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RNA-seq

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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.
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: May 9, 2026

An Efficient Protocol for CUT&RUN Analysis of FACS-Isolated Mouse Satellite Cells

An Efficient Protocol for CUT&RUN Analysis of FACS-Isolated Mouse Satellite Cells

Published on: July 7, 2023

Statistical analysis of ChIP-seq data with MOSAiCS.

Guannan Sun¹, Dongjun Chung, Kun Liang

¹Department of Statistics, University of Wisconsin, Madison, WI, USA.

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

|July 23, 2013

Summary

This summary is machine-generated.

This study introduces a statistical protocol for analyzing chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) data. It provides guidelines and examples for identifying ChIP-enriched regions, improving epigenomic profile analysis.

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Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is a key technique.
It's used for genome-wide transcription factor binding and epigenomic profiling.
Standardized analysis protocols are crucial for reliable results.

Purpose of the Study:

To present a robust statistical protocol for ChIP-seq data analysis.
To offer guidelines for data preprocessing and quality control.
To demonstrate ChIP-enriched region identification using the 'mosaics' Bioconductor package.

Main Methods:

Development of a statistical analysis protocol for ChIP-seq data.
Implementation of data preprocessing and quality control steps.
Application of the 'mosaics' Bioconductor package for peak calling.

Main Results:

A comprehensive protocol for ChIP-seq data analysis is presented.
Guidelines for quality control and preprocessing are detailed.
Successful identification of ChIP-enriched regions is demonstrated.

Conclusions:

The presented protocol enhances the analysis of ChIP-seq data.
This work facilitates accurate genome-wide binding and epigenomic profiling.
The 'mosaics' package provides a reliable tool for ChIP-seq analysis.