Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells.

By Ran Elkon, Chaim Linhart, Roded Sharan, Ron Shamir, Yosef Shiloh. Genome Research, 2003

332 citations recorded by the source corpus.

Dissection of regulatory networks that control gene transcription is one of the greatest challenges of functional genomics. Using human genomic sequences, models for binding sites of known transcription factors, and gene expression data, we demonstrate that the reverse engineering approach, which infers regulatory mechanisms from gene expression patterns, can reveal transcriptional networks in human cells. To date, such methodologies were successfully demonstrated only in prokaryotes and low eukaryotes. We developed computational methods for identifying putative binding sites of transcription factors and for evaluating the statistical significance of their prevalence in a given set of promoters. Focusing on transcriptional mechanisms that control cell cycle progression, our computational analyses revealed eight transcription factors whose binding sites are significantly overrepresented in promoters of genes whose expression is cell-cycle-dependent. The enrichment of some of these factors is specific to certain phases of the cell cycle. In addition, several pairs of these transcription factors show a significant co-occurrence rate in cell-cycle-regulated promoters. Each such pair indicates functional cooperation between its members in regulating the transcriptional program associated with cell cycle progression. The methods presented here are general and can be applied to the analysis of transcriptional networks controlling any biological process.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0dd180087019a8e055e60c7d4e84d88265503704

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Citations in the corpus

Integrative analysis of the cancer transcriptome (2005, background): “demonstrated this approach first in humans, identifying known transcription factor–binding sites enriched among promoters of genes expressed in various phases of the cell cycl”
oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes (2005, background): “Other resources are available for detecting and visualizing binding sites within the conserved regions of human genes [Consite (11), rVISTA (38), dbTSS (39), CONREAL (40), CORG (41)], as well as for identifying statistically over-represented motifs in the promoters of related sequences [Clover (42),”
MatInspector and beyond: promoter analysis based on transcription factor binding sites (2005, background): “Sites outside regulatory regions are known to bind their TFs (Kodadek (1998)), and it is the context that differentiates a functional binding site affecting gene regulation from a mere physical binding site (Elkon et al. (2003)).”
From signatures to models: understanding cancer using microarrays (2005, background): “Nevertheless, several researchers have identified regulatory circuits in expression data from synchronized HeLa cells 26,29 , both finding known cell cycle– regulatory elements and targets, and suggesting new ones ( Fig.”
Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network (2007, methodology): “A common statistical test in the field, previously used in the context of promoter motifs and TF binding site [26–28], is the cumulative hypergeometric statistic.”
Identification of functional modules using network topology and high-throughput data (2007, methodology): “When using the PRIMA motif finding tool [35], six subnetworks showed significant enrichment (p < 10-5) for at least one TF (Table 2).”
Reverse engineering of regulatory networks in human B cells (2005, methodology): “As a result, these methods have been successful only in the study of organisms with relatively simple genomes, such as Saccharomyces cerevisiae , or have produced networks with only a handful of interaction”
Detection of functional DNA motifs via statistical over-representation. (2004, methodology): “(7) use a more intricate procedure, counting the number of sequences with zero matches, one match, two matches or three or more matches in the target and control sets.”