=Paper=
{{Paper
|id=Vol-1546/paper_19
|storemode=property
|title=Towards the Semantic Standardization of Orthology Content
|pdfUrl=https://ceur-ws.org/Vol-1546/paper_19.pdf
|volume=Vol-1546
|authors=Jesualdo Tomás Fernández-Breis,María Del Carmen Legaz-García,Hirokazu Chiba,Ikuo Uchiyama
|dblpUrl=https://dblp.org/rec/conf/swat4ls/Fernandez-Breis15
}}
==Towards the Semantic Standardization of Orthology Content==
https://ceur-ws.org/Vol-1546/paper_19.pdf
Towards the semantic standardization of
orthology content
Jesualdo Tomás Fernández-Breis1 , Marı́a del Carmen Legaz-Garcı́a1 , Hirokazu
Chiba2 , Ikuo Uchiyama2
1
Facultad de Informática, Universidad de Murcia, IMIB-Arrixaca, Murcia, Spain
2
National Institute for Basic Biology, National Institute of Natural Sciences,
Okazaki, Japan
Abstract. The amount of resources and data about orthology, together
with the increasing interest in orthologs in biomedical research has trig-
gered the need for the shareability of the content generated by the differ-
ent tools and stored in the different databases. In recent years OrthoXML
permitted to advance in the standardization of the content for exchange
within the orthology community, but the interest in exchanging content
with other communities suggested the research on the use of Semantic
Web languages like RDF and to use ontologies for making the meaning
of the content explicit. This possibility was reinforced by the existence
of initial efforts for using Semantic Web technologies for representing or-
thology content. In this work, we describe the advances done with the
objective of the semantic standardization of orthology content. The need
for a common ontology has permitted to obtain a draft of an orthology
ontology built by reusing existing ontologies and following best practices
in ontology construction. This ontology should serve as knowledge frame-
work for the semantic standardization of orthology content. A mapping
between OrthoXML and this orthology ontology has been designed, spec-
ified and applied to sample OrthoXML datasets. For this purpose, the
Semantic Web Integration Tool (SWIT) has been used, which provides
support for doing the previously described actions and permit to create
an integrated repository from multiple orthology content sources.
1 Introduction
In recent years, the number of genome sequences determined has significantly
increased and many on-going research projects will permit to know not only the
reference genome of many organisms but also the genome of individuals. In this
new era, being able to perform computational comparative analysis might bring
many opportunities to biomedical research. Homology information can play a
central role in integrating and comparing multiple genomes, because homology
permit establishing evolutionary relations between genes from multiple species.
Three basic concepts, namely, homologs, orthologs, and paralogs need to be
distinguished in this context[5]: (1) homologous genes have diverged from an
ancestral gene; homology may hold for genes of the same species or from different
species; (2) orthologous genes have diverged by speciation from an ancestral gene,
and their biological functions are usually conserved; orthology may hold between
genes from different species; (3) paralogous genes have diverged by duplication
from an ancestral gene; paralogy may hold between genes of the same species or
from different species.
Despite homology relations are usually obtained in particular studies from
a pairwise perspective, they are usually calculated and represented as clusters,
that is, groups of genes holding homologous relationship. Homolog clusters are
further divided into two types: ortholog groups consist of genes derived from a
speciation event and paralog groups consist of genes derived from a duplication
event. Thus, ortholog and paralog groups can be represented in a form of nested
hierarchy according to their evolutionary history, where each orthologous group
is associated with a taxonomic range that corresponds to a set of organisms
derived from a specific speciation event.
Ortholog information is a useful resource to link the corresponding genes
of different species and transfer the biological knowledge of model organisms
to organisms with newly sequenced genomes. In addition, ortholog groups are a
vital resource for the comparative analysis of multiple genomes, and they provide
a basis for the analysis of phylogenetic profiles [11].
