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Related Experiment Video

Updated: Mar 28, 2026

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

RNA-Seq workflow: gene-level exploratory analysis and differential expression.

Michael I Love¹, Simon Anders², Vladislav Kim³

¹Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.

|December 18, 2015

Summary

This summary is machine-generated.

This study presents a complete gene-level RNA-Seq differential expression workflow using Bioconductor. It covers data alignment, count matrix generation, exploratory analysis, and result visualization for RNA sequencing data.

Keywords:

Bioconductor RNA-seq differential expression gene expression genomics high-throughput sequencing statistical analysis visualization

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Published on: October 3, 2025

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA sequencing (RNA-Seq) is a powerful technology for gene expression profiling.
Analyzing RNA-Seq data requires robust and reproducible workflows.

Purpose of the Study:

To provide an end-to-end gene-level RNA-Seq differential expression workflow.
To demonstrate the use of Bioconductor packages for RNA-Seq analysis.

Main Methods:

Alignment of FASTQ files to a reference genome.
Generation of a gene-level count matrix.
Exploratory data analysis (EDA) for quality assessment and sample relationships.
Differential gene expression analysis.
Visualization of analysis results.

Main Results:

A complete and reproducible workflow for gene-level RNA-Seq differential expression analysis was established.
The workflow successfully processed raw sequencing reads to identify differentially expressed genes.
Exploratory data analysis provided insights into sample quality and relationships.

Conclusions:

The presented Bioconductor-based workflow facilitates comprehensive RNA-Seq differential expression analysis.
This approach enables robust identification and visualization of gene expression changes.
The workflow serves as a valuable resource for researchers in genomics and bioinformatics.