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RNA-seq

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...

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Related Experiment Video

Updated: Aug 20, 2025

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Strategy for RNA-Seq Experimental Design and Data Analysis.

Gregory Gimenez¹, Peter A Stockwell², Euan J Rodger²

¹Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand. gregory.gimenez@otago.ac.nz.

Methods in Molecular Biology (Clifton, N.J.)

|November 23, 2022

Summary

This summary is machine-generated.

This guide explains RNA sequencing (RNA-Seq) experimental design and bioinformatics analysis. It helps researchers effectively study gene expression and cellular function for various biological questions.

Keywords:

Alignment Gene expression RNA-Seq Sequence reads Sequencing Transcript

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Last Updated: Aug 20, 2025

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Published on: March 7, 2018

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

Ribonucleic acids (RNAs) are crucial for genome regulation and cellular function.
Accurate RNA transcript analysis is vital for understanding cell behavior, disease, and phenotypes.
RNA sequencing (RNA-Seq) offers large-scale analysis of RNA expression and function.

Purpose of the Study:

To outline critical considerations for RNA-Seq experimental design.
To provide a step-by-step bioinformatics workflow for RNA-Seq data analysis.
To serve as a guide for researchers addressing diverse biological questions using RNA-Seq.

Main Methods:

Review of key experimental design principles for RNA sequencing.
Detailed description of a bioinformatics pipeline for RNA-Seq data processing.
Explanation of data analysis strategies tailored to specific biological applications.

Main Results:

Identification of critical factors influencing RNA-Seq experimental design.
A structured bioinformatics workflow for robust RNA-Seq data analysis.
Emphasis on how design choices impact data interpretation.

Conclusions:

Effective RNA-Seq experimental design and data analysis are essential for reliable biological insights.
This guide provides a framework for optimizing RNA-Seq studies.
Proper methodology ensures accurate understanding of gene expression and cellular processes.