eye what kinds of things exist? what are the relationships between these things? ommatidium sense...

eye

what kinds of things exist?

what are the relationships between these things?

ommatidium

sense organ

eye disc

is_a

part_of

developsfrom

A biological ontology is:

A machine interpretable representation of some aspect of biological reality

Three practical considerations

Acknowledge the substantive issues of sociology and operations to build community

Follow the principles of ontology construction hygiene

Annotation is the rate-limiting step. It can be facilitated by improved ontologies

Drosophila

Meeting the goal: Drawing inferences

A B C DSP:1234 SP:8723 SP:19345?

PMID:5555 PMID:4444

BSP:48392

B CSP:48291 SP:38921

Direct evidence Direct evidence

Indirect evidence

Indirect evidence

PMID:8976

PMID:9550 PMID:3924

Human

Xenopus

Overview Following basic rules helps make better ontologies

We will work through the principles-based treatment of relations in ontologies, to show how ontologies can become more reliable and more powerful

Why do we need rules for good ontology?

Ontologies must be intelligible both to humans (for annotation) and to machines (for reasoning and error-checking)

Unintuitive rules for classification lead to entry errors (problematic links)

Facilitate training of curators Overcome obstacles to alignment with other ontology and terminology systems

Enhance harvesting of content through automatic reasoning systems

Tactile senseTactionTactition

?

The Challenge of Univocity:People call the same thing by

different names

Tactile senseTactionTactition

perception of touch ; GO:0050975

Univocity: GO uses 1 term and many characterized synonyms

= bud initiation

= bud initiation

= bud initiation

The Challenge of Univocity: People use the same words to describe different things

Bud initiation? How is a computer to know?

= bud initiation

sensu Dentists

= bud initiation

sensu yeasties

= bud initiation

sensu agronomists

Univocity: GO adds “sensu” descriptors to discriminate

among organisms

The Importance of synonyms for utility:

How do we represent the function of tRNA?

Biologically, what does the tRNA do?Identifies the codon and inserts the amino acid in the growing polypeptide

Molecular_function

Triplet_codon amino acid adaptor activity

GO Definition: Mediates the insertion of an amino acid at the correct point in the sequence of a nascent polypeptide chain during protein synthesis.

Synonym: tRNA

The Challenge of Positivity

Some organelles are membrane-bound.A centrosome is not a membrane bound organelle,but it still may be considered an organelle.

The Challenge of Positivity: Sometimes absence is a

distinction in a Biologist’s mind

non-membrane-bound organelle

GO:0043228 membrane-bound organelle

GO:0043227

Positivity

Note the logical difference between “non-membrane-bound organelle” and “not a membrane-bound organelle”

The latter includes everything that is not a membrane bound organelle!

The Challenge of Objectivity: Database users want to know if

we don’t know anything (Exhaustiveness with respect

to knowledge)

We don’t know anything about a gene product with

respect to these

We don’t know anything about the ligand that

binds this type of GPCR

Objectivity

How can we use GO to annotate gene products when we know that we don’t have any information about them? Currently GO has terms in each ontology to describe unknown

An alternative might be to annotate genes to root nodes and use an evidence code to describe that we have no data.

Similar strategies could be used for things like receptors where the ligand is unknown.

Single Inheritance

GO has a lot of is_a diamonds Some are due to incompleteness of the graph

Some are due to a mixture of dissimilar classes within the graph at the same level

Is_a diamond in GO Process

behavior

locomotory behavior larval behavior

larval locomotory behavior

Is_a diamond in GO Function

enzyme regulator activity

GTPase regulatoractivity

enzyme activatoractivity

GTPase activatoracivity

Is_a diamond in GO Cellular Component

organelle

non-membrane boundorganelle

intracellularorganelle

non-membrane boundintracellular organelle

Technically the diamonds are correct, but could

be eliminatedlocomotory behavior larval behavior





What do these pairs have in common?

