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<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>Drosophila</it>.</p> <p>Conclusion</p> <p>Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.</p>