José Antonio Miñarro-Giménez

OGO: an ontological approach for integrating knowledge about orthology

BackgroundThere exist several information resources about orthology of genes and proteins, and there are also systems for querying those resources in an integrated way. However, caveats with current approaches include lack of integration, since results are shown sequentially by resource, meaning that there is redundant information and the users are required to combine the results obtained manually.ResultsIn this paper we have applied the Ontological Gene Orthology approach, which makes use of a domain ontology to integrate the information output from selected orthology resources. The integrated information is stored in a knowledge base, which can be queried through semantic languages. A friendly user interface has been developed to facilitate the search; consequently, users do not need to have knowledge on ontologies or ontological languages to obtain the relevant information.ConclusionThe development and application of our approach allows users to retrieve integrated results when querying orthology information, providing a gene product-oriented output instead of a traditional information resource-oriented one. Besides this benefit for users, it also allows a better exploitation and management of orthology information and knowledge.

An Ontology-Based, Mobile-Optimized System for Pharmacogenomic Decision Support at the Point-of-Care

Background The development of genotyping and genetic sequencing techniques and their evolution towards low costs and quick turnaround have encouraged a wide range of applications. One of the most promising applications is pharmacogenomics, where genetic profiles are used to predict the most suitable drugs and drug dosages for the individual patient. This approach aims to ensure appropriate medical treatment and avoid, or properly manage, undesired side effects. Results We developed the Medicine Safety Code (MSC) service, a novel pharmacogenomics decision support system, to provide physicians and patients with the ability to represent pharmacogenomic data in computable form and to provide pharmacogenomic guidance at the point-of-care. Pharmacogenomic data of individual patients are encoded as Quick Response (QR) codes and can be decoded and interpreted with common mobile devices without requiring a centralized repository for storing genetic patient data. In this paper, we present the first fully functional release of this system and describe its architecture, which utilizes Web Ontology Language 2 (OWL 2) ontologies to formalize pharmacogenomic knowledge and to provide clinical decision support functionalities. Conclusions The MSC system provides a novel approach for enabling the implementation of personalized medicine in clinical routine.

Populating Ontologies in the eTourism Domain

The semantic Web vision is based on structuring the knowledge that is present in the current Web so that it is understandable by machines without human intervention. Ontologies are the backbone technology for the semantic Web. Thus, the realization of the semantic Web vision largely depends on the design and instantiation of ontologies. While several methodologies for designing ontologies and automating ontology learning have been proposed, ontology population has not received much attention so far. This paper presents a methodology for populating ontologies from natural language Web documents. For this, semantic Web technologies and natural language technologies have been used. This approach has been applied in the eTourism domain.

Generation of open biomedical datasets through ontology-driven transformation and integration processes

BackgroundBiomedical research usually requires combining large volumes of data from multiple heterogeneous sources, which makes difficult the integrated exploitation of such data. The Semantic Web paradigm offers a natural technological space for data integration and exploitation by generating content readable by machines. Linked Open Data is a Semantic Web initiative that promotes the publication and sharing of data in machine readable semantic formats.MethodsWe present an approach for the transformation and integration of heterogeneous biomedical data with the objective of generating open biomedical datasets in Semantic Web formats. The transformation of the data is based on the mappings between the entities of the data schema and the ontological infrastructure that provides the meaning to the content. Our approach permits different types of mappings and includes the possibility of defining complex transformation patterns. Once the mappings are defined, they can be automatically applied to datasets to generate logically consistent content and the mappings can be reused in further transformation processes.ResultsThe results of our research are (1) a common transformation and integration process for heterogeneous biomedical data; (2) the application of Linked Open Data principles to generate interoperable, open, biomedical datasets; (3) a software tool, called SWIT, that implements the approach. In this paper we also describe how we have applied SWIT in different biomedical scenarios and some lessons learned.ConclusionsWe have presented an approach that is able to generate open biomedical repositories in Semantic Web formats. SWIT is able to apply the Linked Open Data principles in the generation of the datasets, so allowing for linking their content to external repositories and creating linked open datasets. SWIT datasets may contain data from multiple sources and schemas, thus becoming integrated datasets.

