Mapping nucleolus-associated chromatin interactions by nucleolus-Hi-C reveals repression network
ABSTRACT: Nucleolus plays a key role in organizing global nuclear structure and facilitating gene regulation, but the three dimensional information within the nucleolus associated chromatins are still unknown, so here we development the Nucleolus Hi-C (NHi-C) experimental technique, which combines the nucleolus isolation with Hi-C experiment, and study the interactions within and surrounding nucleolus in human cells with a high resolution. With NHi-C experiment, we report how the nucleolus-associated chromatin regions interact at genome scale, and how these interactions organize the repressive nucleus architecture. Overall design: in situ Hi-C and Nucleolus Hi-C data in human cells to detect the 3D genome information, the detailed information about all files uploaded to GEO for this study are explained in "DataFiles.README"
Project description:Chromosome conformation capture-based methods such as Hi-C have become mainstream techniques for the study of the 3D organization of genomes. These methods convert chromatin interactions reflecting topological chromatin structures into digital information (counts of pair-wise interactions). Here, we describe an updated protocol for Hi-C (Hi-C 2.0) that integrates recent improvements into a single protocol for efficient and high-resolution capture of chromatin interactions. This protocol combines chromatin digestion and frequently cutting enzymes to obtain kilobase (kb) resolution. It also includes steps to reduce random ligation and the generation of uninformative molecules, such as unligated ends, to improve the amount of valid intra-chromosomal read pairs. This protocol allows for obtaining information on conformational structures such as compartment and topologically associating domains, as well as high-resolution conformational features such as DNA loops.
Project description:Chromosome conformation capture-based methods such as Hi-C have become mainstream techniques for the study of the 3D organization of genomes. These methods convert chromatin interactions reflecting topological chromatin structures into digital information (counts of pair-wise interactions). Here, we describe an updated protocol for Hi-C (Hi-C 2.0) that integrates recent improvements into a single protocol for efficient and high-resolution capture of chromatin interactions. This protocol combines chromatin digestion and frequently cutting enzymes to obtain kilobase (Kb) resolution. It also includes steps to reduce random ligation and the generation of uninformative molecules, such as unligated ends, to improve the amount of valid intra-chromosomal read pairs. This protocol allows for obtaining information on conformational structures such as compartment and topologically associating domains, as well as high-resolution conformational features such as DNA loops. Overall design: HI-C 2.0
Project description:We develop Split-Pool Recognition of Interactions by Tag Extension (SPRITE), which enables genome-wide detection of higher-order interactions that occur simultaneously within the nucleus in a proximity-ligation independent manner. We generated SPRITE maps in two mammalian cell types – mouse embryonic stem cells (mES) and human lymphoblastoid cells (GM12878). We generated ~1.5 billion sequencing reads from each sample and recapitulate known genome structures identified by Hi-C, including chromosome territories, compartments, topologically associated domains, and loop structures, and identify that many of these occur within higher-order structures in the nucleus. Because SPRITE does not rely on proximity-ligation, we find that SPRITE identifies interactions that occur across larger spatial distances than can be observed by Hi-C. These long-range interactions include two major hubs of inter-chromosomal interactions. We extended SPRITE to enable simultaneous measurements of RNA and DNA interactions and observe ribosomal RNA interactions across specific regions on the genome that correspond to DNA organization around the nucleolus. We show that gene-dense regions that are highly transcribed by PolII organize around nuclear speckles and gene poor, and therefore transcriptionally inactive, regions that are centromere-proximal organize around the nucleolus. In addition to the regions that directly associate around these nuclear bodies, we find that a substantial fraction of the genome exhibits preferential spatial positioning in the nucleus relative to each of these nuclear bodies and that the spatial preferences identified by SPRITE are highly correlated with 3D distances measured by microscopy. Overall design: DNA SPRITE was used to study 3D genome organization in mouse ES cells and human lymphoblasts. RNA-DNA SPRITE was used to map RNA and DNA interactions simultaneously in mouse ES cells. Heatmaps are included in tar archives at the foot of this record in addition to the cluster data. Please see the file SPRITE_readme_heatmaps.rtf for information on heatmap generation.