The Quest for Orthologs (QfO) Consortium has identified more than forty
resources about orthology (http://questfororthologs.org/orthology databases),
which represent different needs of information management in the orthology
field. Many of these resources store information about prediction of gene evo-
lutionary relations and there is a diversity of objectives for these databases.
There is heterogeneity in how data are stored and shared by these resources.
For example, Inparanoid [13] stores orthology relations between two species,
whereas OrthoMCL [1] and MBGD [20] stores ortholog groups among multi-
ple genomes. OMA [3] provides various types of orthology relations including
pairwise orthologs and hierarchical ortholog groups. Many resources use their
own representation format based on tabular files, despite this community has
developed the OrthoXML and SeqXML [15] formats to standardize the repre-
sentation of orthology data. OrthoXML permits the comparison and integration
of orthology data from different resources within the orthology community.
In recent years, semantic web formats have been used for representing or-
thology data. OGO [8] was created with the purpose of providing an integrated
resource of information about genetic human diseases and orthologous genes.
OGO integrated information from orthology databases such as Inparanoid [13],
OrthoMCL [1], and OMIM [7]. This resource developed an OWL ontology for
representing the domain knowledge. More recently, RDF has been used by shar-
ing the content of the Microbial Genome Database for Comparative Analysis
(MBGD) [2] database. This resource also developed an OWL ontology for repre-
senting the domain knowledge, called OrthO, which had similar concepts to the
OGO one, despite being developed independently.
The report of the 2013 QfO meeting [19] identified a series of aspects about
semantics that have been the key drivers of our activities: (1) the orthology
community should use shared ontologies to facilitate data sharing; (2) exploiting
automated reasoning should be beneficial for the QfO consortium. In this work,
we describe the development of a draft on an orthology ontology, built from exist-
ing related ontologies and we explain how we are approaching the transformation
and exploitation of existing orthology datasets using semantic web technologies.
We believe this work provides a step forward towards the standardization in the
orthology community.
2 The Orthology Ontology
In this section, we describe the development of the orthology ontology. The
design of the ontology has followed two basic principles: (1) reusing content
from existing ontologies to facilitate interoperability across biomedical domains:
(2) modelling based on membership of genes to clusters of homologs, orthologs
or paralogs, so pairwise relations are obtained by analysing the structure and
content of the dataset.
2.1 Related ontologies
We studied how orthology related concepts and properties were covered in po-
tentially related ontologies. Our initial selection contained the following ontolo-
gies: Comparative Data Analysis Ontology (CDAO) [12], Relations Ontology
(RO) [18], Semanticscience Integrated Ontology (SIO) [4], Homology Ontology
(HO) [14], National Cancer Institute Thesaurus (NCIt) [17], and Clusters of Or-
thologous Groups (COG) Analysis Ontology [6]. It should be noted that these
ontologies present some overlaps and, in such cases, we inspected the axioms
and the textual content in order to make our decisions. For example, HO, NCIt
and COG were not finally reused, since we found the other ontologies more ap-
propriate. The CDAO provides a series of classes of interest that we decided to
reuse for phylogenetic purposes. It contains a taxonomic module that defines
types of Trees, which we found useful for defining phylogenetic trees. It also pro-
vides a taxonomic module of hereditary changes, which we can use in order to
define concepts like orthologs or paralogs. The RO and the SIO include a series
of properties of interest for our domain like has part, is part of, in taxon and
many evolutionary relations. It seems that the recent versions of the RO include
most classes of the HO as properties. The SIO also defines types of genes and
concepts like databases in a way that can be effectively re-used for our purpose,
so we also selected it. We also reused the NCBI taxonomy for representing the
species.
