RNA-seq Analysis Pipeline

Author: Sagar Tank
Email: tank.sag@northeastern.edu

Overview

Single-end RNA-seq analysis pipeline using FastQC, Trimmomatic, HISAT2, and featureCounts.

Workflow

FASTQ → FastQC → Trimmomatic → HISAT2 → featureCounts

Tools Used

• FastQC v0.12.1 - Quality control
• Trimmomatic v0.40 - Adapter and quality trimming
• HISAT2 v2.2.1 - Read alignment
• featureCounts v2.1.1 - Gene quantification

Requirements

• macOS (tested on M1 Pro)
• Conda/Miniforge
• Reference genome: GRCh38
• GTF annotation: Ensembl 106

Installation

# Install tools via conda
conda install -c bioconda fastqc trimmomatic hisat2 samtools subread

# Download reference genome
curl -O https://genome-idx.s3.amazonaws.com/hisat/grch38_genome.tar.gz
tar -xzf grch38_genome.tar.gz

# Download GTF annotation
curl -O http://ftp.ensembl.org/pub/release-106/gtf/homo_sapiens/Homo_sapiens.GRCh38.106.gtf.gz
gunzip Homo_sapiens.GRCh38.106.gtf.gz

Usage

# Place your FASTQ file in data/fastq/
# Run the pipeline
bash rnaseq_pipeline.sh

Results

• FastQC reports: data/*_fastqc.html
• Trimmed reads: data/demo_trimmed.fastq
• Alignment: HISAT2/demo_trimmed.bam
• Gene counts: quants/demo_featurecounts.txt

Example Results

From demo dataset (1,250,000 reads):

• Trimming: 94.74% reads retained
• Alignment: 93.98% mapping rate
• Gene assignment: 62.3% (843,012 reads)

Project Structure

rnaseq-pipeline/
├── rnaseq_pipeline.sh    # Main pipeline script
├── data/                 # FastQC reports
├── HISAT2/              # BAM alignment files
└── quants/              # Gene count tables

License

MIT License