3D Chromosome Modeling with Semi-Definite Programming and Hi-C Data

Zhang, Zhizhuo; Li, Guo Liang; Toh, Kim-Chuan; Sung, Wing-Kin

doi:10.1089/cmb.2013.0076

Cited by 122 publications

(205 citation statements)

References 24 publications

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“…, where I F ij and d ij are the interaction frequency and the spatial distance between the two beads, respectively (1,(11)(12)(13)(14). Some methods try to optimize α (11,13,14) instead of using a fixed value.…”

Section: Conversion Of Interaction Frequency To Spatial Distancementioning

confidence: 99%

“…Some methods try to optimize α (11,13,14) instead of using a fixed value. Our method utilizes this conversion function and searches for …”

Section: Conversion Of Interaction Frequency To Spatial Distancementioning

confidence: 99%

“…The role of β is to scale interaction frequencies to control the noise level of simulated interaction frequencies. In our experiment, β in the range [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] gives reasonable levels of noise to evaluate methods. Each value of β, we generated one data set and measured the signal to noise ratio (SNR), the higher the SNR, the better quality the data set is.…”

Section: Preparation Of Synthetic Data Setsmentioning

confidence: 99%

“…The first kind uses the polymer physics of the chromatin to build models that are consistent with observed chromosomal contact data (9,10). The second one treats each chromosomal contact as a restraint, and then solves a spatial optimization problem to find chromosome or genome conformations that satisfy the contact restraints as well as possible (11)(12)(13)(14)(15)(16)(17)(18)(19). A common approach used by these methods is to convert chromosomal contacts into spatial distance restraints, and then search for the optimal conformations (models) that satisfy the restraints best according to an objective function.…”

Section: Introductionmentioning

confidence: 99%

“…We benchmarked LorDG together with existing methods (12)(13)(14). These methods use a similar approach in deriving distance restraints from contacts, but differ in how restraints are satisfied.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

3D Genome Structure Modeling by Lorentzian Objective Function

Trieu

Cheng

2017

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

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The 3D structure of the genome plays a vital role in biological processes such as gene interaction, gene regulation, DNA replication and genome methylation. Advanced chromosomal conformation capture techniques, such as Hi-C and tethered conformation capture, can generate chromosomal contact data that can be used to computationally reconstruct 3D structures of the genome. We developed a novel restraint-based method that is capable of reconstructing 3D genome structures utilizing both intra-and inter-chromosomal contact data. Our method was robust to noise and performed well in comparison with a panel of existing methods on a controlled simulated data set. On a real Hi-C data set of the human genome, our method produced chromosome and genome structures that are consistent with 3D FISH data and known knowledge about the human chromosome and genome, such as, chromosome territories and the cluster of small chromosomes in the nucleus center with the exception of the chromosome 18. The tool and experimental data are available at https://missouri.box.com/v/LorDG.

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Section: Conversion Of Interaction Frequency To Spatial Distancementioning

confidence: 99%

“…Some methods try to optimize α (11,13,14) instead of using a fixed value. Our method utilizes this conversion function and searches for …”

Section: Conversion Of Interaction Frequency To Spatial Distancementioning

confidence: 99%

Section: Preparation Of Synthetic Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

3D Genome Structure Modeling by Lorentzian Objective Function

Trieu

Cheng

2017

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

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3D modeling of chromatin structure: is there a way to integrate and reconcile single cell and population experimental data?

Dily

Serra

Martí‐Renom

2017

WIREs Comput Mol Sci

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The genome is organized in a hierarchical fashion within the nucleus in interphase. This nonrandom folding of the chromatin fiber is thought to play important roles in the processing of the genetic information. Therefore, a better knowledge of the mechanisms underlying the threedimensional structure of the genome appears essential to fully understand nuclear processes including transcription and replication. Fluorescent In Situ Hybridization (FISH) and molecular biology methods deriving from the Chromosome Conformation Capture technique are the methods of choice to study genome 3D organization at different levels. Although these single cell and population methods allowed to highlight similar chromatin structures, they also show frequent discrepancies which might be better understood by improving the capacity to generate actual 3D models of organization based on the different types of data available. This review aims at giving an overview of the principles, advantages and limits of microscopy and molecular biology methods of analysis of genome structure and at discussing the different approaches of modeling of chromatin classically used and the improvements that are necessary to reach a better understanding on the links between chromatin structure and its spatial organization.

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A modern challenge of polymer physics: Novel ways to study, interpret, and reconstruct chromatin structure

Fiorillo

Bianco

Esposito

et al. 2019

WIREs Comput Mol Sci

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The constant development of sophisticated technologies is allowing to dissect three‐dimensional chromatin structure at high resolution level. The tremendous amount of quantitative experimental data available today requires a conceptual framework able to make sense of them. In this perspective, polymer physics offers a key tool to interpret chromatin architecture data and to unveil the basic mechanisms shaping its structure. In the very last years, several polymer models have been proposed and have allowed to capture complex features emerging from the data. The major peculiarity distinguishing the different models is represented by the more or less complicated physical mechanism used to explain chromatin folding. Here, we review very popular models which have been recently developed and which represent brilliant examples from this interdisciplinary research field. In order to highlight the wide range of practical applications they have, we discuss the cases of the murine Pitx1 and the human EPHA4 loci, showing that polymer physics allows to effectively study chromatin structure in different cell lines and to predict the impact of pathogenic structural variants on the genome three‐dimensional architecture. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Theoretical and Physical Chemistry > Statistical Mechanics

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3D Chromosome Modeling with Semi-Definite Programming and Hi-C Data

Cited by 122 publications

References 24 publications

3D Genome Structure Modeling by Lorentzian Objective Function

3D Genome Structure Modeling by Lorentzian Objective Function

3D modeling of chromatin structure: is there a way to integrate and reconcile single cell and population experimental data?

A modern challenge of polymer physics: Novel ways to study, interpret, and reconstruct chromatin structure

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