Abstract:BackgroundResearch on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites and in deciding the future direction of ongoing as well as pla… Show more
“…OPL is currently being used in the Semantic Problem Solving Environment (SPSE) for T. cruzi project along with the Parasite Experiment Ontology (PEO) [ 33 , 34 ]. T. cruzi SPSE is a collaborative effort among researchers at the Kno.e.sis Center at the Wright State University, the Center for Tropical and Emerging Global Diseases (CTEGD) at the University of Georgia, and the National Center for Biomedical Ontologies (NCBO) at the Stanford University.…”
BackgroundGenome 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.ResultsThe 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., Leishmaniasp., 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.ConclusionOPL 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.
“…OPL is currently being used in the Semantic Problem Solving Environment (SPSE) for T. cruzi project along with the Parasite Experiment Ontology (PEO) [ 33 , 34 ]. T. cruzi SPSE is a collaborative effort among researchers at the Kno.e.sis Center at the Wright State University, the Center for Tropical and Emerging Global Diseases (CTEGD) at the University of Georgia, and the National Center for Biomedical Ontologies (NCBO) at the Stanford University.…”
BackgroundGenome 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.ResultsThe 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., Leishmaniasp., 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.ConclusionOPL 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.
“…Semantic associations between the specific labels are obtained by finding the shortest paths from the LCA to the ontology elements (step labeled 5). The semantic paths are then passed to an enhanced version of Cuebee [ 4 ], which transforms the RDF triples into computational SPARQL queries and retrieves the answers (labeled 6). Fig.…”
Section: Methodsmentioning
confidence: 99%
“…AskCuebee forms the new question-answering interface for the semantic parasite knowledge repository [ 4 ]. Data in the repository utilizes the RDF data model and is annotated by two OWL ontologies: PEO and the Ontology for Parasite Lifecycle [ 66 ].…”
Section: Methodsmentioning
confidence: 99%
“…The RDF data model has the advantage of making the relationships between the data items explicit, and provides a straightforward way for annotating data using ontologies. An example of this is the semantic problem solving environment for the immunology of the parasite, Trypanasoma cruzi (T. cruzi) , which utilizes an RDF triple store for hosting the parasite’s genomic (microarray), proteomic (transcriptome) and pathway data [ 4 ]. The data is annotated using the parasite experiment ontology (PEO) and queried using the open-source Cuebee [ 5 ] that provides an interface for facilitating the parasitologist’s formulation of SPARQL queries.…”
BackgroundLarge quantities of biomedical data are being produced at a rapid pace for a variety of organisms. With ontologies proliferating, data is increasingly being stored using the RDF data model and queried using RDF based querying languages. While existing systems facilitate the querying in various ways, the scientist must map the question in his or her mind to the interface used by the systems. The field of natural language processing has long investigated the challenges of designing natural language based retrieval systems. Recent efforts seek to bring the ability to pose natural language questions to RDF data querying systems while leveraging the associated ontologies. These analyze the input question and extract triples (subject, relationship, object), if possible, mapping them to RDF triples in the data. However, in the biomedical context, relationships between entities are not always explicit in the question and these are often complex involving many intermediate concepts.ResultsWe present a new framework, OntoNLQA, for querying RDF data annotated using ontologies which allows posing questions in natural language. OntoNLQA offers five steps in order to answer natural language questions. In comparison to previous systems, OntoNLQA differs in how some of the methods are realized. In particular, it introduces a novel approach for discovering the sophisticated semantic associations that may exist between the key terms of a natural language question, in order to build an intuitive query and retrieve precise answers. We apply this framework to the context of parasite immunology data, leading to a system called AskCuebee that allows parasitologists to pose genomic, proteomic and pathway questions in natural language related to the parasite, Trypanosoma cruzi. We separately evaluate the accuracy of each component of OntoNLQA as implemented in AskCuebee and the accuracy of the whole system. AskCuebee answers 68 % of the questions in a corpus of 125 questions, and 60 % of the questions in a new previously unseen corpus. If we allow simple corrections by the scientists, this proportion increases to 92 %.ConclusionsWe introduce a novel framework for question answering and apply it to parasite immunology data. Evaluations of translating the questions to RDF triple queries by combining machine learning, lexical similarity matching with ontology classes, properties and instances for specificity, and discovering associations between them demonstrate that the approach performs well and improves on previous systems. Subsequently, OntoNLQA offers a viable framework for building question answering systems in other biomedical domains.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0029-x) contains supplementary material, which is available to authorized users.
“…Finally, NHLBI-funded investigators recognized a need to improve access to and integration of existing data on Chagas disease to increase the potential for learning from the extant knowledge base. They used bioinformatic approaches to integrate myriad data sources on Chagas disease and create a resource for investigators [53]. …”
Section: Nhlbi–funded Research On Chagas Diseasementioning
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