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ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C.

ABSTRACT: Capture Hi-C (CHi-C) is a new technique for assessing genome organization based on chromosome conformation capture coupled to oligonucleotide capture of regions of interest, such as gene promoters. Chromatin loop detection is challenging because existing Hi-C/4C-like tools, which make different assumptions about the technical biases presented, are often unsuitable. We describe a new approach, ChiCMaxima, which uses local maxima combined with limited filtering to detect DNA looping interactions, integrating information from biological replicates. ChiCMaxima shows more stringency and robustness compared to previously developed tools. The tool includes a GUI browser for flexible visualization of CHi-C profiles alongside epigenomic tracks.

SUBMITTER: Ben Zouari Y

PROVIDER: S-EPMC6532271 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C.

Ben Zouari Yousra Y Molitor Anne M AM Sikorska Natalia N Pancaldi Vera V Sexton Tom T

Genome biology 20190522 1

Capture Hi-C (CHi-C) is a new technique for assessing genome organization based on chromosome conformation capture coupled to oligonucleotide capture of regions of interest, such as gene promoters. Chromatin loop detection is challenging because existing Hi-C/4C-like tools, which make different assumptions about the technical biases presented, are often unsuitable. We describe a new approach, ChiCMaxima, which uses local maxima combined with limited filtering to detect DNA looping interactions, ...[more]

PMID: 31118054

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ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C (4C validation)

Project description:Capture Hi-C (CHi-C) is a new technique for assessing genome organization based on chromosome conformation capture coupled to oligonucleotide capture of regions of interest, such as gene promoters. Chromatin loop detection is challenging because existing Hi-C/4C-like tools, which make different assumptions about the technical biases presented, are often unsuitable. We describe a new approach, ChiCMaxima, which uses local maxima combined with limited filtering to detect DNA looping interactions, integrating information from biological replicates. ChiCMaxima shows more stringency and robustness compared to previously developed tools. The tool includes a GUI browser for flexible visualization of CHi-C profiles alongside epigenomic tracks. The results of the analysis on existing mouse ES Capture Hi-C data (ArrayExpress E-MTAB-2414) were validated by two 4C-seq experiments, submitted here.

2019-04-17 | GSE129884 | GEO

CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data.

Project description:Capture Hi-C (CHi-C) is a method for profiling chromosomal interactions involving targeted regions of interest, such as gene promoters, globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments. We implement these procedures in CHiCAGO ( http://regulatorygenomicsgroup.org/chicago ), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.

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CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

Project description:Capture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions are captured, such as in Promoter CHi-C. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs. Three human CHi-C biological replicates were generated (comprising 1, 2and 3 technical replicates). Two mouse CHi-C biological replicates were generated (both comprising three technical replicates) and a mouse Hi-C dataset. The publicly available HiCUP pipeline (doi: 10.12688/f1000research.7334.1) was used to process the raw sequencing reads. This pipeline was used to map the read pairs against the mouse (mm9) and human (hg19) genomes, to filter experimental artefacts (such as circularized reads and re-ligations), and to remove duplicate reads. For the CHi-C data, the resulting BAM files were processed into CHiCAGO input files, retaining only those read pairs that mapped, at least on one end, to a captured bait. CHiCAGO then identified Hi-C restriction fragments interacting, with statistical significant, to captured baits.

2016-05-18 | E-GEOD-81503 | biostudies-arrayexpress

ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C (4C validation)

Project description:ChiCMaxima: a robust and simple pipeline for detection and visualization of chromatin looping in Capture Hi-C (4C validation)

| PRJNA533097 | ENA

CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

2016-05-18 | GSE81503 | GEO

MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments.

Project description:Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.

| S-EPMC9262194 | biostudies-literature

CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

Project description:CHiCAGO: Robust Detection of DNA Looping Interactions in Capture Hi-C data

| PRJNA321822 | ENA

Fine mapping chromatin contacts in capture Hi-C data.

Project description:BACKGROUND:Hi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest. RESULTS:We hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data. Although we use only the CHi-C count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of <10% of the number in the full run, suggesting that greater resolution can be extracted from existing datasets. CONCLUSIONS:Our results appear largely complementary to those from a per-fragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHi-C experiments.

| S-EPMC6343296 | biostudies-literature

Capture of associated targets on chromatin links long-distance chromatin looping to transcriptional coordination.

Project description:Here we describe a sensitive and novel method of identifying endogenous DNA-DNA interactions. Capture of Associated Targets on CHromatin (CATCH) uses efficient capture and enrichment of specific genomic loci of interest through hybridization and subsequent purification via complementary biotinylated oligonucleotide. The CATCH assay requires no enzymatic digestion or ligation, requires little starting material, provides high-quality data, has excellent reproducibility and is completed in less than 24?h. Efficacy is demonstrated through capture of three disparate loci, which demonstrate unique subsets of long-distance chromatin interactions enriched for both enhancer marks and oestrogen receptor-binding sites. In each experiment, CATCH-seq peaks representing long-distance chromatin interactions were centred near the TSS of genes, and, critically, the genes identified as physically interacting are shown to be transcriptionally coexpressed. These interactions could potentially create transcriptional hubs for the regulation of gene expression programmes.

| S-EPMC5028417 | biostudies-literature

HiCORE: Hi-C Analysis for Identification of Core Chromatin Looping Regions with Higher Resolution.

Project description:Genome-wide chromosome conformation capture (3C)- based high-throughput sequencing (Hi-C) has enabled identification of genome-wide chromatin loops. Because the Hi-C map with restriction fragment resolution is intrinsically associated with sparsity and stochastic noise, Hi-C data are usually binned at particular intervals; however, the binning method has limited reliability, especially at high resolution. Here, we describe a new method called HiCORE, which provides simple pipelines and algorithms to overcome the limitations of single-layered binning and predict core chromatin regions with three-dimensional physical interactions. In this approach, multiple layers of binning with slightly shifted genome coverage are generated, and interacting bins at each layer are integrated to infer narrower regions of chromatin interactions. HiCORE predicts chromatin looping regions with higher resolution, both in human and Arabidopsis genomes, and contributes to the identification of the precise positions of potential genomic elements in an unbiased manner.

| S-EPMC8718365 | biostudies-literature

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