RNA-seq Analysis Pipeline

A streamlined, user-friendly pipeline for processing RNA-seq data from raw FASTQ files to aligned BAMs with quality control metrics.

Overview

This pipeline automates the following steps:

• Quality control using FastQC
• Read trimming with Trimmomatic
• Alignment using STAR
• Duplicate marking with Picard
• Quality metrics collection
• Summary report generation with MultiQC

Quick Start

Installation

1. Clone this repository:

git clone https://github.com/IkramInf/rnaseq-pipeline.git
cd rnaseq-pipeline

2. Run the installation script:

bash install.sh

3. Activate the environment:

source ~/activate_rna_seq_pipeline.sh

Running the Pipeline

Basic usage:

./rna_seq_pipeline.sh -i /path/to/fastq/files -g hg38 -o results

For help and all options:

./rna_seq_pipeline.sh --help

Requirements

• Linux-based operating system (Ubuntu/Debian or CentOS/RHEL recommended)
• 16GB+ RAM (more for large genomes)
• 100GB+ disk space
• Python 3.8+
• Java Runtime Environment 11+

Command-line Options

Option	Description	Default
-i, --input	Directory containing FASTQ files	Current directory
-g, --genome	Reference genome (hg19 or hg38)	hg19
-o, --output	Output directory	results
-c, --crop	Crop reads to this length	50
-l, --leading	Remove leading low quality bases	0
-t, --trailing	Remove trailing low quality bases	0
-p, --threads	Number of threads to use	All but one CPU
-h, --help	Display help message	-

Output Structure

results/
├── sample1/
│   ├── fastqc_reports/
│   ├── sample1_PE_Aligned.sortedByCoord.out.bam
│   ├── sample1_marked_duplicates.bam
│   ├── sample1_RNA_Metrics.txt
│   ├── sample1_mapping_metrics.txt
│   ├── sample1_duplicate_metrics.txt
│   └── multiqc_report.html
├── sample2/
...

Workflow Details

1. Quality Control: FastQC analyzes read quality
2. Trimming: Trimmomatic removes adapters and low-quality bases
3. Alignment: STAR aligns reads to the reference genome
4. Metrics Collection: Picard tools collect RNA-seq and alignment metrics
5. Duplicate Marking: Picard identifies PCR duplicates
6. Summary Report: MultiQC creates a comprehensive report

Customization

The pipeline supports both single-end and paired-end data automatically. It will detect FASTQ file pairs based on the standard naming convention: _R1.fastq.gz/_R2.fastq.gz.

Troubleshooting

Not enough memory for STAR:

• Reduce the number of threads with -p option
• Run on a machine with more RAM

Missing dependencies:

• Re-run the installation script
• Check system requirements

Citation

If you use this pipeline, please cite:

• STAR aligner: Dobin A, et al. (2013) PMID: 23104886
• Trimmomatic: Bolger AM, et al. (2014) PMID: 24695404
• Picard: https://broadinstitute.github.io/picard/
• FastQC: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
• MultiQC: Ewels P, et al. (2016) PMID: 27312411

License

This project is licensed under the MIT License - see the LICENSE file for details.