Weaving and untangling the GO

•

is_a

completeness ~9 slides • granularity & BP ~3 slides • Linking MF to BP ~15 slides • Sensu ~13 slides – linguistic qualifiers vs relations • Linking GO to other ontologies ~40 slides – GO+Cell

Tangled DAGs and complexity

• paths increasing • GO process

in general

has a multiple axes of classification – qualifier -ve +ve – anatomy • structural • spatial – chemical • structural • functional

is_a

completeness

GO and

is_a

completeness

• Why?

• What’s wrong with every term having at least one is_a

or

part_of parent?

– this is the way we’ve always done things

Ontologies should be complete

• • No errors of omission

is_a

completeness is the ontologically correct thing to do – every entity type is a subtype of some other thing • Accurate ontologies = accurate queries – currently a query for “find all kinds of development” does not return “ovarian follicle development” • this is wrong

missing

is_a

s hinders common tool use

• We should play nicely with the others in the playground • Most (non-GOC) tools expect is_a completeness – GO looks funny when viewed in other tools • the standard is to show only

is_a

relations in default tree view – missing

is_a

s breaks reasoners

Filling

is_a

gaps brings practical benefits

• • Easier for tools to find inconsistencies in GO

We can start to untangle displays

Example: current displays mix relations

• it’s a mess

untangling

is_a

and

part_of

• difficult if

is_a

hierarchy is incomplete –

is_a is_a

orphans show up at root node in pure display • not everything must have an asserted

part_of

parent – can infer from

is_a

parents

The new complete cellular component

• Current CC: – 277 is_a orphans / 1688 terms – avg is-a-paths-to-root 1.4

– avg mixed-paths-to-root 6.97

• Jane’s fixed CC: – 0 is_a orphans – avg is-a-paths-to-root 3.36

– avg mixed-paths-to-root 38.6

Granularity and the organisation of GO:BP

Fixing the upper levels of BP

• The upper portion of any ontology is very important for organisation • Design decisions percolate down • Many users exploring GO top-down see this first • Diamonds are particularly bad in the upper level – significantly increases tangledness

others cellular process biological process physiological process cellular physiological process organismal physiological process

biological process Processes that are carried out

at the cellular level

, but are not necessarily restricted to a single cell. For example, cell communication occurs among more than one cell, but occurs at the cellular level cellular process A phenomenon marked by changes that lead to a particular result, mediated by one or more gene products Those processes specifically pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms physiological process The processes pertinent to the integrated function of

a cell

cellular physiological process organismal physiological process The processes pertinent to the function of an organism

above the cellular level

; includes the integrated processes of tissues and organs

Consider… (long term view)

• Making top division by

granularity of the process itself

– biological process • molecular level process?

• cellular level process • (multi-cellular) level process • These types are

disjoint

• But what about physiological process?

– this is not disjoint from the granularity of the process itself

Relations between GO ontologies

Outline

• We focus on MF & BP • biological example from David • the types and relations in reality – maintaining the ALL-SOME definition of relations • how should this be implemented in the GO?

– what links should be manifested – retain some level of redundancy, or eliminate it?

GO:0006548 Histidine catabolism GO:0019557 Histidine catabolism to glutamate and formate formamide GO:0004397 Histidine ammonia GO:0050480 imidazolopropionase activity Formimidoyl GO:0050416 Formimidoylglutamate deiminase activity formiminotetrahydrofolate Overbeek, et al. The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes. NAR 2005, 33-17:5691-5702

Ontological Representation

• I will try and be clear when I am talking about – types in reality – types we wish to manifest as terms in the GO (or in other ontologies) • all GO terms should be types • not all types need to have terms created - we limit for practical reasons

What are the relations in reality?

• Between types in the same ontology, different levels of granularity – part_of • Between functions and processes (at the same level of granularity) – functioning_of • Between component and function – has_function • Between process and component – located_in

What are the instances and relations in reality?

some gene product instance has function some molecular function instance functioning of function some multistep process instance part_of some molecular function

ING

instance process

What are the types and type level relations in reality?

some type of gene product has function some type of molecular function function functioning of some type of multistep process part (direction?) some type of molecular function

ING

process

types example

issues: -- ALL-SOME structure histidine catabolism part?

coarse histidine ammonia lyase

function

function functioning of histidine ammonia lyase

reaction

process fine

What are the types and relations in reality?

issues: -- ALL-SOME structure histidine catabolism to glutamate and formate has part?