They are all differentiated from the parent term by a

different factorlocomotory behavior larval behavior





Type of behavior vs. what is behaving

What is regulated vs. type of regulator

Type of organelle vs. location of organelle

Insert an intermediate grouping term

behavior

locomotory behavior larval behavior

larval locomotory behavior

behavior of a thing

descriptive behavior

Why insert terms that no one would use?

behavior

locomotory behavior larval behavior rhythmic behavior adult behavior

By the structure of this graph, locomotory behaviorhas the same relationship to larval behavior

as to rhythmic behavior

Why insert terms that no one would use?

behavior

locomotory behavior larval behaviorrhythmic behavior adult behavior

But actually, locomotory behavior/rhythmic behaviorand larval behavior/adult behavior group naturally

Descriptivebehavior

Behaviorof athing

This type of single step differentiation of terms between levels would allow us to use distancesbetween nodes and levels to compare similarity.

GO DefinitionsA definition written by

a biologist:necessary & sufficient

conditions written definition(not computable)

Graph structure: necessary conditions

formal(computable)

Relationships and definitions

The set of necessary conditions is determined by the graph This can be considered a partial definition

Important considerations: Placement in the graph- selecting parents

Appropriate relationships to different parents

True path violation

The importance of relationships Cyclin dependent protein kinase

Complex has a catalytic and a regulatory subunit

How do we represent these activities (function) in the ontology?

Do we need a new relationship type (regulates)?Catalytic activity

protein kinase activity

protein Ser/Thr kinase activity

Cyclin dependent protein kinase activity

Cyclin dependent protein kinase regulator activity

Molecular_function

Enzyme regulator activity

Protein kinase regulator activity

Relationships provide the computable definitions

The definition of a class lower down in the hierarchy is provided by specifying the parent of the class together with the relevant differentia.

Differentia tells us what marks out instances of the defined class within the wider parent class.

Structured definitions contain both genus and differentiae

Essence = Genus + Differentiae

neuron cell differentiation =Genus: differentiation (processes whereby a relativelyunspecialized cell acquires the specialized features of..)Differentiae: acquires features of a neuron

Alignment of the Two Ontologies will permit the generation of consistent

and complete definitions

id: CL:0000062name: osteoblastdef: "A bone-forming cell which secretes an extracellular matrix. Hydroxyapatite crystals are then deposited into the matrix to form bone." [MESH:A.11.329.629]is_a: CL:0000055relationship: develops_from CL:0000008relationship: develops_from CL:0000375

GO

Cell type

New Definition

+

=Osteoblast differentiation: Processes whereby an osteoprogenitor cell or a cranial neural crest cell acquires the specialized features of an osteoblast, a bone-forming cell which secretes extracellular matrix.

Alignment of the Two Ontologies will permit the generation of consistent and complete definitions

id: GO:0001649

name: osteoblast differentiationsynonym: osteoblast cell differentiationgenus: differentiation GO:0030154 (differentiation)differentium: acquires_features_of CL:0000062 (osteoblast)definition (text): Processes whereby a relatively unspecialized cell acquires the specialized features of an osteoblast, the mesodermal cell that gives rise to bone

Formal definitions with necessary and sufficient conditions, in both human readable and computer readable forms

Basis in Reality

GO is designed by a consortium As long as egos don’t get in the way, GO represents universals rather than concepts

Large-scale developments of the GO are a result of compromise

Gene Annotators have a large say in GO content Annotators are experts in their fields Annotators constantly read the scientific literature

But, since GO is representing a science, GO actually represents paradigms.

Therefore, it is essential that GO is able to change!

Classes and Instances

When should we create a new class as opposed to multiple annotations?

When the the biology represents a universal principal. Receptor signaling protein tyrosine kinase activity does not represent receptor signaling protein activity and tyrosine kinase activity independently.

