4DGenome: a comprehensive database of chromatin interactions

Teng, Li; He, Bing; JiaHui, Wang; Tan, Kai

doi:10.1093/bioinformatics/btw375

Cited by 88 publications

(33 citation statements)

References 16 publications

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“…With a threshold derived from the correlation coefficients obtained in a permuted data set (Appendix Fig S6D and F), we then identified enhancer–promoter pairs within 100‐kb windows (Dataset EV10). We found that 68% of these pairs were also listed as spatial contacts in the 4D genome database (Teng et al , ), suggesting that 2/3 of the promoter–enhancer pairs involve physical contacts. In total, 3,955 promoter–enhancer pairs were identified, with most promoters being connected to one enhancer at mean and median distances of 32 and 20 kb (CLL) and 23 and 10 kb (NBCs), respectively (Appendix Fig S6E and G).…”

Section: Resultsmentioning

confidence: 86%

Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks

Mallm

Iskar

Ishaque

et al. 2019

Molecular Systems Biology

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In chronic lymphocytic leukemia ( CLL ), a diverse set of genetic mutations is embedded in a deregulated epigenetic landscape that drives cancerogenesis. To elucidate the role of aberrant chromatin features, we mapped DNA methylation, seven histone modifications, nucleosome positions, chromatin accessibility, binding of EBF 1 and CTCF , as well as the transcriptome of B cells from CLL patients and healthy donors. A globally increased histone deacetylase activity was detected and half of the genome comprised transcriptionally downregulated partially DNA methylated domains demarcated by CTCF . CLL samples displayed a H3K4me3 redistribution and nucleosome gain at promoters as well as changes of enhancer activity and enhancer linkage to target genes. A DNA binding motif analysis identified transcription factors that gained or lost binding in CLL at sites with aberrant chromatin features. These findings were integrated into a gene regulatory enhancer containing network enriched for B‐cell receptor signaling pathway components. Our study predicts novel molecular links to targets of CLL therapies and provides a valuable resource for further studies on the epigenetic contribution to the disease.

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Section: Resultsmentioning

confidence: 86%

Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks

Mallm

Iskar

Ishaque

et al. 2019

Molecular Systems Biology

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“…S2I). In order to assess the validity of our eDMR-gene pair predictions, we compared our results to other methods that identify enhancer-promotor associations based on physical interactions IM-PET (He et al 2014), ChIA-PET, Hi-C (Teng et al 2015), and transcriptional activities of interacting enhancer-promoters (cap analysis gene expression; CAGE) (Andersson et al 2014). Our model predicted eDMR-gene pairs separated by 400 kb or less at a precision rate of 75% or better ( Fig.…”

Section: Differential Methylation Patterns Of Edmrs Clusters Tumors Amentioning

confidence: 99%

Enhancer methylation dynamics contribute to cancer plasticity and patient mortality

et al. 2016

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During development, enhancers play pivotal roles in regulating gene expression programs; however, their involvement in cancer progression has not been fully characterized. We performed an integrative analysis of DNA methylation, RNA-seq, and small RNA-seq profiles from thousands of patients, including 25 diverse primary malignances and seven body sites of metastatic melanoma. We found that enhancers are consistently the most differentially methylated regions (DMR) as cancer progresses from normal to primary tumors and then to metastases, compared to other genomic features. Remarkably, identification of enhancer DMRs (eDMRs) enabled classification of primary tumors according to physiological organ systems, and in metastasis eDMRs are the most correlated with patient outcome. To further understand the eDMR role in cancer progression, we developed a model to predict genes and microRNAs that are regulated by enhancer and not promotor methylation, which shows high accuracy with chromatin architecture methods and was experimentally validated. Interestingly, among all metastatic melanoma eDMRs, the most correlated with patient survival were eDMRs that "switched" their methylation patterns back and forth between normal, primary, and metastases and target cancer drivers, e.g., KIT. We further demonstrated that eDMR target genes were modulated in melanoma by the bone metastasis microenvironment, suggesting that eDMRs respond to microenvironmental cues in metastatic niches. Our findings that aberrant methylation in cancer cells mostly affects enhancers, which contribute to tumor progression and cancer cell plasticity, will facilitate development of epigenetic anticancer approaches.

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“…This browser allows visual exploration of contact maps both as matrices/heatmaps and as arcs, while also containing a rich database of annotated data sets. A few other repositories that contain chromatin interaction data include ‘GEO' [92], ‘Array Express' [93], and the ‘3DGD database’ [94, 167]. Different labs have generated their own browsers to visualize the data sets they have produced.…”

Section: Bioinformatics Challengesmentioning

confidence: 99%

In the loop: promoter–enhancer interactions and bioinformatics

Mora¹,

Sandve²,

Gabrielsen³

et al. 2015

Brief Bioinform

109

104

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Enhancer–promoter regulation is a fundamental mechanism underlying differential transcriptional regulation. Spatial chromatin organization brings remote enhancers in contact with target promoters in cis to regulate gene expression. There is considerable evidence for promoter–enhancer interactions (PEIs). In the recent years, genome-wide analyses have identified signatures and mapped novel enhancers; however, being able to precisely identify their target gene(s) requires massive biological and bioinformatics efforts. In this review, we give a short overview of the chromatin landscape and transcriptional regulation. We discuss some key concepts and problems related to chromatin interaction detection technologies, and emerging knowledge from genome-wide chromatin interaction data sets. Then, we critically review different types of bioinformatics analysis methods and tools related to representation and visualization of PEI data, raw data processing and PEI prediction. Lastly, we provide specific examples of how PEIs have been used to elucidate a functional role of non-coding single-nucleotide polymorphisms. The topic is at the forefront of epigenetic research, and by highlighting some future bioinformatics challenges in the field, this review provides a comprehensive background for future PEI studies.

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4DGenome: a comprehensive database of chromatin interactions

Cited by 88 publications

References 16 publications

Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks

Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks

Enhancer methylation dynamics contribute to cancer plasticity and patient mortality

In the loop: promoter–enhancer interactions and bioinformatics

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