Contextual data integration in drug discovery

Abstract

Introduction: The interpretation of high-throughput profiling data depends on the pathway analysis database. Currently, pathway analysis often has to rely on a set of interactions and pathways measured in every possible human tissue, due to insufficient knowledge about interactions and pathways in the context of the profiling experiment. However, a recent global scale analysis of human tissue proteomes and interactomes reveals significant differences among tissues, suggesting that interaction and pathway data that are used out of biological context are the major source of inaccuracies and noise in the analysis of profiling data.

Areas covered: In this review, the major classes of biological context used for experimental detection of molecular interactions and pathways in molecular biology are described. Furthermore, the author reviews methods for predicting biological interactions in order to evaluate the applicability of various contextual interaction data in pathway analysis. Using the results from recent publications that study large-scale tissue composition, the article provides an estimation of the gain in pathway analysis accuracy if only the interactions predicted for the context of a molecular profiling experiment are used, relative to the analysis performed with a context-independent knowledge base.

Expert opinion: It is of the author's opinion that the major source of inaccuracy in pathway analysis is the lack of knowledge about tissue-specific transcriptional regulation. It is therefore suggested that the accuracy of the analysis can be substantially improved if only context-specific interactions and pathways are used for interpretation.

Keywords::

• gene expression microarray
• interparalog
• iterolog
• pathway analysis
• pathway reconstruction
• profiling data
• tissue specificity

Acknowledgements

The author thanks L Wilson, a freelance technical writer, for the review of the paper's language.

Notes

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