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Related Experiment Video

Updated: May 2, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Protocol for identifying differentially expressed genes using the RumBall RNA-seq analysis platform.

Luis Augusto Eijy Nagai¹, Seohyun Lee¹, Ryuichiro Nakato¹

¹Laboratory of Computational Genomics, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo, Tokyo 113-0032, Japan.

|March 10, 2024

Summary

This study introduces a Dockerized protocol for RNA sequencing analysis, enabling identification of differentially expressed genes. The method ensures a thorough understanding of gene expression analysis from raw data to interpretation.

Keywords:

Bioinformatics RNA-seq Sequence analysis

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Related Experiment Videos

Last Updated: May 2, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

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

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-Seq) is a powerful technique for transcriptome analysis.
Identifying differentially expressed genes (DEGs) is crucial for understanding biological processes and disease mechanisms.
Standardized and reproducible protocols are essential for robust RNA-Seq data analysis.

Purpose of the Study:

To present a comprehensive and user-friendly protocol for RNA sequencing data analysis.
To enable the identification of differentially expressed genes (DEGs) using the RumBall pipeline within a Docker environment.
To provide clear guidance on all analysis steps, from raw data processing to biological interpretation.

Main Methods:

Utilized FASTQ files from public datasets as starting material.
Employed the RumBall pipeline within a self-contained Docker system for reproducibility.
Detailed procedures for software setup, data acquisition, read mapping, normalization, statistical modeling, and gene ontology enrichment analysis.

Main Results:

Successfully established a step-by-step protocol for RNA-Seq analysis.
Demonstrated the capability to identify differentially expressed genes (DEGs).
Provided methods for result interpretation using plots and tables, facilitating biological insights.

Conclusions:

The presented Dockerized protocol offers a reproducible and comprehensive approach to RNA sequencing analysis.
RumBall streamlines the identification and interpretation of differentially expressed genes.
This protocol empowers researchers to gain a deeper understanding of gene expression patterns.