RNA-seq Analysis Pipeline

A professional, end-to-end bioinformatics pipeline for bulk RNA-seq data analysis. This project covers the entire workflow from raw FASTQ files to advanced systems biology analysis (WGCNA).

Description

This repository provides a standardized framework for:

• Raw data quality control and trimming.
• Efficient read alignment and post-processing.
• Gene-level quantification.
• Differential expression analysis (DEA) with high-quality visualizations.
• Functional interpretation via GO, KEGG, and GSEA.
• Gene co-expression network construction (WGCNA).

Repository Structure

• scripts/: Contains all modular Bash and R scripts.
• README.md: Documentation and usage guide.
• LICENSE: MIT License.

---

Workflow Overview

1. Upstream Processing (Shell Scripts)

All upstream steps are automated in shell scripts located in /scripts.

• Environment Setup: Configuration using Conda and SRA-Toolkit.
• Preprocessing: Quality control with FastQC and adapter trimming with Cutadapt.
  • Script: scripts/01_preprocessing.sh
• Alignment: Supports Hisat2 and STAR for mapping reads to a reference genome.
  • Script: scripts/02_alignment.sh
• Quantification: Counting reads per gene using featureCounts.
  • Script: scripts/03_quantification.sh

2. Downstream Analysis (R Scripts)

Downstream statistical analysis is performed using R (v4.0+).

• Differential Expression: Utilizing DESeq2 for normalization and identifying DEGs.
  • Script: scripts/04_deseq2_analysis.R
• Functional Enrichment: GO/KEGG Over-representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA).
  • Script: scripts/05_enrichment.R
• Network Analysis: WGCNA for identifying highly correlated gene modules.
  • Script: scripts/06_wgcna.R

---

Quick Start

Prerequisites

Ensure you have Conda installed. Create the environment:

conda create -n rnaseq_env bioconda::sra-tools bioconda::fastqc bioconda::cutadapt bioconda::hisat2 bioconda::subread
conda activate rnaseq_env