Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.

ABSTRACT: Current computational methods on Hi-C analysis focused on identifying Mb-size domains often failed to unveil the underlying functional and mechanistic relationship of chromatin structure and gene regulation. We developed a novel computational method HiSIF to identify genome-wide interacting loci. We illustrated HiSIF outperformed other tools for identifying chromatin loops. We applied it to Hi-C data in breast cancer cells and identified 21 genes with gained loops showing worse relapse-free survival in endocrine-treated patients, suggesting the genes with enhanced loops can be used for prognostic signatures for measuring the outcome of the endocrine treatment. HiSIF is available at https://github.com/yufanzhouonline/HiSIF .

SUBMITTER: Zhou Y

PROVIDER: S-EPMC7425017 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Modeling and analysis of Hi-C data by HiSIF identifies characteristic promoter-distal loops.

Zhou Yufan Y Cheng Xiaolong X Yang Yini Y Li Tian T Li Jingwei J Huang Tim H-M TH Wang Junbai J Lin Shili S Jin Victor X VX

Genome medicine 20200812 1

Current computational methods on Hi-C analysis focused on identifying Mb-size domains often failed to unveil the underlying functional and mechanistic relationship of chromatin structure and gene regulation. We developed a novel computational method HiSIF to identify genome-wide interacting loci. We illustrated HiSIF outperformed other tools for identifying chromatin loops. We applied it to Hi-C data in breast cancer cells and identified 21 genes with gained loops showing worse relapse-free surv ...[more]

PMID: 32787954

Similar Datasets

Genome-wide chromatin interactions identify characteristic promoter-distal loops

Project description:We developed a novel computational model, HiSIF (Hi-C Significant Interacting Fragments), which uses a Poisson Mixture Model (PMM) with a power-law decay background. We compared its performance to some existing programs with publicly available Hi-C data, and then applied it to in situ Hi-C data in breast cancer sensitive and resistant cells.

2020-12-01 | GSE128676 | GEO

Analysis of Hi-C data using SIP effectively identifies loops in organisms from C. elegans to mammals.

Project description:Chromatin loops are a major component of 3D nuclear organization, visually apparent as intense point-to-point interactions in Hi-C maps. Identification of these loops is a critical part of most Hi-C analyses. However, current methods often miss visually evident CTCF loops in Hi-C data sets from mammals, and they completely fail to identify high intensity loops in other organisms. We present SIP, Significant Interaction Peak caller, and SIPMeta, which are platform independent programs to identify and characterize these loops in a time- and memory-efficient manner. We show that SIP is resistant to noise and sequencing depth, and can be used to detect loops that were previously missed in human cells as well as loops in other organisms. SIPMeta corrects for a common visualization artifact by accounting for Manhattan distance to create average plots of Hi-C and HiChIP data. We then demonstrate that the use of SIP and SIPMeta can lead to biological insights by characterizing the contribution of several transcription factors to CTCF loop stability in human cells. We also annotate loops associated with the SMC component of the dosage compensation complex (DCC) in Caenorhabditis elegans and demonstrate that loop anchors represent bidirectional blocks for symmetrical loop extrusion. This is in contrast to the asymmetrical extrusion until unidirectional blockage by CTCF that is presumed to occur in mammals. Using HiChIP and multiway ligation events, we then show that DCC loops form a network of strong interactions that may contribute to X Chromosome-wide condensation in C. elegans hermaphrodites.

| S-EPMC7111518 | biostudies-literature

Genome-wide chromatin interactions identify characteristic promoter-distal loops

Project description:Genome-wide chromatin interactions identify characteristic promoter-distal loops

| PRJNA528483 | ENA

SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data.

Project description:Single-cell Hi-C (scHi-C) analysis has been increasingly used to map chromatin architecture in diverse tissue contexts, but computational tools to define chromatin loops at high resolution from scHi-C data are still lacking. Here, we describe Single-Nucleus Analysis Pipeline for Hi-C (SnapHiC), a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. Using scHi-C data from 742 mouse embryonic stem cells, we benchmark SnapHiC against a number of computational tools developed for mapping chromatin loops and interactions from bulk Hi-C. We further demonstrate its use by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells, which uncovers cell type-specific chromatin loops and predicts putative target genes for noncoding sequence variants associated with neuropsychiatric disorders. Our results indicate that SnapHiC could facilitate the analysis of cell type-specific chromatin architecture and gene regulatory programs in complex tissues.

| S-EPMC8440170 | biostudies-literature

Sequence-based multiscale modeling for high-throughput chromosome conformation capture (Hi-C) data analysis.

