ArticleBMC bioinformatics2008
c-REDUCE: incorporating sequence conservation to detect motifs that correlate with expression.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Modeling considerations for using expression data from multiple species.Statistics in medicine · 2013Article
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2 authors.
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Abstract
backgroundComputational 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.
resultsIn 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.
conclusionOur results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.
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