Rationale
Molecular biology has spent the past two decades cataloguing genes, expression levels, proteins, molecular interactions and more. The combination of all these catalogues should enable a biologist to start building a comprehensive picture of a biological system rather than only looking at the individual components. The formation of representations of these components into a network that describes a biological system constitutes the first step in allowing a biologist to develop an understanding of the behavior of a system. If adequate kinetic and other parameters can be obtained or estimated, such models can be used for network simulations in a mathematical framework, making them particularly useful to study the emergent properties of such a system
Complementary to such model-dependent hypothesis generation, the field of computational reasoning promises to provide a powerful additional source of new hypotheses concerning biological network components. The integration of biological knowledge from various sources and the alignment of their representations into one common representation are recognized as critical steps toward hypothesis building
Bio-ontologies
While it is easy to compare nucleic acid or polypeptide sequences from different bioinformatics resources, the biological knowledge contained in these resources is very difficult to compare as it is represented in a wide variety of lexical forms
Ontologies represent knowledge about a specific scientific domain, and support a consistent and unambiguous representation of entities within that domain. This knowledge can be integrated into a single model that holds these domain entities and their term labels, as well as their connecting relationships
Ontologies not only provide the foundation for knowledge integration, but also the basis for advanced computational reasoning to validate hypotheses and make implicit knowledge explicit
Generally speaking, ontologies that model domain knowledge are developed through an iterative process of refinement, an approach common in the field of software engineering
Biological information is known to be difficult to integrate and analyze
Application ontologies in the life sciences and their role in systems biology
Application ontologies define relevant concepts for a particular application or use
Local neighborhood of the SWI4_YEAST protein
Example of the local neighborhood of the protein SWI4_YEAST: some of the types of relationships used within CCO depict how a given protein (SWI4_YEAST) is connected to the organism it belongs to (
A successful application ontology may form the core of an efficient and effective management system. Such a system combines data extraction methods, data format conversions and a variety of information sources. To illustrate the potential use of application ontologies for the life sciences, we have designed and built a knowledge management system that facilitates the analysis of cell cycle control.
Why focus on the cell cycle process?
The eukaryotic cell cycle, or cell division cycle, is the series of events that happen between two consecutive cell divisions that underlie cell multiplication. The molecular events that control the cell cycle are ordered and directional; that is, each process occurs in a sequential fashion and it is impossible to reverse the cycle. The cell cycle control network is complex and is thought to include hundreds of proteins
Although the basic principles of cell cycle control are now well documented
Part of this knowledge has already been incorporated into dynamic system models that are being exploited to test, refine and generate hypothesis
Results and discussion
The Cell Cycle Ontology application ontology
CCO is built to provide laboratory biologists with a one-stop shop for cell cycle knowledge and to have access to an integrated knowledge system that can be used to explore the potential power of automated reasoning. CCO comprises information from a number of resources that contain relevant information about the cell cycle process, such as GO
Bio-ontologies and their presentations have been made accessible through existing software tools (such as OBO-Edit
CCO is extensible, and the CCO integration architecture can accommodate additional ontologies if necessary. In addition, a broad range of export formats from CCO (in particular, OWL and Resource Description Framework (RDF)) enables virtual integration with external sources (controlled vocabularies translated into RDF such as Medical Subject Headings (MeSH)
Knowledge representation in the Cell Cycle Ontology
CCO is a resource that can directly support systems biology. Systems biology is essentially a model-driven approach to biological research, in which a model of a biological process serves to integrate all the available information (network components and their interactions). A model simulation allows for an understanding of network behavior, including changes to the entities, describing these changes in terms of what these entities are, where they are located and when these statements hold. To this end, the knowledge of entities and their interactions needs to be represented in a mathematical framework that facilitates dynamic simulations.
