Endothelial Cell RNA-Seq Data: Differential Expression and Functional Enrichment Analyses to Study Phenotypic
Guillermo Díez Pinel1, Joseph L Horder2, John R King3
1Neuronal and Vascular Biology Group, UCL Institute of Ophthalmology, University College London, London, UK.
Methods in Molecular Biology (Clifton, N.J.)
|January 31, 2022
Summary
This study presents a user-friendly bioinformatics workflow for RNA sequencing (RNA-seq) data analysis, transforming raw sequencing reads into interpretable gene expression results. The workflow employs established tools for quality control, alignment, quantification, and differential expression analysis, facilitating biological discovery.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- RNA sequencing (RNA-seq) is crucial for understanding gene expression but often poses computational challenges for researchers.
- Many researchers lack experience in the complex bioinformatics analysis required for RNA-seq data.
Purpose of the Study:
- To provide a user-friendly, step-by-step bioinformatics workflow for RNA-seq data analysis.
- To guide researchers from raw sequencing data to interpretable gene expression and functional enrichment results.
- To demonstrate the workflow using publicly available endothelial cell (HUVEC) data.
Main Methods:
- Data quality assessment using FastQC and read trimming with Cutadapt.
- Read alignment to a reference genome using STAR, followed by alignment analysis with Qualimap.
- Gene quantification with featureCounts and differential expression analysis using DESeq2.
- Functional enrichment analysis utilizing clusterProfiler against GO, KEGG, and Reactome databases.
Main Results:
- A comprehensive bioinformatics pipeline is detailed, covering all essential steps from raw reads to functional insights.
- Differential gene expression analysis identifies key genes between experimental conditions.
- Functional enrichment analysis reveals significantly enriched biological pathways and gene sets.
- Example figures illustrate the interpretation of functional enrichment outcomes.
Conclusions:
- This workflow simplifies RNA-seq data analysis, making advanced bioinformatics accessible to a broader range of researchers.
- The presented methods enable robust identification of differentially expressed genes and biological pathways.
- The workflow serves as a valuable resource for researchers studying gene expression in various biological contexts.
