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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...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
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 28, 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

Normalization, testing, and false discovery rate estimation for RNA-sequencing data.

Jun Li1, Daniela M Witten, Iain M Johnstone

  • 1Department of Statistics, Stanford University, Stanford, CA 94305, USA. junli07@stanford.edu

Biostatistics (Oxford, England)
|October 18, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel log-linear model for analyzing RNA sequencing data, addressing count-based outcomes and normalization challenges. The method accurately identifies significant genes and estimates the false discovery rate (FDR) efficiently.

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Last Updated: May 28, 2026

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • RNA sequencing data are count-based, making Gaussian models unsuitable.
  • Normalization is complex due to varying read counts across experiments.
  • Existing methods may not be optimal for comparative genomic analyses.

Purpose of the Study:

  • To develop a robust statistical framework for identifying genes associated with outcomes in sequencing experiments.
  • To introduce a novel normalization approach for count-based genomic data.
  • To provide an accurate and efficient method for false discovery rate (FDR) estimation.

Main Methods:

  • Utilized a log-linear model incorporating a new normalization strategy.
  • Developed a novel procedure for estimating the false discovery rate (FDR).
  • Applied the method to quantitative, two-class, and multiple-class outcome data.

Main Results:

  • The proposed log-linear model effectively handles count data and normalization challenges.
  • The novel FDR estimation procedure demonstrates high accuracy.
  • The method shows potential advantages over Poisson and negative binomial models.

Conclusions:

  • The developed pipeline offers a fast and accurate approach for the significance analysis of sequencing data.
  • This method provides a valuable tool for comparative genomics and gene identification.
  • The approach is scalable and efficient for large datasets.