Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors

Serra, François; Baù, Davide; Goodstadt, Mike; Castillo, David; Filion, Guillaume J.; Martí‐Renom, Marc A.

doi:10.1371/journal.pcbi.1005665

Cited by 274 publications

(280 citation statements)

References 45 publications

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“…They allow visualization of different genomic features in the 3D context. Several software tools have been developed to render the inferred model in 3D (Asbury, Mitman, Tang, & Zheng, 2010;Nowotny et al, 2016;Serra et al, 2017). In the context of modeling single-cell haploid Hi-C data, the consensus assumption is more justifiable and has been used to provide 3D visualization of the Hi-C data (Nagano et al, 2017;Stevens et al, 2017).…”

Section: Visualization Of 3d Genome Structurementioning

confidence: 99%

Computational methods for analyzing and modeling genome structure and organization

Lin

Bonora

Yardımcı

2018

WIREs Mechanisms of Disease

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Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D genome organization to essential biological processes. In this article, we review the state of the art in analytic and modeling techniques with a focus on their application to answering various biological questions related to chromatin structure. We summarize the limitations of these computational techniques and suggest future directions, including the importance of incorporating multiple sources of experimental data in building a more comprehensive model of the genome. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Models of Systems Properties and Processes > Mechanistic Models.

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Section: Visualization Of 3d Genome Structurementioning

confidence: 99%

Computational methods for analyzing and modeling genome structure and organization

Lin

Bonora

Yardımcı

2018

WIREs Mechanisms of Disease

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“…Models derived from Hi-C interactions using TADbit 48 We determined cohesin islands by comparing the RIS cohesin ChIP-seq libraries with and 520 without DRB treatment (transcription elongation inhibitor). We then selected significantly differentially bound regions larger than 2 kb.…”

mentioning

confidence: 99%

Transcription-driven cohesin repositioning rewires chromatin loops in cellular senescence

Olan

Parry

Schoenfelder

et al. 2019

Preprint

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Senescence is a phenotypic state of stable proliferative arrest, typically occurring in lineagecommitted cells and triggered by various stimuli. It is generally accompanied by activation of a secretory program (senescence-associated secretory phenotype, SASP), which modulates 35 both local (tissue microenvironment) and systemic (ageing) homeostasis 1,2 . Enhancerpromoter interactions play a key role in gene regulation 3-5 , facilitated by chromatin loops, mostly formed via CCCTC binding factor (CTCF) and cohesin tethering 6-8 . The threedimensional chromatin structure of senescent cells has been characterised 9-11 mostly in terms of macro-domain structures, but its relevance in gene expression remains elusive. 40Here, we use Hi-C and capture 13 to show that oncogenic HRAS-induced senescence (RIS) in human diploid fibroblasts (HDFs) is accompanied by extensive enhancer-promoter rewiring, which is closely connected with dynamic cohesin binding to the genome. We find de novo cohesin peaks at the 3' end of a subset of active genes, reminiscent of the transcription-driven 'cohesin islands' recently discovered in mouse embryonic fibroblasts 45 deficient in both CTCF and the cohesin release factor Wings apart-like (Wapl) 14 . RIS de novo cohesin peaks are also transcription-dependent and enriched for SASP genes, as exemplified by IL1B, where de novo cohesin binding is involved in new loop formation. Cytokine induction is associated with similar cohesin islands appearance and enhancerpromoter rewiring during the terminal differentiation of monocytes to macrophages 15 , but not 50 upon acute TNFa treatment of HDFs 16 . These results suggest that RIS represents a fatedetermined process in which gene expression is regulated beyond the cell type specific 3D chromatin framework, in part through cohesin redistribution. 3 MainOncogene-induced senescence (OIS) has been linked to dynamic alterations of the chromatin 60 landscape, through the formation of three-dimensional heterochromatic foci 17 , altered distributions of histone modifications and chromatin accessibility 2,18 or the appearance of new 'super-enhancers' 19 . While it has been shown that regulation of acute stress-responsive genes can be achieved through transcription factor (TF) recruitment to largely pre-existing enhancer-promoter (EP) contacts 16,20,21 , whether or not a similar mechanism is employed 65 during OIS (sustained oncogenic stress) is unknown. To study gene regulatory mechanisms in the 3D chromatin context at high resolution, we performed in situ Hi-C experiments as well as capture Hi-C (cHi-C) for 62 selected genomic regions of interest ( Supplementary Table 1) in normal growing and oncogenic HRAS-G12V-induced senescent (RIS) IMR90 human diploid fibroblasts (HDFs), using the 4-hydroxytamoxifen (4OHT)-inducible estrogen receptor 70 (ER) HRAS fusion system 22 (ER:HRAS G12V Fig. 1a). Growing (three replicates) and RIS (two replicates) Hi-C libraries yielded a total of 523 and 286 million valid reads respectively, after removal of artefacts and duplicate...

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“…The CD solutions from Multi-CD are in good agreement with the previously proposed CDs, obtained from several different methods. Specifically, CDs correspond to the sub-TADs [17] in the priorfree solution at λ = 0, to the TADs [24,49] at λ ≈ 10, and to the compartments [17] at λ ≈ 90 (see Fig. S6).…”

Section: Validation Of Domain Solutions From Multi-cdmentioning

confidence: 99%

A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C

Kim

Bak

Liu

et al. 2019

Preprint

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Identifying chromatin domains (CDs) from Hi-C data is currently a central problem in genome research. Here we present Multi-CD (https://github.com/multi-cd), a unified method to discover CDs at various genomic scales. Integrating approaches from polymer physics, financial market fluctuation analysis and Bayesian inference, Multi-CD identifies the CDs that best represent the global pattern of correlation manifested in Hi-C, and reveals the multi-scale structure of chromosome. At each scale, the CDs are consistent with the results of existing methods, as well as with biological data from independent sources. CD solutions compared across different scales and cell types allow us to quantify the hierarchy between four major families of CDs, and to glean the principles of chromatin organization: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by distinct organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. CDs acquired from our interpretation of Hi-C data using Multi-CD not only provide new insights into chromatin organization, but also offer a quantitative account for its cell-type-dependence and function.with γ ij = (σ ii + σ jj − 2σ ij )/2, where σ ij (= δr i · δr j ) is the positional covariance, determined by the topology of polymer network [42]. Indeed, distance distributions measured using fluorescence measurement between chromatin loci justifies this hypothesis (see Fig. S1). Importantly, our interpretation of chromosome conformation as a locally equilibrated, quasi-stable polymer network enables a one-to-one mapping of the contact probability p ij = rc 0 dxP (x; γ ij ) to the positional covariance σ ij , and hence to the cross-correlation matrix, (C) ij = σ ij / √ σ ii σ jj (see Methods). The cross-correlation matrix C normalizes the wide numer-

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Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors

Cited by 274 publications

References 45 publications

Computational methods for analyzing and modeling genome structure and organization

Computational methods for analyzing and modeling genome structure and organization

Transcription-driven cohesin repositioning rewires chromatin loops in cellular senescence

A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C

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