Predicting three-dimensional genome organization with chromatin states

Qi, Yuanming; Zhang, Bin

doi:10.1371/journal.pcbi.1007024

Cited by 121 publications

(81 citation statements)

References 99 publications

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“…2b) and the contact probability as a function of genomic separation averaged over all chromosomes inactive (B) chromatin. 4,38 However, we found that these finer structures were not necessary for an accurate modeling of chromosome positioning and inter-chromosomal interactions, indicating that sub-compartments might only contribute to local chromatin folding rather than global genome organization.…”

Section: Simulated Contact Map Reproduces Experimental Hi-c Datamentioning

confidence: 87%

“…While the polymer model is motivated to recapitulate various biological mechanisms of genome organization, it is also data-driven and all of its parameters can be determined from haploid Hi-C data with an iterative optimization algorithm. 27,38 Model parameterization is possible because mathematical expressions for the various energy terms in U Genome (r) were designed such that their ensemble averages can be mapped onto combinations of contact frequencies measured in Hi-C. As shown in Fig. 1, we first perform molecular dynamics simulations to collect an ensemble of genome structures.…”

Section: A Data-driven Mechanistic Model For the Diploid Human Genomementioning

confidence: 99%

“…Values of these parameters can then be fine tuned with an iterative algorithm to ensure that ensemble averages calculated using simulated genome structures match with Hi-C constraints. 27,38 Detailed mathematical expressions for the constraints are provided in the SI.…”

Section: Energy Function Of the Whole Genome Modelmentioning

confidence: 99%

“…Coupling the data-and hypothesis-driven approaches together can potentially produce a powerful strategy for modeling genome organization. [38][39][40] This strategy ensures the biological relevance of simulated genome structures since all model parameters will be derived from Hi-C data. In the meantime, it will be well suited for mechanistic investigation as the polymer model's energy function will be designed explicitly from biological factors that are known to contribute to genome organization.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven polymer model for mechanistic exploration of diploid genome organization

Reyes

Johnstone

et al. 2020

Preprint

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Chromosomes are positioned non-randomly inside the nucleus to coordinate with their transcriptional activity. The molecular mechanisms that dictate the global genome organization and the nuclear localization of individual chromosomes are not fully understood. We introduce a polymer model to study the organization of the diploid human genome: it is data-driven as all parameters can be derived from Hi-C data; it is also a mechanistic model since the energy function is explicitly written out based on a few biologically motivated hypotheses. These two features distinguish the model from existing approaches and make it useful both for reconstructing genome structures and for exploring the principles of genome organization. We carried out extensive validations to show that simulated genome structures reproduce a wide variety of experimental measurements, including chromosome radial positions and spatial distances between homologous pairs. Detailed mechanistic investigations support the importance of both specific inter-chromosomal interactions and centromere clustering for chromosome positioning. We anticipate the polymer model, when combined with Hi-C experiments, to be a powerful tool for investigating large scale rearrangements in genome structure upon cell differentiation and tumor progression.

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Section: Simulated Contact Map Reproduces Experimental Hi-c Datamentioning

confidence: 87%

Section: A Data-driven Mechanistic Model For the Diploid Human Genomementioning

confidence: 99%

Section: Energy Function Of the Whole Genome Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-driven polymer model for mechanistic exploration of diploid genome organization

Reyes

Johnstone

et al. 2020

Preprint

Self Cite

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“…Advances in resolving three-dimensional chromatin structure and how it varies (Kishi & Gotoh, 2018;Marti-Renom et al, 2018;Qi & Zhang, 2019) hold long term hope for progress here. Figure 1: Workflow of the method for identification of probable causative variants.…”

Section: Scope For Improvementmentioning

confidence: 99%

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Pal

Kundu

Yuan

et al. 2019

Preprint

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Precise identification of causative variants from whole‐genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole‐genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state‐of‐the‐art pipeline. The patients have a range of eye, neurological, and connective‐tissue disorders. We used a gene‐centric approach to address this problem, assigning each gene a multiphenotype‐matching score. Mutations in the top‐scoring genes for each phenotype profile were ranked on a 6‐point scale of pathogenicity probability, resulting in an approximately equal number of top‐ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding.

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Microscopic Chromosomal Structural and Dynamical Origin of Cell Differentiation and Reprogramming

Chu

Wang

2020

Advanced Science

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As an essential and fundamental process of life, cell development involves large-scale reorganization of the 3D genome architecture, which forms the basis of gene regulation. Here, a landscape-switching model is developed to explore the microscopic chromosomal structural origin of embryonic stem cell (ESC) differentiation and somatic cell reprogramming. It is shown that chromosome structure exhibits significant compartment-switching in the unit of topologically associating domain. It is found that the chromosome during differentiation undergoes monotonic compaction with spatial repositioning of active and inactive chromosomal loci toward the chromosome surface and interior, respectively. In contrast, an overexpanded chromosome, which exhibits universal localization of loci at the chromosomal surface with erasing the structural characteristics formed in the somatic cells, is observed during reprogramming. An early distinct differentiation pathway from the ESC to the terminally differentiated cell, giving rise to early bifurcation on the Waddington landscape for the ESC differentiation is suggested. The theoretical model herein including the non-equilibrium effects, draws a picture of the highly irreversible cell differentiation and reprogramming processes, in line with the experiments. The predictions provide a physical understanding of cell differentiation and reprogramming from the chromosomal structural and dynamical perspective and can be tested by future experiments.

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Predicting three-dimensional genome organization with chromatin states

Cited by 121 publications

References 99 publications

Data-driven polymer model for mechanistic exploration of diploid genome organization

Data-driven polymer model for mechanistic exploration of diploid genome organization

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Microscopic Chromosomal Structural and Dynamical Origin of Cell Differentiation and Reprogramming

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