- Open Access
The Ontology for Parasite Lifecycle (OPL): towards a consistent vocabulary of lifecycle stages in parasitic organisms
© Parikh et al.; licensee BioMed Central Ltd. 2012
Received: 7 October 2011
Accepted: 4 May 2012
Published: 23 May 2012
Genome sequencing of many eukaryotic pathogens and the volume of data available on public resources have created a clear requirement for a consistent vocabulary to describe the range of developmental forms of parasites. Consistent labeling of experimental data and external data, in databases and the literature, is essential for integration, cross database comparison, and knowledge discovery. The primary objective of this work was to develop a dynamic and controlled vocabulary that can be used for various parasites. The paper describes the Ontology for Parasite Lifecycle (OPL) and discusses its application in parasite research.
The OPL is based on the Basic Formal Ontology (BFO) and follows the rules set by the OBO Foundry consortium. The first version of the OPL models complex life cycle stage details of a range of parasites, such as Trypanosoma sp., Leishmania sp., Plasmodium sp., and Shicstosoma sp. In addition, the ontology also models necessary contextual details, such as host information, vector information, and anatomical locations. OPL is primarily designed to serve as a reference ontology for parasite life cycle stages that can be used for database annotation purposes and in the lab for data integration or information retrieval as exemplified in the application section below.
OPL is freely available at http://purl.obolibrary.org/obo/opl.owl and has been submitted to the BioPortal site of NCBO and to the OBO Foundry. We believe that database and phenotype annotations using OPL will help run fundamental queries on databases to know more about gene functions and to find intervention targets for various parasites. The OPL is under continuous development and new parasites and/or terms are being added.
Parasitic diseases cause a burden throughout the world , but most importantly in the tropics and subtropics. The protozoan parasite that causes malaria remains a major threat to global health. In addition to this, important vector-borne parasites include the protozoa that cause American trypanosomiasis or Chagas disease, leishmaniasis, Human African Trypanosomiasis (HAT) or sleeping sickness, and the water-borne helminth that causes Schistosomiasis (also known as Bilharzia) . Currently, treatment for many of these parasitic diseases is far from ideal. The availability of genome sequences has become central to research on the biology of these parasites and has reinvigorated the aim of identifying novel intervention targets [3–5]. The integration and mining of available data resources is a key challenge to achieve this goal and several projects, such as the T. cruzi SPSE , EuPathDB , and VectorBase , are focused on creating effective integration platforms for parasite and vector datasets.
The complexity of parasites and their lifecycle stages requires the use of expressive and well-defined representation formats that can be consistently and unambiguously interpreted. To illustrate the complexity of parasite lifecycle stages we consider the Trypanosoma cruzi parasite, which has three stages, namely amastigote, epimastigote, and trypomastigote. The amastigote is an intracellular form that is found within nucleated cells of human/vertebrate hosts of the parasite, the epimastigote is found in the midgut of an insect vector and the trypomastigote is found in the bloodstream of a vertebrate host. Further, similar lifecycle stages in different organisms may have different locations and vectors. For example, the epimastigote stage of T. cruzi occurs in the midgut of the triatomine kissing bug, but in T. brucei one type of epimastigote occurs in the salivary gland of the tsetse fly, Glossina morsitans, whereas another is present in the proventricles.
In addition to the complexity of modeling parasite life cycle stages, the parasitic protozoa datasets are available from multiple genome specific databases. For example,RMgmDB , PlasmoDB , andGeneDB , for Plasmodium sp; GeneDB, TriTrypDB , Trypsproteome , VSGDB , and TrypanoCyc  for the kinetoplastids, and EupathDB , CryptoDB , or GeneDB for other parasitic protozoans, such as Cryptosporidium sp., and Eimeria sp. There are now at least 5 different open access databases for T. brucei[11–14, 18] that exist solely to capture and store experimental results. Furthermore, there are several additional databases that have information on species-specific phenotypes and many datasets produced by individual laboratories that are freely available. Each of these data sources represents parasite information, and insufficient and/or distinct annotations make it difficult to effectively integrate, query and retrieve relevant data.
