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Related Experiment Video

Updated: Jun 18, 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

An RNA-Seq Data Analysis Pipeline.

Soham Sengupta¹, Rajeev K Azad²

¹Center for Pediatric Neurological Disease Research, St. Jude Children's Research Hospital, Memphis, TN, USA.

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

|July 27, 2024

Summary

This summary is machine-generated.

This chapter details a reproducible RNA-Seq analysis pipeline, from raw data processing to identifying differentially expressed genes and their functions. The workflow ensures reliable gene expression insights for biological discovery.

Keywords:

Alignment Differential gene expression Expression quantification RNA-Seq

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Related Experiment Videos

Last Updated: Jun 18, 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

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

RNA sequencing (RNA-Seq) is a powerful technology for measuring gene expression.
Analyzing RNA-Seq data requires a robust and reproducible computational pipeline.
Understanding gene expression patterns is crucial for elucidating biological processes.

Purpose of the Study:

To present a comprehensive, step-by-step pipeline for RNA-Seq data analysis.
To enable researchers to identify differentially expressed genes and perform functional characterization.
To provide accessible scripts and data for reproducibility and customization.

Main Methods:

Data acquisition and quality control assessment of raw sequencing reads.
Gene-level expression quantification across samples.
Differential gene expression analysis to compare conditions and identify significant changes.

Main Results:

A complete RNA-Seq analysis pipeline is established and described.
The pipeline facilitates the identification of differentially expressed genes.
Functional characterization of identified genes is supported.

Conclusions:

The presented pipeline offers a reliable framework for RNA-Seq data analysis.
Accessibility through an online repository enhances reproducibility.
The workflow empowers researchers to gain insights into gene expression and biological mechanisms.