Dataset Information

Inferring Spatial Organization of Individual Topologically Associated Domains via Piecewise Helical Model.

ABSTRACT: The recently developed Hi-C technology enables a genome-wide view of chromosome spatial organizations, and has shed deep insights into genome structure and genome function. However, multiple sources of uncertainties make downstream data analysis and interpretation challenging. Specifically, statistical models for inferring three-dimensional (3D) chromosomal structure from Hi-C data are far from their maturity. Most existing methods are highly over-parameterized, lacking clear interpretations, and sensitive to outliers. In this study, we propose a parsimonious, easy to interpret, and robust piecewise helical model for the inference of 3D chromosomal structure of individual topologically associated domain from Hi-C data. When applied to a real Hi-C dataset, the piecewise helical model not only achieves much better model fitting than existing models, but also reveals that geometric properties of chromatin spatial organization are closely related to genome function.

SUBMITTER: Zhang R

PROVIDER: S-EPMC7202374 | biostudies-literature | 2020 Mar-Apr

REPOSITORIES: biostudies-literature

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Inferring Spatial Organization of Individual Topologically Associated Domains via Piecewise Helical Model.

Zhang Rongrong R Hu Ming M Zhu Yu Y Qin Zhaohui Z Deng Ke K Liu Jun S JS

IEEE/ACM transactions on computational biology and bioinformatics 20180815 2

The recently developed Hi-C technology enables a genome-wide view of chromosome spatial organizations, and has shed deep insights into genome structure and genome function. However, multiple sources of uncertainties make downstream data analysis and interpretation challenging. Specifically, statistical models for inferring three-dimensional (3D) chromosomal structure from Hi-C data are far from their maturity. Most existing methods are highly over-parameterized, lacking clear interpretations, an ...[more]

PMID: 30113897

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Project description:In simulation models of populations or communities, individual plants have often been obfuscated in favor of aggregated vegetation. This simplification comes with a loss of biological detail and a smoothing out of the demographic noise engendered by stochastic individual-scale processes and heterogeneities, which is significant among others when studying the viability of small populations facing challenging fluctuating environmental conditions. This consideration has motivated the development of precise plant-centered models. The accuracy gained in the representation of plant biology has then, however, often been balanced by the disappearance in models of important plant-soil interactions (esp. water dynamics) due to the inability of most individual-based frameworks to simulate complex continuous processes. In this study, we used a hybrid modeling approach, namely integrated System Dynamics (SD)-Individual-based (IB), to illustrate the importance of individual plant dynamics to explain spatial self-organization of vegetation in arid environments. We analyzed the behavior of this model under different parameter sets either related to individual plant properties (such as seed dispersal distance and reproductive age) or the environment (such as intensity and yearly distribution of precipitation events). While the results of this work confirmed the prevailing theory on vegetation patterning, they also revealed the importance therein of plant-level processes that cannot be rendered by reaction-diffusion models. Initial spatial distribution of plants, reproductive age, and average seed dispersal distance, by impacting patch size and vegetation aggregation, affected pattern formation and population survival under climatic variations. Besides, changes in precipitation regime altered the demographic structure and spatial organization of vegetation patches by affecting plants differentially depending on their age and biomass. Water availability influenced non-linearly total biomass density. Remarkably, lower precipitation resulted in lower mean plant age yet higher mean individual biomass. Moreover, seasonal variations in rainfall greater than a threshold (here, ±0.45 mm from the 1.3 mm baseline) decreased mean total biomass and generated limit cycles, which, in the case of large variations, were preceded by chaotic demographic and spatial behavior. In some cases, peculiar spatial patterns (e.g., rings) were also engendered. On a technical note, the shortcomings of the present model and the benefit of hybrid modeling for virtual investigations in plant science are discussed.

Dataset Information

Inferring Spatial Organization of Individual Topologically Associated Domains via Piecewise Helical Model.

Publications

Inferring Spatial Organization of Individual Topologically Associated Domains via Piecewise Helical Model.

Similar Datasets

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