Ontologies are being increasingly used to address the issue of data heterogeneity in biomedical research. Ontologies mitigate terminological heterogeneity, ensure consistent interpretation of terms through use of formal logic languages, and also facilitate automated discovery of implicit knowledge in large datasets. For example, The Gene Ontology (GO)  has enabled a standardized, cross-database, description of gene products. However, until recently, there has not been a consistent vocabulary for the description of lifecycle stages in parasitic organisms. In addition, availability of limited parasite lifecycle and related terms for the bioinformatics tools make it challenging in analyzing parasite datasets and pose a barrier for linking these tools together. Thus, the Infectious Disease Ontology (IDO) project (http://infectiousdiseaseontology.org/page/Main_Page) has initiated development of a family of ontologies including one for vector borne diseases that already includes an extension covering malaria .
The Ontology for Parasite Lifecycle (OPL) complements the efforts of the IDO project by developing an ontology that models complex details of parasite lifecycle stages including location within the host at each lifecycle stage (within tissue cells of a vertebrate host or midgut of an insect vector, etc.). OPL also describes host and vector information and is comprised of rich computational logics that would help query over multiple data repositories as shown in the Application section below. The current version of OPL covers the parasites that cause Malaria, Chagas disease, Leishmaniasis, Human African Trypanosomiasis, and Schistosomiasis. Like other ontologies, OPL is also under continuous development and it will later cover additional eukaryotic parasites within the GeneDB and EuPathDB databases, to enable consistent curation and annotation across all of the parasite genome databases currently maintained by these projects.
Statistics of OPL specific terms and imported terms from different resources
Ontology for Parasites Lifecycle (OPL)
Basic Formal Ontology (BFO)
NCB ITaxonomy (NCBITAXON)
Uber anatomy ontology (UBERON)
Infectious Disease Ontology (IDO)
Cell Type Ontology (CL)
Malaria Ontology (IDOMAL)
Brend a Tissue Ontology (BTO)
Common Anatomy Reference Ontology (CARO)
Ontology for Biomedical Investigations (OBI)
Information Artifact Ontology (IAO)
Obo In Owl
OPL modeling and class details
Modeling of lifecycle stages
Like other processes, lifecycle stages require location information as well. It is very important to model the location of the lifecycle stage since different stages may have different hosts or may occur in different anatomical structures of the same host. For example, the T. cruzi epimastigote stage occurs in the midgut of the Triatominae and the T. brucei epimastigote stage occurs in the salivary gland of Glossina flies. To capture such facts, we used the unfolds_in relationship that is used between a ‘processual_entity’ and a ‘continuant’  to describe ‘T. cruzi epimastigote stage’ > unfolds_in > ‘Triatominae midgut’. For this purpose, terms for organism-specific anatomical structures were also created as mentioned in the section below.
Modeling of parasite organisms and anatomical structures
Two subclasses of Material Entity, Organism and Anatomical Structure are imported from OBI and Common Anatomy Reference Ontology (CARO) , respectively. Some subclasses of Organism are imported from the IDOMAL and NCBI taxonomy (http://purl.bioontology.org/ontology/NCBITaxon). However, new OPL-specific classes have been created under specific organisms to describe the organism in a particular lifecycle stage, such as ‘T. cruzi epimastigote’, ‘P. falciparum sporozoite’, etc. (Figure 1). Further, the location of the parasite in a particular lifecycle stage was captured using the located_in property used between two continuants ; i.e., ‘T. cruzi epimastigote’ > located_in > some ‘Triatominae midgut’. A connection of the parasite to the lifecycle stage, however, was captured using participates_in property. For example, ‘T. cruzi epimastigote’ > participates_in > only ‘T. cruzi epimastigotestage’.
