection_of: PATO:0000583 ! decreased weight

intersection_of: MA:0002405 ! adult mouse

phenotypic information captured differently across different domains
Phenotypic information captured differently across different domains
  • MP:0001265 – decreased body size
    • MP:0001255 – decreased body height
  • WBPhenotype0000229 – small
  • OMIM %210710 – short stature
logical definitions allow for cross species domain links
Logical definitions allow for cross species – domain links

[Term]

id: MP:0001265 ! decreased body size

intersection_of: PATO:0000587 ! decreased size

intersection_of: inheres_in MA:0002405 ! adult mouse

[Term]

id: MP:0001255 ! decreased body height

intersection_of: PATO:0000569 ! decreased height

intersection_of: inheres_in MA:0002405 ! adult mouse

[Term]

id: WBPhenotype0000229 ! small

intersection_of: PATO:0000587 ! decreased size

intersection_of: OBO_REL:inheres_in WBls:0000041 ! Adult

[Term]

id: OMIM:xxxxxxx ! short stature

intersection_of: PATO:0000587 ! decreased size

intersection_of: OBO_REL:inheres_in FMA!:20394 ! Body

[Term]

id: OMIM:xxxxxxx ! short stature

intersection_of: ATO:0000569 ! decreased height

intersection_of: OBO_REL:inheres_in FMA:20394 ! Body

experimental design
Experimental Design
  • Annotate 11 human disease genes, and their homologs
  • Develop search algorithm that utilizes the ontologies for comparison
  • Test search algorithm by asking, “given a set of phenotypic descriptions (EQ stmts), can we find…”
    • alleles of the same gene
    • homologs in different organisms
    • members of a pathway (same organism)
    • members of a pathway (other organisms)
strategy for annotation
Strategy for Annotation

Leverage OMIM gene and related disease records

Use FMA, CL, GO, EDHAA, CHEBI, PATO ontologies

Annotate 5 (in parallel) to check for curator consistency

Annotate fly & fish orthologs (FB, ZFA)

Import mouse ortholog data (MA, MP)

testing the methodology
Testing the methodology

Annotated 11 gene-linked human diseases described in OMIM, and their homologs in zebrafish and fruitfly:

experimental design37
Experimental Design
  • Annotate 11 human disease genes, and their homologs
  • Develop search algorithm that utilizes the ontologies for comparison
  • Test search algorithm by asking, “given a set of phenotypic descriptions (EQ stmts), can we find…”
    • alleles of the same gene
    • homologs in different organisms
    • members of a pathway (same organism)
    • members of a pathway (other organisms)
ontology based similarity scoring
Ontology-based similarity scoring

Measure IC of any node:

Compute ‘similarity’ by finding IC ratios between any genotypes, genes, classes, etc.

ontology based search algorithm
Ontology-based Search Algorithm

c ∈ A(q) iff link(r,q,c)

link(influences,sox9,curvature-of-tibia) → link(influences,sox9,morphology-of-bone)

Given a query node q, we try to find hits h1, h2,... that are of the same type as q, and are similar to q in terms of their annotation profile, A(q).

First step: create an annotation profile for the thing to be searched (i.e., a gene)

The annotation profile is the set of classes used to annotate that entity, and their ancestors

Comparing annotation profiles using same similarity IC metric

experimental design41
Experimental Design
  • Annotate 11 human disease genes, and their homologs
  • Develop search algorithm that utilizes the ontologies for comparison
  • Test search algorithm by asking, “given a set of phenotypic descriptions (EQ stmts), can we find…”
    • alleles of the same gene
    • homologs in different organisms
    • members of a pathway (same organism)
    • members of a pathway (other organisms)
uberon an anatomical linking ontology
UBERON: an anatomical linking ontology

Each organism has its own anatomical ontology

To connect annotations across species, need a way to link the anatomies

Wanted an ontology that incorporated both functional homology and anatomical similarity

Created an ontology linking anatomies from ZFA, FMA, XAO, MA, MIAA, WBbt, FBbt

experimental design45
Experimental Design
  • Annotate 11 human disease genes, and their homologs
  • Develop search algorithm that utilizes the ontologies for comparison
  • Test search algorithm by asking, “given a set of phenotypic descriptions (EQ stmts), can we find…”
    • alleles of the same gene
    • homologs in different organisms
    • members of a pathway (same organism)
    • members of a pathway (other organisms)
results thus far
Results thus far
  • Annotate 11 human disease genes, and their homologs
  • Develop search algorithm that utilizes the ontologies for comparison
  • Test search algorithm by asking, “given a set of phenotypic descriptions (EQ stmts), can we find…”
    • alleles of the same gene
    • homologs in different organisms
    • members of a pathway (same organism)
    • members of a pathway (other organisms)
conclusions
Conclusions

Ontologies help

Promising new directions for ontology-based phenotype annotation

Promising ways for identifying novel pathway members, generating hypotheses to test at the bench

acknowledgements
Acknowledgements

NCBO-Berkeley

  • Christopher Mungall
  • Nicole Washington
  • Mark Gibson
  • Rob Bruggner

U of Oregon

  • Monte Westerfield
  • Melissa Haendel

Cambridge

  • Michael Ashburner
  • George Gkoutos (PATO)
  • David Osumi-Sutherland

National Institutes of Health