Similarly, to computationally reason about temporal and spatial aspects of a biological process, this knowledge should be represented by a semantically rich and strict language (for example, OWL) to exploit computational reasoning tools. Automated reasoners for OWL do not directly support either temporal or spatial reasoning. It is possible, however, to make representations of temporal and spatial aspects of knowledge and then reason about them in a way that is adequate for many application settings.
Within cell cycle related research, a scientist may be interested in a particular protein (what) for which the localization (where) and specific phase of the cell cycle (when) are important analysis components. To represent the linkage between all these different terms, CCO uses relationships as follows. Let: B be a protein; C be a cellular location in which B might be present; G be the gene that codes for B; P be a biological process in which B participates; I be an interaction in which B takes part; and T be the organism that is the source of B.
These relationships provide the basis for the atomic elements of knowledge about the protein B: 'B located in C', 'B coded by G', 'B participates in P', 'B participates in I', and 'B has source T'. The existing relationships also have an inverse relationship such as 'P has participant B', 'G codes for B', 'C location of B', 'T source of B'. An example is shown in Figure
Cell Cycle Ontology contents
CCO supports four model organisms:
Organism-specific ontology figures
Ontology
Entity
At
Hs
Sc
Sp
Total
Proteins
3,572
26,220
14,685
2,388
46,865
Genes
3,027
8,699
4,498
1,439
17,663
Protein protein interactions
1,524
8,707
9,903
447
20,581
The numbers shown are of some important entities presently contained in CCO (for example, cell cycle genes) for each of the organism-specific ontologies (
CCO protein figures
Ontology
Type of proteins
At
Hs
Sc
Sp
Total
Core cell cycle
3,276
9,114
1,648
1,348
15,386
Added from IntAct
166
1,671
2,777
80
4,694
Modified proteins added from UniProt
126
15,328
10,200
926
26,580
Total
3,572
26,220
14,685
2,388
46,865
This table shows the number of cell cycle related proteins that were integrated into the four species-specific ontologies for the model organisms:
Integrated ontology figures
Ontology
Entity
At
Hs
Sc
Sp
Total
Proteins
14,892
54,109
18,007
3,635
90,643
Genes
4,595
10,005
4,695
1,744
21,039
Orthology types
-
-
-
-
5,772
Figures are shown for the composite ontology (CCO): union of the four organism-specific ontologies (
Main features of the Cell Cycle Ontology
CCO is protein centric, meaning that proteins are used as 'hubs' to integrate and connect knowledge. The semantic integration of knowledge creates synergy by allowing queries that would not otherwise be possible. For example, OBO ontologies can be queried by tools such as OBO-Edit
CCO has been made available in a wide range of formats to accommodate a suite of popular visualization and analysis tools, ensuring maximum flexibility of interaction with the ontology: OBOF, OWL
OBOF is the
An RDF model is a collection of triple patterns, also simply named 'triples', comprising a subject, a predicate and an object (Figure
RDF triple sample
Simple RDF triple sample showing the subject (Nucleus), the predicate (part_of) and the object (Cell).
RDF matching model
RDF matching model: while querying an RDF model, a matching process is performed against the graph model. In the sample, the triples '?protein is_a CCO_B0000000' and '?protein rdfs:label ?protein label' are matched against the graph on the left.
