=Paper=
{{Paper
|id=None
|storemode=property
|title=Semantic representation of Gene Ontology terms by using Gene Regulation Ontology
|pdfUrl=https://ceur-ws.org/Vol-754/kim_krmed2010.pdf
|volume=Vol-754
}}
==Semantic representation of Gene Ontology terms by using Gene Regulation Ontology==
https://ceur-ws.org/Vol-754/kim_krmed2010.pdf
Semantic representation of Gene Ontology
terms by using Gene Regulation Ontology
Jung-jae Kim1,*, Vivian Lee2 and Dietrich Rebholz-Schuhmann2
1
School of Computer Engineering, Nanyang Technological University, Singapore;
2
European Bioinformatics Institute, Cambridge, U.K.
Abstract. Gene Ontology (GO) has been developed to provide concepts
for the functional annotation of biological entities. This development has
already contributed to significant biomedical research results.
Nonetheless, GO could have provided even stronger support to
biomedical text mining, if it delivered domain-independent logical
definitions of its concepts.
We present a method that extracts the semantic structures of GO terms by
using the Gene Regulation Ontology (GRO). The method annotates
substrings of GO terms with, if any, corresponding concepts of OBO
ontologies and then converts the syntactic structures of GO terms into
GRO-based semantic structures. We show that the semantic structures
can be used to deduce implied relations from GO terms.
1. Introduction
Gene Ontology (GO) provides a controlled vocabulary for describing the functions and
properties of gene products. The big size and steady growth of the ontology leads us to
the need of automated aids for maintenance tasks such as consistency checking
(Verspoor et al., 2009) and functional annotation (Camon et al., 2005). One of the
obstacles in the maintenance of GO is that GO terms are long and have complex
syntactical structures (Ogren et al., 2005). One solution to this problem would be to
decompose GO terms into basic concepts and then combine the concepts into a
compositional structure that represents relations between the involved basic concepts.
The cross-product extensions of GO (Mungall et al., 2009) is the ongoing work for
such a solution that has formal descriptions on the internal structures of GO terms,
called logical definitions. Each definition is an intersection of participant-role relations.
The participants are in turn characterized as other concepts of OBO ontologies,
including GO, Cell Ontology, ChEBI, and Sequence Ontology. They have used roles
that are formally defined in the OBO Relation Ontology.
However, the cross-products have several issues to deal with. First, many of their
relation types, or roles (e.g. results_in_transport_of), are not event-independent. This
unnecessarily constrains the usage of the cross-products for text mining systems as they
mostly assume event-independent roles (e.g. has_agent, has_patient) for the purpose of
the uniform identification of various event types from text (Carreras & Màrquez, 2005;
Kim et al., 2009). Second, Mungall and colleagues (2009) reported that no reasoner is
capable to reason over all cross-product sets and all referenced ontologies.
We propose to use basic relations to represent the semantic structure of GO terms,
including agent-patient relations, part-of relations, space-related relations, time-related
relations, and biological relations. In fact, we utilize Gene Regulation Ontology (GRO)
as the framework for the semantic structures. GRO is a conceptual model for the
domain of gene regulation that defines the basic concepts and relations of the domain
(Beisswanger et al., 2008). The concepts of GRO are cross-linked to OBO ontologies.
We implemented a system that first represents substrings of a GO term, which
correspond to existing OBO ontology concepts, with the cross-linked GRO concepts,
and then associates the GRO concepts with each other through the basic relations of
GRO. The resultant semantic structures of GO terms are nested type-value frames like
the cross-products of (Mungall et al., 2009). Note that in contrast to the GO cross-
products, it is possible for a reasoner to reason over all the semantic structures
represented with GRO.
Furthermore, we have successfully applied the semantic structures of GO terms for a
task of semantic similarity analysis. We show that the analysis leads us to the discovery
of implied relations between concepts.
2. Methods
We present a method of identifying logical definitions of GO terms. It has three steps:
term recognition, parsing, and pattern matching. The term recognition step is to label
substrings of GO terms with appropriate GRO concepts. The GRO concepts labeled are
used as the base units for pattern matching. The parsing step is to identify the syntactic
structures of GO terms. The pattern matching is applied to the syntactic structures to
generate the semantic structures. The pattern matching method is based on our rule-
based system for event extraction from text (Hahn et al., 2009).
