A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for Trypanosoma cruzi

Effective research in parasite biology requires analyzing experimental lab data in the context of constantly expanding public data resources. Integrating lab data with public resources is particularly difficult for biologists who may not possess significant computational skills to acquire and process heterogeneous data stored at different locations. Therefore, we develop a semantic problem solving environment (SPSE) that allows parasitologists to query their lab data integrated with public resources using ontologies. An ontology specifies a common vocabulary and formal relationships among the terms that describe an organism, and experimental data and processes in this case. SPSE supports capturing and querying provenance information, which is metadata on the experimental processes and data recorded for reproducibility, and includes a visual query-processing tool to formulate complex queries without learning the query language syntax. We demonstrate the significance of SPSE in identifying gene knockout targets for T. cruzi. The overall goal of SPSE is to help researchers discover new or existing knowledge that is implicitly present in the data but not always easily detected. Results demonstrate improved usefulness of SPSE over existing lab systems and approaches, and support for complex query design that is otherwise difficult to achieve without the knowledge of query language syntax

A unified framework for managing provenance information in translational research

<p>Abstract</p> <p>Background</p> <p>A critical aspect of the NIH <it>Translational Research </it>roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the <it>provenance </it>metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists.</p> <p>Results</p> <p>We identify a common set of challenges in managing provenance information across the <it>pre-publication </it>and <it>post-publication </it>phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata:</p> <p>(a) Provenance <b>collection </b>- during data generation</p> <p>(b) Provenance <b>representation </b>- to support interoperability, reasoning, and incorporate domain semantics</p> <p>(c) Provenance <b>storage </b>and <b>propagation </b>- to allow efficient storage and seamless propagation of provenance as the data is transferred across applications</p> <p>(d) Provenance <b>query </b>- to support queries with increasing complexity over large data size and also support knowledge discovery applications</p> <p>We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for <it>Trypanosoma cruzi </it>(<it>T.cruzi </it>SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness.</p> <p>Conclusions</p> <p>The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis.</p

Aligning the Parasite Experiment Ontology and the Ontology for Biomedical Investigations Using AgreementMaker

Tremendous amounts of data exist in life sciences along with many bio-ontologies. Though these databases contain important information about gene, proteins, functions, etc., this information is not well utilized due to the heterogeneous formats of these databases. Therefore, ontology alignment (OA) is now very critical for life science domain. Our work utilizes AgreementMaker for OA and describes results, difficulties faced in the process, and lessons learned. We aligned two real-world ontologies, the Parasite Experiment Ontology (PEO) and the Ontology for Biomedical Investigations (OBI). The former is more application- oriented and the latter is a reference ontology for any biomedical or clinical investigations. Our study led to several enhancements to AgreementMaker: annotation profiling, mapping provenance information, and tailored lexicon building. These enhancements, which are applicable to any OA system, greatly improved the alignment of these real world ontologies, producing 90% precision with 60% recall from the BSMlex+, the Base Similarity Matcher, and 57% precision with 67% recall from the PSMlex+, the Parametric String Matcher, both using lexicon lookup for synonyms. The mappings obtained through this study are posted on BioPortal site for public use

Aligning the Parasite Experiment Ontology and the Ontology for Biomedical Investigations Using AgreementMaker

Tremendous amounts of data exist in life sciences along with many bio-ontologies. Though these databases contain important information about gene, proteins, functions, etc., this information is not well utilized due to the heterogeneous formats of these databases. Therefore, ontology alignment (OA) is now very critical for life science domain. Our work utilizes AgreementMaker for OA and describes results, difficulties faced in the process, and lessons learned. We aligned two real-world ontologies, the Parasite Experiment Ontology (PEO) and the Ontology for Biomedical Investigations (OBI). The former is more application- oriented and the latter is a reference ontology for any biomedical or clinical investigations. Our study led to several enhancements to AgreementMaker: annotation profiling, mapping provenance information, and tailored lexicon building. These enhancements, which are applicable to any OA system, greatly improved the alignment of these real world ontologies, producing 90% precision with 60% recall from the BSMlex+, the Base Similarity Matcher, and 57% precision with 67% recall from the PSMlex+, the Parametric String Matcher, both using lexicon lookup for synonyms. The mappings obtained through this study are posted on BioPortal site for public use