Project description:Hi-C analysis has revealed the three-dimensional architecture of chromosomes in the nucleus. Although Hi-C data contains valuable information on long-range interactions of chromosomes, the data is not yet widely utilized by molecular biologists because of the quantity of data.We developed a web tool, ChromContact, to utilize the information obtained by Hi-C. The web tool is designed to be simple and easy to use. By specifying a locus of interest, ChromContact calculates contact profiles and generates links to the UCSC Genome Browser, enabling users to visually examine the contact information with various annotations.ChromContact provides wide-range of molecular biologists with a user-friendly means to access high-resolution Hi-C data. One of the possible applications of ChromContact is investigating novel long-range promoter-enhancer interactions. This facilitates the functional interpretation of statistically significant markers identified by GWAS or ChIP-seq peaks that are located far from any annotated genes. ChromContact is freely accessible at http://bioinfo.sls.kyushu-u.ac.jp/chromcontact/ .
Project description:SUMMARY:Capture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. Chicdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalization and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression. AVAILABILITY AND IMPLEMENTATION:Chicdiff is implemented as an R package that is publicly available at https://github.com/RegulatoryGenomicsGroup/chicdiff. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
Project description:Bulk chromatin motion has not been analyzed at high resolution. We present Hi-D, a method to quantitatively map dynamics of chromatin and abundant nuclear proteins for every pixel simultaneously over the entire nucleus from fluorescence image series. Hi-D combines reconstruction of chromatin motion and classification of local diffusion processes by Bayesian inference. We show that DNA dynamics in the nuclear interior are spatially partitioned into 0.3-3-?m domains in a mosaic-like pattern, uncoupled from chromatin compaction. This pattern was remodeled in response to transcriptional activity. Hi-D can be applied to any dense and bulk structures opening new perspectives towards understanding motion of nuclear molecules.
Project description:The High-throughput Chromosome Conformation Capture (Hi-C) technique combines the power of the Next Generation Sequencing technologies with chromosome conformation capture approach to study the 3D chromatin organization at the genome-wide scale. Although such a technique is quite recent, many tools are already available for pre-processing and analyzing Hi-C data, allowing to identify chromatin loops, topological associating domains and A/B compartments. However, only a few of them provide an exhaustive analysis pipeline or allow to easily integrate and visualize other omic layers. Moreover, most of the available tools are designed for expert users, who have great confidence with command-line applications. In this paper, we present HiCeekR (https://github.com/lucidif/HiCeekR), a novel R Graphical User Interface (GUI) that allows researchers to easily perform a complete Hi-C data analysis. With the aid of the Shiny libraries, it integrates several R/Bioconductor packages for Hi-C data analysis and visualization, guiding the user during the entire process. Here, we describe its architecture and functionalities, then illustrate its capabilities using a publicly available dataset.
Project description:We describe a method, Hi-C, to comprehensively detect chromatin interactions in the mammalian nucleus. This method is based on Chromosome Conformation Capture, in which chromatin is crosslinked with formaldehyde, then digested, and re-ligated in such a way that only DNA fragments that are covalently linked together form ligation products. The ligation products contain the information of not only where they originated from in the genomic sequence but also where they reside, physically, in the 3D organization of the genome. In Hi-C, a biotin-labeled nucleotide is incorporated at the ligation junction, enabling selective purification of chimeric DNA ligation junctions followed by deep sequencing. The compatibility of Hi-C with next generation sequencing platforms makes it possible to detect chromatin interactions on an unprecedented scale. This advance gives Hi-C the power to both explore the biophysical properties of chromatin as well as the implications of chromatin structure for the biological functions of the nucleus. A massively parallel survey of chromatin interaction provides the previously missing dimension of spatial context to other genomic studies. This spatial context will provide a new perspective to studies of chromatin and its role in genome regulation in normal conditions and in disease.
Project description:MOTIVATION:With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. RESULTS:Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. AVAILABILITY AND IMPLEMENTATION:multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
Project description:Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits and diseases. However, most of them are located in the non-protein coding regions, and therefore it is challenging to hypothesize the functions of these non-coding GWAS variants. Recent large efforts such as the ENCODE and Roadmap Epigenomics projects have predicted a large number of regulatory elements. However, the target genes of these regulatory elements remain largely unknown. Chromatin conformation capture based technologies such as Hi-C can directly measure the chromatin interactions and have generated an increasingly comprehensive catalog of the interactome between the distal regulatory elements and their potential target genes. Leveraging such information revealed by Hi-C holds the promise of elucidating the functions of genetic variants in human diseases.In this work, we present HiView, the first integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants. HiView is able to display Hi-C data and statistical evidence for chromatin interactions in genomic regions surrounding any given GWAS variant, enabling straightforward visualization and interpretation.We believe that as the first GWAS variants-centered Hi-C genome browser, HiView is a useful tool guiding post-GWAS functional genomics studies. HiView is freely accessible at: http://www.unc.edu/~yunmli/HiView .