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Related Experiment Video

Updated: Oct 5, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
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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
PubMed
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.

Keywords:
DESEQ2Differential gene expression analysisEndothelial transcriptomicsRNASeq

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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.