weaving and untangling the go
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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 completene
ss
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_as 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_as 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 orphans show up at root node in
pure is_a 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
biologicalprocess
cellularprocess
physiologicalprocess
organismalphysiological
process
cellularphysiological
process
others
The processes pertinent to the function of an organism above the cellular level; includes the integrated processes of
tissues and organs
The processes
pertinent to the
integrated function of a
cell
A phenomenon marked by changes that lead to a particular result, mediated by one or more gene
products
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
Those processes specifically pertinent to the functioning of integrated living units: cells,
tissues, organs, and organisms
biologicalprocess
cellularprocess
physiologicalprocess
organismalphysiological
process
cellularphysiological
process
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:0006548Histidine catabolism
GO:0004397Histidine ammonia
lyase activityGO:0016153
Urocanate hydratase activity
GO:0050480imidazolopropionase
activityGO:0030409Glutamate- Formimidoyl transferase
GO:0050415Formimidoyl-Glutamase
activity
GO:0050129N-formylglutamate
deformylaseactivity
GO:0050416Formimidoylglutamate
deiminaseactivity
GO:0019557Histidine catabolism
to glutamate and formate
GO:0019556Histidine catabolism
to glutamate and formamide
GO:????????Histidine catabolism
to glutamate and 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 molecular function instance
some molecular
functionING instance
some multistep process instance
functioningof
part_of
some gene product instance
hasfunction
function process
What are the types and type-level relations in
reality?
some type of molecular function
some type of molecular
functionING
some type of multistep process
functioningof
part(direction?)
some type of gene product
hasfunction
function process
types example
histidine ammonia
lyase function
histidine ammonia
lyase reaction
histidine catabolism
functioningof
part?
issues: -- ALL-SOME structure
function process
coarse
fine
What are the types and relations in reality?
Formimidoylglutmate
deiminase function
Formimidoylglutmatedeiminase reaction
histidine catabolism to glutamate and
formate
functioningof
issues: -- ALL-SOME structure
function process
haspart?
coarse
fine
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?
some type of molecular function
some type of molecular
functionING
some type of multistep process
functioningof
haspart(?)
issues: -- not all function terms have a functionING corresponding term -- even if they do, redundancy is generally to be avoided
coarse
fine
function process
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
some type of molecular function
some type of molecular
functionING
some type of process
functioningof
haspart(?)
coarse
fine
function process
• one relation standing for two
most functionings are implicit
histidine ammonia
lysase function
histidine ammonia
lyase REACTION
histidine catabolism
functioningof
haspart(?)
coarse
fine
function process
• current paradigm
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
histidine ammonia
lysase reaction
histidine ammonia
lyase function
histidine catabolism
functioningof
function process
some type of gene product
hasfunction part
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• functioning 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 of entity
– a kind 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 in OBO relations ontology
• Standard logical rules can be applied
photosystem I
photosystem I,in cyanobacteria
is_a
cyanobacteria
inorganism
thylakoid
thylakoid,in cyanobacteria
is_a
inorganism
partof
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 a contextual synonyms• 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
biosynthesisis_ametabolism
cysteineis_aserine family amino acidis_aamino acidis_aamine
cysteineis_aserine family amino acidis_aamino acidis_aserine
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 biosynthesisintersection_of: GO:0009058 ! biosynthesisintersection_of: outputs CHEBI:15356 ! cysteine
Now we have the ability to untangle
• Process axis view (primary is_as, 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:0009058intersection_of: outputs CHEBI:15356intersection_of: input CHEBI:17822
Composite terms can be nested
• regulation of cysteine biosynthesis
intersection_of: GO:0050789 ! regulation of biological processintersection_of: regulates GO:0019344 ! cysteine biosynthesis
id: GO:0019344 ! cysteine biosynthesisintersection_of: GO:0009058intersection_of: outputs CHEBI:15356
Composite terms can optionally be
manufactured in bulk• Generic term:
{metabolism,biosynthesis}• Differentia: has_output {serine,
cysteine, …}• 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 cellactivation
inflammatoryresponse
part_ofA change in morphology and behavior of a macrophage resulting from exposure to a cytokine, chemokine, cellular ligand, pathogen, or soluble factor
text def:macrophage
activation
Normal DAG links only capture necessary
conditions, not essence
macrophageactivation
immune cellactivation
is_ainflammatory
response
part_of
macrophage
activates
essence captured by genus-differentia
macrophageactivation
immune cellactivation
is_ainflammatory
response
part_of
id: GO:macrophage_activationintersection_of: GO:cell_activationintersection_of: activates CL:macrophage
essence captured by genus-differentia
macrophageactivation
immune cellactivation
is_ainflammatory
response
part_of
id: GO:macrophage_activationintersection_of: GO:cell_activationintersection_of: activates CL:macrophage
A change in morphology and behavior of a macrophage resulting from exposure to a cytokine, chemokine, cellular ligand, pathogen, or soluble factor
text def:
essence captured by genus-differentia
macrophageactivation
immune cellactivation
is_ainflammatory
response
part_of
cellactivation
macrophage
(genus)
activates
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 and compatible– proviso: if done properly
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
go.obo oboedit
obolreport
cell.obocell.obocell.obo
cjm
GOeditorOBO
editor
obolconfig
nameparser
go+ldefs
reasoner
go‘fixed’
obol
go.obo oboedit
obol
obolreport
cell.obocell.obocell.obo
cjm
GOeditorOBO
editor
obolconfig
nameparser
Ego.obo
reasoner
go‘fixed’
Obol produces genus-differentia logical definitions
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
go.obo oboedit
obol
cell.obocell.obocell.obo
cjm
GOeditorOBO
editor
obolconfig
nameparser ego-cell
.obo
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 neuroblastT cell differentiation has_participant T cellT cell homeostasis has_participant T cellcell homeostasis has_participant homeostasissperm incapacitation has_participant spermsperm 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
cvs
cvs
gene_ontology_edit.obo oboedit
cell.obo
GOeditor
CellOeditor
cvs
rel.obo
gene_ontology.obo
filter
normal downstream stuff(website, amigo, users)unaffected
power users &advanced applications
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
Term ID … Slots
AABC1 GO:0030301(cholesterol transport)
OBOREL:located_in[MA:liver]
AABC2 GO:0048663(neuron fate development)
OBOREL:has_primary_participant[FBbt:Y_neuron]
AABC3 GO:000003
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
id: GO:larval_locomotory_behaviorintersection_of: GO:locomotory_behavorintersection_of: occurs_in FBbt:larval_stage
Solution• Curator asserts:
• Oboedit infers diamond:
id: GO:larval_locomotory_behaviorintersection_of: GO:locomotory_behavorintersection_of: occurs_in FBbt:larval_stageis_a: GO:locomotory_behavor ! genusis_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