coarse Formimidoylglutmat e deiminase

function

function functioning of Formimidoylglutmat e deiminase

reaction

fine process

We want to capture these real relationships between biological types

• Between granular levels • Between orthogonal ontologies • But first we must be clear on the definitions of these types, and which types should be manifested as GO terms

Can we just manifest this in the GO?

issues: -- not all function terms have a function

ING

corresponding term some type of multistep -- even if they do, redundancy is generally to be avoided process has part(?) coarse some type of molecular function function functioning of some type of molecular function

ING

process fine

We already have some redundancy

• function & process redundancy • iron transport (BP) • iron transporter (MF) • function & component redundancy • voltage-gated ion channel function • voltage-gated ion channel complex • If we retain this redundancy, these relations can be trivially added • But we don’t always have this redundancy – not all functions have a corresponding functioning term

Manifest shortcut relationships

• one relation standing for two some type of process has part(?) coarse some type of molecular function function functioning of some type of molecular function

ING

process fine

most functionings are implicit

• current paradigm coarse histidine catabolism has part(?) histidine ammonia lysase function function functioning of histidine ammonia lyase REACTION process fine

When do we manifest functions and processes?

• Need consistent stable policy • Nothing in function ontology should have activity suffix – even though to a biochemist activity==potential, this is still confusing • Beyond this, do we retain current policy – some redundancy • Or take a more extreme approach – eliminate redundancy – eliminate current ‘activity’ MF terms and manifest corresponding reaction terms in BP (Amelia)

‘purist process’ approach

some type of gene product has function histidine ammonia lyase

function

function functioning of histidine catabolism part histidine ammonia lysase

reaction

process

When is it safe to eliminate redundancy?

• Does functioning always imply function?

– iron transport does not imply iron transporter – but we could still extend annotation to allow for specification of functioning-as-function • Reactions and other ‘single-step’ processes involving no helper – function and corresponding functioning imply one another • Redundancy between function and component should be retained • Any obsoletion obviously causes disruption

Difficult functionings

• Structural constituents • function

ing

happens at lower level of granularity than is covered by GO • these will not be linked to process - for now

Implementation

• Still need to curate the actual links – trivial links can be computed automatically • Can proceed independently of resolving ontological issues – most likely retain current policy re: manifesting terms – need maintain 3 kinds of links • granular (part, same ontology) • functioning_of (function and functioning) • ‘diagonal’ – ALL-SOME definition

Sensu

Sensu - outline

• Original use – A linguistic qualifier – denote differing community usage of a terminological entity (a term) • Perverted use – A type qualifier – Used for when the part_of structure is specific to an organism type • The fix – provide separate mechanisms for each

Terms vs kinds

• The term ‘term’ is confusing – Term (sensu GO) – Term (sensu normal usage) • strings, tokens • GO is not a terminology • A GO ID identifies a

type

– a

kind

of entity of entity – a

universal

(as opposed to instance) – more specific than a

class

– but not a concept

Sensu - original usage

• Sometimes the same

string

refers to different

types

– nucleus (sensu particle physicist) – nucleus (sensu astrophysicist) – nucleus (sensu biologist) • Canonical GO example: –

bud

• no longer relevant, terms obsoleted –

trichome

Linguistic qualifiers are about language, not biological reality

• No ontological requirement for linguistically related terms to be ontologically related – current GO docs are not correct • trichome, sensu plant community – should not state that there is some biological relation between an instance of a trichome and the plant community

The original usage has been conflated

• Organism type specificity is a genuine challenge for the GO – ‘contextual’ part_ofs – e.g. X part_of Y in species Z • Sensu has been wrongly recruited to fix this – standard pattern: • X, sensu Z

part_of

Y • X, sensu Z

is_a

Z • Two problems – conflation of meaning of sensu – conflation results in lack of precision • “as in, but not restricted to taxon” not rigorous enough

Two problems, two solutions

• Retain sensu as a linguistic qualifier only – re-interpret as:

sensu S community

– no requirement for taxon IDs – no ontology structure requirements • Introduce a new relation for genuine organism-type specific terms –

in_organism

– standard inference rules can be used •

e.g.

–

X in_organism X’, Y in_organism Y’, X is_a Y <=> X’ is_a Y’

Contextual synonyms

[Term] name: trichome (sensu insecta) synonym: EXACT “hair” [] synonym: EXACT “trichome” [] {context=insecta} def: “ a polarized cellular extension that covers much of the insect epidermis ” [Term] name: trichome (sensu plant) synonym: EXACT “trichome” [] {context=plant} def: “ An outgrowth from the epidermis. Trichomes vary in size and complexity and include hairs, scales, and other structures and may be glandular. In Arabidopsis, patterning of trichome development is not random but does not appear to be lineage-based like stomata ”

Advantages

• Lexical qualifiers dealt with use lexical oboedit tags • No need to be as specific as a taxon – only as specific as is needed to decontextualise • No false reasoning is done over synonyms – cellular component types and cell types should not be siblings • Big user-friendliness win?