Similarly, new classes for anatomical structures were created for specific organisms, for example, ‘Anopheles salivary gland’ or ‘Homo sapiens hepatocyte.’ This was done to avoid any confusion in the similarity of anatomical structures of various organisms. Many times insect anatomical structures are made out of only one kind of tissue in contrast to vertebrates, in which there are more than one tissue or cell types present in a typical organ. Thus, to capture the difference between the mosquito salivary gland and human salivary gland, anatomical structures were classified into organism-specific structures. Such concepts were modeled using part_of property. For example, the term ‘Anopheles salivary gland’ was defined as “UBERON: Salivary Gland and (part_of some NCBI Taxon: Anopheles).” The class ‘Salivary Gland’ was imported from UBERON  since it defines salivary gland in general and not any species specific anatomical structure. Further, OPL captures restrictions between parasites and their hosts using located_in property. For instance, ‘P. falciparum sporozoites’ > located in > some (‘Anopheles salivary gland’ or ‘human hepatocyte’). Finally, some classes under Anatomical Structure are imported from the Cell type Ontology (CL)  and BRENDA Tissue Ontology (BTO) .
Database annotations and queries
- A.T. brucei bloodstream form trypomastigote stage
Tb927.10.10390 Trypanothione Reductase.
Current curation = loss-of-function mutant phenotype: lethal during bloodstream stage.
Tb927.8.2210 Pteridine Reductase.
Current curation = RNAi phenotype: essential in bloodstream form.
Tb927.7.5930 Protein Associated With Differentiation.
Current curation = expressed in stumpy form.
- B.T. brucei procyclic trypomastigote stage
Tb927.5.2900 Histone Deacetylase 4.
Tb11.02.2260 MCAK-like kinesin, putative.
Tb927.3.4290 Paraflagellar Rod Protein.
Current curation = conditional null mutant phenotype: essential for fly midgutcolonisation.
Current curation = gene deletion: no effect on growth in procyclic form
Current curation = RNAi: decreased cell motility during procyclic stage.
In both of these examples, the OPL term will be used to replace the ambiguous and inconsistent terms currently in use for annotation. As a team of curators carries out phenotype annotation manually, OPL usage will remove any ambiguity and inconsistency in the descriptions of lifecycle stages associated with phenotypes. EuPathDB will also use OPL for annotation of investigations in which lifecycle stages represent an important context . To address community needs, EuPathDB collects datasets for a wide variety of eukaryotic parasite species from both individual investigations and external resources. OPL and other ontologies that follow OBO Foundry principles (such as GO and OBI) will be used to provide consistent representation of experimental contexts such as the lifecycle stage for which phenotype data was collected. Consistent usage of terms from OPL and other ontologies will facilitate sharing and exchanging data among different resources, integration of data from external resources, such as genome data from GeneDB, and data exploration and analysis.
Data integration and knowledge discovery
Compare the gene expression profile of different parasites that are found in human macrophages.
Compare all the P. falciparum genes that are expressed during vector-based lifecycle stages and not during vertebrate-host lifecycle stages.
Show all the enzymes expressed during the slender trypomastigote stage in the glycosome and not expressed in the procyclic stage.
List all the T. cruzi genes that are expressed in amastigote stage only. Also, how many of these genes are involved in only one metabolic pathway.
Some databases including EuPathDB do not collect information regarding parasite vectors and anatomical locations in a structured manner. OPL will be very important for such databases and it will enable users to query the databases for such information (please see Query 1 and 2), by providing an additional, potential interoperability tool between, for example, EuPathDB and VectorBase. OPL can also be used along with other ontologies, such as GO or PEO to find vaccination, drug, or gene knockout targets in parasites (Query 3 and 4) .