2.2 Description of the orthology ontology
The orthology ontology has been implemented in OWL using Protégé and it is
currently available at https://github.com/qfo/OrthologyOntology. Figure 1 rep-
resents an excerpt of this ontology. There, classes are represented as boxes and
properties as arrows. The entities without prefix are defined in our orthology
ontology. The prefixes cdao, sio and ro represent entities reused from the corre-
sponding ontologies. We can see HomologsCluster is a subclass of GeneTreeNode,
and it has two descendants, namely, OrthologsCluster and ParalogsCluster, which
are associated with cdao:speciation and cdao:geneDuplication respectively. Gen-
eTreeNode is a subclass of cdao:Node, which is not shown in the figure. The
members of HomologsCluster are instances of GeneTreeNode, which means that
they can be clusters of homologs or sequence units. The class SequenceUnit
has three subclasses, Gene, Subgene and Protein. The membership property is
hasHomologous, which is a subproperty of sio:has part. We use this property in-
stead of two hasOrthologous and hasParalogous, because the pairwise relations
are obtained by analysing the tree. Finally, genes and proteins are linked to
ncbi:organisms through the property ro:in taxon.
Fig. 1. Excerpt of the orthology ontology
3 Semantic Transformation of Orthology Data
The availability of the orthology ontology enables the possibility of using it as
schema for the generation of RDF datasets from existing orthology resources.
As it has been previously mentioned, OrthoXML has been a format proposed
for the exchange of orthology content. Hence, we have defined a mapping be-
tween OrthoXML and the orthology ontology, whose main entities can be seen
in Table 1. The first four rows show mappings corresponding to entities in
the ontology. The rest are examples of mappings to properties in the ontol-
ogy. For instance orthologGroup/paralogGroup means that a group of paralogs
has been defined in an group of orthologs and in the ontology this is represented
through the property hasHomologous. The complete mapping file is available at
https://github.com/qfo/OrthologyOntology.
Table 1. Main mappings between OrthoXML and the Orthology Ontology
OrthoXML Orthology Ontology
orthologGroup OrthologsCluster
paralogGroup ParalogsCluster
gene Gene
NCBITaxId organisms
orthologGroup/orthologGroup
orthologGroup/paralogGroup
orthoXML/species/database
One of the technical objectives of the work is to be able to use tools available
for supporting the different processes, so the managers of orthology resources
do not need to create their own transformation scripts into semantic formats.
To this end, we have used the SWIT tool (http://sele.inf.um.es/swit), which
was used in our research group to develop and maintain the OGO Linked Open
Dataset [9]. SWIT provides a web interface through which the user is guided to
perform all the steps of the process:
1. Inputs and outputs: the user has to provide the OrthoXML schema and the
orthology ontology, as well as the corresponding datasets to be transformed.
SWIT is able to generate the dataset in OWL or RDF formats, which can
be downloaded or directly stored into a triple store like Virtuoso. This tool
also enables the user to define the structure of the URI for the RDF/OWL
instances (by default, the ontology base URI).
2. Definition of the mappings: the user can define the mappings between Or-
thoXML and the orthology ontology. Alternatively, mappings created in pre-
vious sessions can be uploaded and re-used. In this case, the file containing
the mappings between OrthoXML and the orthology ontology described in
the previous section was uploaded.
3. Once the mappings have been defined, they can be executed to generate the
corresponding repository.
SWIT applies the mapping rules to the data source to generate the se-
mantic content. Briefly speaking, the mapping rules associate entities and at-
tributes of the OrthoXML schema with owl:Class, owl:DatatypeProperty and
owl:ObjectProperty defined in the ontology. SWIT also generates integrated
repositories from multiple sources. For supporting the integration process, SWIT
permits the definition of identity rules, which aim to avoid the creation of redun-
dant individuals in the semantic repository. Automated reasoning ensures that
only logically consistent content is transformed. To this end, SWIT is currently
using Hermit [16] as reasoner.
4 Calculating Pairwise Relationships
In this section we focus on how we can obtain pairwise relationships between
genes in a given tree. Figure 2 will be used to illustrate some concepts. In the next
subsections, we will describe how we can obtain them from the RDF repository.
