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2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...

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Updated: Apr 25, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Two-dimensional segmentation for analyzing Hi-C data.

Celine Lévy-Leduc¹, M Delattre¹, T Mary-Huard²

¹AgroParisTech/INRA MIA 518, 75005 Paris and UMR de Génétique Végétale, INRA/Univ. Paris-Sud/CNRS, 91190 Gif-sur-Yvette, France.

Bioinformatics (Oxford, England)

|August 28, 2014

Summary

This summary is machine-generated.

This study introduces a new computational method for identifying cis-interacting regions in chromosome conformation data. The approach simplifies the 2D segmentation problem into a 1D version, improving the analysis of chromatin organization.

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Last Updated: Apr 25, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Chromosome spatial conformation significantly impacts gene regulation and expression.
Hi-C technology measures spatial proximity between genomic loci, generating data matrices with self-interacting regions.
Accurate delimitation of these interacting regions is crucial for understanding chromatin spatial organization.

Purpose of the Study:

To develop a computational method for detecting cis-interacting regions in Hi-C data.
To address the 2D segmentation problem inherent in analyzing chromatin spatial organization.

Main Methods:

A block-wise segmentation model was developed for detecting cis-interacting regions.
The model reformulates the 2D segmentation problem into a 1D segmentation problem solvable with dynamic programming.
The HiCseg R package implements the proposed methodology.

Main Results:

The method effectively identifies cis-interacting regions, which are prominent in Hi-C data.
Simulation studies on synthetic and resampled data demonstrate the method's performance.
Comparative analysis on public data shows good agreement with biologically validated regions.

Conclusions:

The proposed method provides an effective solution for segmenting Hi-C data to identify cis-interacting regions.
This facilitates a deeper understanding of chromatin spatial organization and its role in gene regulation.
The availability of the HiCseg R package enables broader application of this technique.