Regulatory element detection using correlation with expression

By Harmen J. Bussemaker, Hao Li, Eric D. Siggia. Nature Genetics, 2001

794 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/28aecc0a1c2a9551e8b6394135448152650bf485

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In Molecular Biology

Citations in the corpus

The topology of the transcription regulatory network in the yeast (2002, background): “The combination of microarray data with the presence of known and putative regulatory motifs in the promoter regions of the expressed genes [22,23] suggests the combinatorial activity of a small number of transcription factors are responsible for a complex set of expression patterns under diverse co”
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections (2014, background): “The stringbased approaches rely on the detection of statistically overrepresented words (oligonucleotides or spaced motifs) compared to a given background [83–88].”
ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells (2009, background): “9% on various datasets from yeast to human (5, 7, 10, 11), even after considering TF–TF interaction.”
PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data (2011, methodology): “In the spirit of previously published work [47-49], we used a linear regression model for the interaction site binding evidence, which closely resembles the cERMIT scoring strategy described above [8].”
Defining transcription modules using large-scale gene expression data (2004, background): “, 2002), to predict novel cis-regulatory elements (Bussemaker et al., 2001; Hughes et al., 2000a) and to elucidate design principles of transcriptional regulation (Wang et al.”
Identifying regulatory networks by combinatorial analysis of promoter elements (2001, methodology): “A recent study used S. cerevisiae microarray data to ﬁt a linear model that describes the additive effect of oligomers on the expression levels of individual genes at particular time points 33 .”
Identifying protein interaction subnetworks by a bagging Markov random field-based method (2012, methodology): “The network score in Equation (8) allows us to properly evaluate a given subnetwork, but finding the maximally scoring subnetwork in the full PPI network is an NP-hard problem.”
Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering (2002, background): “The ribosomal protein genes in this group are regulated by the factor Rap1p and contain multiple copies of its binding site within their promoters [45,46], while other genes in this group contain two putative regulatory sequences that have been previously identified as enriched in the promoters of m”
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks (2015, background): “Predicting the functional output of DNA sequences is a fundamental problem in computational biology with a long history of approaches (Bussemaker et al. 2001; Beer and Tavazoie 2004; Segal et al. 2003).”
Network-based global inference of human disease genes (2008, methodology): “Note that the basic assumption of our model parallels the regression model in transcription factor-binding motif discovery from gene expression data, which assumes additivity of the contributions from different transcription factors to target gene expressions (Bussemaker et al , 2001).”
LOGOS: a modular Bayesian model for de novo motif detection (2003, background): “( 6 ) and (7) is the isomorphism of their righthand sides to those of the Eqs.”
Dicer ablation affects antibody diversity and cell survival in the B lymphocyte lineage. (2008, positive): “Linear regression analysis was performed for all 16384 possible nucleotide motifs of length seven, as previously described ( Bussemaker et al., 2001; Roven and Bussemaker, 2003; Sood et al., 2006).”
Linear modes of gene expression determined by independent component analysis (2002, background): “The 'reduce' model (Bussemaker et al., 2001) is based on the occurrence of common motifs in the genes' promoter sequences, assuming that each regulator acts on the genes via a particular promoter element.”