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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...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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Updated: May 18, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

The bench scientist's guide to statistical analysis of RNA-Seq data.

Craig R Yendrek1, Elizabeth A Ainsworth, Jyothi Thimmapuram

  • 1USDA ARS Global Change and Photosynthesis Research Unit, 1201 W. Gregory Drive, Urbana, IL 61801, USA. Craig.Yendrek@ars.usda.gov

BMC Research Notes
|September 18, 2012
PubMed
Summary

RNA sequencing (RNA-Seq) offers accurate transcript quantification. However, using multiple bioinformatics tools is crucial for a conservative list of differentially expressed genes due to method variability.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • RNA sequencing (RNA-Seq) is a powerful technique for quantifying transcript abundance.
  • Analysis of large transcriptomic datasets has traditionally required specialized bioinformatics expertise.
  • Potential sources of error in RNA-Seq analysis can impact the interpretation of gene expression changes.

Purpose of the Study:

  • To provide a step-by-step guide for RNA-Seq data analysis.
  • To outline a strategy for obtaining a conservative list of differentially expressed genes.
  • To discuss potential errors in RNA-Seq analysis and their impact on gene expression interpretation.

Main Methods:

  • Comparison of statistical tools for differential gene expression analysis.
  • Utilizing negative binomial distribution-based methods (edgeR and DESeq).
  • Analysis of RNA-Seq data from soybean leaf tissue under elevated O3 conditions.

Main Results:

  • edgeR identified 11,995 differentially expressed genes, while DESeq identified 11,317.
  • Only 10,535 differentially expressed genes were common between edgeR and DESeq.
  • Limitations include overly stringent significance parameters and increased Type II error for high-abundance transcripts.

Conclusions:

  • High variability exists between different bioinformatics tools for RNA-Seq differential expression analysis.
  • Employing multiple bioinformatics tools is recommended to ensure a conservative gene list.
  • RNA-Seq provides highly accurate transcript abundance quantification, comparable to qRT-PCR, despite analytical limitations.