Fig. 2. Sample phylogenetic tree
4.1 Pairwise orthologs
We can define pairwise orthologs as pairs of genes from different species whose
common ancestor has a speciation event associated. If we inspect Figure 2, X1
has two ancestral nodes associated with speciation events: one is the common
ancestor of gene X1 and gene Y1, and the other is the common ancestor of gene
X1 and gene Z1. Therefore, we can say that Y1 and Z1 are orthologs of X1,
so the pairs (Y1, X1) and (Z1, X1). Table 2 shows the query for that purpose.
It can be explained as the search for ?common ancestor, which is a cluster of
orthologs (so it has a speciation event associated) and is the common ancestor
of two nodes of trees (?tree node1 and ?tree node2 ) to which two genes (?gene1
and ?gene2 ) from different species belong. We have omitted the taxonomic range
in the query for simplicity.
4.2 Pairwise paralogs
We can define pairwise paralogs as pairs of genes whose common ancestor has
a duplication event associated. This means that the genes may be found in the
same species or in different ones. If we inspect Figure 2, X2 and Y2 are paralogs to
X1, since their common ancestor have a duplication event associated. Therefore,
a similar query to the previous one for orthologs can work for this purpose
by introducing two changes: use of ParalogsCluster instead of OrthologsCluster
and not using the condition for species in the FILTER clause. This part is not
shown. However, this query cannot work for Inparanoid in which in-paralogs
are included in OrthologsCluster and not defined as ParalogsCluster. Thus, in
this case, another query (see Table 3) is necessary to search for members of
OrthologsCluster from the same species. In fact, many orthology resources use
flat structure of ortholog clusters where all member genes are directly associated
with the top-level speciation node. In such cases, although the same query as in
Table 3 can be used to list all pairwise intra-species inparalogy relationships, it
is impossible to distinguish between orthologs (e.g. X1 and Y1) and inter-species
inparalogs (e.g. X1 and Y2).
Table 2. The SPARQL query for pairwise orthologs
SELECT ?gene1 ?species1 ?gene2 ?species2 ?common ancestor
WHERE {
?common ancestor a orthology:OrthologsCluster .
?common ancestor orthology:hasHomologous ?tree node1 .
?common ancestor orthology:hasHomologous ?tree node2 .
?tree node1 orthology:hasHomologous* ?gene1 .
?tree node2 orthology:hasHomologous* ?gene2 .
?gene1 a orthology:Gene .
?gene2 a orthology:Gene .
?gene1 obo:RO 0002162 ?species1 .
?gene2 obo:RO 0002162 ?species2 .
FILTER (?tree node1 != ?tree node2 && ?species1 != ?species2) }
Table 3. The SPARQL query for second condition for pairwise paralogs
SELECT ?gene1 ?species1 ?gene2 ?species2 ?common ancestor
WHERE {
?common ancestor a orthology:OrthologsCluster .
?common ancestor orthology:hasHomologous ?tree node1 .
?common ancestor orthology:hasHomologous ?tree node2 .
?tree node1 orthology:hasHomologous* ?gene1 .
?tree node2 orthology:hasHomologous* ?gene2 .
?gene1 a orthology:Gene .
?gene2 a orthology:Gene .
?gene1 obo:RO 0002162 ?species1 .
?gene2 obo:RO 0002162 ?species2 .
FILTER (?tree node1 != ?tree node2 && ?species1 = ?species2) }
5 Integrated Data Exploitation
The mapping defined between OrthoXML and the orthology ontology can be
applied over specific datasets. In this work, we have transformed data from two
orthology resources, Inparanoid 8 [10] and OMA Sept 2014 hierarchical ortholo-
gous groups [3]. These two resources provide data in OrthoXML format, but they
structure orthology data in a different manner, so they constitute an interesting
exploratory use case. For example, OMA uses clusters of orthologs and clusters
of paralogs in a hierarchical manner but Inparanoid only considers clusters of
orthologs, which may contain paralogs generated by duplications after the spe-
ciation of the two target species (termed in-paralogs [13]). This means that all
paralogy relations have to be inferred from Inparanoid datasets. Besides, OMA
stores the taxonomic range associates with a certain cluster, but Inparanoid does
not. Provided that both resources use OrthoXML we are able to reuse the same
mapping file for transforming the data. SWIT is able to execute the rules that
can be applied to each dataset, which permits to skip in the case of Inparanoid,
for instance, the rule for transforming the taxonomic range. The transformed
RDF contains 8798758 genes from OMA, 1713180 genes for Homo sapiens or-
thologs and 1367940 genes for Mus musculus orthologs from Inparanoid. Table 4
shows the SPARQL query for getting the orthologs of the human gene OR4D2 in
Mus Musculus, which would include the results from both Inparanoid and OMA.
