Managing and querying gene expression data using Curray.

Managing and querying gene expression data using Curray.

BACKGROUND
In principle, the gene expression data can be seen as providing only a three-valued expression profile relative biological element target experiment at hand. Although complicated, collect expression profile does not pose much of a challenge from the standpoint of the query language. What is interesting is how the expression profiles are used to tease out information from a vast array of repository information that ascribe meaning to the expression profile.

Because of the inherent experimenting explanation of certain functions, much the same way as the query in the database, developing a query system for gene expression data seems futile. Instead, develop tools and techniques to support the individual tasks have been considered prudent in contemporary research.


RESULTS
We propose gene expression data management and query system that is capable of supporting the pre-expression, expression and analysis of post-expression levels and reducing the impedance mismatch between the system analysis. To this end, we propose a new, platform-independent and general purpose query language called curray, for a query language Custom microarrays, to support the online data expression analysis using distributed resources.

It includes features for designing pipelines using expression analysis at the conceptual level language constructs. The ability to include user function defined as a class language feature facilitates the support limited analysis and eliminates the limitations of language. We show that this curray declarative and extensible agile feature enables the modeling of flexible and space for customization.


CONCLUSION
The development proposed in this article allows the user to see their expression data from a conceptual standpoint – experiment, probe, expression, mapping, etc. at various levels of representation and is independent of the underlying chip technology. It also allows transparent roll-up and drill-down along a hierarchical representation of the raw data to standard as MIAME and MAGE-ML uses linguistic construction. Curray also allows integration with distributed web resources through its LifeDB system which is a part.

 Managing and querying gene expression data using Curray.
Managing and querying gene expression data using Curray.

EMERALD contribution of the project to assess and improve the quality of microarray data.

While the minimum information about the trial microarray (MIAME) standards have helped to increase the value of microarray data deposited into the general database as ArrayExpress and Gene Expression Omnibus (GEO), limited means have been provided to assess the quality of this data or to identify the procedures used to normalize and transforms raw data.

The EMERALD FP6 Coordination Action is designed to provide an approach to assess and improve the overall quality of microarray data and to disseminate to the public microarray approach through a comprehensive series of workshops, tutorials, and symposia. Tools developed to assess the quality of data used to demonstrate how low-quality data deletion can increase the power of statistical analysis and facilitate the analysis of multiple microarray data sets together.

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The tool quality metrics have been disseminated through publications and through arrayQualityMetrics software package. Within the framework provided by the Ontology for Biomedical Investigations, ontology developed to describe the transformation of the data, and software ontology developed for gene expression analysis software. In addition, the consortium has advocated for the development and use of an external reference standard in microarray hybridization and create Molecular Methods (MolMeth) database, which provides the main source for the methods and protocols focusing on microarray-based technology.

Trevor

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