Abstract:

  The cooperative binding of transcription factors to specific 
  regulatory sequence elements is a primary mechanism for controlling 
  gene expression. In the talk I will describe a framework for finding 
  recurrent regulatory sequence patterns in a selected set of genes and 
  scoring their statistical significance. Proceeding from a database of 
  identified binding site motifs and their genomic locations, we seek 
  motifs whose frequency in the selected set is different than in a 
  background set.  I will present a statistical test designed for this 
  purpose.  I will then provide a hashing algorithm for detecting 
  combinations of these motifs that co-occur in modules within the 
  selected genes. The significance of such co-occurrences is evaluated 
  using novel statistical scores.  Our methods are combined in CREME, 
  a suite of software which includes a browser for viewing the pattern 
  of occurrence of selected modules. We applied CREME to find modules 
  within human-mouse conserved promoter segments, focusing on cell 
  cycle regulated genes and stress response related genes. 
  To validate the biological significance of the identified modules 
  we tested whether the associated genes tended to be co-expressed 
  or share similar function. In the cell cycle set five of the seven 
  identified sets of genes were coherently expressed.  On the 
  stress response data four of the six detected sets fell predominantly 
  into well-defined functional sub-categories.
 
  Joint work with Ivan Ovcharenko, Asa Ben-Hur and Richard Karp.