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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 microarray-based...

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Updated: Jun 8, 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

Processing and analyzing ChIP-seq data: from short reads to regulatory interactions.

Marion Leleu¹, Grégory Lefebvre, Jacques Rougemont

¹Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Briefings in Functional Genomics

|September 24, 2010

Summary

This summary is machine-generated.

Chromatin-immunoprecipitation and sequencing (ChIP-seq) analysis, while challenging, is crucial for understanding chromatin states. This overview covers established genome mapping, normalization, and recent advancements in peak calling and analysis.

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

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Published on: March 7, 2018

Area of Science:

Genomics and Molecular Biology
Epigenetics and Chromatin Biology

Background:

Chromatin-immunoprecipitation and sequencing (ChIP-seq) is a powerful, maturing technology for analyzing chromatin states.
Despite advancements, the primary challenge in ChIP-seq lies in downstream data analysis.

Purpose of the Study:

To provide an overview of established and emerging ChIP-seq data analysis methods.
To highlight key steps including genome mapping, data normalization, peak calling, and statistical analysis.

Main Methods:

Review of established genome mapping and data normalization techniques in ChIP-seq.
Description of recent progress in ChIP-seq peak calling algorithms and statistical analysis.
Overview of common follow-up analyses: genomic feature categorization and motif discovery.

Main Results:

Established methods for genome mapping and normalization are essential for reliable ChIP-seq data.
Recent advancements in peak calling provide more accurate identification of protein-DNA interactions.
Follow-up analyses enable functional interpretation of ChIP-seq data, such as identifying regulatory elements.

Conclusions:

Effective downstream data analysis is critical for maximizing the potential of ChIP-seq technology.
Understanding these analytical steps is key for researchers utilizing ChIP-seq to study chromatin dynamics and gene regulation.