The OWL version of CCO is the most expressive one and exceeds the other versions in information content as new axioms (see Materials and methods) have been added to exploit its language capabilities (the other versions are equivalent in content to the original ontologies in OBOF). OWL also allows integration of other ontologies within CCO by using an importing mechanism based on URIs, meaning that extant encoded knowledge from other resources can be effectively added and exploited. Ontologies expressed in OWL, however, often cause performance limitations to the extent that it is prohibitive for specific tools, such as Protégé, when launching very complex queries. OWL reasoners (Pellet
XML allows efficient data processing and programmatic access to the ontology. XML has less expressivity than RDF or OWL in terms of semantics. The structured document enabled in XML also supports querying (for example, with technologies such as XQuery
GML, XML (visANT) and DOT allow visual exploration of CCO by tools such as Cytoscape
Querying the Cell Cycle Ontology with SPARQL
The SPARQL syntax is based on the triple pattern of RDF and, therefore, allows for a detailed specification of a small graph pattern, thus a collection of interconnected triples, for which the graph should be queried. When performing a query with SPARQL, a small RDF graph pattern is built in which any of the elements of any triple can be a variable (variable names are prepended in the query with the sign
A query can also specify which variables in the query pattern should be shown in the answer. One of SPARQL's strengths is its ability to specify various target graphs that could be used in the same query, resulting in their subsequent combination and effectively constituting an efficient data integration mechanism. As the pointers to the graphs are URIs, knowledge represented in dispersed RDF resources can be combined in a powerful way.
In order to design SPARQL queries on CCO, it is sometimes necessary to deal with CCO identifiers. The following query shows how to retrieve a term name (called 'label' in RDF) corresponding to a given CCO identifier ('CCO_B0000000' in this example). First, a base URL is defined (
A similar query can be employed to retrieve a CCO identifier using a term label. The following query retrieves the CCO identifier ('CCO_B0002337') of the protein with the label 'WEE1_ARATH':
More sophisticated searches based on regular expressions can also be performed as illustrated in the following query that retrieves all the terms having the keyword 'p53' anywhere within the label (the flag 'i' enables case-insensitive expression lookups):
Consider the simple query 'retrieve the names (labels) of all core cell cycle proteins from
The following SPARQL query on the
The query returns 48 proteins (for example, DMC1_ARATH, SEM12_ARATH) having an interaction with a documented nuclear protein, meaning their own cellular location is also likely to include 'nucleus' at some point. These results and, more generally, any answer to a query on CCO simply reflects the information in the original sources, but their integration enables the construction of new hypotheses. For some questions, the integrated CCO graph must be used. For instance, to retrieve the orthologs of the protein TIP41_YEAST from
The query returns 63 distinct putative orthologs, of which 55 are not documented to participate in any known process. Thus, with this result these proteins can be hypothesized to participate in the same process as 'TIP41_SCHPO'. To retrieve the identity of the processes in which 'TIP41_SCHPO' participates, a new query must be built that returns the answer 'G2/M transition of mitotic cell cycle':
More examples of biological queries can be found at
Finally, we used SPARQL to analyze the subcellular distribution of cell cycle proteins. For that, we used the core cell cycle proteins subset of the CCO. First, we analyzed the distribution among the three major cellular compartments - the cytoplasm, nucleus and cell membrane. We found that the majority of cell cycle proteins are located in the nucleus (755) and the cytoplasm (356), where the majority of cell cycle events are known to take place
We looked in more detail at the distribution of cell cycle proteins in the cytoplasm. As expected, the majority of cell cycle proteins are found in the cytosol (280). We also wanted to see if there were cell cycle proteins in the membrane bounded organelles other than in the nucleus. To our surprise, all of the analyzed organelles contained cell cycle proteins: the endoplasmic reticulum (46), the Golgi apparatus (19) and the mitochondrion (43). One could hypothesize that the cell cycle proteins located in the first two compartments are involved in the build-up of a new cell membrane and cell wall between the two daughter cells. It is much more difficult, however, to envision how mitochondrial proteins could be involved in the cell cycle. Even more strikingly, six mitochondrial proteins were found to play a role in the regulation of the cell cycle. Provided the cellular compartment annotations are correct, and if taken up by cell cycle researchers, these results may possibly lead to the discovery of novel mechanisms of cell cycle regulation.
