title: Identifying cis-regulatory sequences by word profile similarity.
abstract: BACKGROUND
  Recognizing regulatory sequences in genomes is a continuing challenge, despite a wealth of available genomic data and a growing number of experimentally validated examples.
  
  
  RESULTS
  We discuss here a simple approach to search for regulatory sequences based on the compositional similarity of genomic regions and known cis-regulatory sequences. This method, which is not limited to searching for predefined motifs, recovers sequences known to be under similar regulatory control. The words shared by the recovered sequences often correspond to known binding sites. Furthermore, we show that although local word profile clustering is predictive for the regulatory sequences involved in blastoderm segmentation, local dissimilarity is a more universal feature of known regulatory sequences in Drosophila.
  
  
  CONCLUSIONS
  Our method leverages sequence motifs within a known regulatory sequence to identify co-regulated sequences without explicitly defining binding sites. We also show that regulatory sequences can be distinguished from surrounding sequences by local sequence dissimilarity, a novel feature in identifying regulatory sequences across a genome. Source code for WPH-finder is available for download at http://rana.lbl.gov/downloads/wph.tar.gz.
authors.affiliation: University of California Berkeley and University of California San Francisco Joint Graduate Group in Bioengineering, University of California, Berkeley, California, United States of America. garmay@berkeley.edu
authors.forename: Garmay
authors.initials: G
authors.lastname: Leung
authors.forename: Michael B
authors.initials: MB
authors.lastname: Eisen
mesh_terms: D020816:Amino Acid Motifs; D000818:Animals; D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence / Q000235:genetics; D030541:Databases, Genetic; D004330:Drosophila / Q000235:genetics; D016678:Genome; D023281:Genomics; D006801:Humans; D007802:Language; D008957:Models, Genetic; D012045:Regulatory Sequences, Nucleic Acid; D012984:Software