Discussion and conclusion
OPL is a collaborative effort among national and international institutes with expertise in parasite and vector research, ontology development, semantic web, and bioinformatics. OPL describes complex lifecycle stage details of various parasites along with their host, vector, and anatomical location information. Since the sequences of many parasite genomes are available publicly on GeneDB, GenBank, and EuPathDB, annotation of gene products has become a priority. Moreover, consistent labeling of parasite data including external databases, experimental data, and literature, is essential in data integration, cross database comparison, and uniform data access. Currently the only aspect of annotation that is structured uses the Gene Ontology (GO) in conjunction with an evidence code that describes the experiment type used to generate the data. This gives users the ability to search the database according to GO annotations. Since not every phenotype can be described using GO terms alone, GeneDB is building a controlled phenotype curation system that will utilize GO in combination with other relevant ontologies. For such a curation system, the annotation of parasite lifecycle stages for the experimental data is very desirable to GeneDB and EuPathDB users. Since a lifecycle ontology for parasitic organisms was not already available, the creation of OPL is expected to have a significant impact on the curation process of parasite data. Using OPL and other ontologies, the vast wealth of parasite knowledge can be used effectively by parasite experts, database developers, and technicians developing decision support tools to control such diseases. Moreover, such ontologies can be used to structure the parasite phenotype data and to allow queries on these databases. The data integration and annotations using OPL also helps parasite researchers to (i) advance their understanding of gene function, (ii) interpret functional genomics datasets, (iii) query phenotypes to find intervention targets, and (iv) facilitate integration of various data sources as exemplified in the Application section above.
OPL complies with the OBO Foundry principles and reuses many terms from existing OBO Foundry ontologies, such as OBI, CL, CARO, UBERON, and BTO. As more data annotated using OPL become available, its utility to the broader parasite research community will continue to increase. The latest version of OPL (current is v 2.0) is always available at http://purl.obolibrary.org/obo/opl.owl and also posted on BioPortal site of NCBO (http://purl.bioontology.org/ontology/OPL) for public use.
In summary, OPL represents a significant effort to model the complex lifecycle of various parasites including information on their host, vector, and anatomical location. As shown in the application and use cases, we believe that consistent labeling of parasite data and database annotations using OPL will facilitate data integration and uniform data access, and also help parasitologists answer the fundamental question of what a gene product does in a specific lifecycle stage.
OPL is developed using the Basic Formal Ontology (BFO)  as upper level ontology and follows the principles set by the OBO Foundry consortium . OPL is expressed in the W3C standard Web Ontology Language (OWL) 1.0 as it supports richer semantics than the Open Biomedical Ontologies (OBO) format, another commonly used language in the biomedical domain. The OWL Description Logic (OWL-DL) was chosen to gain maximum expression and support for the automated reasoning, inferences, and consistency-checking are important for ontology development and maintenance. The meta-data schema of OPL is implemented as OWL annotation properties defined in the Information Artifact Ontology (IAO, http://purl.obolibrary.org/obo/iao), which have been widely used by many OBO foundry ontologies, such as IDO  and OBI. Two key external ontologies, BFO and IAO meta-data part, are directly imported into OPL using the owl:import mechanism which imports the whole ontologies.
To eliminate the redundant efforts and ensure orthogonality, OPL maximizes the use of existing ontologies already listed by the OBO foundry. Since importing the whole ontology is impractical, Minimal Information Reference External Ontology Term (MIREOT) strategy  is adopted to import the terms from the external resources. This strategy provides the consistency, flexibility, and scalability of referring the external resources. The minimal information of an external term includes source ontology URI, source term URI, and target direct superclass URI. The OPL mainly references terms in four reference ontologies, IDO for existing parasite related terms, NCBI taxon (http://purl.bioontology.org/ontology/NCBITaxon) for organism, Cell Ontology for cell type, and UBERON Ontology for cross species anatomical structure. OPL provides the textual definitions for all the terms and logical definitions for each term when possible. The ontology was initially developed using Protégé 4.1 (http://protege.stanford.edu/), later switching to the WebProtege ontology editor  since it provides a better collaborative working environment and allows the developers to track changes made by others. The reasoner tools, such as Hermit (http://hermit-reasoner.com/) or Pellet , were used for consistency checking and inferences.