Consequences of inconsistencies

Hard to synchronize manually can be automated currently requires text mining

Inconsistent user-search results Problems likely to resurface with other ontologies embedded in the GO chemical, protein multiple species-specific anatomical ontologies

OBO ontologies should inform GO

Combinatorial annotation

Allow combinatorial annotations in appropriate context phenotypic annotation

eg “tail fin placement ventralized at stage pharyngula:prim25”

combinatorial GO annotation eg “activation of MAPK in mesenchymal cell”

New AmiGO will allow for these annotations

Animal models

Mutant Gene

Mutant or missing Protein

Mutant Phenotype

Animal disease models

Humans Animal models

Mutant Gene


Mutant Phenotype

(disease)

Mutant Gene


Mutant Phenotype

(disease model)

Animal disease models

SHH-/+ SHH-/-

shh-/+ shh-/-

Phenotype (clinical sign) = entity + attribute


P1 = eye + hypoteloric



P2 = midface + hypoplastic



P2 = midface + hypoplastic

P3 = kidney + hypertrophied


P1 = eye + hypoteloricP2 = midface + hypoplastic P3 = kidney + hypertrophied

PATO: hypoteloric

hypoplastic

hypertrophied

ZFIN: eye

midface

kidney

+


Anatomical ontology

Cell & tissue ontology

Developmental ontology

Gene ontology

biological process

molecular function

cellular component

+ PATO(phenotype and trait ontology)


P1 = eye + hypoteloricP2 = midface + hypoplastic P3 = kidney + hypertrophied

Syndrome = P1 + P2 + P3 (disease)

= holoprosencephaly

Human holo-prosencephaly

Zebrafishshh

Zebrafishoep

OMIM gene

ZFIN gene

FlyBase gene

FlyBase mut pub

ZFIN mut pub

mouse rat SNOMED

OMIM disease

LAMB1 lamb1 LanB1 5 15 39 -

FECH fech Ferro-

chelatase

2 5 2 29 Protoporphyria, Erythropoietic

GLI2 gli2a ci 388 41 22 -

SLC4A1 slc4a1 CG8177 7 7 19 Renal Tubular Acidosis, RTADR

MYO7A myo7a ck 84 5 9 3 16 Deafness; DFNB2; DFNA11

ALAS2 alas2 Alas 1 7 14 Anemia, Sideroblastic, X-Linked

KCNH2 kcnh2 sei 27 3 12 -

MYH6 myh6 Mhc 166 3 1 12 Cardiomyopathy, Familial Hypertrophic; CMH

TP53 tp53 p53 64 3 3 19 11 Breast Cancer

ATP2A1 atp2a1 Ca-P60A 32 6 1 11 Brody Myopathy

EYA1 eya1 eya 251 5 4 6 Branchiootorenal Dysplasia

SOX10 sox10 Sox100B 1 17 4 4 Waardenburg-Shah Syndrome

CBiO Bioinformatics Goals

1. Apply the ontology Software toolkit for “annotation”

applications

2. Manage the data Database and interface to store & view

annotations

3. Investigate and compare Linking human diseases to genetic models

4. Maintain Ongoing reconciliation of ontologies

with annotations

Ontology requirements

Ontologies attribute ontology GO anatomical ontologies

cell species-specific

Ontology integration technical semantic

Review of proposed (2003) EAV model

A phenotype is described using an Entity-Attribute-Value triple similiar “slot-value” frame-based systems

Entities are drawn from various OBO ontologies

Attributes and Values in one ontology - PATO

Separation of concerns

Not phenotypes: Genotype Environment Assay, measurement systems Images

Association = Genotype Phenotype Environment AssayPhenotype = Entity Attribute ValueEntity = OBOClassIDAttribute | Value = PATOClassID

schema:

2003 Pilot study

Trial of EAV model on small collection of genotypes FlyBase ZFIN

Genes were non-orthologous New curations - in progress

orthologous genes with clinical relevance Use the same data model and exchange format?

