THEA: ontology-driven analysis of microarray data

TL;DR

THEA is an integrated system that automates annotation and analysis of microarray data by linking it to biological knowledge bases, facilitating interpretation and data mining.

Contribution

It introduces an ontology-driven tool that bridges the gap between microarray data analysis and biological knowledge integration.

Findings

Automates annotation of microarray data with biological information

Enables browsing and data mining of annotated data

Facilitates interpretation of high-throughput experiments

Abstract

MOTIVATION: Microarray technology makes it possible to measure thousands of variables and to compare their values under hundreds of conditions. Once microarray data are quantified, normalized and classified, the analysis phase is essentially a manual and subjective task based on visual inspection of classes in the light of the vast amount of information available. Currently, data interpretation clearly constitutes the bottleneck of such analyses and there is an obvious need for tools able to fill the gap between data processed with mathematical methods and existing biological knowledge. RESULTS: THEA (Tools for High-throughput Experiments Analysis) is an integrated information processing system allowing convenient handling of data. It allows to automatically annotate data issued from classification systems with selected biological information coming from a knowledge base and to either manually search and browse through these annotations or automatically generate meaningful generalizations according to statistical criteria (data mining). AVAILABILITY: The software is available on the website http://thea.unice.fr/