title: c-REDUCE: incorporating sequence conservation to detect motifs that correlate with expression.
abstract: BACKGROUND
  Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes.
  
  
  RESULTS
  In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in Drosophila.
  
  
  CONCLUSIONS
  Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.
authors.affiliation: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, 4200 East Ninth Avenue, B-119, Denver, CO 80262, USA. katerina.kechris@ucdenver.edu
authors.forename: Katerina
authors.initials: K
authors.lastname: Kechris
authors.forename: Hao
authors.initials: H
authors.lastname: Li
mesh_terms: D000818:Animals; D001483:Base Sequence; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence / Q000235:genetics*; D004330:Drosophila / Q000235:genetics; D015870:Gene Expression; D012984:Software; D014157:Transcription Factors / Q000235:genetics; D015003:Yeasts / Q000235:genetics
chemical_list: D014157:Transcription Factors