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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. 
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Ribosome Profiling

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Related Experiment Video

Updated: Jun 6, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

A survey of statistical software for analysing RNA-seq data.

Dexiang Gao1, Jihye Kim, Hyunmin Kim

  • 1Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA. Dexiang.Gao@UCDenver.edu

Human Genomics
|November 26, 2010
PubMed
Summary

This review compares edgeR, DEGseq, and baySeq for RNA sequencing analysis. It highlights their normalization, statistical models, and testing methods for gene expression studies.

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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

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Last Updated: Jun 6, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput RNA sequencing is a powerful tool for gene expression analysis.
  • Several software packages are available for analyzing RNA sequencing data.

Purpose of the Study:

  • To review and compare three Bioconductor software packages: edgeR, DEGseq, and baySeq.
  • To focus on normalization, statistical models, and testing methods within these packages.
  • To discuss the advantages and limitations of each software package.

Main Methods:

  • Comparative review of RNA sequencing analysis software.
  • Focus on normalization techniques.
  • Evaluation of statistical models and hypothesis testing approaches.

Main Results:

  • Detailed comparison of edgeR, DEGseq, and baySeq.
  • Analysis of normalization strategies.
  • Assessment of statistical rigor and performance.

Conclusions:

  • Provides insights into the strengths and weaknesses of edgeR, DEGseq, and baySeq.
  • Aids researchers in selecting appropriate software for RNA sequencing data analysis.
  • Highlights key considerations for gene expression studies using RNA sequencing.