ArticleNucleic acids research2001
PROSPECT improves cis-acting regulatory element prediction by integrating expression profile data with consensus pattern searches.
Article in Nucleic acids research, 2001. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.Plant molecular biology · 2009Article
- Computational framework for the prediction of transcription factor binding sites by multiple data integration.BMC neuroscience · 2006Review
- Quantitative statistical analysis of cis-regulatory sequences in ABA/VP1- and CBF/DREB1-regulated genes of Arabidopsis.Plant physiology · 2005Article
- A functional analysis reveals dependence on the anaphase-promoting complex for prolonged life span in yeast.Genetics · 2004Article
- POBO, transcription factor binding site verification with bootstrapping.Nucleic acids research · 2004Article
- SiteSeer: Visualisation and analysis of transcription factor binding sites in nucleotide sequences.Nucleic acids research · 2003Article
- EZ-Retrieve: a web-server for batch retrieval of coordinate-specified human DNA sequences and underscoring putative transcription factor-binding sites.Nucleic acids research · 2002Article
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3 authors.
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Abstract
Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data.
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