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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: Dec 30, 2025

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Published on: June 28, 2018

User-Friendly and Interactive Analysis of ChIP-Seq Data Using EaSeq.

Mads Lerdrup^1,2, Klaus Hansen³

¹Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, Center for Chromosome Stability, University of Copenhagen, Copenhagen, Denmark. mads.lerdrup@sund.ku.dk.

Methods in Molecular Biology (Clifton, N.J.)

|January 22, 2020

Summary

This summary is machine-generated.

EaSeq provides an integrated platform for visualizing and analyzing genome-wide ChIP-seq data, accelerating discoveries in stem cell biology and differentiation. This user-friendly tool connects biological interpretations with underlying genomic signal distributions.

Keywords:

Analysis ChIP-sequencing Epigenetics Exploration Genomics Next-generation sequencing Visualization

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Chromatin immunoprecipitation sequencing (ChIP-seq) is crucial for understanding genome-wide regulatory networks in stem cells and during differentiation.
Analysis of ChIP-seq data aids in generating new hypotheses and accelerating biological discovery.
Effective visualization and analysis tools are needed to bridge abstract interpretations with raw genomic data.

Purpose of the Study:

To introduce EaSeq, a versatile, user-friendly, and interactive platform for integrated exploration of genome-wide ChIP-seq data.
To demonstrate how EaSeq connects biological insights with signal distribution at specific genomic loci.
To guide users through workflows for analyzing transcription factor binding sites and histone marks.

Main Methods:

EaSeq offers integrated visualization and analysis of genome-wide data.
The platform supports exploration of ChIP-seq data, including transcription factor binding sites and histone marks.
Workflows cover data acquisition, basic plotting, peak analysis (finding, annotating, sorting, filtering), genome browsing, signal measurement, ratio calculation, and data import/export.

Main Results:

EaSeq enables scientists to analyze and visualize genome-wide ChIP-seq data effectively.
The tool facilitates the discovery phase by connecting biological understanding with data.
Example workflows demonstrate comprehensive genome-wide analysis and visualization capabilities.

Conclusions:

EaSeq enhances the exploration and analysis of ChIP-seq data, accelerating scientific discovery.
The platform's interactive and visual approach improves the interpretation of regulatory networks.
EaSeq empowers researchers to perform detailed genome-wide analyses of epigenetic marks and transcription factor binding.