ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data

Yang, Jianhua; Li, Junhao; Jiang, Shan; Zhou, Hui; Qu, Liang‐Hu

doi:10.1093/nar/gks1060

Cited by 287 publications

(229 citation statements)

References 98 publications

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“…Much of the experimental evidence in support of the four models has been collected using reporter assays or single endogenous elements as a model system. Fortunately, with the advent of new technologies that allow investigation of the genome-wide in vivo binding patterns of TFs (75)(76)(77), along with comprehensive gene expression and DNA methylation analyses, it is now possible to investigate the relationship between DNA methylation, TF binding, and gene expression on a global scale in a variety of cell types under diverse physiological and developmental conditions. Future studies that intersect TF-binding sites with binding sites of DNMTs or repressive histone-modifying complexes may identify factors that help establish or reinforce DNA methylation.…”

Section: Discussionmentioning

confidence: 99%

Cross-talk between Site-specific Transcription Factors and DNA Methylation States

Blattler

Farnham

2013

Journal of Biological Chemistry

174

147

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DNA methylation, which occurs predominantly at CpG dinucleotides, is a potent epigenetic repressor of transcription. Because DNA methylation is reversible, there is much interest in understanding the mechanisms by which it can be regulated by DNA-binding transcription factors. We discuss several models that, by incorporating sequence motifs, CpG density, and methylation levels, attempt to link the binding of a transcription factor with the acquisition or loss of DNA methylation at promoters and distal regulatory elements. Additional in vivo genome-wide characterization of transcription factor binding patterns and high-resolution DNA methylation analyses are clearly required for stronger support of each model. DNA methylation is a potent epigenetic repressor of transcription; promoters and enhancers that display high levels of methylation are essentially inactive (1, 2). Patterns of DNA methylation are tightly regulated in a tissue-specific manner, resulting in epigenetic specification of gene expression. Additionally, widespread genomic DNA hypomethylation in conjunction with local promoter-specific hypermethylation (which leads to inappropriate gene silencing) is centrally implicated in a myriad of human diseases from immunodeficiency-centromeric instability-facial anomalies syndrome to cancer (3-7). DNA methylation is potentially reversible (8), and there is much interest in understanding the mechanisms by which DNA methylation patterns are established. In this review, we focus on regulation of methylation patterns at mammalian genomic elements such as promoters and enhancers; for a summary of the regulation of large scale changes in methylation during development, we suggest a recent review by Smith and Meissner (9). It has been proposed that site-specific regulation of DNA methylation is mediated by DNA-binding transcription factors (TFs) 3 that interact with specific promoter or enhancer regions (10 -14). The functional relationship between DNA methylation and TF binding has been a subject of much interest for decades. Several models have been put forth that attempt to integrate the ability of a TF to bind to hyper-or hypo-methylated DNA with the acquisition and/or loss of DNA methylation at regulatory elements (Fig. 1). These include (a) protection from acquisition of DNA methylation upon binding of a TF to unmethylated DNA, (b) promotion of DNA methylation upon binding of a TF to unmethylated DNA, (c) reversal of DNA methylation upon binding of a TF to a region containing methylated DNA, and (d) reinforcement of repression upon binding of a TF to methylated DNA. In mammalian genomes, DNA methylation occurs predominantly at CpG dinucleotides. Regions of the genome are generally classified as those that have high CpG density (e.g. promoters) versus low CpG density (most of the genome). Additionally, the genome can be divided into categories based on CpG methylation levels: low methylation (promoters), intermediate methylation (distal regulatory elements), and high CpG methylation (most of the genome) (15)...