The results of this query are shown in Table 5. In this use case, we have used
Virtuoso 7 as the triple store. Some sample queries exploiting this integrated
dataset are available at https://github.com/qfo/OrthologyOntology.
Table 4. Orthologs of the human gene OR4D2 in Mus Musculus. 9606 and 10090 stand
for the taxonomic identifier for Homo sapiens and Mus Musculus, respectively
SELECT ?gene ?species ?database
WHERE {
?common ancestor a orthology:OrthologsCluster .
?common ancestor orthology:hasHomologous ?tree node1 .
?common ancestor orthology:hasHomologous ?tree node2 .
?common ancestor void:inDataset ?dataset .
?dataset orthology:hasSource ?database .
?tree node1 orthology:hasHomologous* ?gene1 .
?tree node2 orthology:hasHomologous* ?gene2 .
?gene1 a orthology:Gene .
?gene2 a orthology:Gene .
?gene1 obo:RO 0002162 ?species1 .
?gene2 obo:RO 0002162 ?species2 .
?gene1 dcterms:identifier ?id .
?gene2 dcterms:identifier ?gene .
?species2 rdfs:label ?species .
FILTER (?tree node1 != ?tree node2 && ?species1 != ?species2
&& regex(?species1, ” 9606$”) && regex(?species2, ” 10090$”))
VALUES (?id ) {(”OR4D2”)} }
6 Discussion and Conclusion
In this paper we have described how we can approach the standardization of
orthology content using semantic web technologies. We have been able to build
a draft of the orthology ontology by reusing existing ones. We have been able
Table 5. The results of the query shown in Table 4
gene species database
”Olfr463” ”Mus musculus” ”InParanoid”
”Olfr462” ”Mus musculus” ”InParanoid”
”MOUSE03761” ”Mus musculus” ”OMA”
”MOUSE03760” ”Mus musculus” ”OMA”
”MOUSE03762” ”Mus musculus” ”OMA”
to use state-of-the-art tools for all the processes involved: construction of the
ontology, definition of mappings, transformation and exploitation of the data.
Our effort would permit the systematic application of the process to any Or-
thoXML database to generate an integrated knowledge base and to carry out
evaluation and testing processes in both usefulness and performance. There are
also remaining tasks and challenges. First, the draft of the ontology needs to be
improved in terms of classes, properties and documentation of what has already
been produced. We have started by defining queries of pairwise paralogs and or-
thologs, but other relations and concepts like in-paralogs, out-paralogs, etc. could
be defined and encoded as queries. So far, there is no friendly way to query the
transformed content, only through SPARQL endpoints, which are for machines
rather than for humans. Efforts in that sense should be made, taking into account
that there will be distributed SPARQL endpoints of orthology-related content.
The future work also includes the development of a Linked Data API over the
triple store that has method calls enabling the exploitation of the datasets.
Acknowledgements
This work was supported by the Ministerio de Economı́a y Competitividad and
the FEDER programme through grant TIN2014-53749-C2-2-R2, the Fundación
Séneca through grant 15295/PI/10, and the National Bioscience Database Cen-
ter, Japan Science Technology Agency. We would like to thank the organizers of
the BioHackathon meeting (http://www.biohackathon.org) for providing us an
invaluable opportunity to accomplish this work.
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