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. 2005 Jan 12;33(1):272-9.
doi: 10.1093/nar/gki167. Print 2005.

Inferring Combinatorial Regulation of Transcription in Silico

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Free PMC article

Inferring Combinatorial Regulation of Transcription in Silico

Nils Blüthgen et al. Nucleic Acids Res. .
Free PMC article

Abstract

In this paper, we propose a functional view on the in silico prediction of transcriptional regulation. We present a method to predict biological functions regulated by a combinatorial interaction of transcription factors. Using a rigorous statistic, this approach intersects the presence of transcription factor binding sites in gene upstream sequences with Gene Ontology terms associated with these genes. We demonstrate that for the well-studied set of skeletal muscle-related transcription factors Myf-2, Mef and TEF, the correct functions are predicted. Furthermore, starting from the well-characterized promoter of a gene expressed upon lipopolysaccharide stimulation, we predict functional targets of this stimulus. These results are in excellent agreement with microarray data.

Figures

Figure 1
Figure 1
Data flow in our method: Using the Cluster-Buster algorithm, we search for clusters of binding sites in putative promoter regions. The list of genes having a cluster in their promoter are then passed to GOSSIP, which detects association with biological processes using the Gene Ontology. The significantly associated processes are reported.
Figure 2
Figure 2
Results for the set of transcription factors Mef-2, Myf and TEF, known to regulate the expression of muscle-specific genes (14). The black and gray boxes correspond to significantly overrepresented biological processes of the Gene Ontology within the predicted target genes [thresholds of FDR ≤ 0.01 and FDR ≤ 0.05, respectively]. The diamond shows the root node for biological processes. (a) An illustration of the complexity of the analysis: overrepresented terms drawn in the context of all 655 terms assigned to the genes with predicted clusters of binding sites. (b) A fragment emphasizing all significantly overrepresented terms.
Figure 3
Figure 3
Black and gray boxes indicate the significantly associated biological processes [FDR ≤ 0.01 and FDR ≤ 0.05, respectively] with (a) the set of transcription factors CREB, CEBP, p50/p65, Sp-1, ETS and AP1 as predicted by our method; (b) up-regulated genes upon LPS stimulation in monocytes (microarray data from the alliance for cellular signaling).
Figure 4
Figure 4
Normalized cumulative histograms of mean fold changes in the microarray data set for all genes (open bars), genes where Cluster-Buster detected a cluster of binding sites (gray bars) and after additional filtering with Gene Ontology (closed bars).

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