Dataset Information

Biased visibility in Hi-C datasets marks dynamically regulated condensed and decondensed chromatin states genome-wide.

ABSTRACT: BACKGROUND:Proximity ligation based techniques, like Hi-C, involve restriction digestion followed by ligation of formaldehyde cross-linked chromatin. Distinct chromatin states can impact the restriction digestion, and hence the visibility in the contact maps, of engaged loci. Yet, the extent and the potential impact of digestion bias remain obscure and under-appreciated in the literature. RESULTS:Through analysis of 45 Hi-C datasets, lamina-associated domains (LADs), inactive X-chromosome in mammals, and polytene bands in fly, we first established that the DNA in condensed chromatin had lesser accessibility to restriction endonucleases used in Hi-C as compared to that in decondensed chromatin. The observed bias was independent of known systematic biases, was not appropriately corrected by existing computational methods, and needed an additional optimization step. We then repurposed this bias to identify novel condensed domains outside LADs, which were bordered by insulators and were dynamically associated with the polycomb mediated epigenetic and transcriptional states during development. CONCLUSIONS:Our observations suggest that the corrected one-dimensional read counts of existing Hi-C datasets can be reliably repurposed to study the gene-regulatory dynamics associated with chromatin condensation and decondensation, and that the existing Hi-C datasets should be interpreted with cautions.

SUBMITTER: Chandradoss KR

PROVIDER: S-EPMC7036197 | BioStudies | 2020-01-01

SECONDARY ACCESSION(S): 10.15252/msb.20156492

REPOSITORIES: biostudies

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Publications

Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation.

Fraser James J Ferrai Carmelo C Chiariello Andrea M AM Schueler Markus M Rito Tiago T Laudanno Giovanni G Barbieri Mariano M Moore Benjamin L BL Kraemer Dorothee C A DC Aitken Stuart S Xie Sheila Q SQ Morris Kelly J KJ Itoh Masayoshi M Kawaji Hideya H Jaeger Ines I Hayashizaki Yoshihide Y Carninci Piero P Forrest Alistair R R AR Semple Colin A CA Dostie Josée J Pombo Ana A Nicodemi Mario M

Molecular systems biology 20151223 12

Mammalian chromosomes fold into arrays of megabase-sized topologically associating domains (TADs), which are arranged into compartments spanning multiple megabases of genomic DNA. TADs have internal substructures that are often cell type specific, but their higher-order organization remains elusive. Here, we investigate TAD higher-order interactions with Hi-C through neuronal differentiation and show that they form a hierarchy of domains-within-domains (metaTADs) extending across genomic scales ...[more]

PMID: 26700852

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Project description:BACKGROUND:The genome architecture mapping (GAM) technique can capture genome-wide chromatin interactions. However, besides the known systematic biases in the raw GAM data, we have found a new type of systematic bias. It is necessary to develop and evaluate effective normalization methods to remove all systematic biases in the raw GAM data. RESULTS:We have detected a new type of systematic bias, the fragment length bias, in the genome architecture mapping (GAM) data, which is significantly different from the bias of window detection frequency previously mentioned in the paper introducing the GAM method but is similar to the bias of distances between restriction sites existing in raw Hi-C data. We have found that the normalization method (a normalized variant of the linkage disequilibrium) used in the GAM paper is not able to effectively eliminate the new fragment length bias at 1?Mb resolution (slightly better at 30?kb resolution). We have developed an R package named normGAM for eliminating the new fragment length bias together with the other three biases existing in raw GAM data, which are the biases related to window detection frequency, mappability, and GC content. Five normalization methods have been implemented and included in the R package including Knight-Ruiz 2-norm (KR2, newly designed by us), normalized linkage disequilibrium (NLD), vanilla coverage (VC), sequential component normalization (SCN), and iterative correction and eigenvector decomposition (ICE). CONCLUSIONS:Based on our evaluations, the five normalization methods can eliminate the four biases existing in raw GAM data, with VC and KR2 performing better than the others. We have observed that the KR2-normalized GAM data have a higher correlation with the KR-normalized Hi-C data on the same cell samples indicating that the KR-related methods are better than the others for keeping the consistency between the GAM and Hi-C experiments. Compared with the raw GAM data, the normalized GAM data are more consistent with the normalized distances from the fluorescence in situ hybridization (FISH) experiments. The source code of normGAM can be freely downloaded from http://dna.cs.miami.edu/normGAM/.

Project description:<h4>Background</h4>Hi-C and its variant techniques have been developed to capture the spatial organization of chromatin. Normalization of Hi-C contact map is essential for accurate modeling and interpretation of high-throughput chromatin conformation capture (3C) experiments. Hi-C correction tools were originally developed to normalize systematic biases of karyotypically normal cell lines. However, a vast majority of available Hi-C datasets are derived from cancer cell lines that carry multi-level DNA copy number variations (CNVs). CNV regions display over- or under-representation of interaction frequencies compared to CN-neutral regions. Therefore, it is necessary to remove CNV-driven bias from chromatin interaction data of cancer cell lines to generate a euploid-equivalent contact map.<h4>Results</h4>We developed the HiCNAtra framework to compute high-resolution CNV profiles from Hi-C or 3C-seq data of cancer cell lines and to correct chromatin contact maps from systematic biases including CNV-associated bias. First, we introduce a novel 'entire-fragment' counting method for better estimation of the read depth (RD) signal from Hi-C reads that recapitulates the whole-genome sequencing (WGS)-derived coverage signal. Second, HiCNAtra employs a multimodal-based hierarchical CNV calling approach, which outperformed OneD and HiNT tools, to accurately identify CNVs of cancer cell lines. Third, incorporating CNV information with other systematic biases, HiCNAtra simultaneously estimates the contribution of each bias and explicitly corrects the interaction matrix using Poisson regression. HiCNAtra normalization abolishes CNV-induced artifacts from the contact map generating a heatmap with homogeneous signal. When benchmarked against OneD, CAIC, and ICE methods using MCF7 cancer cell line, HiCNAtra-corrected heatmap achieves the least 1D signal variation without deforming the inherent chromatin interaction signal. Additionally, HiCNAtra-corrected contact frequencies have minimum correlations with each of the systematic bias sources compared to OneD's explicit method. Visual inspection of CNV profiles and contact maps of cancer cell lines reveals that HiCNAtra is the most robust Hi-C correction tool for ameliorating CNV-induced bias.<h4>Conclusions</h4>HiCNAtra is a Hi-C-based computational tool that provides an analytical and visualization framework for DNA copy number profiling and chromatin contact map correction of karyotypically abnormal cell lines. HiCNAtra is an open-source software implemented in MATLAB and is available at https://github.com/AISKhalil/HiCNAtra .

Dataset Information

Biased visibility in Hi-C datasets marks dynamically regulated condensed and decondensed chromatin states genome-wide.

Publications

Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation.

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