The terms in OPL were collected from our collaborators and the community members who intend to use OPL. The ontology will continue to expand to meet the needs of a parasite research community to cover more eukaryotic parasites and their lifecycle stages. The BioPortal or Trykipedia site (http://wiki.knoesis.org/index.php/Trykipedia) will be used to collect more terms in the future. The current developmental version of OPL is available at: http://webprotege.stanford.edu/#OPL. We plan to release new inferred versions of OPL quarterly, if required, to provide any new updates on the ontology. During the release process, the permanent OPL identifiers will be assigned and the compliance with OBO policies will be checked. The releases of OPL will also be uploaded to both the NCBO BioPortal and the OBO Foundry repository for public use.
Availability of supporting data
The web Protégé link for OPL development, http://webprotege.stanford.edu/, shows the change history and developers’ comments, discussion, and the reason for changes made.
The queries and results of the Semantic Problem Solving Environment (SPSE) for T. cruzi project where OPL was used along with other ontologies can be found here: http://wiki.knoesis.org/index.php/Manuscript_Details.
We thank Drs. Barry Smith and Mathias Brochhausen for their initial feedback and help with the ontology. We also thank Christopher Thomas and Cory Henson at the Kno.e.sis Center for scientific discussion on modeling of the ontology. We thank Dr. Alan Ruttenberg for providing a script to add unique OPL identifiers and Dr. Tania Tudorache for her technical support with the OPL loaded in the Stanford WebProtege server.
This research was supported by the NIH R01 Grant# 1R01HL087795-01A1, and in part by 5R01GM93132-1 and by the National Institute of Allergy and Infectious Diseases at the National Institutes of Health Contract# HHSN272200900039C, the EVIMALAR network of excellence (Grant Agreement # 242095), and the Wellcome Trust (Grant # 098051 and 085822).
- World Health Organization: Drug development and evaluation for helminths and other neglected tropical diseases - 2008 annual report. 2009, Available at: http://apps.who.int/tdr/svc/publications/about-tdr/annual-reports/drug-development-2008Google Scholar
- Parasitic Diseases. available at: http://www.cdc.gov/ncidod/dpd/aboutparasites.htm
- Winzeler EA: Malaria research in the post-genomic era. Nature. 2008, 455: 751-756. 10.1038/nature07361.View ArticleGoogle Scholar
- Rassi A, Rassi A, Marin-Neto JA: Chagas disease. Lancet. 2010, 375: 1388-1402. 10.1016/S0140-6736(10)60061-X.View ArticleGoogle Scholar
- Chitsulo L, Loverde P, Engels D: Focus: Schistosomiasis. Nat Rev Micro. 2004, 2: 12-13.View ArticleGoogle Scholar
- The Semantics and Services enabled Problem Solving Environment for Trypanosoma cruzi. 2008, Available at: http://wiki.knoesis.org/index.php/Trykipedia
- Aurrecoechea C, Heiges M, Wang H, Wang Z, Fischer S, Rhodes P, Miller J, Kraemer E, Stoeckert CJ, Roos DS, Kissinger JC: ApiDB: integrated resources for the apicomplexan bioinformatics resource center. Nucleic Acids Res. 2007, 35: D427-D430. 10.1093/nar/gkl880.View ArticleGoogle Scholar
- Lawson D, Arensburger P, Atkinson P, Besansky NJ, Bruggner RV, Butler R, Campbell KS, Christophides GK, Christley S, Dialynas E: VectorBase: a data resource for invertebrate vector genomics. Nucleic Acids Res. 2009, 37: D583-D587. 10.1093/nar/gkn857.View ArticleGoogle Scholar
