Microarray technology makes it possible to identify changes in gene expression of an organism, under various conditions. The challenge to researchers that employ microarray expression profiling is that once pre-processing is completed, and a cluster of co-expressed genes obtained, is to derive biological meaning from this data. Data mining is thus essential for deducing significant biological information such as the identification of new biological mechanisms or putative drug targets. While many algorithms and software have been developed for analysing gene expression, the extraction of relevant information from experimental data is still a substantial challenge, requiring significant time and skill. MADIBA (MicroArray Data Interface for Biological Annotation) facilitates the assignment of biological meaning to gene expression clusters by automating the post-processing stage. A relational database has been designed to store the data from gene to pathway for Plasmodium falciparum, Oryza sativa (rice), Arabidopsis thaliana, and Pectobacterium atrosepticum (Pba) As input, the user submits a cluster of genes, either the gene identifiers or the gene sequences. Tools within the web interface allow rapid analyses for the identification of the Gene Ontology terms relevant to each cluster; visualising the metabolic pathways where the gene products are implicated, their genomic localisations, putative common transcriptional regulatory elements in the upstream sequences, and an analysis specific to the organism being studied. The user has the option of outputting selected results of the analyses, either in PDF or plain text formats. MADIBA is an integrated, online tool that will assist researchers in interpreting their results and understand the meaning of the co-expression of a cluster of genes. Functionality of MADIBA was used to analyse a number of gene clusters from several experiments – expression profiling of the Plasmodium falciparum life cycle, a Ralstonia solanacearum infection ofArabidopsis thaliana, a rice treatment with BTH, a millet SA- and MeJ-treatment experiment, and an expI mutant experiment in Pectobacterium atrosepticum. Data from the Plasmodium falciparum and rice were used to illustrate MADIBA’s functionality. For the A. thaliana analyses, the DRASTIC database was implemented to identify how genes respond to various treatments. In addition, a method named PCA Experiment Comparer was developed, which compares the expression values of the numerous experiments in NASCArrays. Using the A. thaliana-R. solanacearum interaction data several related experiments matched in both the susceptible and resistant interactions. In the millet analyses, besides defence related genes being identified, several genes also involved in photosynthesis were found, possibly suggesting a relation between light and defence signalling. The Pba data identified genes involved in quorum sensing, as well as some associated genes with no known function that may also be related to this regulatory process. With the advent of whole genome microarray chips and an increasing number of organisms being sequenced, tools such as MADIBA will become even more significant in understanding the underlying biology. MADIBA provides access to several genomic data sources and analyses, allowing users to quickly annotate and visualise the results. MADIBA is freely available and can be accessed at http://www.bi.up.ac.za/MADIBA/. Copyright / Dissertation (MSc)--University of Pretoria, 2009. / Biochemistry / unrestricted