Functional genomics opens many opportunities, but is limited by our ability to handle huge amounts of data, relate it to existing knowledge, and extract novel information. Within GABI II the software IMAGEANNOTATOR was developed to help analyze microarray data.

Within the GABI –FUTURE funded MAPMEN project, we are improving the performance and deepen the functionality of the existing modules, and develop new modules. While these software modules will be usable as stand-alones, the goal is to provide a pipeline that helps the user:

• Define the experimental design with a dedicated language and graphical machine-readable interface (XEML DESIGNER)

• Normalize, quality check and statistically analyze large amounts of raw microarray data (ROBIN)

• Continually improve and extend the hierarchical ontologies (SCAVENGERs). Existing modules will be periodically updated using emerging information, and further crops added. This will be supported by a database (MAPCAVE).

• Integrate results with knowledge that is not captured in the ontology. IMAGE-ANNOTATOR will be extended to allow single-gene queries, and genome-wide filtering using large numbers of attributes in a background database.

• Condense global responses to a few biologically-related features, by statistically analyzing and visualizing the responses at the level of the ontology. This will aid comparison across experiments and species (PAGEMAN).

We will also investigate the comparability of responses between different experiments and enable data-mining by combining ontologies and cutting edge bioinformatics, and perform a pilot experiment to test the utility of these tools for cross-species systems analysis.