An alternative hypothesis to explain a cell cycle role for proteins known to be located in membrane bounded organelles other than the nucleus is to suggest that these proteins are also present outside of those organelles. For example, if a protein can be located in both the mitochondrion and the cytosol, then the cell cycle function of the protein can be exerted in the cytosol, but not in the mitochondrion where it may fulfill a different role. Therefore, we analyzed alternative locations of the proteins in question. We identified 9, 5 and 15 core cell cycle proteins from the endoplasmic reticulum, Golgi apparatus and mitochondrion, respectively, that have additionally cytosolic or nuclear localization. These proteins have an unusual combination of locations, and merit further investigation with respect to the molecular mechanisms underlying their ability to be localized to apparently incompatible locations. This also highlights the need to indicate when and where functions assigned to a protein are valid.
Automated reasoning over bio-ontologies
Description logics and automated reasoners
Description Logics (DL)
DL reasoners are computational tools to: ensure that an ontology does not contain any contradictory facts (consistency checking); compute the subclass relation between each named class to create the class hierarchy (classification); find the most specific classes to which an individual belongs (realization); and retrieve information from an ontology (querying).
Ontology curators can use DL reasoners to minimize the term redundancy, while maintaining sufficiently detailed descriptions and consistency of the contents
Representing biological knowledge with OWL
OWL-DL queries can be more fine-grained than RDF queries since the semantic model of OWL-DL allows more expressivity. The OWL semantics is based on sets (classes) of instances (individuals). Classes can be subclasses of other classes, if and only if all the instances of the subclass are also instances of the superclass, although the superclass has other instances that do not belong to the subclass. For example, in GO the well-known 'is a' hierarchy is founded on this concept.
Relationships in OWL-DL are interpreted as existing between pairs of individuals. Restrictions on classes define which and how many relationships the instances of that class must hold. When a restriction is defined, an anonymous class is defined (Figure
OWL property (part_of) sample
OWL property (part_of) sample: the property 'part of' links individuals belonging to a class (for example, 'Nucleus') to individuals of the class 'Cell'. A restriction of the type 'some part_of Cell' on the class 'Nucleus' defines an anonymous class (dotted shape), and will imply that individuals belonging to the class 'Nucleus' also belong to (are 'part_of') the class 'Cell'.
If the restriction is added as a superclass of the class that is being defined (the class being defined is a subclass of the restriction, as in the example above), the restriction is known as a 'necessary condition'. A necessary condition is a condition that all the instances of the class must fulfill, but is not enough in itself to define class membership. Therefore, if an instance is found that has at least one 'part_of' relationship to 'Cell', it does not mean that it is a member of the class 'Nucleus'. If a restriction is added as an equivalent class of the class that is being described, then this restriction is known as a 'necessary and sufficient' condition (if an instance with at least one 'part of Cell' relationship is found, it is a member of the class 'Nucleus'). Restrictions can also be composed and nested such as 'part of some (participates in only (mitosis or meiosis))', providing a powerful mechanism for expressing complex conditions.
OWL works under the Open World Assumption (OWA), in which something not known to be true is not assumed to be false but merely to be 'unknown'. This is in contrast to database management systems that work with the Closed World Assumption. In database management systems, anything that is not demonstrated by the stored data to be true is assumed to be false. As an example, let us assume that in a system the following and only the following is stated: 'protein CDC25_YEAST participates in the regulation of the cell cycle process', and if the query: 'does protein CDC25_YEAST participate in the regulation of spindle elongation?' is launched, the answer will depend on the type of system. In a Closed World Assumption-based system (for example, a typical database management system), the answer will be 'no', whereas in an OWA-based system (for example, an OWL knowledge base) the answer will be 'unknown'; in other words, protein CDC25 YEAST might also participate in other processes, and will remain 'unknown' until it is explicitly stated that it either does or does not participate in that process. The OWA model complies well with the Semantic Web vision, in which knowledge is continuously evolving, and efficiently accommodates domains with incomplete knowledge such as the cell cycle process.