2.1. Term Recognition
First, we have used SwissProt for recognizing gene/protein names and Enzyme
Nomenclature for enzyme names in GO terms. Once the names are located, they are
labeled with the GRO concepts Gene and Enzyme, respectively.
Second, we have used OBO ontologies, including Sequence Ontology, ChEBI, MeSH,
and GO, for recognizing ontological concepts in GO terms. The terms from ChEBI are
labeled with the GRO concept Chemical.
For other ontologies, we have constructed mapping tables linking their terms to GRO
concepts. These mappings are is-a relations. In fact, many of the mappings are
equivalence relations, while there are non-equivalence relations such as the mapping
from the GO concept “biological process” to the GRO concept “Process”. We have
mapped to GRO only the GO concepts that are not descendants of “regulation of gene
expression” (GO:0010468). The mappings are available at the project homepage
(http://www.ntu.edu.sg/home/jungjae.kim/GO2GRO/ .).
Those mappings of is-a relations are used to recognize the synonyms of GRO concepts
and also hyponyms to GRO concepts. If an ontological term is located in a GO term
and if this term or one of its ancestor terms is mapped to a GRO concept, then it is
labeled with the GRO concept. If many ancestors of an ontological term are mapped to
GRO, we use only the GRO concept mapped to the nearest ancestor according to the
ontology concept hierarchy.
2.2. Parsing
Our method for analyzing the semantic structures of GO terms works by matching
syntactic patterns to the syntactic structures of GO terms. We have adopted Enju, a
HPSG parser (Sagae et al., 2007), to identify the syntactic structures of GO terms. We
converted the predicate-argument structures produced by Enju into dependency
structures.
We integrate the GRO labels annotated by the term recognition module into the
dependency structures. If an ontological term consists of only one word, we simply
annotate its semantic labels onto the node of a dependency structure which corresponds
to the word. If a term has more than one word, we merge the corresponding nodes into
a single node and then annotate the semantic labels onto the node.
2.3. Pattern Matching
A GO term often has a complex structure which involves different participants and
cascading relations between the participants, where a participant can be an event again.
To deal with the cascaded structure, our method compositionally matches multiple
patterns to a single GO term (see Hahn et al., 2009, for details). Figure 1 depicts the
semantic analysis for the GO term “positive regulation of gene expression”. The
numbers 1, 2, and 3 in the figure indicate the order of pattern matching in a cascaded
approach.
Fig. 1. Pattern matching for the GO term “positive regulation of gene expression”
3. Evaluation
3.1. Input GO terms
For evaluation, we have focused on the domain of gene regulation. The version of
Gene Ontology published on January 29, 2010 contains 428 concepts under the concept
“regulation of gene expression”, designated ROGE. If a GO term includes phrases such
as “DNA-dependent” and “gene-specific”, we ignore the phrases since they are mainly
used to distinguish the GO term from others that indicate similar processes but with
different mechanisms (e.g. "RNA-dependent", "mating-type specific"). By ignoring the
phrases, we treat the GO term as default, compared to the others with different
mechanisms.
3.2. Pattern Construction
We have manually constructed 123 patterns to analyze the semantics of the GO terms.
If a pattern encodes a biologically ambiguous relation, we represent the relation with
the GRO concept Process which includes all possible biological relations. For instance,
the relation between A and B of the pattern “A, B” can be any biological relation (e.g.
‘binding’, ‘regulation’). We represent it as . In fact, this
semantic representation does not explicitly express the fact that it is a child concept of
A. The ideal representation would be , where the involvement in B
is regarded as an attribute of A. However, we have discarded this option since the
property ‘involvedIn’ is too artificial and has not been defined in any other ontologies.
It is still possible to deduce the ideal semantics from the proposed semantics by
inference.
