Selected papers from the 13th Annual Bio-Ontologies Special Interest Group Meeting.

Over the years, the Bio-Ontologies SIG at ISMB has provided a forum for discussion of the latest and most innovative research in the application of ontologies and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences. The ten papers selected for this supplement are extended versions of the original papers presented at the 2010 SIG. The papers span a wide range of topics including practical solutions for data and knowledge integration for translational medicine, hypothesis based querying , understanding kidney and urinary pathways, mining the pharmacogenomics literature; theoretical research into the orthogonality of biomedical ontologies, the representation of diseases, the representation of research hypotheses, the combination of ontologies and natural language processing for an annotation framework, the generation of textual definitions, and the discovery of gene interaction networks.

Integrating scientific data for drug discovery and development using the Life Sciences Grid.

BACKGROUND: There are many daunting challenges for companies who wish to bring novel drugs to market. The information complexity around potential drug targets has increased greatly with the introduction of microarrays, high-throughput screening and other technological advances over the past decade, but has not yet fundamentally increased our understanding of how to modify a disease with pharmaceuticals. Further, the bar has been raised in getting a successful drug to market as just being new is no longer enough: the drug must demonstrate improved performance compared with the ever increasing generic pharmacopeia to gain support from payers and government authorities. In addition, partly as a consequence of a climate of concern regarding the safety of drugs, regulatory authorities have approved fewer new molecular entities compared to historical norms over the past few years. OBJECTIVE: To overcome these challenges, the pharmaceutical industry must fully embrace information technology to bring better understood compounds to market. An important first step in addressing an unmet medical need is in understanding the disease and identifying the physiological target(s) to be modulated by the drug. Deciding which targets to pursue for a given disease requires a multidisciplinary effort that integrates heterogeneous data from many sources, including genetic variations of populations, changes in gene expression and biochemical assays. METHOD: The Life Science Grid was developed to provide a flexible framework to integrate such diverse biological, chemical and disease information to help scientists make better-informed decisions. RESULTS/CONCLUSION: The Life Science Grid has been used to rapidly and effectively integrate scientific information in the pharmaceutical industry and has been placed in the open source community to foster collaboration in the life sciences community.

Advances in systems biology: measurement, modeling and representation.

Systems biology is frequently defined as the study of all of the elements in a biological system and their relationship to one another in response to perturbation. Advances in science and technology are enabling the development of this emerging and cross-disciplinary field by allowing researchers to explore how biological components function as a network in cells, tissues and organisms. Recently, pharmaceutical companies have begun to embrace systems approaches in an effort to better understand physiology, pathogenic processes and pharmacological responses. This review focuses on recent advances within three core areas of systems biology: data collection, data analysis, and the integration and sharing of data.

Oracle Database 10g: a platform for BLAST search and Regular Expression pattern matching in life sciences.

As database management systems expand their array of analytical functionality, they become powerful research engines for biomedical data analysis and drug discovery. Databases can hold most of the data types commonly required in life sciences and consequently can be used as flexible platforms for the implementation of knowledgebases. Performing data analysis in the database simplifies data management by minimizing the movement of data from disks to memory, allowing pre-filtering and post-processing of datasets, and enabling data to remain in a secure, highly available environment. This article describes the Oracle Database 10g implementation of BLAST and Regular Expression Searches and provides case studies of their usage in bioinformatics. http://www.oracle.com/technology/software/index.html.