– Displays customised for particular users may choose to display contextual exact synonyms in place of the wordier sensu name

in_organism • Standard ALL-SOME definition: • Type level definition: – P in_organism O • for all instances p of P, there exists some organism o of type O, and some time t, such that p in_organism o at time t • More specific relation than

located_in

OBO relations ontology in • Standard logical rules can be applied

thylakoid

is_a

thylakoid, in cyanobacteria

in organism

photosystem I

part of is_a

photosystem I, in cyanobacteria

in organism

cyanobacteria

Open question

• Sometimes the relation between two types is largely lexical – eg trichome • Sometimes it isn’t so clear • Can we have both a relation to a taxon,

and

contextual synonyms a • Is ‘eye’ an exact contextual synonym for ‘compound eye’ for the arthropod community?

Practical considerations

• Use NCBI Taxonomy as our organism ontology • xref or relationship tags?

– xrefs are more lightweight – relationship tags are more accurate – relationship tags would be ‘dangling’ unless organism ontology is loaded • See next section…

Composite terms in GO finally…

Composite terms - outline

• The problems inherent in composite terms and diamonds - brief review • Actively managing composite terms in GO – big change: parseable

logical definitions

• Implementation plan • Progress so far: logical definitions referring to cell types • Pre vs post composition – composite terms in ontologies and annotations

biosynthesis

is_a

metabolism

cysteine

is_a

serine family amino acid

is_a

amino acid

is_a

amine

cysteine

is_a

serine family amino acid

is_a

amino acid

is_a

serine

Composed terms currently cause problems

– No link to external ontology term – Redundancy – Inconsistency – Extra work – Annotation bottleneck – Tangled DAGs and confusing displays • we have no way to disentangle • Solution so far: – fix errors based on results of term name parsing (Obol) • reactive, not proactive

Solution:

actively manage

composed terms

• Composed terms should now/soon be generated using oboedit plugin – building block terms are

recorded in ontology

along with composite term • Correct DAG structure can be inferred from external ontologies – placement & consistency checking automated – additional work can be automated • synonyms, text definitions

How will composite terms be recorded by oboedit?

• How do we record a definition for a composite term?

– using a

logical definition

(computational

essence

) • A logical definition consists of: – a

generic

term (aka genus) – relationships to other terms which serve to

discriminate

this specific term from other is_a children of the generic term (aka differentiae) • Can be written in natural language as: – A <

generic term

> which <

discriminating characteristics

>

Example of composite term record

• cysteine biosynthesis – generic term: • biosynthesis – discriminating characteristics: •

outputs

cysteine – a biosynthesis process which

outputs

cysteine id: GO:0019344 ! cysteine biosynthesis intersection_of: GO:0009058 ! biosynthesis intersection_of: outputs CHEBI:15356 ! cysteine

Now we have the ability to untangle

• Process axis view (primary

is_a

s, via generic term): – biological_process • metabolism – biosynthesis » cysteine biosynthesis • Process participant axis view: – amine • amino acid – serine family amino acid » cysteine • Combined view – (same as current tangled diamond lattice)

Recording the relationship is important

• Why not just a simple cross-product?

– e.g. biosynthesis x cysteine • Relationships are important for reasoning and querying – Consider: • cysteine biosynthesis from serine • mRNA export from nucleus during heat stress • Without the relations, the logical definition is not specific enough – the

essence

is not captured

Multiple discriminating characteristics are allowed

• Cysteine biosynthesis from serine – Generic term: • biosynthesis – Discriminating characteristics: • •

output

cysteine

input

serine intersection_of: GO:0009058 intersection_of: outputs CHEBI:15356 intersection_of: input CHEBI:17822

Composite terms can be nested

• regulation of cysteine biosynthesis intersection_of: GO:0050789 ! regulation of biological process intersection_of: regulates GO:0019344 ! cysteine biosynthesis id: GO:0019344 ! cysteine biosynthesis intersection_of: GO:0009058 intersection_of: outputs CHEBI:15356

Composite terms can optionally be manufactured in bulk

• Generic term: {metabolism,biosynthesis} • Differentia:

has_output

cysteine, …} {serine, • With caution… – Sparse vs dense matrices – not all combinations are types

On the importance of necessary

and sufficient

conditions

• Why intersection_of ?

