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Related Concept Videos

RNA-seq03:21

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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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Published on: September 18, 2021

DEGseq: an R package for identifying differentially expressed genes from RNA-seq data.

Likun Wang1, Zhixing Feng, Xi Wang

  • 1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing 100084, China.

Bioinformatics (Oxford, England)
|October 27, 2009
PubMed
Summary

This study introduces DEGseq, an R package for analyzing RNA sequencing data to find differences in gene expression. It offers novel methods for detecting and visualizing gene expression variations between samples.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput RNA sequencing (RNA-seq) is a key technology for transcriptome profiling.
  • Analyzing RNA-seq data requires robust statistical methods to identify gene expression differences.

Purpose of the Study:

  • To present DEGseq, an R package designed for identifying differentially expressed genes and isoforms from RNA-seq data.
  • To provide researchers with a tool for detecting and visualizing gene expression variations.

Main Methods:

  • Integration of three established methods for differential gene expression analysis.
  • Introduction of two novel methods utilizing MA-plot for enhanced detection and visualization of expression differences.
  • Development of an R package (DEGseq) for streamlined analysis.

Main Results:

  • The DEGseq package provides a comprehensive approach to differential gene expression analysis.
  • The integrated and novel methods facilitate the identification of significant gene expression changes.
  • MA-plot based methods offer effective visualization of expression differences.

Conclusions:

  • DEGseq is a valuable R package for researchers utilizing RNA-seq data.
  • The package enhances the ability to detect and visualize differential gene expression.
  • It supports quantitative transcriptome profiling through advanced analytical methods.