In an OWL ontology, the knowledge is asserted by the user, and the reasoner makes the axioms that are implicit in such asserted knowledge explicit, therefore inferring axioms that flag unnoticed items of knowledge. Therefore, the complexity of OWL queries depends on the complexity of the asserted and inferred knowledge present on the ontology. An OWL query can be regarded as an 'anonymous class', and, therefore, the user may ask the reasoner for different answers (for example, retrieve superclasses, ancestor classes, equivalent classes, subclasses, descendant classes or instances of the anonymous class). CCO provides an attractive starting point to exploit all these querying possibilities by enriching it with particular/customized axioms. These axioms could be limited to subsets of CCO that will enable focusing on particular aspects of cell cycle research such as endoreduplication. The examples described below only show some of the added value that could be gained from having an ontology expressed in OWL.
Examples of automated reasoning in the Cell Cycle Ontology
Consider the simple query: 'Which cell cycle related proteins participate in a reported interaction?' In the Manchester OWL syntax
where the classes 'protein' and 'interaction type' have CCO_U0000005 and CCO_Y0000001, respectively, as their CCO identifiers. When a query is launched, an anonymous class is built on the fly. The extension of that anonymous class is the set of individuals that are members of the class 'protein' with at least one relationship (due to the 'some' keyword) along the property 'participates_in' to an individual of the class 'interaction type'. The expressions to create an anonymous class can be of arbitrary complexity: combination of constructors, nested expressions, combinations of different types of restrictions, and so on. For example, if we want to know which proteins (CCO_U0000005) participate in 'mitosis' (CCO_P0000081) or any process that is a part of it we may use the expression below, which shows the versatility and power of OWL expressions:
The extension of this anonymous class is the set of instances that are members of the class 'protein' (CCO_U0000005) and have at least one relationship along 'participates in' to an individual of the class 'mitosis' (CCO_P0000081) or to an individual that has at least one relationship along 'part_of' to an individual of 'mitosis'. Reasoning is used to exploit the transitivity of the 'part_of' and subsumption relationships. Thus, a process that is not directly part of mitosis, but that is part of a process that is part of mitosis, will be taken into account, even though such knowledge is not asserted in the ontology: such knowledge is inferred by the reasoner, and it is implicit (asserted) in the axioms of the ontology. Another example is retrieving proteins that have at least one interaction with other proteins, and can be located in the cytoplasm as well as in the nucleus:
This query defines an anonymous class formed by 'proteins' (CCO_U0000005) that participate in at least one 'interaction' (CCO_U0000007), and are located in the 'cytoplasm' (CCO_C0000323) or any part of it, and the 'nucleus' (CCO_C0000251) or any part of it.
As another example, let us consider the query that defines an anonymous class formed by the entities that are the location of proteins participating in the 'S phase' (CCO_P0000014) or any process that is a part of it. This query returns 13 proteins for
The same procedure can be applied to any defined query with the OWL-DL query service provided by Protégé 4 and also programmatically via the OWL application programming interface (OWL application programming interface)
The OWL-DL version of CCO constitutes a structured and integrated knowledge framework that may serve as a basis for advanced reasoning approaches. Reasoning services can be applied to CCO at different levels, depending on who interacts with the system (such as a molecular biologist or an ontology specialist
Cell Cycle Ontology integrated into a platform for cell cycle research
The querying and analysis of results of SPARQL queries can be intimidating for lay users. We therefore set out to build a visualizer of biological networks specifically for CCO (in the framework of the EU FP6 project DIAMONDS
CCO visualization applet
CCO visualization applet. The CCO terms are shown as clickable, color-coded nodes, and mouse actions of the user are translated into pre-defined SPARQL queries that operate on the RDF representation of CCO. The results are returned as an XML file, which is translated into new nodes and edges that are then shown in the display. This visualization sample shows the local neighborhood of the protein WEE1_SCHPO ('Mitosis inhibitor protein kinase wee1').