3.3. Experiment Results
We have applied our method to 428 GO terms under ROGE and successfully obtained
correct semantic structures for 321 GO terms (75%). The mappings were evaluated by
the authors, including one scientific curator who is experienced in annotations for GO
and text mining competitions (e.g. BioCreative). The correct semantic structures are
available at the project homepage.
3.3.1. Error analysis
Table 1 shows a summary of terms that did not correctly map to our representation.
The categories of errors are not mutually exclusive such that the incorrect semantic
structure of a GO term may fall into multiple categories. For simplicity, however, we
chose only one error type for each incorrect result, roughly preferring higher category
to lower one in Table 1.
Table 1. The results of error analysis
Fail to recognize Count
Word 40 (9%)
‘host’, ‘symbiont’, ‘mating-type’ [24]
Adjectives (e.g. ‘small’, ‘other’) [14]
Nouns (e.g. ‘integration’) [2]
Named entity 32 (7%)
Gene/Protein [15]
Chemical, Sequence [6]
Other [11]
Comma 9 (2%)
Parse Error 24 (6%)
Other 2 (1%)
Total 107 (25%)
Many of the incorrect semantics result from the low coverage of the term recognition
module. For example, the module does not recognize gene/protein family names (e.g.
“survival gene”, “gap gene”).
We have not addressed GO terms that contain ‘host’ or ‘symbiont’, in the sense that
these GO terms are likely to be expressed in text, not with the two words, but with
specific species names whose roles as host and symbiont are well described in the
context. Furthermore, our system does not yet properly deal with such adjectives as
‘small’ and ‘other’.
It also fails when GO terms have commas with a particular usage. For instance, the
lectin pathway in GO:0001868 (regulation of complement activation, lectin pathway) is
one of the pathways for complement activation, and our system does not recognize this
is-a relation due to the lack of such domain knowledge.
Enju is one of the state-of-the-art parsers in the biomedical domain, but it still produces
incorrect results in parsing GO terms. One notorious example is “modification by virus
of host polysomes” (GO:0046783), where the prepositional phrase “of host polysomes”
modifies ‘modification’. The parser incorrectly identifies the head of the prepositional
phrase as ‘virus’. This is an ambiguous example which cannot be resolved without
deep domain knowledge.
For the rest of the paper, we have used only the correctly recognized semantic
structures of the 321 GO terms.
3.3.2. Comparison with GO cross-products
We compared the GRO-based semantic representation of GO terms with the GO cross-
products (published on 15 January 2010). Our method successfully identifies the
semantic structure of 321 GO terms (75%), while the cross-products have logical
definitions for 63 GO terms (15%). Let us compare the following representations of
GO:0060967 (negative regulation of gene silencing by RNA):
(1)
(2) a. intersection_of: GO:0060969 ! negative regulation of gene silencing
b. intersection_of: OBO_REL:mediated_by CHEBI:33697 ! ribonucleic acids
The cross-product of a GO term usually includes the is-a relation with its parent term
(e.g. GO:0060969) as in (2a). The logical definition of a GO term is complete only
when the parent term is also logically defined. According to this notion, the cross-
products have complete logical definitions for only 15 GO terms under ROGE (4%).
The difference between the two representations lies in the difference between the two
relation types (i.e. hasAgent, mediated_by), while the other parts (i.e. gene silencing,
RNA) are in essence identical to each other.
The relation types used by our method can be roughly classified into six groups: 1)
agent-patient relations (i.e. hasAgent, hasPatient), 2) part-of relations (i.e. partOf,
hasPart), 3) space-related relations (i.e. startFromLocus, locatedIn, actsOn, moveFrom),
4) time-related relations (i.e. precededBy), 5) biological relations (i.e. fromSpecies,
encodedIn), and 6) other attributes (i.e. hasPolarity, hasQuality).
Among them, only “encodedIn” involves an event in itself, where it represents the
relation between an ‘unregistered’ protein and the gene encoding the protein. First
order logic can be used to replace this relation type with the ontology concept of
“protein encoding” while linking the protein and the gene to the concept by using
variables. Our method does not support variables, and we leave this issue to future
improvements.
