c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression

Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, 4200 East Ninth Avenue, B-119, Denver, CO 80262, USA.
BMC Bioinformatics (Impact Factor: 2.58). 12/2008; 9(1):506. DOI: 10.1186/1471-2105-9-506
Source: PubMed


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.
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.
Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.

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