Mining 3D genome structure populations identifies major factors governing the stability of regulatory communities

Dai, Chao; Li, Wenyuan; Tjong, Harianto; Hao, Shengli; Zhou, Yonggang; Li, Qingjiao; Chen, Lin; Alber, Frank; Zhou, Xianghong Jasmine

doi:10.1038/ncomms11549

Cited by 36 publications

(37 citation statements)

References 53 publications

(69 reference statements)

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“…Each kinetochore lies at the apex of a DNA loop that extends on average about 400 nm from the sister chromatid axis in metaphase. The centromere loops have been found in 3C maps from yeast (Yeh et al 2008; Duan et al 2010), and Hi-C maps of lymphoblastoid cells in mammals (Dai et al 2016). The size of the loops can be estimated from the region over which the structural maintenance of chromosome proteins (SMC’s cohesin and condensin) are enriched.…”

Section: Characteristics Of Centromere Chromatin—primary Folding Of Amentioning

confidence: 99%

Centromere Structure and Function

Bloom

Costanzo

2017

Progress in Molecular and Subcellular Biology

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The centromere is the genetic locus that specifies the site of kinetochore assembly, where the chromosome will attach to the kinetochore microtubule. The pericentromere is the physical region responsible for the geometry of bi-oriented sister kinetochores in metaphase. In budding yeast the 125 bp point centromere is sufficient to specify kinetochore assembly. The flanking region is enriched (3X) in cohesin and condensin relative to the remaining chromosome arms. The enrichment spans about 30-50 kb around each centromere. We refer to the flanking chromatin as the pericentromere in yeast. In mammals, a 5-10 Mb region dictates where the kinetochore is built. The kinetochore interacts with a very small fraction of DNA on the surface of the centromeric region. The remainder of the centromere lies between the sister kinetochores. This is typically called centromere chromatin. The chromatin sites that directly interface to microtubules cannot be identified due to the repeated sequence within the mammalian centromere. However in both yeast and mammals, the total amount of DNA between the sites of microtubule attachment in metaphase is highly conserved. In yeast the 16 chromosomes are clustered into a 250 nm diameter region, and 800 kb (16 × 50 kb) or ~1 Mb of DNA lies between sister kinetochores. In mammals, 5-10 Mb lies between sister kinetochores. In both organisms the sister kinetochores are separated by about 1 μm. Thus, centromeres of different organisms differ in how they specify kinetochore assembly, but there may be important centromere chromatin functions that are conserved throughout phylogeny. Recently, centromeric chromatin has been reconstituted in vitro using alpha satellite DNA revealing unexpected features of centromeric DNA organization, replication, and response to stress. We will focus on the conserved features of centromere in this review.

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Section: Characteristics Of Centromere Chromatin—primary Folding Of Amentioning

confidence: 99%

Centromere Structure and Function

Bloom

Costanzo

2017

Progress in Molecular and Subcellular Biology

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“…Dai et al addressed this challenge by identifying frequently occurring chromatin clusters [76], which are often enriched in binding of specific regulatory factors.…”

Section: Approaches To Integrative Modellingmentioning

confidence: 99%

Integrative modelling of cellular assemblies

Joseph

Polles

Alber

et al. 2017

Current Opinion in Structural Biology

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A wide variety of experimental techniques can be used for understanding the precise molecular mechanisms underlying the activities of cellular assemblies. The inherent limitations of a single experimental technique often requires integration of data from complementary approaches, to gain sufficient insights into the assembly structure and function. Here, we review popular computational approaches for integrative modelling of cellular assemblies, including protein complexes and genomic assemblies. We provide recent examples of integrative models generated for such assemblies by different experimental techniques, especially including 3D electron microscopy (3D-EM) and HiC data, respectively. We highlight general concepts in integrative modelling and discuss the need for careful formulation and merging of different types of information.

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“…This variability poses a great challenge to interpreting ensemble Hi-C contact frequencies, which are averaged across an ensemble of cells. Long-range and inter-chromosomal interactions, which have low frequencies to begin with, are particularly difficult to integrate into consistent 3D models 8,14–20 . To address this challenge, we recently introduced the concept of population-based genome structure modeling.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, because the generated population contains many different structural states, mutually exclusive interactions (often low-frequency, long-range interactions) can be accommodated by incorporating these in different structures. As a result, almost all observed chromatin interactions can be considered and alternative chromatin structural states can be analyzed in detail 20 . Our method is sufficiently flexible to integrate additional experimental information (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the population represents a deconvolution of the Hi-C data into individual structures and domain contacts; it is the best approximation to the underlying true population of genome structures in the Hi-C experiment, given the available data and assumptions. Because our approach considers the stochastic nature of chromosome conformations it allows analysis of alternate chromatin structural states 20 . Our expectation-maximization (EM) modeling framework is extendable for integration of other data sources, for example combining Hi-C with Lamina DamID data 21 .…”

Section: Introductionmentioning

confidence: 99%

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Producing genome structure populations with the dynamic and automated PGS software

et al. 2018

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Hi-C technologies are widely used to investigate the spatial organization of genomes. Because genome structures can vary considerably between individual cells of a population, interpreting ensemble-averaged Hi-C data can be challenging, in particular for long-range and inter-chromosomal interactions. We pioneered a probabilistic approach for generating a population of distinct diploid 3D genome structures consistent with all the chromatin-chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome’s organization in space and time. We provide a user- friendly software package, called PGS, which runs on local machines (for toy runs) and high- performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix along with information about genome segmentation and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and provides tools to extract and analyze the 3D coordinates of specific domains. Basic Linux command line knowledge is sufficient for using this software. A typical running time of the pipeline is about 3 days with 300 cores on a computer cluster to generate a population of 1,000 diploid genome structures at TAD level resolution.

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Mining 3D genome structure populations identifies major factors governing the stability of regulatory communities

Cited by 36 publications

References 53 publications

Centromere Structure and Function

Centromere Structure and Function

Integrative modelling of cellular assemblies

Producing genome structure populations with the dynamic and automated PGS software

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