Developing a kidney and urinary pathway knowledge base

Abstract

BackgroundChronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration.ResultsWe present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney.ConclusionsThe KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain’s ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself.AvailabilityThe KUPKB may be accessed via http://www.e-lico.eu/kupkb.

Unifying the integration, analysis and interpretation of multi-omic datasets: Exploration of the disease networks of Obstructive Nephropathy in children

“…The KUPKB [14] uses Semantic Web technologies to integrate data and knowledge related to the Kidney and Urinary pathways, aiming to assist in biomarker discovery and molecular pathway modeling of diseases related to the urinary system. All the derived elements from the multipleomics dataset analysis were visualized in EGAN [9].…”

Stealthy annotation of experimental biology by spreadsheets

“…Once the template is populated, Populous uses the Ontology PreProcessing Language (OPPL) [15,16] to map the data in the columns to variables within the design pattern to generate axioms for the growing ontology. Populous has been successfully used in the e-LICO (http://www.e-lico.eu) project for the construction of an ontology describing the Kidney and Urinary Pathway (KUP) [17]. The KUP ontology (KUPO) is used to annotate experimental data held in the KUP Knowledge-base (KUPKB).…”

Using semantic web technologies to manage complexity and change in biomedical data

“…We have developed the KUP ontology (KUPO) in the Web Ontology Language (OWL) to organise these data and populated it with genes, proteins, metabolites, and experiments, covering the transcription and protein complements of cells and urine across the KUP domain [5]. The KUPO uses ontologies for the anatomy of the kidney, cells, gene product functionality, disease, metabolism and investigations to cover the KUP domain.…”

“…Added to this are the rapid changes in experimental technique and variations upon experimental themes. In biology we now have experimental techniques that will let biologists explore the genome, the expression of the genes it contains, the proteins within a cell, the metabolism of a cell, and so on-all at a system rather than individual entity level [5]. All these experimental techniques produce new knowledge about a domain, but knowledge that has to be interpreted in the context of the factors used in the experiment and the broader biological context in which the experiment was performed.…”

“…We have used Semantic Web technologies to manage the complexity of these data. We have developed the KUP knowledgebase (KUPKB) [5] as a resource description framework (RDF) [6] store that uses a KUP ontology (KUPO) as a flexible schema to capture the breadth of the domain's knowledge and to use it as a basis for data mining experiments.…”