Example data records

Genotype Entity

Attribute Value

npo gut structure dysplastic

gut relative size small

r210 retina

pattern irregular

brain structure fused

ZFIN schema extension: stages

Genotype Stage Entity Attribute Value

npo Hatching:Pec-fin gut structure dysplastic

Hatching:Pec-fin gut relative size

small

r210 Hatching:Long-pec retina pattern irregular

Larval:Protruding-mouth


Stages

Association = Genotype Phenotype Environment AssayPhenotype = Stage* Entity Attribute ValueEntity = OBOClassIDStage = OBOAnatomicalStageClassIDAttribute | Value = PATOClassID

* means zero or more

Interpretation of the schema

How should EAV data records be interpreted by a computer? What are the instances?

Is the EAV schema just to improve database searching? Can it be used for meaningful cross-species comparisons?

What is the Entity slot used for?

Genotype Entity Attribute Value



r210 retina pattern irregular


tm84 d/v pattern formation

qualitative abnormal

blood islands

relative number number increased

Bsb[2] elongation of arista literal

process arrested

C-alpha[1D] adult behaviour

behavioral activity uncoordinated

2003 trial data: FB & ZFIN

What is the Entity slot used for?

In practical terms: An ID from one of the following ontologies

GO CC GO BP GO MF Species-specific anatomical ontology OBO Cell

Or a cross-product e.g. acidification GO:0045851

which has_locationOBOREL midgut FBbt00005383

[example from FBal0062296: Acidification in the midgut of homozygous larvae is often less than in wild-type larvae]

But what does it mean in the context of an annotation?

Universals and particulars

An ontology consist of universals (classes) fruitfly wing flight

Experimental data generally concerns particulars (instances) that instantiate universals

this particular wing of this particular fruitfly this particular fruitfly participating in this particular flight from here to there

in annotation we often use a class ID as a proxy for an (unnamed) instance (or collection of instances)

It is important to always keep this distinction in mind

What is an Entity?UBO - Upper Bio OntologyBFO - Basic Formal Ontology

What is the Attribute slot used for?








blood islands



process arrested



2003 trial data: FB & ZFIN

Attributes: a proposal

Treat as Qualities a dependent entity

a quality must have independent entity(s) as bearer

the quality inheres_in the bearer

Examples The particular shape of this ball The particular structure of this wing The particular length of this tail The particular rate of synaptic transmission between these two neurons

Attribute universals vs Attribute particulars

In an EAV annotation, a PATO class ID typically serves as a proxy for an unnamed attribute instance

Universals (classes) must always be defined in terms of their instances

A Formal Theory of Substances, Qualities, and Universals Fabian NEUHAUS Pierre GRENON Barry SMITH

How is the value slot used?








blood islands



process arrested



Redundancy in EAV triples

entity attribute value

fin shape irregular shape

eye color hue blue

mesenchyme relative thickness

thin


retinal cells

relative orientation

disoriented

Proposal: EA modelentity attribute value


eye color hue blue


thin


retinal cells


disoriented

Proposal: EA modelentity attribute attribute


eye color hue blue


thin


retinal cells


disoriented

Association = Genotype Phenotype Environment AssayPhenotype = Stage* Entity Attribute ValueEntity = OBOClassIDAttribute = PATOVersion2ClassID

Proposed schema modification

Monadic and relational attributes

Monadic: the quality/attribute inheres in a single entity

Relational: the quality/attribute inheres in two or more entities

sensitivity of an organism to a kind of drug sensitivity of an eye to a wavelength of light

can turn relational attributes into cross-product monadic attributes

e.g. sensitivityToRedLight better to use relational attributes

avoids redundancy with existing ontologies

Association = Genotype Phenotype Environment AssayPhenotype = Stage* Entity Attribute Entity*Entity = OBOClassIDAttribute = PATOVersion2ClassID

Incorporating relational attributes

Example data record:Phenotype = “organism” sensitiveTo “puromycin”

Measurable attributes

Some attributes are inexact and implicitly relative to a wild-type or normal attribute relatively short, relatively long, relatively reduced

easier than explicitly representing: this tail length shorter-than ‘canonical mouse’ wild-type tail length