- Janse CJ, Kroeze H, van Wigcheren A, Mededovic S, Fonager J, Franke-Fayard B, Waters AP, Khan SM: A genotype and phenotype database of genetically modified malaria-parasites. Trends Parasitol. 2011, 27: 31-39. 10.1016/j.pt.2010.06.016.View ArticleGoogle Scholar
- Aurrecoechea C, Brestelli J, Brunk BP, Dommer J, Fischer S, Gajria B, Gao X, Gingle A, Grant G, Harb OS: PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res. 2009, 37: D539-D543. 10.1093/nar/gkn814.View ArticleGoogle Scholar
- Logan-Klumpler FJ, De Silva N, Boehme U, Rogers MB, Velarde G, McQuillan JA, Carver T, Aslett M, Olsen C, Subramanian S: GeneDB: an annotation database for pathogenes. Nucleic Acids Res. 2012, 40: D98-D108. 10.1093/nar/gkr1032.View ArticleGoogle Scholar
- Aslett M, Aurrecoechea C, Berriman M, Brestelli J, Brunk BP, Carrington M, Depledge DP, Fischer S, Gajria B, Gao X: TriTrypDB: a functional genomics resource for the Trypanosomatidae. Nucleic Acids Res. 2010, 38: D457-D462. 10.1093/nar/gkp851.View ArticleGoogle Scholar
- Trypanosoma brucei Proteome. Available at: www.TrypsProteome.org
- Marcello L, Menon S, Ward P, Wilkes J, Jones N, Carrington M, Barry JD: VSGdb: a database for trypanosome variant surface glycoproteins, a large and diverse family of coiled coil proteins. BMC Bioinforma. 2007, 8: 143-10.1186/1471-2105-8-143.View ArticleGoogle Scholar
- Chukualim B, Peters N, Fowler C, Berriman M: TrypanoCyc - a metabolic pathway database for Trypanosoma brucei. BMC Bioinforma. 2008, 9: P5-View ArticleGoogle Scholar
- Aurrecoechea C, Brestelli J, Brunk BP, Fischer S, Gajria B, Gao X, Gingle A, Grant G, Harb OS, Heiges M: EuPathDB: a portal to eukaryotic pathogen databases. Nucleic Acids Res. 2010, 38: D415-D419. 10.1093/nar/gkp941.View ArticleGoogle Scholar
- Puiu D, Enomoto S, Buck GA, Abrahamsen MS, Kissinger JC: CryptoDB: the Cryptosporidium genome resource. Nucleic Acids Res. 2004, 32: D329-D331. 10.1093/nar/gkh050.View ArticleGoogle Scholar
- Trypanosoma brucei Annotations. Available at: http://www.metexplore.fr/TrypAnnotEnz/
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT: Gene Ontology: tool for the unification of biology. Nat Genet. 2000, 25: 25-29. 10.1038/75556.View ArticleGoogle Scholar
- Topalis P, Mitraka E, Bujila I, Deligianni E, Dialynas E, Siden-Kiamos I, Troye-Blomberg M, Louis C: IDOMAL: an ontology for malaria. Malar J. 2010, 9: 230-10.1186/1475-2875-9-230.View ArticleGoogle Scholar
- Brinkman R, Courtot M, Derom D, Fostel J, He Y, Lord P, Malone J, Parkinson H, Peters B, Rocca-Serra P: Modeling biomedical experimental processes with OBI. J Biomed Semantics. 2010, 1: S7-10.1186/2041-1480-1-S1-S7.View ArticleGoogle Scholar
- Eilbeck K, Lewis S, Mungall C, Yandell M, Stein L, Durbin R, Ashburner M: The Sequence Ontology: a tool for the unification of genome annotations. Genome Biol. 2005, 6: R44-10.1186/gb-2005-6-5-r44.View ArticleGoogle Scholar
- Smith B, Ceusters W, Klagges B, Kohler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector A, Rosse C: Relations in biomedical ontologies. Genome Biol. 2005, 6: R46-10.1186/gb-2005-6-5-r46.View ArticleGoogle Scholar
- The National Center for Biomedical Ontology.Google Scholar
- Good BM, Wilkinson MD: The OBO Foundry: The Life Sciences Semantic Web is full of creeps!. Brief Bioinform. 2006, 7: 275-286. 10.1093/bib/bbl025.View ArticleGoogle Scholar