• Why not just make normal links in the GO DAG?

– normal relationships are for necessary conditions only – we want

both

necessary and sufficient conditions • captures the

essence

of the term

Normal DAG links only capture

necessary conditions

, not

essence

immune cell activation inflammatory response text def: A change in morphology and behavior of a macrophage resulting from exposure to a cytokine, chemokine, cellular ligand, pathogen, or soluble factor macrophage activation part_of

Normal DAG links only capture necessary conditions, not essence

macrophage immune cell activation is_a inflammatory response activates macrophage activation part_of

essence

captured by genus differentia

immune cell activation is_a inflammatory macrophage activation part_of id: GO:macrophage_activation intersection_of: GO:cell_activation intersection_of: activates CL:macrophage response

essence

captured by genus differentia

text def: A change in morphology and behavior of a macrophage resulting from exposure to a cytokine, chemokine, cellular ligand, pathogen, or soluble factor immune cell activation is_a inflammatory response macrophage activation part_of id: GO:macrophage_activation intersection_of: GO:cell_activation intersection_of: activates CL:macrophage

essence

captured by genus differentia

cell activation (genus) activates macrophage immune cell activation is_a macrophage activation inflammatory part_of response

The power of reason

• with genus-differentia definitions that are computationally parseable, we can do a lot more consistency checking

Pre- vs post- composition

• It makes sense to pre-compose terms and maintain them as part of GO • Annotations can post-compose terms if they choose to do so – MGI, DictyBase are doing this already • results remain local to MOD – AmiGO-NG will allow querying of these • The two approaches are

complementary compatible

– proviso: if done properly and

SO already contains composite terms

• A silenced gene is a

gene

which has the quality of being

silenced

Plan: outline

• We want all new composite terms to be created using appropriate oboedit plugin – logical definitions automatically recorded – term management automated • Changes: – editors

must

now be ‘OBO-aware’ – annotators and end-users can remain unaware of changes

if they choose to do so

• but using the logical defs can bring benefits • But first we need to find logical definitions for all the existing composite terms

Where we were at, 2005

• Lots of terms to be retrofitted – Where to start?

• Previous strategy: – Obol guesses logical def for each term – Obol uses logical def to reason • errors of omission • inconsistencies – Batch reports to curators

OBO editor cjm obol config go.obo

oboedit name parser go+ ldefs reasoner obol go ‘fixed’ GO editor obol report

Obol produces genus-differentia logical definitions

OBO editor go.obo

oboedit GO editor cjm obol config name parser Ego.obo

reasoner obol go ‘fixed’ obol report

Limitations of this approach

• Good as proof-of-principle • But..

– only the

end results

are evaluated – Obol makes the identical mistakes in

guessing logical definitions

each iteration – we want to evaluate and preserve the logical definitions that are generated by Obol

What we’ve been doing since then

• Focused on OBO Cell ontology • Used Obol to infer logical defs • Manually curate logical defs • Feed back results to improve Obol • Iterate and refine • Use oboedit reasoner to check consistency between GO & CellO • Next: incorporate into curation process

OBO editor cjm obol config go.obo

oboedit name parser GO editor ego-cell .obo

obol

Results so far

• Test set of 337 logical definitions curated – only a fraction of the composite terms in GO • Relations not finalised • Composite terms involving CellO present some interesting challenges • …but first, here’s a demo

Open issues: what relations do we use?

• We are concerned for now with relations between processes and cells – neuroblast activation & neuroblast – T cell differentiation & T cell – T cell homeostasis & T cell – cell homeostasis & homeostasis – sperm incapacitation & sperm – sperm motility & sperm

OBO Relations ontology

• OBO Relations ontology has – has_participant • sub-relations: – has_agent (active participant) – has_patient (inactive participant) » (not in obo-rel yet) – between a process and a

continuant

– follows standard ALL-SOME structure

has_participant •

P

has_participant

C

if and only if: given any process

p

that instantiates

P

there is some continuant

c

, and some time

t

, such that:

c

instantiates

C

at

t

and

c

participates in

p

at

t

• has_participant is a primitive instance-level relation between a process, a continuant, and a time at which the continuant participates in some way in the process. The relation obtains, for example, when this particular process of oxygen exchange across this particular alveolar membrane has_participant this particular sample of hemoglobin at this particular time

Is this the appropriate relation?

