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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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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.
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Related Experiment Video

Updated: Nov 16, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Processing and Analysis of RNA-seq Data from Public Resources.

Yazeed Zoabi¹, Noam Shomron²

¹Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

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

|February 19, 2021

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) provides vast RNA sequencing data. This guide covers major datasets like GTEx and TCGA, offering analysis pipeline and interpretation strategies for researchers.

Keywords:

Databases Differential expression analysis GTEx Genomics NGS RNA sequencing RNA-seq TCGA

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Last Updated: Nov 16, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

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Published on: March 7, 2018

Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

Next-generation sequencing (NGS) has generated extensive transcriptome data.
Large-scale projects like GTEx and TCGA offer valuable gene expression resources.

Purpose of the Study:

To guide researchers in utilizing publicly available RNA sequencing datasets.
To provide an overview of analysis pipelines and data interpretation for transcriptome data.

Main Methods:

Overview of commonly used RNA sequencing datasets (e.g., GTEx, TCGA).
Description of the initial steps in an RNA sequencing analysis pipeline.
Guidance on exploring and interpreting transcriptome data.

Main Results:

Identification of key publicly available RNA sequencing resources.
A foundational understanding of RNA sequencing data analysis workflows.
Strategies for effective interpretation of gene expression data.

Conclusions:

Publicly available transcriptome data offers significant research potential.
Standardized approaches facilitate the analysis and interpretation of large-scale RNA sequencing studies.
Researchers can leverage these resources for gene expression investigations.