Transcription factor binding element detection using functional clustering of mutant expression data

As a powerful tool to reveal gene functions, gene mutation has been used extensively in molecular biology studies. With high throughput technologies, such as DNA microarray, genome-wide gene expression changes can be monitored in mutants. Here we present a simple approach to detect the transcription-factor-binding motif using microarray expression data from a mutant in which the relevant transcription factor is deleted. A core part of our approach is clustering of differentially expressed genes based on functional annotations, such as Gene Ontology (GO). We tested our method with eight microarray data sets from the Rosetta Compendium and were able to detect canonical binding motifs for at least four transcription factors. With the support of chromatin IP chip data, we also predict a possible variant of the Swi4 binding motif and recover a core motif for Arg80. Our approach should be readily applicable to microarray experiments using other types of molecular biology techniques, such as conditional knockout/overexpression or RNAi-mediated 'knockdown', to perturb the expression of a transcription factor. Functional clustering included in our approach may also provide new insights into the function of the relevant transcription factor.