Dataset Information

KaBOB: ontology-based semantic integration of biomedical databases.

ABSTRACT: The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license.KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

SUBMITTER: Livingston KM

PROVIDER: S-EPMC4448321 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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KaBOB: ontology-based semantic integration of biomedical databases.

Livingston Kevin M KM Bada Michael M Baumgartner William A WA Hunter Lawrence E LE

BMC bioinformatics 20150423

<h4>Background</h4>The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.<h4>Results</h4>We pres ...[more]

PMID: 25903923

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Project description:BACKGROUND:Biomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology. RESULTS:In this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes. CONCLUSIONS:Taken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications. AVAILABILITY:UFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use.

Project description:This study was undertaken to evaluate the use of ontology-based semantic mapping (OS-Mapping) in chemical toxicity assessment. Nineteen chemical-species phenotypic profiles (CSPPs) were constructed by ontologically annotating the toxicity responses reported in more than seven hundred published studies of ten chemicals on six vertebrate species. The CSPPs were semantically compared to more than 29,000 publicly available phenotypic profiles of genes, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, and diseases based on a cross-species phenotype ontology. OS-Mapping was shown to differentiate chemical toxicities among themselves as well as within and across species. It also revealed cases of chemical by species interactions. In addition to confirming similar MOAs (mechanisms of action) for a few chemicals, OS-Mapping also generated novel insights into the MOAs underlying some seemingly different, yet phenotypically similar, classes of chemicals. The nature of a unified cross-species phenotype ontology and its representation of diverse knowledge domains allowed the construction of a complete phenotypic continuum for the 17?-ethynylestradiol_fathead minnow across the biological levels of organization, which complemented a similar one derived from the Comparative Toxicogenomics Database but based primarily on 17?-ethynylestradiol-induced molecular phenotypes. Overall, OS-Mapping has been demonstrated to offer a powerful approach to help bridge the gap between the molecular and non-molecular phenotypes of chemicals characterized by using high throughput or traditional omics methods and their apical endpoints of greater regulatory relevance, which are typically phenotypes found at the higher levels of biological organization. OS-Mapping also enables comparative toxicity assessment among chemicals, both within and across species. Furthermore, the semantic analysis of phenotypes can reveal additional novel MOAs for some well-known chemicals and discover candidate MOAs for chemicals that are less molecularly characterized. A full phenotypic continuum based on OS-Mapping will also be conducive to the future development of adverse outcome pathways. As phenomics continues to advance and the ontological annotation of literature becomes more automated, the power of OS-Mapping will be further enhanced.

Project description:BackgroundThe Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular component. It has become widely used in many bioinformatics applications for annotating genes and measuring their semantic similarity, rather than their sequence similarity. Generally speaking, semantic similarity measures involve the GO tree topology, information content of GO terms, or a combination of both.ResultsHere we present a new semantic similarity measure called TopoICSim (Topological Information Content Similarity) which uses information on the specific paths between GO terms based on the topology of the GO tree, and the distribution of information content along these paths. The TopoICSim algorithm was evaluated on two human benchmark datasets based on KEGG pathways and Pfam domains grouped as clans, using GO terms from either the biological process or molecular function. The performance of the TopoICSim measure compared favorably to five existing methods. Furthermore, the TopoICSim similarity was also tested on gene/protein sets defined by correlated gene expression, using three human datasets, and showed improved performance compared to two previously published similarity measures. Finally we used an online benchmarking resource which evaluates any similarity measure against a set of 11 similarity measures in three tests, using gene/protein sets based on sequence similarity, Pfam domains, and enzyme classifications. The results for TopoICSim showed improved performance relative to most of the measures included in the benchmarking, and in particular a very robust performance throughout the different tests.ConclusionsThe TopoICSim similarity measure provides a competitive method with robust performance for quantification of semantic similarity between genes and proteins based on GO annotations. An R script for TopoICSim is available at http://bigr.medisin.ntnu.no/tools/TopoICSim.R .

Dataset Information

KaBOB: ontology-based semantic integration of biomedical databases.

Publications

KaBOB: ontology-based semantic integration of biomedical databases.

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