Some attributes are determinable use a measure function

unit, value, {time} this tail has length L

measure(L, cm) = 2

Keep measurements separate from (but linked to) attribute ontology

Incorporating measurements

Association = Genotype Phenotype Environment AssayPhenotype = Stage* Entity Attribute Entity* Measurement*Measurement = Unit Value (Time)Entity = OBOClassIDAttribute = PATOVersion2ClassID

Example data record:Phenotype = “gut” “acidic” Measurement = “pH” 5

Composite phenotype classes

Mammalian phenotype has composite phenotype classes e.g. “reduced B cell number”

Compose at annotation time or ontology curation time? False dichotomy

Core 2 will help map between composite class based annotation and EA annotation

Interpreting annotations

Annotations are data records typically use class IDs implicitly refer to instances

How do we map an annotation to instances?

Important for using annotations computationally

Interpreting annotations (1)

What does an EA (or EAV) annotation mean? Annotation:

Genotype=“FBal00123” E=“brain” A=“fused” presumed implied meaning:

this organism has_part x, where

x instance_of “brain”x has_quality “fused”

or in natural language: “this organism has a fused brain”

Various built-in assumptions

Interpreting annotations (II)

What does this mean: annotation:

Genotype=“FBal00123” E=“wing” A=“absent” using same mapping as annotation I:

fly98 has_part x, where x instance_of “wing” x has_quality “absent”

or in natural language: this fly has a wing which is not there !

What we really intend: NOT(this organism has_part x, where x instance_of “wing”)

Interpreting annotations (II)


Genotype=“FBal00123” E=“wing” A=“absent” using same mapping as annotation I:

this organism has_part x, where x instance_of “wing” x has_quality “absent”

or in natural language: this fly has a wing which is not there !

What we really intend: this organism has_quality “wingless” “wingless” = the property of having count(has_part “wing”)=0

Are our computational representations intended to capture linguistic statements or reality?

Does this matter?

Logical reasoners will compute incorrect results unless explicitly provided with specific rules for certain attributes such as “absent”

What are the consequences? Basic search will be fine

e.g. “find all wing phenotypes” But computers will not be able to reason correctly

Interpreting annotations (III)


E=“digit” A=“supernumery” using same interpretation as annotation I:

this organism has_part x, where x instance_of “digit” x has_quality “supernumery”

or in natural language: this organism has a particular finger which is supernumery

What we really intend: this person has_quality “supernumery finger” “supernumery finger” = the property of having count(has_part “digit”) > wild-type”

!!!

Interpreting annotations (IV)


Gt=“mp001” E=“brown fat cell” A=“increased quantity”

using same mapping as annotation I: this organism has_part x, where

x instance_of “brown fat cell” x has_quality “increased quantity”

or in natural language: this organism has a particular brown fat cell which is increased in quantity

What we really intend: this organism has_part population_of(“brown fat cell”) which has_quality increased size

Other use cases

spermatocyte devoid of asters Homeotic transformations increased distance between wing veins

Some vs all

Alternate perspectives

process vs state regulatory processes:

acidification of midgut has_quality reduced rate

midgut has_quality low acidity

development vs behavior wing development has_quality abnormal flight has_quality intermittent

granularity (scale) chemical vs molecular vs cell vs tissue vs anatomical part

Summary

Define attributes in terms of instances

Evaluate proposed new schema measurement proposal relational attribute proposal

Complexity trade-off create library of use cases Core2 will create tools to present user-friendly layer

Alternate perspective annotations are useful

Ontologies must adapt over time

Getting it right It is impossible to get it right the 1st (or 2nd, or 3rd, …) time.

What we know about biology is continually growing

This “standard” requires versioning.

Improve

Collaborate and Learn

Our Outlook

“The needs of the many outweigh the needs of the few, or of the one.”

eye what kinds of things exist? what are the relationships between these things? ommatidium sense...

Documents

membrane bound organelle

nonmembranebound organelle

membranebound organellego

membranebound organellethe

biological ontology

challenge of objectivity

biologists mindnonmembrane

good ontology