ITFP: an integrated platform of mammalian transcription factors

Zheng, Guangyong; Tu, Kang; Yang, Qing; Xiong, Yun; Wei, Chaochun; Xie, Lu; Zhu, Yu; Li, Yixue

doi:10.1093/bioinformatics/btn439

Cited by 142 publications

(106 citation statements)

References 6 publications

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“…[10], see Methods for details) and mutual ranks of gene co-expression rates (provided by COXPRESdb[11]). We controlled the effect of transcription factors on co-expression using transcription control similarity (TCS, see Methods for details) [12], and used 6,084 genes with both TF annotation (from ITFP[13]) and co-expression information for analyses. Then, 960,507 intra chromatin gene pairs were extracted by these genes.…”

Section: Resultsmentioning

confidence: 99%

“…Mutual ranks of gene coexpression scores are from COXPRESdb [11], which includes co-expression data for 19,777 genes in human. The information about human transcription factor are from the Integrated Transcription Factor Platform (ITFP, version 1.0 Aug 2008)[13], which under current release includes 4,105 putative TFs and 69,496 potential TF-target pairs for human. And for the GO anotation of human genes, we use the GO.db package (version 2.4.1) for R (http://www.r-project.org/).…”

Section: Methodsmentioning

confidence: 99%

“…We took 6,084 genes within both COXPRESdb [11] and ITFP [13] for analyses. There are 960,507 intra chromatin gene pairs from them.…”

Section: Methodsmentioning

confidence: 99%

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Human transcriptional interactome of chromatin contribute to gene co-expression

Dong

Chen

et al. 2010

BMC Genomics

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BackgroundTranscriptional interactome of chromatin is one of the important mechanisms in gene transcription regulation. By chromatin conformation capture and 3D FISH experiments, several chromatin interactions cases among sequence-distant genes or even inter-chromatin genes were reported. However, on genomics level, there is still little evidence to support these mechanisms. Recently based on Hi-C experiment, a genome-wide picture of chromatin interactions in human cells was presented. It provides a useful material for analysing whether the mechanism of transcriptional interactome is common.ResultsThe main work here is to demonstrate whether the effects of transcriptional interactome on gene co-expression exist on genomic level. While controlling the effects of transcription factors control similarities (TCS), we tested the correlation between Hi-C interaction and the mutual ranks of gene co-expression rates (provided by COXPRESdb) of intra-chromatin gene pairs. We used 6,084 genes with both TF annotation and co-expression information, and matched them into 273,458 pairs with similar Hi-C interaction ranks in different cell types. The results illustrate that co-expression is strongly associated with chromatin interaction. Further analysis using GO annotation reveals potential correlation between gene function similarity, Hi-C interaction and their co-expression.ConclusionsAccording to the results in this research, the intra-chromatin interactome may have relation to gene function and associate with co-expression. This study provides evidence for illustrating the effect of transcriptional interactome on transcription regulation.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Human transcriptional interactome of chromatin contribute to gene co-expression

Dong

Chen

et al. 2010

BMC Genomics

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“…A database containing 3134 putative transcription factors for mouse was used. To detect downstream regulated genes of specific TFs, a reverse engineering algorithm named ARACNE (26,27) was adopted that used gene expression profile data and TF terms as inputs and produced TF and target pairs as the outcome (28). The gene expression profile data were downloaded from the Gene Expression Omnibus (GEO) database and refined to build primary materials for the algorithm.…”

Section: Prediction Of Transcription Factors and Theirmentioning

confidence: 99%

Concurrent Quantification of Proteome and Phosphoproteome to Reveal System-wide Association of Protein Phosphorylation and Gene Expression

Dai

Yang

et al. 2009

Molecular & Cellular Proteomics

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Reversible phosphorylation of proteins is an important process modulating cellular activities from upstream, which mainly involves sequential phosphorylation of signaling molecules, to downstream where phosphorylation of transcription factors regulates gene expression. In this study, we combined quantitative labeling with multidimensional liquid chromatography-mass spectrometry to monitor the proteome and phosphoproteome changes in the initial period of adipocyte differentiation. The phosphorylation level of a specific protein may be regulated by a kinase or phosphatase without involvement of gene expression or as a phenomenon that accompanies the alteration of its gene expression. Concurrent quantification of phosphopeptides and nonphosphorylated peptides makes it possible to differentiate cellular phosphorylation changes at these two levels. Furthermore, on the system level, certain proteins were predicted as the targeted gene products regulated by identified transcription factors. Among them, several proteins showed significant expression changes along with the phosphorylation alteration of their transcription factors. This is to date the first work to concurrently quantify proteome and phosphoproteome changes during the initial period of adipocyte differentiation, providing an approach to reveal the system-wide association of protein phosphorylation and gene expression. Protein phosphorylation has been considered as a central role for cell regulation and signaling transduction. It is estimated that one-third of eukaryotic proteins are phosphorylated (1). In eukaryotes, the residues that undergo protein phosphorylation are mainly serine, threonine, and to a lesser extent tyrosine (2). Reversible phosphorylation of proteins is an important process for modulating cellular activities. At the upstream level, regulation of signaling transduction mainly relies on the appropriate phosphorylation events, such as sequential phosphorylation of various signaling molecules. At the downstream level, however, signaling transduction in the nucleus generally involves transcription of certain genes. Transcription factor activity, besides being regulated at the level of gene expression, is prominently regulated via post-translational events such as protein phosphorylation, processing, or localization (3). Phosphorylation or dephosphorylation of transcription-associated factors plays a crucial role in transcriptional regulation (see Fig. 1A).Most of the previous studies have focused on the initial period of signaling transduction, which mainly refers to the dynamic changes of phosphorylation events only. Recent developments in mass spectrometry promise to provide novel insights into the dynamics of protein expression and activities regulated by post-translational modifications (7,8). However, the isolation of phosphoproteins from complex mixtures and the determination of phosphorylation sites still remain a challenge. Different strategies have been applied for enrichment of phosphorylated peptides and proteins, including immuno...

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“…From the 1374 regulated genes between differentiated cardiomyocytes and undifferentiated ESCs 69 genes are defined as TFs. For these TFs, TF target information was retrieved from both integrated transcription factor platform (ITFP) (Zheng et al, 2008) and PAZAR (Portales-Casamar et al, 2009) database. Primary regulation network connections between regulated TFs and regulated genes were predicted by applying undirected Gaussian graphical Markov models on expression data of 69 regulated TFs and 1374 regulated genes during cardiomyocyte-specific differentiation with a non-rejection rate threshold of 0.8 (Castelo and Roverato, 2009).…”

Section: Tfs Tf Targets and Tf Regulation Networkmentioning

confidence: 99%