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Updated: Jun 4, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing

Kevin P McCormick1, Matthew R Willmann2, Blake C Meyers1

  • 1Department of Plant and Soil Sciences and Delaware Biotechnology Institute, University of Delaware, Newark, DE 19711, USA.

Silence
|March 2, 2011
PubMed
Summary
This summary is machine-generated.

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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. 
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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Next-generation sequencing revolutionized small RNA (sRNA) discovery, overcoming limitations of older methods. This review guides designing sRNA sequencing experiments and analyzing data, including normalization and differential expression.

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Small RNA (sRNA) discovery was historically limited by Sanger sequencing, restricting insights to abundant molecules.
  • Next-generation sequencing (NGS) technologies have dramatically expanded the understanding of sRNA biology, diversity, and abundance.

Purpose of the Study:

  • To review critical aspects of designing and analyzing small RNA sequencing experiments.
  • To provide guidance on experimental design, data preprocessing, normalization, and differential expression analysis for sRNA sequencing.

Main Methods:

  • Discussion of sequencing platform selection and inherent biases in sRNA measurements.
  • Outline of data preprocessing steps for sRNA sequencing data.
  • Review of normalization principles and current options.

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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Published on: June 8, 2020

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Last Updated: Jun 4, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

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

  • Exploration of differential expression analysis methods with and without biological replicates.

    • Main Results:

    • NGS enables comprehensive analysis of sRNA populations, revealing greater diversity and abundance.
    • Identification of key considerations for minimizing bias and ensuring robust results in sRNA sequencing.
    • Standardized approaches for data preprocessing, normalization, and differential expression analysis are crucial for reliable findings.

    Conclusions:

    • The review provides a framework for effective small RNA sequencing experiment design and data analysis.
    • The principles discussed are broadly applicable to other RNA sequencing applications.
    • Advancements in sequencing technology continue to enhance our understanding of regulatory RNA networks.