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This article is part of the supplement: Selected Proceedings of the 6th International Symposium on Bioinformatics Research and Applications (ISBRA'10)

Open Access Proceedings

Managing and querying gene expression data using Curray

Hasan Jamil* and Aminul Islam

Author affiliations

Department of Computer Science, Wayne State University, Michigan, USA

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Citation and License

BMC Proceedings 2011, 5(Suppl 2):S10  doi:10.1186/1753-6561-5-S2-S10

Published: 28 April 2011

Abstract

Background

In principle, gene expression data can be viewed as providing just the three-valued expression profiles of target biological elements relative to an experiment at hand. Although complicated, gathering expression profiles does not pose much of a challenge from a query language standpoint. What is interesting is how these expression profiles are used to tease out information from the vast array of information repositories that ascribe meaning to the expression profiles. Since such annotations are inherently experiment specific functions, much the same way as queries in databases, developing a querying system for gene expression data appears to be pointless. Instead, developing tools and techniques to support individual assignment has been considered prudent in contemporary research.

Results

We propose a gene expression data management and querying system that is able to support pre-expression, expression and post-expression level analysis and reduce impedance mismatch between analysis systems. To this end, we propose a new, platform-independent and general purpose query language called Curray, for

    Cu
stom Microa
    rray
query language, to support online expression data analysis using distributed resources. It includes features to design expression analysis pipelines using language constructs at the conceptual level. The ability to include user defined functions as a first-class language feature facilitates unlimited analysis support and removes language limitations. We show that Curray’s declarative and extensible features nimbly allow flexible modeling and room for customization.

Conclusions

The developments proposed in this article allow users to view their expression data from a conceptual standpoint - experiments, probes, expressions, mapping, etc. at multiple levels of representation and independent of the underlying chip technologies. It also allows transparent roll-up and drill-down along representation hierarchies from raw data to standards such as MIAME and MAGE-ML using linguistic constructs. Curray also allows seamless integration with distributed web resources through its LifeDB system of which it is a part.