neuroblast activation

has_participant

neuroblast T cell differentiation

has_participant

T cell T cell homeostasis

has_participant

T cell cell homeostasis

has_participant

homeostasis sperm incapacitation

has_participant

sperm sperm motility

has_participant

sperm these are all correct… …but are they too general?

more specific kinds of participation

• has_agent (has_active_participant) – As for has_participant, but with the additional condition that the component instance is causally active in the relevant process • has_patient (has_inactive_participant) – Yes, this is a daft name – The component instance is acted upon • (not yet in OBO REL)

Cell differentiation

• T cell differentiation – A cell differentiation instance in which a cell

acquires_features_of

T cell • problem: – not a simple relation between the process (T cell differentiation) and the cell (T cell) • 3-place relation: process, instance, type

Cell differentiation, attempt 2

• T cell differentiation

has_output

T cell – Compare to: • cysteine biosynthesis

has_output

cysteine • We should distinguish between participation relations in which the continuant relations are – transformation_of – derives_from • e.g. something made (biosynthesis) vs something transformed (differentiation)

Cell differentiation, attempt 3

• T cell differentiation

has_transformed_output_participant

T cell – …not exactly catchy…

has_primary_participant

• T cell differentiation

has_primary_participant

T cell – aka has_theme • ontologically a good relation?

• Meaning partly resides in the process term • Can be migrated to other relations later

To decompose or not to decompose

• We could have a logical definition for sperm incapacitation – genus: incapacitation – differentia:

has_participant

sperm • Requires creating a new term – incapacitation • Not used in any other logical def • Logical def does not capture full essence – this term is a little more complex • involves at least three continuants • Instead just use a relationship to capture

necessary conditions

only

‘Anonymous’ terms

• border follicle cell delamination – The splitting off of border cells from the anterior epithelium • genus: delamination – no such term • we can create as ‘anonymous’ term – exists only in order to make logical definitions • ..or we can just create a normal term

Implementation

• We have 337 logical definitions (nearly) ready • When can we merge them into the GO?

adding logical defs to the GO

• Will this cause disruption to users?

• gene_ontology.obo file exactly the same as before, but will have – fewer inconsistencies!

– new intersection_of tags • specified in obo v1.2

• can easily be ignored by parsers • oboedit users must either: – load cell.obo, relationship.obo at same time as go.obo

– OR select “allow dangling terms” • may still confuse some users – ‘anonymous’ terms

power users & advanced applications cvs gene_ontology _edit.obo

filter gene_ontology.obo

cvs rel.obo

oboedit cvs cell.obo

normal downstream stuff (website, amigo, users) unaffected GO editor CellO editor

Applications may want to take advantage of enhanced GO

• enhanced GO isn’t just to help curation • queries possible with ego: – find genes associated with blood cells • annotations to microglial cell activation – differentiation of any microglial precursor • annotations to monocyte differentiation

Post-composition

• This approach is highly compatible with post composition • We should extend the annotation format to allow denoting more specific classes – e.g.

• cholesterol transport

in

liver – advanced applications can query this – standard applications suffer no loss – extended annotations can be used to help seed new terms in the ontology • This is already being done (MGI,Dicty) – we just want to capture this in interopeable way

Post-composition in gene association files

• New column in file format Gene Product AABC1 Term ID … Slots AABC2 AABC3 GO:0030301 (cholesterol transport) GO:0048663 (neuron fate development) GO:000003 OBOREL:located_in[MA:liver] OBOREL:has_primary_participant[FB bt:Y_neuron]

Important note on post composition

• This is not an either-or situation • We will retain pre-composed terms – terms will continue to be created for real biological types • Annotation post-composition can be used to

further

refine existing pre-composed terms – if the post-composed term is later created in the GO, the annotation can be

automatically

migrated • Tools can ignore post-composition for small loss in specificity – defaults to the current paradigm

Avoiding diamonds

• Surely larval locomotory behavior involves a diamond?

• yes, but we can disentangle the two axes of classification

Solution

• Curator

asserts

: id: GO:larval_locomotory_behavior intersection_of: GO:locomotory_behavor intersection_of: occurs_in FBbt:larval_stage • Oboedit

infers

diamond: id: GO:larval_locomotory_behavior intersection_of: GO:locomotory_behavor intersection_of: occurs_in FBbt:larval_stage is_a: GO:locomotory_behavor ! genus is_a: GO:larval_behavior ! inferred

Next Steps

• • Tidy up cell logical definitions

integrate them into curation process

• Look at composite terms within GO – larval locomotory behaviour – regulation • Chemicals • Anatomical entities