The terms in CCO are basically divided into three types: terms that represent a protein class (for example, 'UBC11_SCHPO'); terms in the upper level ontology (for example, 'biological process'); other ontological terms (for example, 'nucleolus').
The RDF contains explicit 'rdf:type' links from every term node to its type as well as 'rdfs:label' links to their names. All the pre-defined SPARQL queries operate on the URIs of the CCO terms, but they retrieve the 'rdf:type' and 'rdfs:label' for the visual presentation of the results. The types are used for a color coding of the nodes. The links between the terms also have names, defined by the relation types in CCO (for example, interacts with, encoded by, and so on). Some of the relation types, such as the protein interactions, are visualized with special arrows. For more information, please see the DIAMONDS deliverable document D5.4
Yet another integrated system?
Efficient use of biological information that, in practice, is widely dispersed strongly relies on its means of access. However, each individual resource mostly informs the researchers about only a part of their biological question. Therefore, life scientists often need to combine several resources in order to gain new insights. Computational systems that provide transparent and integrative access should increase research productivity. Some systems, such as Reactome
CCO adopts a data integration paradigm that can be readily applied to any other domain. The system is readily expandable and can accommodate virtually any other data related to the cell cycle (for example, cell cycle related information from the Kyoto Encyclopedia of Genes and Genomes (KEGG)
Currently, building complex queries over CCO may require some training in semantic technologies. It has always been difficult to develop systems that provide a balanced trade-off between user-friendliness and the possibility of asking complex questions. Nevertheless, it is fair to assume that Semantic Web research will deliver such features in the future. The rich ontology developed to study the cell cycle process highlights the advantages of semantically representing knowledge for further analysis and ontology-driven hypothesis generation. We envision that by improving both content and semantics, the utility of CCO can be considerably increased.
Materials and methods
Data integration pipeline
CCO is built from scratch every three months, and only the identifiers are kept for consistency between releases. This automatic pipeline encompasses the typical life cycle of an integrated system: set-up, data integration and system maintenance. All the integrated information is cross-referenced to the original sources to ensure data provenance. The integration pipeline relies on the ability to programmatically manipulate ontologies, terms and relations, a functionality offered by ONTO-PERL
CCO pipeline
CCO pipeline. The CCO data integration pipeline scheme plots the principal phases: set-up, data integration and system's life cycle. In the 'set-up' phase, several existing ontologies are integrated and merged: the Gene Ontology, the Relations Ontology, the Molecular Interactions ontology, an upper level ontology (see 'An upper level ontology for application ontologies in the life sciences' section) and an ontology holding taxonomical terms for the four model organisms supported by CCO (
Set-up
In this initial phase, the ontology structure and its lexicon (for formal ontology definitions, see
Every entity within CCO is given a unique identifier of the form CCO:Xnnnnnnn where 'CCO' represents the ontology namespace, 'X' the entity subnamespace, and 'nnnnnnn' a sequence of seven digits. The legal subnamespaces include: 'C' for cellular components, 'P' for biological processes, 'F' for molecular functions, 'B' for proteins, 'G' for genes, 'T' for taxon terms, 'I' for interactions, 'O' for orthology terms, 'Y' for interaction types and 'U' for ULO terms. All the original identifiers of the imported terms are stored in the 'xref' section. Additionally, an association table keeps track of the mapping between GO identifiers and CCO identifiers. Next, the RO is fully incorporated and the 'interaction type' branch from the MI is integrated and then a specific taxonomy is built based on the NCBI taxonomy for
Data integration
Data integration follows a protein centric model in which the representations of proteins are considered as integration pivots that connect their relevant data (such as the molecular functions in which they participate).
