RNA-seq Differential Gene Expression Analysis

Overview

This project performs differential gene expression analysis between control and treatment RNA-seq samples.

The complete RNA-seq workflow includes quality assessment, read trimming, genome alignment, read quantification, and statistical identification of differentially expressed genes using DESeq2.

Dataset

NCBI SRA Project: PRJNA925118

Two paired-end RNA-seq samples were analyzed:

Condition	SRA Accession
Control	SRR23108228
Treatment	SRR23108232

The samples were analyzed as single-end reads for downstream processing.

RNA-seq Analysis Workflow

1. Quality Control

Tool used: FastQC

Quality assessment was performed on raw sequencing reads before trimming.

Outputs:

• FastQC HTML reports

2. Read Trimming

Tool used: Trimmomatic

Adapter sequences and low-quality bases were removed to improve read quality.

3. Genome Alignment

Tool used: HISAT2

Cleaned reads were aligned against the reference genome.

Alignment outputs were processed for downstream gene quantification.

4. Read Quantification

Tool used: featureCounts

Aligned reads were assigned to genes to generate a gene-level count matrix.

5. Differential Expression Analysis

Tool used: DESeq2 (R/Bioconductor)

Differential expression analysis was performed between:

• Control group
• Treatment group

Statistical analysis identified significantly upregulated and downregulated genes.

Results

The analysis generated:

• Gene count matrix
• Differential expression results
• Volcano plot
• Heatmap visualization
• Quality control reports

Visualization

Volcano Plot

Heatmap

Repository Structure

RNA-seq-DESeq2-Analysis/

├── counts/
│   └── gene_counts.txt
│
├── deseq2/
│   ├── DESeq2_results.csv
│   ├── counts_matrix.csv
│   ├── metadata.csv
│   ├── deseq2_analysis.R
│   ├── volcano_plot.R
│   ├── heatmap_plot.R
│   ├── volcanoPlot.png
│   └── Heatmap.png
│
├── qc_raw/
│   └── FastQC reports
│
├── qc_trimmed/
│   └── FastQC reports after trimming
│
└── .gitignore

Tools & Technologies

• FastQC
• Trimmomatic
• HISAT2
• featureCounts
• R
• DESeq2
• ggplot2
• pheatmap

Key Skills Demonstrated

• Next-generation sequencing (NGS) data analysis
• RNA-seq preprocessing
• Quality control assessment
• Genome alignment
• Gene expression quantification
• Differential expression analysis
• Biological data visualization

Author

Fiza Ahmad
BS Bioinformatics Student