Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae.

By J. Hughes, P. Estep, Saeed Tavazoie, George M. Church. Journal of Molecular Biology, 2000

1,116 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cfbfd5f15f8c1e8a6d63d024f9dbf00c4a91dfb6

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Citations in the corpus

Arc diagrams: visualizing structure in strings (2002, neutral): “This is potentially related to a recent finding [ H00 ] that many regulatory motifs are more likely to appear in this same restricted region.”
A Gibbs Sampling Method to Detect Overrepresented Motifs in the Upstream Regions of Coexpressed Genes (2001, positive): “For instance, when using AlignACE (Roth et al. , 1998) the user should estimate the total number of motif occurrences in the complete data set, while in our approach there is only a maximum number per sequence to be set.”
Mapping global histone acetylation patterns to gene expression. (2004, methodology): “We searched for overrepresented DNA sequence motifs upstream Jacobson, R.H., Ladurner, A.G., King, D.S., and Tjian, R. (2000).of genes within each acetylation cluster as previously described Structure and function of a human TAFII250 double bromodomain(Hughes et al., 2000; Tavazoie et al., 1999).”
FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data (2007, methodology): “As an example, promoters of genes belonging to the same transcriptional clusters in a yeast cell cycle exper-iment were found to be enriched for specific sequence motifs, likely to serve as transcription factor binding sites [2,3].”
A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling (2001, methodology): “The most popular probabilistic models published so far (Bailey and Elkan, 1995; Hughes et al., 2000; Lawrence et al., 1993; Liu et al., 1995; Neuwald et al., 1995; Roth et al., 1998) use a simple background model based on the frequency of the nucleotides A, C, G, and T in the data set to represent a”
CEAS: cis-regulatory element annotation system (2006, methodology): “The current best de novo motif finding methods for ChIP–chip includes MEME (11), AlignACE (12), Mascan (13) and their combinations (14).”
Discrimination between paralogs using microarray analysis: application to the Yap1p and Yap2p transcriptional networks. (2002, methodology): “We thank Semyon Kruglyak for the computer code for the QTClust algorithm, Jason Hughes for the ScanACE and AlignACE programs, Fred Winston for strains and plasmids, and members of the Church lab for discussions and critical readings of the manuscript.”
Computational prediction of transcription-factor binding site locations (2003, background): “In prokaryotes and yeast, motif-finding studies generally need to search only a few hundred base-pairs upstream of predicted translational start sites [36,37,52].”
Genome-wide prediction of transcription factor binding sites using an integrated model (2010, methodology): “Many computational methods [6-15] (for a survey, see [16]) have been developed to identify DNA segments recognized by TFs.”
Detection of functional DNA motifs via statistical over-representation. (2004, background): “While this 2-fold excess criterion is intuitive, a more rigorous test using the hypergeometric distribution is available (8, 10 ).”
Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes (2002, methodology): “[6], and the CompareACE software [7] available from the same web source.”