- Haendel MA, Neuhaus F, Osumi-Sutherland D, Mabee PM, Mejino JLV, Mungall CJ, Smith B: Anatomy Ontologies for Bioinformatics: Principles and Practice. CARO - The Common Anatomy Reference Ontology. 2007, 327-350.Google Scholar
- Haendel M, Gkoutos G, Lewis S, Mungall C: Uberon: towards a comprehensive multi-species anatomy ontology. Nature Precedings. 2009,http://precedings.nature.com/documents/3592/version/1,Google Scholar
- Bard J, Rhee S, Ashburner M: An ontology for cell types. Genome Biol. 2005, 6: R21-10.1186/gb-2005-6-2-r21.View ArticleGoogle Scholar
- Gremse M, Chang A, Schomburg I, Grote A, Scheer M, Ebeling C, Schomburg D: The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources. Nucleic Acids Res. 2011, 39: D507-D513. 10.1093/nar/gkq968.View ArticleGoogle Scholar
- Zheng J, Harb O, Stoeckert C: Ontology Driven Data Collection for EuPathDB. International Conference on Biomedical Ontology (ICBO). 2011, NY: University of BuffaloGoogle Scholar
- Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream M-Al, Barrell B: Artemis: sequence visualization and annotation. Bioinformatics. 2000, 16: 944-945. 10.1093/bioinformatics/16.10.944.View ArticleGoogle Scholar
- Ajith Ranabahu, Parikh P, Panahiaza M, Sheth A, Logan-Klumpler F: A Generic Document Management System for Biologists Using SA-REST and Faceted Search. Fifth IEEE International Conference on Semantic Computing. 2011, Palo Alto, CA, USA: Stanford UniversityGoogle Scholar
- Sahoo S, Weatherly D, Mutharaju R, Anantharam P, Sheth A, Tarleton R: Ontology-Driven Provenance Management in eScience: An Application in Parasite Research. OnTheMove Federated Conferences & Workshops (OTM 2009) - ODBASE'09. pp. 992–1009. 2009, Vilamoura, Algarve-Portugal: Springer Verlag, 992-1009.Google Scholar
- Parikh PP, Minning TA, Nguyen V, Lalithsena S, Asiaee AH, Sahoo SS, Doshi P, Tarleton R, Sheth AP: A semantic problem solving environment for integrative parasite research: Identification of intervention targets for Trypanosoma cruzi. PLoS Negl Trop Dis. 2012, 6 (1):Google Scholar
- Mendes PN: Cuebee: Knowledge Driven Query Formulation. Technical Report. 2010, Dayton OH: Kno.e.sis Center, Wright State UniversityGoogle Scholar
- Grenon P, Smith B, Goldberg L: Biodynamic ontology: applying BFO in the biomedical domain. Stud Health Technol Inform. 2004, 102: 20-38.Google Scholar
- Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, Goldberg LJ, Eilbeck K, Ireland A, Mungall CJ: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007, 25: 1251-1255. 10.1038/nbt1346.View ArticleGoogle Scholar
- Sintchenko V, Cowell LG, Smith B: Infectious Disease Ontology. Infectious Disease Informatics. 2010, Springer New York, New York Dordrecht Heidelberg London, 373-395.View ArticleGoogle Scholar
- Melanie C, Frank G, Allyson LL, James M, Daniel S, Ryan RB, Alan R: MIREOT: The minimum information to reference an external ontology term. Appl Ontol. 2011, 6: 23-33.Google Scholar
- Tudorache T, Vendetti JL, Noy NF: Web-Protege: A Lightweight OWL Ontology Editor for the Web. Fifth OWLED Workshop on OWL: Experiences and Directions. 2008, Karlsruhe Germany, 432-Google Scholar
- Sirin E, Parsia B, Grau BC, Kalyanpur A, Katz Y: Pellet: a practical OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World Wide Web. 2007, 5: 51-53. 10.1016/j.websem.2007.03.004.View ArticleGoogle Scholar
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