c-REDUCE: incorporating sequence conservation to detect motifs that correlate with expression.
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.
Kechris
Katerina
K
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
Li
Hao
H
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
D014157:Transcription Factors