Extraction of transcription regulatory signals from genome-wide DNA-protein interaction data

Abstract

Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The ‘location data’ in yeast is a comprehensive resource that provides transcription factor–DNA interaction information in vivo. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike.

Delineation of the Salmonella enterica Serovar Typhimurium HilA Regulon through Genome-Wide Location and Transcript Analysis

“…Furthermore, in contrast to the prediction of target genes by in silico motif detection, GWLA is supported by in vivo experimental evidence and does not rely only on the sometimes-spurious presence of consensus DNA binding sites (49). But while GWLA alone is prone to noise, combining it with the results of other methods should reduce the number of false positives (24,37). Moreover, it offers the additional advantage that only those target genes are selected for whose expression is controlled by HilA under the applied conditions.…”

Comparative promoter analysis allows de novo identification of specialized cell junction-associated proteins

“…Beer and Tavazoie (19) used a combinatorial approach for characterizing regulatory DNA elements in yeast that could be used to predict gene expression patterns. Studies in higher organisms have also used computational searches to identify TFBSs combinations in phylogenetically conserved sequences controlling cell cycle-dependent transcription of G 2 ͞M genes (20). A slightly different approach was used by Dohr et al (6) to define potential regulatory networks by in silico promoter analysis by first finding potentially coregulated subgroups without a priori knowledge.…”

Age-dependent methylation in epigenetic clock CpGs is associated with G-quadruplex, co-transcriptionally formed RNA structures and tentative splice sites

“…Hotknots [53,54] was applied to identify pseudoknot structures in the 100 nt region adjacent to clock CpGs, using Perl-based batch submissions to the Hotknots Web server. Folding predictions and their corresponding energy were also simulated using the local version of Kinefold [55]. Both tools predict the presence of co-transcriptionally formed pseudoknots in RNA, while Kinefold can also predict pseudoknots in DNA.…”