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Related Concept Videos

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

The cationic polymerization mechanism consists of three steps: initiation, propagation, and termination. In the initiation step of the polymerization process, the π bond of a monomer gets protonated by the Lewis acid catalyst, which is formed from boron trifluoride and water. The protonation of the π bond generates a carbocation stabilized by the electron‐donating group. In the propagation step, the π bond of the second monomer acts as a nucleophile and attacks the generated carbocation,...
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...

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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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PHi-C: deciphering Hi-C data into polymer dynamics.

Soya Shinkai1,2, Masaki Nakagawa2,3, Takeshi Sugawara2,4

  • 1Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.

NAR Genomics and Bioinformatics
|February 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces PHi-C, a novel 4D simulation method to dynamically model genome organization from 2D Hi-C data. It bridges the gap between static 3D models and live-cell 4D genome dynamics.

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Area of Science:

  • Genomics
  • Computational Biology
  • Biophysics

Background:

  • Genomes exhibit dynamic spatiotemporal organization within the cell nucleus.
  • Chromosome conformation capture (Hi-C) reveals 3D genome architecture, but static models lack dynamic insights.
  • Live-cell imaging demonstrates the functional 4D nature of genomes, which current computational methods struggle to capture from 2D Hi-C data.

Purpose of the Study:

  • To develop a computational method for depicting 4D genome features from 2D Hi-C data.
  • To enable the interpretation of 2D Hi-C data as physical interaction parameters for polymer modeling.
  • To simulate and analyze the dynamic characteristics of genomic loci and chromosomes.

Main Methods:

  • Developed PHi-C (polymer dynamics deciphered from Hi-C data), a 4D simulation method.
  • Utilized polymer modeling to translate 2D Hi-C contact frequencies into physical interaction parameters.
  • Applied these parameters in simulations to explore dynamic genome behavior.

Main Results:

  • PHi-C successfully depicts 4D genome features from 2D Hi-C data.
  • The method allows interpretation of Hi-C data as physical interaction parameters within chromosomes.
  • Simulations demonstrate dynamic genomic locus and chromosome behaviors consistent with live-cell observations.

Conclusions:

  • PHi-C provides a novel approach to explore the 4D nature of genome organization using existing 2D Hi-C data.
  • This method bridges the gap between static 3D genome models and dynamic functional observations.
  • PHi-C facilitates a deeper understanding of genome dynamics through polymer modeling.