Semantic integration of information about orthologs and diseases: The OGO system

Semantic Web technologies like RDF and OWL are currently applied in life sciences to improve knowledge management by integrating disparate information. Many of the systems that perform such task, however, only offer a SPARQL query interface, which is difficult to use for life scientists. We present the OGO system, which consists of a knowledge base that integrates information of orthologous sequences and genetic diseases, providing an easy to use ontology-constrain driven query interface. Such interface allows the users to define SPARQL queries through a graphical process, therefore not requiring SPARQL expertise.

A semantic query interface for the OGO platform

In the last years, a number of semantic biomedical systems have been developed to store biomedical knowledge in an accessible manner. However, their practical usage is limited, since they require expertise in semantic languages by the user, or, in the other hand, their query interfaces do not fully exploit the semantics of the knowledge represented. Such drawbacks were present in the OGO system, a resource that semantically integrates knowledge about orthologs and human genetic diseases, developed by our research group. In this paper, we present an extension of the OGO system for improving the process of designing advanced semantic queries. The query module requires the users to know and to manage only the OGO ontology, which represents the domain knowledge, simplifying the process of query building.

Linking Genome Annotation Projects with Genetic Disorders using Ontologies

Genome sequencing projects generate vast amounts of data of a wide variety of types and complexities, and at a growing pace. Traditionally, the annotation of such sequences was difficult to share with other researchers. Despite the fact that this has improved with the development and application of biological ontologies, such annotation efforts remain isolated since the amount of information that can be used from other annotation projects is limited. In addition to this, they do not benefit from the translational information available for the genomic sequences. In this paper, we describe a system that supports genome annotation processes by providing useful information about orthologous genes and the genetic disorders which can be associated with a gene identified in a sequence. The seamless integration of such data will be facilitated by an ontological infrastructure which, following best practices in ontology engineering, will reuse existing biological ontologies like Sequence Ontology or Ontological Gene Orthology.

Publishing Orthology and Diseases Information in the Linked Open Data Cloud

The Linked Data initiative offers a straight method to publish structured data in the World Wide Web and link it to other data, resulting in a world wide network of semantically codified data known as the Linked Open Data cloud. The size of the Linked Open Data cloud, i.e. the amount of data published using Linked Data principles, is growing exponentially, including life sciences data. However, key information for biological research is still missing in the Linked Open Data cloud. For example, the relation between orthologs genes and genetic diseases is absent, even though such information can be used for hypothesis generation regarding human diseases. The OGOLOD system, an extension of the OGO Knowledge Base, publishes orthologs/diseases information using Linked Data. This gives the scientists the ability to query the structured information in connection with other Linked Data and to discover new information related to orthologs and human diseases in the cloud.

Flexible semantic querying of clinical archetypes

In the last years, a number of semantic biomedical systems have been developed. However, their query interfaces are not easy to use for biomedical researchers since they require expertise in semantic languages. Consequently, its practical usage is limited. In this paper, we address this issue by moving the complexity in the design of the semantic query from knowing such query languages to exploring the domain ontology. We also report how this system has been applied to query a semantic repository of clinical archetypes.

Exploitation of translational bioinformatics for decision-making on cancer treatments

The biological information involved in hereditary cancer and medical diagnoses have been rocketed in recent years due to new sequencing techniques. Connecting orthology information to the genes that cause genetic diseases, such as hereditary cancers, may produce fruitful results in translational bioinformatics thanks to the integration of biological and clinical data. Clusters of orthologous genes are sets of genes from different species that can be traced to a common ancestor, so they share biological information and therefore, they might have similar biomedical meaning and function. Linking such information to medical decision support systems would permit physicians to access relevant genetic information, which is becoming of paramount importance for medical treatments and research. Thus, we present the integration of a commercial system for decisionmaking based on cancer treatment guidelines, ONCOdata, and a semantic repository about orthology and genetic diseases, OGO. The integration of both systems has allowed the medical users of ONCOdata to make more informed decisions.