Exploring chromatin hierarchical organization via Markov State Modelling

Tan, Zhen Wah; Guarnera, Enrico; Berezovsky, Igor N.

doi:10.1371/journal.pcbi.1006686

Cited by 12 publications

(12 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We investigate here large-scale changes in chromatin structure captured in Hi-C chromatin interaction data for OSNs from 4 SARS-CoV-2-infected patients and 2 control subjects, by reconstructing of the chromatin ensemble structure (Figure 1 and Supplementary File, Figure S1). Our approach consists of two steps, by first (i) identifying megabase-level structural partitions using a Markov state model (MSM) of Hi-C interactions (33), and second (ii) obtaining ensemble reconstructions at the level of these partitions using a stochastic embedding procedure (SEP) (32), as implemented in software pipelines published previously.…”

Section: Methodsmentioning

confidence: 99%

“…Our approach consists of two steps, by first (i) identifying megabase-level structural partitions using a Markov state model (MSM) of Hi-C interactions (33), and second (ii) obtaining ensemble reconstructions at the level of these partitions using a stochastic embedding procedure (SEP) (32), as implemented in software pipelines published previously.…”

Section: Reconstruction Of 3d Chromatin Ensemble Structurementioning

confidence: 99%

“…We adopted a two-pronged approach in characterizing chromatin structural differences in SARS-CoV-2-infected patients and controls, on one hand studying large-scale structural shifts by spatial reconstructions at the genome level, and on the other hand zooming in to fine-scale organization of key gene clusters. Firstly, we use a Markov state model to identify coarse Mb-scale structural units (33), then apply a stochastic embedding procedure (32) to obtain genome-level reconstructions of the chromatin ensemble. Secondly, to understand chromatin organization and packing at the local level we devised a novel method for identifying localized structural units at the ~30 to 200-500 kbp scale, mapping out how strong links between these units shape physical packing of the chromatin fiber from chromatin loops (34)(35)(36) to (sub)TADs (37).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Disrupted chromatin architecture in olfactory sensory neurons: A missing link from COVID-19 infection to anosmia

Tan

Toon

Guarnera

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

We tackle here genomic mechanisms of a rapid onset and recovery from anosmia - a useful diagnostic indicator for early-stage COVID-19 infection. On the basis of earlier observed specifics of olfactory receptors (ORs) regulation in the mice chromatin structures, we hypothesized that the disruption of OR function can be caused by chromatin reorganization taking place upon SARS-CoV-2 infection. We reconstructed the chromatin ensembles of ORs obtained from COVID-19 patients and control samples using our original computational framework for the whole-genome chromatin ensemble 3D reconstruction. We have also developed here a new procedure for the analysis of fine structural hierarchy in local, megabase scale, parts of chromosomes containing the OR genes and corresponding epigenetic factors. We observed structural modifications in COVID-19 patients on different levels of chromatin organization, from alteration of the whole genome structure and chromosomal intermingling to reorganization of contacts between the chromatin loops at the level of topologically associating domains. While complementary data on known regulatory elements point to pathology-associated changes within the overall picture of chromatin alterations, further investigation using additional epigenetic factors mapped on 3D reconstructions with improved resolution will be required for better understanding of anosmia caused by SARS-CoV-2 infection.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Reconstruction Of 3d Chromatin Ensemble Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disrupted chromatin architecture in olfactory sensory neurons: A missing link from COVID-19 infection to anosmia

Tan

Toon

Guarnera

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…After 50–60 years, the problem of predicting the three-dimensional structures of proteins from their sequences remains unsolved. With the apparent stalling in the progress of the ‘true’ ab-initio folding from ‘first principles’, focus has shifted to three components in modeling algorithms: the energy function, the conformational search, and the model selection [112,113,114,115,116]. Great progress was reported recently by AlphaFold, a deep learning approach by Google’s DeepMind team that outperformed other teams for about half of the targets in the 2018 Critical Assessment on protein Structure Prediction (CASP) community-wide competition [117].…”

Section: Challenges In Computational Structural Biologymentioning

confidence: 99%

Computational Structural Biology: Successes, Future Directions, and Challenges

et al. 2019

View full text Add to dashboard Cite

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous ‘big data’ integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells’ actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

show abstract

“…S. Wright 10 first proposed the adaptive landscape concept in 1932 to describe population dynamics. Better known Waddington’s epigenetic landscape 11,12 was proposed in the 1950s and has shown increasing importance in molecular and cell biology 13,14 , which has widely been applied to analyze cancer 15 , study evolutionary theory 16 , quantify cell fate decisions 17 and model the hierarchical organization of chromatin 18 in recent years.…”

Section: Introductionmentioning

confidence: 99%

MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data

Wang

Zhong

et al. 2020

Preprint

View full text Add to dashboard Cite

Distinguishing cell types and cell states is one of the fundamental questions in single-cell studies. Meanwhile, exploring the lineage relations among cells and finding the path and critical points in the cell fate transition are also of great importance. Existing unsupervised clustering methods and lineage trajectory reconstruction methods often face several challenges such as clustering data of arbitrary shapes, tracking precise trajectories and identifying critical points. Certain adaptive landscape approach, which constructs a pseudo-energy landscape of the dynamical system, may be used to explore such problems. However, algorithms based on rigorous metastability theory for constructing the landscape of individual cells are still lacking. Thus, we propose Markov hierarchical clustering algorithm (MarkovHC), which reconstructs multi-scale pseudo-energy landscape by exploiting underlying metastability structure in an exponentially perturbed Markov chain. A Markov process describes the random walk of a hypothetically traveling cell in the corresponding pseudo-energy landscape over possible gene expression states. Technically, MarkovHC integrates tasks of cell classification, trajectory reconstruction, and critical point identification in a single theoretical framework consistent with topological data analysis (TDA). In addition to the algorithm development and simulation tests, we also applied MarkovHC to diverse types of real biological data: single-cell RNA-Seq data, cytometry data, and single-cell ATAC-Seq data. Remarkably, when applying to single-cell RNA-Seq data of human ESC derived progenitor cells, MarkovHC could not only successfully identify known cell types, but also discover new cell types and stages. In addition, when using MarkovHC to analyze single-cell RNA-Seq data of human preimplantation embryos in early development, the hierarchical structure of the lineage trajectories were faithfully reconstituted. Furthermore, the critical points representing important stage transitions had also been identified by MarkovHC from early gastric cancer data. In summary, these results demonstrate that MarkovHC is a powerful tool based on rigorous metastability theory to explore hierarchical structures of biological data, identify cell population (basin) and critical point (stage transition), and track lineage trajectory (differentiation path).

show abstract

Exploring chromatin hierarchical organization via Markov State Modelling

Cited by 12 publications

References 75 publications

Disrupted chromatin architecture in olfactory sensory neurons: A missing link from COVID-19 infection to anosmia

Disrupted chromatin architecture in olfactory sensory neurons: A missing link from COVID-19 infection to anosmia

Computational Structural Biology: Successes, Future Directions, and Challenges

MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data

Contact Info

Product

Resources

About