http://mashup.pubs.dbs.uni-leipzig.de/files/Belleau2008BioRDFTowardsamashuptobuildbioinformaticsknowledge.pdf

Bio2RDF: Towards a mashup to build bioinformatics knowledge systems

State of the nation in data integration for bioinformatics

Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited We have created a single repository called Chem2Bio2RDF by aggregating data from multiple chemogenomics repositories that is cross-linked into Bio2RDF and LODD. We have also created a linked-path generation tool to facilitate SPARQL query generation, and have created extended SPARQL functions to address specific chemical/biological search needs. We demonstrate the utility of Chem2Bio2RDF in investigating polypharmacology, identification of potential multiple pathway inhibitors, and the association of pathways with adverse drug reactions. We have created a new semantic systems chemical biology resource, and have demonstrated its potential usefulness in specific examples of polypharmacology, multiple pathway inhibition and adverse drug reaction - pathway mapping. We have also demonstrated the usefulness of extending SPARQL with cheminformatics and bioinformatics functionality.

/pdf/chem2bio2rdf-a-semantic-framework-for-linking-and-data-1mrb5lw0wk.pdf

Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data

New knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.

Biological knowledge management: the emerging role of the Semantic Web technologies

Methodological Review: HCLS 2.0/3.0: Health care and life sciences data mashup using Web 2.0/3.0

Motivation: As the semantic web technology is maturing and the need for life sciences data integration over the web is growing, it is important to explore how data integration needs can be addressed by the semantic web. The main problem that we face in data integration is a lack of widely-accepted standards for expressing the syntax and semantics of the data. We address this problem by exploring the use of semantic web technologies---including resource description framework (RDF), RDF site summary (RSS), relational-database-to-RDF mapping (D2RQ) and native RDF data repository---to represent, store and query both metadata and data across life sciences datasets.

Results: As many biological datasets are presently available in tabular format, we introduce an RDF structure into which they can be converted. Also, we develop a prototype web-based application called YeastHub that demonstrates how a life sciences data warehouse can be built using a native RDF data store (Sesame). This data warehouse allows integration of different types of yeast genome data provided by different resources in different formats including the tabular and RDF formats. Once the data are loaded into the data warehouse, RDF-based queries can be formulated to retrieve and query the data in an integrated fashion.

Availability: The YeastHub website is accessible via the following URL: http://yeasthub.gersteinlab.org

Contact: kei.cheung@yale.edu

http://archive.gersteinlab.org/papers/e-print/yeasthub/preprint.pdf

Remko Deknikker

Papers

YeastHub: a semantic web use case for integrating data in the life sciences domain