Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome

By Benjamin Paul Berman, Y. Nibu, B. Pfeiffer, Pavel Tomancak, S. Celniker, M. Levine, Gerald M. Rubin, Michael B. Eisen. 2002

684 citations recorded by the source corpus.

Subfields: Molecular Biology

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Citations in the corpus

Iterative random forests to discover predictive and stable high-order interactions (2017, background): “Extensive work has resulted in genome-wide, quantitative maps of DNA occupancy for 23 TFs (30) and 13 histone modifications (6), as well as labels of enhancer status for 7,809 genomic sequences in blastoderm (stage 5) Drosophila embryos (1, 31).”
Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions (2009, methodology): “To address this challenge, the Berkeley Drosophila Transcription Network Project (BDTNP) has initiated an interdisciplinary analysis of the network controlling transcription in the Drosophila melanogaster blastoderm embryo [7-12].”
Transcriptional enhancers: from properties to genome-wide predictions (2014, background): “Available approaches either identify genomic regions that are enriched for transcription factor motif matches 16 or seek individual matches that are highly conserved across species 17,18 .”
Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. (2003, background): “Therefore, a major task of deciphering transcriptional regulation networks is to identify combinations of TFs that cooperate in the regulation of genes and form a recurrent regulation motif, termed a “regulation module.” Recent works successfully undertook a computational approach for genome-wide ma”
Computational prediction of transcription-factor binding site locations (2003, background): “More recently, approaches have been developed to identify higher-order site clusterings [81-93].”
Distribution and intensity of constraint in mammalian genomic sequence. (2005, background): “…including third-position wobble in exons, transcription-factor binding sites containing degenerate bases, and clustering of transcription-factor binding sites that together regulate gene expression (Arnone and Davidson 1997; Berman et al. 2002; Markstein et al. 2002; Brugger et al. 2004).”
The evolution of transcriptional regulation in eukaryotes. (2003, background): “These algorithms rely on large data sets to identify overrepresented sequence motifs (e.g., Sinha and Tompa 2002), clusters of binding sites (e.g., Berman et al. 2002; Rebeiz, Reeves, and Posakony 2002), or correlations with expression data (e.g., Birnbaum, Benfey, and Shasha 2001).”
Cluster-Buster: finding dense clusters of motifs in DNA sequences (2003, methodology): “Most recent methods for finding motif clusters fall into two categories: those that count motif ‘hits’ occurring within a sequence window of some size (2,12,13), and those that employ probabilistic models (5,10,11,14).”
Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments (2013, methodology): “One is using clustering of motif instances for factors known to cooperate to form cis-regulatory modules (92,93).”
Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures (2007, methodology): “In the absence of such data, previous studies have relied on motif clustering 91,94–96 or other sequence characteristics 106 to predict regulatory targets or regions.”