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February 15, Thursday
12:00 – 14:00

Gene expression is directly regulated by protein transcription factors that bind at particular DNA or RNA sites in a sequence specific manner. A comprehensive characterization of these functional non-coding elements,or motifs, remains a formidable challenge, especially for higher eukaryotes.

I will present a rigorous computational methodology for ab-initio motif discovery from expression data, that utilizes the concept of mutual information, and have the following characteristics:

• directly applicable to any type of expression data, thus conceptually unifying existing motif discovery techniques,
• model-independence,i.e.,model-related assumptions commonly made by other methods are not required,
• simultaneously finds DNA motifs in upstream regions and RNA motifs in 3'UTRs and highlights their functional relations,
• scales well to metazoan genomes,
• yields very few false positive predictions if any,
• incorporates systematic analysis of the functional coherence of the predicted motifs, their conservation, positional and orientation biases, cooperativity, and co-localization with other motifs,
• displays predictions via a novel user-friendly graphical interface.

I will present results for a variety of data types, measured for different organisms, including yeast, worm, fly, mouse, human, and the Plasmodium parasite responsible for malaria. I will further discuss in detail surprising observations regarding gene expression regulation that were overlook by previous studies and naturally arise from our analysis. As a shorthand for our methodology we use the acronym FIRE, standing for Finding Informative Regulatory Elements.

Based on joint work with Olivier Elemento and Saeed Tavazoie.