Integration of Known Transcription Factor Binding Site Information and Gene Expression Data to Advance from Co-Expression to Co-Regulation

Clements, Maarten; Someren, Eugene P. van; Knijnenburg, Theo; Reinders, Marcel J. T.

doi:10.1016/s1672-0229(07)60019-9

Cited by 17 publications

(17 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thereafter, bioinformatics technology was used to extract these biological significances from high‐throughput information . There is evidence that many functionally related genes are coexpressed; therefore, coexpressed genes can reveal a number of control mechanisms and also display different levels of expression in different cell types and cell states . For these reasons, this paper used STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) to predict the coexpression scores of differential gene products and to build networks of coexpression through the connection with the given database according to the information in the input sequence itself, such as on structure and features.…”

Section: Methodsmentioning

confidence: 99%

Comparisons of gene expression in normal, lesional, and non‐lesional psoriatic skin using DNA microarray techniques

et al. 2014

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Comparisons of gene expression in normal, lesional, and non‐lesional psoriatic skin using DNA microarray techniques

et al. 2014

View full text Add to dashboard Cite

show abstract

“…An alternative method is to approach this problem from the perspective of the actual biological effects of the TFBS-like sequences by performing a two-dimensional clustering with the PWM scores as a function of gene expression. This would also provide a means of visualization, and currently, several groups are following this approach (54,64).…”

Section: Methodology For Studying Natural Selection On Tfbs Motifsmentioning

confidence: 99%

Mechanisms and Evolution of Control Logic in Prokaryotic Transcriptional Regulation

Hijum

Medema

Kuipers

2009

Microbiol Mol Biol Rev

140

149

View full text Add to dashboard Cite

SUMMARY A major part of organismal complexity and versatility of prokaryotes resides in their ability to fine-tune gene expression to adequately respond to internal and external stimuli. Evolution has been very innovative in creating intricate mechanisms by which different regulatory signals operate and interact at promoters to drive gene expression. The regulation of target gene expression by transcription factors (TFs) is governed by control logic brought about by the interaction of regulators with TF binding sites (TFBSs) in cis-regulatory regions. A factor that in large part determines the strength of the response of a target to a given TF is motif stringency, the extent to which the TFBS fits the optimal TFBS sequence for a given TF. Advances in high-throughput technologies and computational genomics allow reconstruction of transcriptional regulatory networks in silico. To optimize the prediction of transcriptional regulatory networks, i.e., to separate direct regulation from indirect regulation, a thorough understanding of the control logic underlying the regulation of gene expression is required. This review summarizes the state of the art of the elements that determine the functionality of TFBSs by focusing on the molecular biological mechanisms and evolutionary origins of cis-regulatory regions.

show abstract

“…However, due to the limited information present in any dataset, genes in the same cluster might be co-expressed but not necessarily co-regulated [47]–[49]. Therefore, to design an effective algorithm for finding co-regulated genes is our future work.…”

Section: Resultsmentioning

confidence: 99%

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

2013

View full text Add to dashboard Cite

Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.

show abstract

Integration of Known Transcription Factor Binding Site Information and Gene Expression Data to Advance from Co-Expression to Co-Regulation

Cited by 17 publications

References 41 publications

Comparisons of gene expression in normal, lesional, and non‐lesional psoriatic skin using DNA microarray techniques

Comparisons of gene expression in normal, lesional, and non‐lesional psoriatic skin using DNA microarray techniques

Mechanisms and Evolution of Control Logic in Prokaryotic Transcriptional Regulation

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

Contact Info

Product

Resources

About