Both our method and the cross-products have used concepts from OBO ontologies. In
fact, the two methods share five ChEBI concepts. The other three ChEBI concepts and
two Sequence Ontology concepts used by the cross-products are actually defined in
GRO, and our method uses the corresponding GRO concepts. Therefore, the two
methods share all the concepts from ChEBI and Sequence Ontology.
However, the two methods hardly share concepts from the other sources. First, the
cross-products have not used MeSH terms. Though MeSH is not a formal ontology, we
utilize it in order to increase the coverage of text mining applications based on our
results. Second, while we ignored ‘host’ and ‘symbiont’, the cross-products have
defined them in their own concept repository. Third, the two methods share only seven
out of 73 GO concepts. This is because our method uses GO concepts outside ROGE
and makes use of additional sources for entities and concepts, while the cross-products
usually represent is-a relations between parent and child GO concepts.
In summary, our method mostly utilizes event-independent relation types, while the
cross-products often not. The two methods share many concepts from ChEBI and
Sequence Ontology, but do not share other ontology concepts. Our method has
established many more cross-links between the GO concepts under ROGE and those
outside ROGE than the GO cross-products.
4. Application
We have compared the semantic structures of parent GO terms with those of children.
Structural comparison has advantages over string comparison (cf. Verspoor et al.,
2009). Let us consider the following pairs of GO terms:
(3) Parent: regulation of transcription by carbon catabolites
Child: regulation of transcription by glucose
(4) Parent: regulation of transcription from RNA polymerase II promoter
Child: regulation of transcription involved in G1 phase of mitotic cell cycle
The example (3) shows an is-a relation, where the difference between the parent term
and the child term lies in the pair of “carbon catabolites” and “glucose”. We can thus
deduce from the example that glucose is a carbon catabolite. However, we cannot
deduce from the parent term of (4) any relation between “RNA polymerase II promoter”
and “G1 phase of mitotic cell cycle”, not only because of the difference between ‘from’
and “involved in”, but also because the two prepositional phrases modify ‘transcription’
and ‘regulation’, respectively. We cannot understand this difference without the
semantic structures of those GO terms.
Table 2 shows the most frequent cases of such differences. The chemical names found
in GO terms are replaced with their corresponding identifiers of ChEBI. For example,
“CHEBI:17234” indicates glucose.
Table 2. The most frequent cases of structural difference between parent and child GO
terms
Unique part in parent Unique part in child Count
hasPolarity=”negative” 63
hasPolarity=”positive” 58
Multiple attributes are mismatched 47
partOf=Mitosis 12
hasAgent=Stress 9
has Agent= CarbonCatabolite hasAgent= 6
The third row of the table means that only 47 pairs of parents and children out of 488
pairs have differences in multiple attributes. It means that in most cases (90%) the child
differs from its parent only in one attribute. In fact, in many cases, the child has an
additional attribute that is not included in the parent. By using the structural differences,
we can deduce relations between concepts. For instance, we can deduce an is-a relation
between CarbonCatabolite and ‘glucose’ (CHEBI:17234), which is explicitly expressed
neither in Gene Ontology nor in ChEBI. Another example is the part-of relation
between Translation and TranslationInitiation, which is already expressed in Gene
Ontology.
We have enumerated the newly identified relations in Table 3. The first three cases are
of is-a relations. The last case has not been expressed in Gene Ontology due to the lack
of the relationship type that can specify the relation between ‘transcription’ and
“transcription factor activity”.
Table 3. Newly discovered relations from GO terms
Unique part in parent Unique part in child
hasAgent= CarbonCatabolite hasAgent=
hasAgent= CarbonCatabolite hasAgent=
hasAgent=Stress hasAgent=Starvation
hasPatient=Transcription hasPatient=TranscriptionFactorActivity
5. Conclusion
Our results reveal that the GRO-based representation can be better used for text mining
than GO cross-products because of the usages of event-independent relations. This
leads us to future plans: 1) using the GRO-based representation in recognizing GO
terms in text, considering them as complex events, and 2) developing GRO-like
ontologies to recognize other ontology terms in text, ultimately aiming at constructing
the Semantic Web for biomedical literature.
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