Identifying regulatory networks by combinatorial analysis of promoter elements

By Yitzhak Pilpel, P. Sudarsanam, George M. Church. Nature Genetics, 2001

688 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/109e5fe8cc1c277fe47eb3ebb6ba7613cc10d52e

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Citations in the corpus

Associating Genes and Protein Complexes with Disease via Network Propagation (2010, methodology): “Each protein complexes was scored by the mean pairwise Pearson correlation of gene expression profiles among all its members [19].”
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”
Fundamentals of cDNA microarray data analysis. (2003, background): “Analyzing the promoters of this group of genes can often reveal common regulatory motifs and unveil a higher level of network organization in the biological system [41].”
Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs (2002, background): “Assignment of the transcription start sites of TUs The correlation of expression measurements with identified promoter regions permits the association of specific cis -acting elements with particular patterns of expressio”
Computational prediction of transcription-factor binding site locations (2003, background): “Because pairs of sites may correspond to TFs that coregulate expression of the nearby gene(s) [71], a number of approaches have been developed to identify pairs of binding sites [72-78].”
Systematic prediction of drug combinations based on clinical side-effects (2014, background): “However such ‘‘magic bullet’’ sometimes shows limited efficacy, especially for complex 4 , which is often due to factors such as network robustness 5 , redundancy 6 , compensatory and neutralizing actions 7 .”
Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes (2002, methodology): “[6]: There one can see that (1) of all known regulatory motifs, PAC and RRPE show the highest expression coherence for the diauxic shift and (2) viceversa, of the eight experimental conditions considered in Ref.”
A dynamic transcriptional network communicates growth potential to ribosome synthesis and critical cell size. (2004, background): “The presence of RAP1, PAC, RRPE, or PAC + RRPE (P+R) promoter elements is indicated (Pilpel et al. 2001; Jorgensen et al. 2002).”
Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. (2003, background): “cerevisiae (Jelinsky et al. 2000; Pilpel et al. 2001; Tavazoie et al. 1999) These studies demonstrated that genes that are co-expressed over multiple biological conditions are often regulated via common mechanisms, and, hence, share common cis-regulatory elements in their promoters.”
Discovering regulatory and signalling circuits in molecular interaction networks (2002, neutral): “…clustering (Eisen et al., 1998; Tamayo et al., 1999), significance testing (Kerr and Churchill, 2001; Rocke and Durbin, 2001; Ideker et al., 2000), and sequence motif identification (Pilpel et al., 2001) have been indispensable for enabling these discoveries and summarizing the data at each step.”
Network motifs: theory and experimental approaches (2007, background): “Currently, most of the input functions in any organism are unknow”
A universal framework for regulatory element discovery across all genomes and data types. (2007, background): “The temporal and spatial regulation of gene expression often involves combinatorial interactions between tran- Molecula scription factors (Beer and Tavazoie, 2004; Pilpel et al., 2001).”