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Section: Discussionmentioning

confidence: 99%

Cross-talk between Site-specific Transcription Factors and DNA Methylation States

Blattler

Farnham

2013

Journal of Biological Chemistry

174

147

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“…Further experimentations are needed to conduct large-scale studies for understanding this kind of regulatory feedback loops in ESCs and iPSCs. Recently available vast datasets [49] of transcription factor binding sites in the lncRNA regulatory regions, identified by ChIP-Seq, can be explored and further investigated for actual regulatory relationships between pluripotency-associated transcription factors and lncRNAs in pluripotent cells. Association of the lncRNAs with chromatin-modifying complexes in pluripotent cells is comparatively well studied in comparison to their other regulatory mechanisms such as transcriptional or post-transcriptional regulation.…”

Section: Discussionmentioning

confidence: 99%

“…As discussed in the previous section, hundreds of lncRNAs are differentially expressed in pluripotent cells, and many of them are likely to be regulated by the pluripotency-associated transcription factors. The recently published database ChIPBase [49] shows more than 8000 lncRNAs in mouse that harbor binding sites for at least one of nine key pluripotency transcription factors (including Oct4, Sox2, Nanog, c-Myc, n-Myc, Klf4, Zfx, E2F1, and Smad1) in their promoter region; identified by analyzing ChIP-Seq data. In the following section, we will discuss about lncRNAs activated or repressed by pluripotency-associated transcription factors in both ESCs and iPSCs.…”

Section: Transcriptional Activation or Repression Of Lncrnas By Plurimentioning

confidence: 99%

“…Many of these lncRNAs have been identified to be transcriptionally activated or repressed by pluripotencyassociated transcription factors. A recently published database [49] presents a comprehensive annotation of the transcription factor-binding sites in lncRNAs and transcriptional regulatory relationships of transcription factors and lncRNAs based on chromatin immunoprecipitation with the next-generation DNA sequencing (ChIP-Seq) data. This database includes lncRNAs whose promoters are found to have binding sites for pluripotency-associated transcription factors.…”

Section: Introductionmentioning

confidence: 99%

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Long Noncoding RNAs: New Players in the Molecular Mechanism for Maintenance and Differentiation of Pluripotent Stem Cells

Ghosal

Das

Chakrabarti

2013

Stem Cells and Development

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Maintenance of the pluripotent state or differentiation of the pluripotent state into any germ layer depends on the factors that orchestrate expression of thousands of genes through epigenetic, transcriptional, and posttranscriptional regulation. Long noncoding RNAs (lncRNAs) are implicated in the complex molecular circuitry in the developmental processes. The ENCODE project has opened up new avenues for studying these lncRNA transcripts with the availability of new datasets for lncRNA annotation and regulation. Expression studies identified hundreds of long noncoding RNAs differentially expressed in the pluripotent state, and many of these lncRNAs are found to control the pluripotency and stemness in embryonic and induced pluripotent stem cells or, in the reverse way, promote differentiation of pluripotent cells. They are generally transcriptionally activated or repressed by pluripotency-associated transcription factors and function as molecular mediators of gene expression that determine the pluripotent state of the cell. They can act as molecular scaffolds or guides for the chromatin-modifying complexes to direct them to bind into specific genomic loci to impart a repressive or activating effect on gene expression, or they can transcriptionally or post-transcriptionally regulate gene expression by diverse molecular mechanisms. This review focuses on recent findings on the regulatory role of lncRNAs in two main aspects of pluripotency, namely, self renewal and differentiation into any lineage, and elucidates the underlying molecular mechanisms that are being uncovered lately.

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“…In case of PPI, the Human Protein Reference Database provides more than 40 000 PPIs (Keshava Prasad et al ., 2009), the Database of Interacting Proteins more than 7000 interactions (Xenarios et al ., 2002) and the MIPS mammalian protein–protein database roughly 1000 hand‐curated interactions of human proteins (Pagel et al ., 2005). GRN are provided by the ChIPBase (Yang et al ., 2013), which contains six million transcription factor binding sites from >300 experiments. Metabolic reactions are amongst others provided by KEGG.…”

Section: From Omics To Systems Biologymentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

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ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data

Cited by 287 publications

References 98 publications

Cross-talk between Site-specific Transcription Factors and DNA Methylation States

Cross-talk between Site-specific Transcription Factors and DNA Methylation States

Long Noncoding RNAs: New Players in the Molecular Mechanism for Maintenance and Differentiation of Pluripotent Stem Cells

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

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