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Related Concept Videos

RNA-seq03:21

RNA-seq

9.9K
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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Ribosome Profiling02:24

Ribosome Profiling

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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...
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Related Experiment Video

Updated: Jun 25, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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RNA-Seq Data Analysis: A Practical Guide for Model and Non-Model Organisms.

Enrique Pola-Sánchez1, Karen Magdalena Hernández-Martínez2, Rafael Pérez-Estrada3

  • 1Laboratorio Nacional de Genómica para la Biodiversidad-Unidad de Genómica Avanzada, Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV), Unidad Irapuato, Irapuato, México.

Current Protocols
|May 29, 2024
PubMed
Summary
This summary is machine-generated.

This guide offers step-by-step RNA sequencing (RNA-seq) data analysis workflows for researchers and students. It covers gene expression analysis from raw reads to functional insights, applicable to any organism.

Keywords:
RNA‐seqassemblydifferential expressionnon‐model plantsplottingreproducibility

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • RNA sequencing (RNA-seq) is a vital tool for genome-wide gene expression analysis.
  • Analyzing large RNA-seq datasets presents significant challenges for those without bioinformatics expertise.

Purpose of the Study:

  • To provide a comprehensive, step-by-step guide for RNA-seq data analysis.
  • To assist students and researchers in analyzing gene expression data from any organism, with or without a reference genome.

Main Methods:

  • The guide details workflows from raw read processing to functional enrichment analysis.
  • It utilizes recognized bioinformatics tools, discussing their pros and cons.
  • Includes protocols for model organisms with reference genomes and de novo assembly for non-model organisms.

Main Results:

  • A practical and reproducible protocol for RNA-seq data analysis is presented.
  • The guide facilitates the extraction of valuable biological insights from complex datasets.
  • All necessary scripts and a sample dataset are provided via GitHub.

Conclusions:

  • This guide empowers users to confidently perform RNA-seq data analysis.
  • It democratizes access to advanced gene expression analysis techniques.
  • Enhances biological discovery through accessible bioinformatics workflows.