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C-REDUCE: incorporating sequence conservation to detect motifs that correlate with expression.


ABSTRACT: 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.

SUBMITTER: Kechris K 

PROVIDER: S-EPMC2626603 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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

Kechris Katerina K   Li Hao H  

BMC bioinformatics 20081128


<h4>Background</h4>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.<h4>Results</h4>In this work, we describe a method for predicting motifs that combines the correlation-based strategy with  ...[more]

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