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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 10, 2025

3' End Sequencing Library Preparation with A-seq2

3' End Sequencing Library Preparation with A-seq2

Published on: October 10, 2017

A Computational Workflow for Analysis of 3' Tag-Seq Data.

Akshay D Paropkari^1,2, Priyanka S Bapat^1,2, Suzanne S Sindi³

¹Quantitative and Systems Biology Graduate Program, University of California, Merced, California.

Current Protocols

|February 13, 2023

Summary

This summary is machine-generated.

This study introduces a user-friendly computational workflow for analyzing 3' Tag-sequencing (Tag-Seq) data. The workflow efficiently processes RNA sequencing data to identify differentially expressed genes, aiding gene expression profiling.

Keywords:

3′ Tag-Seq RNA sequencing computational pipeline differential gene expression

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Last Updated: Aug 10, 2025

3' End Sequencing Library Preparation with A-seq2

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Published on: October 10, 2017

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

RNA-sequencing (RNA-seq) is a key technology for genome-wide gene expression profiling.
3' Tag-Seq is a cost-effective RNA-seq variation focusing on transcript 3' ends.
Existing analysis methods may not be optimized for 3' Tag-Seq specific data characteristics.

Purpose of the Study:

To present a simple, publicly available computational workflow for analyzing 3' Tag-Seq data.
To provide a streamlined process for differential gene expression analysis from 3' Tag-Seq experiments.
To generate key outputs including differential gene expression tables and visualizations.

Main Methods:

Adapter trimming of raw FASTQ files.
Quality control using FastQC.
Read alignment to the reference genome using STAR.
Differential gene expression analysis with DESeq2.
Generation of MA plots, differential gene tables, and UpSet plots.

Main Results:

A functional computational workflow for 3' Tag-Seq data analysis is established.
The workflow enables efficient identification of differentially expressed genes.
Key analytical outputs are generated for biological interpretation.

Conclusions:

The developed workflow offers a straightforward and accessible method for 3' Tag-Seq data analysis.
This protocol facilitates robust differential gene expression analysis for researchers utilizing 3' Tag-Seq.
The workflow is specifically tailored for 3' Tag-Seq, omitting transcript length normalizations.