Project description:In this paper, we introduce sequence-based multiscale modeling for biomolecular data analysis. We employ spectral clustering method in our modeling and reveal the difference between sequence-based global scale clustering and local scale clustering. Essentially, two types of distances, i.e., Euclidean (or spatial) distance and genomic (or sequential) distance, can be used in data clustering. Clusters from sequence-based global scale models optimize spatial distances, meaning spatially adjacent loci are more likely to be assigned into the same cluster. Sequence-based local scale models, on the other hand, result in clusters that optimize genomic distances. That is to say, in these models, sequentially adjoining loci tend to be cluster together. We propose two sequence-based multiscale models (SeqMMs) for the study of chromosome hierarchical structures, including genomic compartments and topological associated domains (TADs). We find that genomic compartments are determined only by global scale information in the Hi-C data. The removal of all the local interactions within a band region as large as 10 Mb in genomic distance has almost no significant influence on the final compartment results. Further, in TAD analysis, we find that when the sequential scale is small, a tiny variation of diagonal band region in a contact map will result in a great change in the predicted TAD boundaries. When the scale value is larger than a threshold value, the TAD boundaries become very consistent. This threshold value is highly related to TAD sizes. By the comparison of our results with those previously obtained using a spectral clustering model, we find that our method is more robust and reliable. Finally, we demonstrate that almost all TAD boundaries from both clustering methods are local minimum of a TAD summation function.

| S-EPMC5800693 | biostudies-literature

Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data.

Project description:BackgroundCo-localized combinations of histone modifications ("chromatin states") have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points ("chromatin state trajectories") have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs.ResultsWe present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex .ConclusionsTimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time.

| S-EPMC7841892 | biostudies-literature

Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains.

Project description:Proximity-ligation methods such as Hi-C allow us to map physical DNA-DNA interactions along the genome, and reveal its organization into topologically associating domains (TADs). As the Hi-C data accumulate, computational methods were developed for identifying domain borders in multiple cell types and organisms. Here, we present PSYCHIC, a computational approach for analyzing Hi-C data and identifying promoter-enhancer interactions. We use a unified probabilistic model to segment the genome into domains, which we then merge hierarchically and fit using a local background model, allowing us to identify over-represented DNA-DNA interactions across the genome. By analyzing the published Hi-C data sets in human and mouse, we identify hundreds of thousands of putative enhancers and their target genes, and compile an extensive genome-wide catalog of gene regulation in human and mouse. As we show, our predictions are highly enriched for ChIP-seq and DNA accessibility data, evolutionary conservation, eQTLs and other DNA-DNA interaction data.

| S-EPMC5740158 | biostudies-literature

Integrating Hi-C and FISH data for modeling of the 3D organization of chromosomes.

Project description:The new advances in various experimental techniques that provide complementary information about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. Here we propose GEM-FISH, a method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes, for which both Hi-C and FISH data are available, demonstrate that GEM-FISH can outperform previous chromosome structure modeling methods and accurately capture the higher order spatial features of chromosome conformations. Moreover, our reconstructed 3D models of chromosomes revealed interesting patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation.

| S-EPMC6499832 | biostudies-literature

Comparison of computational methods for Hi-C data analysis.

Project description:Hi-C is a genome-wide sequencing technique used to investigate 3D chromatin conformation inside the nucleus. Computational methods are required to analyze Hi-C data and identify chromatin interactions and topologically associating domains (TADs) from genome-wide contact probability maps. We quantitatively compared the performance of 13 algorithms in their analyses of Hi-C data from six landmark studies and simulations. This comparison revealed differences in the performance of methods for chromatin interaction identification, but more comparable results for TAD detection between algorithms.

| S-EPMC5493985 | biostudies-literature

GENE-SWitCH pig promoter Capture Hi-C

Project description: Not available

| S-BSST900 | biostudies-other