2011
DOI: 10.1186/1471-2229-11-39
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Prediction of transcriptional regulatory elements for plant hormone responses based on microarray data

Abstract: BackgroundPhytohormones organize plant development and environmental adaptation through cell-to-cell signal transduction, and their action involves transcriptional activation. Recent international efforts to establish and maintain public databases of Arabidopsis microarray data have enabled the utilization of this data in the analysis of various phytohormone responses, providing genome-wide identification of promoters targeted by phytohormones.ResultsWe utilized such microarray data for prediction of cis-regul… Show more

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Cited by 43 publications
(63 citation statements)
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“…Genome-wide surveys and computational analyses of auxin and brassinosteroid targets have provided clues into the regulatory landscape required for hormone response (Nemhauser et al, 2004;Priest et al, 2009;Keilwagen et al, 2011;Yamamoto et al, 2011). However, these approaches have a limited ability to define the function of specific cis-elements.…”
Section: Discussionmentioning
confidence: 99%
“…Genome-wide surveys and computational analyses of auxin and brassinosteroid targets have provided clues into the regulatory landscape required for hormone response (Nemhauser et al, 2004;Priest et al, 2009;Keilwagen et al, 2011;Yamamoto et al, 2011). However, these approaches have a limited ability to define the function of specific cis-elements.…”
Section: Discussionmentioning
confidence: 99%
“…We used k-mer motifs to illustrate the MERCED algorithm for k = 8, while k could be any number between 6 and 14. We used 8-mers, because the most dominant length of motifs in the TRANSFAC database is eight (Wingender et al, 1996), and many previous studies have successfully identified meaningful motifs in plants and other species using 8-mers (Mariño-Ramírez et al, 2004;Yamamoto et al, 2011). We also predicted 7-mer and 9-mer motifs.…”
Section: Discussionmentioning
confidence: 99%
“…We recently developed a unique methodology for identifying promoter elements, utilizing frequency comparison of short nucleotide sequences present in a library of promoter regions (Yamamoto et al, 2011). Our method is simple but offers considerable advantages in terms of the accuracy and sensitivity of predictions compared with established methods such as Gibbs Sampling and Multiple Expectation Maximization for Motif Elicidation that are based upon extraction of consensus sequences .…”
mentioning
confidence: 99%
“…As a first step in our work, we made use of these microarray data to analyze the ELIP2 promoter using our octamer-based frequency comparison method (Yamamoto et al, 2011). Promoter scans that express the frequency of individual octamers in stress-induced promoters relative to their frequency in all promoters in the genome (the relative appearance ratio [RAR], degree of overrepresentation) provide a means of visualizing regions within a specific promoter that display octamers that are enriched within the population of stress-induced promoters.…”
mentioning
confidence: 99%
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