Modeling gene expression control using Omes Law
TL;DR: A promising strategy for determining which context features are most important for a given TF binding motif is presented, which belongs to a growing class of methods that fit simple mathematical models of transcription regulation to DNA microarray data to map gene regulation networks.
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Abstract: Mol Syst Biol. 2: 2006.0013
The binding of transcription factors (TFs) to specific sites in the genome is a crucial step in the molecular process controlling gene expression. The in vitro sequence specificity of these regulatory proteins can generally be well represented by consensus DNA motifs or slightly more sophisticated sequence profiles called position‐specific scoring matrices. These are widely used to scan genome sequences in order to find novel transcriptional target genes. Unfortunately, usually only a small fraction of the ‘hits’ thus obtained are functional in vivo , where local chromatin structure and TF–TF interactions come into play. Taking into account the context provided by the surrounding noncoding DNA is therefore essential. In a recent study currently published in Molecular Systems Biology, Nguyen and D'haeseleer (2006) present a promising strategy for determining which context features are most important for a given TF binding motif. Their approach belongs to a growing class of methods that fit simple mathematical models of transcription regulation to DNA microarray data to map gene regulation networks.
Many of the molecular players that govern gene expression are known, but our knowledge about their interactions with the DNA and with each other is very incomplete. Information about the gene regulatory network is only implicitly represented in the large volume of functional genomics data now available to us. The strengths of the ‘arrows’ between TFs and their target genes and the condition‐specific activities of the regulatory ‘nodes’ need to be inferred by computational means. A detailed mathematical model that accurately describes the molecular …
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Computational biology of genome expression and regulation--a review of microarray bioinformatics.
TL;DR: A high level of microarray data analysis that focuses on the domain-specific microarray applications such as tumor classification, biomarker prediction, analyzing array CGH experiments, and reverse engineering of gene expression networks is reviewed.
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The contribution of cis-regulatory elements to head-to-head gene pairs’ co-expression pattern
TL;DR: A network component analysis was performed to estimate the impact of cis-elements on gene promoters and their activities under different conditions and reveal how biological system uses those regulatory elements to control the expression pattern of “head-to-head” gene pairs and the whole transcription regulation system.
4
Accuracy and application of the motif expression decomposition method in dissecting transcriptional regulation
Zhihua Zhang,Jianzhi Zhang +1 more
TL;DR: It is found that although MED accurately rebuilds gene expression levels from decomposed motif binding strengths and TF activities, estimates of motifbinding strengths andTF activities are unreliable and thus, judicious use of MED will likely provide useful information about eukaryotic transcriptional regulation.
2
Inferring the influence of cultivation parameters on transcriptional regulation
Theo A. Knijnenburg
- 20 Mar 2009
TL;DR: Computational approaches are presented that not only infer the activity of TFs as a function of the cultivation parameters, but also describe the combinatorial interplay between different TFs on gene promoters to regulate a gene's rate of transcription.
1
Genome-Wide Scanning of Gene Expression
Sung-J. Park
- 01 Jan 2016
TL;DR: Extensive community-wide efforts in gene expression profiling with a practical example of transcriptome data from spermatogenesis are reviewed to expedite the understanding of recent progress in scanning and annotating genome-wide gene expression that offers fundamental resource to communities.
1
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