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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Apr 11, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

FourCSeq: analysis of 4C sequencing data.

Felix A Klein¹, Tibor Pakozdi¹, Simon Anders¹

¹European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany.

Bioinformatics (Oxford, England)

|June 3, 2015

Summary

This summary is machine-generated.

This study introduces FourCSeq, an R package for analyzing Circularized Chromosome Conformation Capture (4C) data. It identifies specific genomic interactions and detects changes between conditions, improving 3D genome structure analysis.

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Last Updated: Apr 11, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

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Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Circularized Chromosome Conformation Capture (4C) investigates spatial genomic interactions from a viewpoint.
Interaction frequencies depend on genomic distance, with specific peaks superimposed.
Detecting these peaks and condition-specific changes is computationally challenging.

Purpose of the Study:

To develop a computational method for identifying specific 3D genome interactions using 4C data.
To enable the detection of differential interactions between experimental conditions or cell types.
To provide an accessible R package for end-to-end 4C data analysis.

Main Methods:

Modeling distance-dependent interaction frequencies with a smooth function.
Calculating z-scores from residuals to identify interaction peaks.
Normalizing fragment counts and using DESEQ2 for differential contact frequency analysis.

Main Results:

Successfully identified specific interaction peaks in 4C data.
Developed a robust method for detecting differential interactions between conditions.
The FourCSeq R package provides a comprehensive pipeline for 4C data analysis.

Conclusions:

FourCSeq offers an effective computational solution for 4C data analysis.
The package facilitates the discovery of condition-specific 3D genome structures.
This work enhances the utility of 4C for studying genome organization.