International Mammalian Genome Society

logo18th International Mouse Genome Conference

17-22 October 2004, Seattle, USA


Peterson KA 2, Richardson JE 1, King BL 1, Dolan ME 1, Welsh IC 1, Bult CJ 1, O'Brien TP 1

1 The Jackson Laboratory, Bar Harbor, United States, 2 The University of Maine, Orono, United States

A major goal in functional genomics is to identify and understand the collection of cis-regulatory modules necessary for coordinating differential gene expression during development. Comparative studies of the complete genomic sequences for mouse and human indicate that the largest conserved fraction is non-coding, and within this non-coding fraction are sequences that perform regulatory function. One step towards deciphering the function of these conserved non-coding sequences (CNCS) is by interpreting the combinatorial clustering of transcription factor binding sites (TFBS) as an informational pattern that forms a regulatory code. Based upon a signaling network model for lung branching morphogenesis, we developed a novel sequence analysis pipeline for integrating comparative genomic approaches to identify shared regulatory motifs that are derived from constraints imposed by a biological network.  The algorithm was trained on a set of genes known to control lung branching morphogenesis (LungNet). This algorithm is designed to identify novel motifs (e.g. binding sites for factors involved in lung branching) as well as map clusters of TFBS to search for regulatory modules on a genome-wide scale. We used the LungNet training set to derive a set of novel motifs and to identify regulatory modules proposed to be important for lung development. This computational pipeline was implemented using a new data analysis platform and visualization tool, BioDX.  We tested our approach by identifying cis-regulatory modules along mouse chromosome 2 (Mmu2) using comparative genomics, motif clustering and position relative to known genes. Genes predicted to be expressed in the branching lung epithelium were confirmed by RT-PCR and in situ hybridization. We are working to validate and extend the LungNet model as a proof-of-concept so that this tool will provide a powerful new approach for investigating the connection between genomic regulatory architecture and dynamic gene expression throughout development.

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