We define the proteins annotated with cell cycle terms (as defined in the section 'Set-up' above) in the corresponding GOA files
The 'core cell cycle' protein section of CCO is then expanded to include the proteins known to interact with the core cell cycle proteins, as documented in IntAct (only one degree of separation). The protein role (prey, bait or neutral component), type of experiment (such as yeast two hybrid), type of interaction (such as physical association), and so on are all retained in CCO. Then, protein information (such as synonym names, encoding genes and cross-references) are retrieved from the UniProt knowledge base. In addition, post-translational modification data, when available in UniProt, are also added by creating new terms defined by their specific modification. Finally, the OrthoMCL clustering utility is used to generate clusters of putative orthologs for the four species included in the CCO. The parameters used for running OrthoMCL have been chosen to obtain a balanced size of the resulting clusters. Compared to the default values, the following parameters were changed to make the clusters more homogeneous:
All the entities in CCO (proteins, including their modified forms, genes, interactions, and so on) are modeled as classes (also known as universals
System maintenance
Semantic improvements are incorporated in this phase, further enriching the ontology (see section "Improving the OWL version of the Cell Cycle Ontology). Validation and verification processes are carried out while building CCO to ensure its soundness. The ontologies generated in the previous phase are manually and automatically checked by ontology editors, validators and reasoners. In addition, the pipeline log execution files are inspected in detail. These files are sufficiently detailed so as to point out any possible problems. This has allowed us to assemble a fully automated pipeline that uploads the ontologies and their exports in the different formats to the CCO website and to all related and supporting repositories: CCO Website
For each release, the entire set of sources (ontologies, databases, and so on) is retrieved from their repositories. Therefore, CCO is a dynamic artifact in which the terms and relationships are updated as new data are released. Versioning servers keep track of all the changes between different releases.
An upper level ontology for application ontologies in the life sciences
A ULO is an ontology that structures very general types of concepts (such as a process) in generic as well as specific domains
Upper level ontology for CCO
Upper level ontology for CCO. The ULO provides a hierarchical scaffold, including generic terms (for example, cell cycle gene), which serve as 'hooks' for hanging the integrated resources. The dashed rectangles represent the type of data residing below the parental nodes: the node called 'Data from GO' under the term 'cellular component' shows the placeholder where the cellular components from GO are placed (for example, nucleus), the node 'Data from UniProt' under the term 'protein' shows the placeholder where protein data from UniProt resides (for example, p53), and so forth.
Improving the OWL version of the Cell Cycle Ontology
The Ontology Pre-Processor Language (OPPL)
OPPL is used to enrich the OWL ontology that the CCO pipeline builds from the original OBOF files. The advantages of OPPL are automatic maintenance and consistent, explicit and flexible development. OPPL also has the capability of performing what would otherwise be prohibitive using a graphical interface. The set of axioms used to enrich CCO via OPPL can be found in
OPPL has also been used to apply ontology design patterns (ODPs) in CCO
Availability
All the software and data of the CCO project are freely available, either upon request or through websites
Abbreviations
CCO: Cell Cycle Ontology; CVS: Concurrent Versions System; DL: Description Logics; GML: Graph Modeling Language; GO: Gene Ontology; GOA: Gene Ontology Annotation; MI: Molecular Interactions ontology; NCBI: National Center for Biotechnology Information; OBO: Open Biomedical Ontology; OBOF: Open Biomedical Ontology Format; ODP: ontology design pattern; OPPL: Ontology Pre-Processor Language; OWA: Open World Assumption; OWL: Web Ontology Language; RDF: Resource Description Framework; RO: Relation Ontology; SVN: Subversion; ULO: upper level ontology; URI: Uniform Resource Identifier; XML: eXtensible Markup Language.
Authors' contributions
EA was the main architect and engineer of CCO. ME contributed expertise in bio-ontologies, ODPs and OPPL. RS provided bio-ontology expertise. WB built sample SPARQL queries and developed the transitive closures. AI and IB developed the CCO visualizer. VM contributed cell cycle expertise, ontology design and platform engineering skills. BDB and MK contributed their expertise about knowledge management in systems biology and led the project. All authors contributed to the writing of the manuscript.