Bulk RNA sequencing pipeline

Snakemake pipeline processes paired-end FASTQ files through quality control, alignment, transcript quantification, differential expression analysis (DESeq2), and GO enrichment analysis

Pipeline Features

• FastQ Quality Control (FastQC, MultiQC)
• Adapter Trimming (Atria)
• Read Alignment (STAR)
• Read Alignment QC (MultiQC)
• Transcript Quantification (Salmon)
• Quality Control Metrics (RSeQC, samtools)
• Differential Expression Analysis (DESeq2)
• GO Enrichment Analysis (clusterProfiler)

📥 Installation

1️⃣ Install Dependencies

conda install -c bioconda snakemake

2️⃣ Clone the Repository

git clone https://github.com/your_username/Bulk_RNA_seq.git
cd Bulk_RNA_seq

User Input Files

Raw fastq files

You should create a folder and rename it to "rawdata". You should have samplename_1.fq.gz and samplename_2.fq.gz for each samplename in the "rawdata" folder. The pipeline will use this as an input and then will output all the results in separate "results" folder.

Sample Sheet

Currently, you can perform analysis on paired-end reads. You must provide a CSV file describing your samples. (If your data are in Excel format, please convert them to CSV or modify the pipeline accordingly.) The sample sheet should contain the following columns:

• group: Experimental group (e.g., cell line or condition)
• sample_id: A unique identifier for each sample, needs to match with samplename
• replicate: Replicate number (e.g., 1, 2, …)
• fastq_1: Path for the raw FASTQ file (read 1), e.g. samplename_1.fq.gz in rawdata folder
• fastq_2: Path for the raw FASTQ file (read 2), e.g. samplename_2.fq.gz in rawdata folder
• genome: Genome build used (e.g., "hg38" or "mm39")

Example:

group,sample_id,replicate,fastq_1,fastq_2,genome
GM12878,SRR3192657,1,s3://nf-core-awsmegatests/rnaseq/input_data/SRX1603629_T1_1.fastq.gz,s3://nf-core-awsmegatests/rnaseq/input_data/SRX1603629_T1_2.fastq.gz,hg38
GM12878,SRR3192658,2,s3://nf-core-awsmegatests/rnaseq/input_data/SRX1603630_T1_1.fastq.gz,s3://nf-core-awsmegatests/rnaseq/input_data/SRX1603630_T1_2.fastq.gz,hg38
K562,SRR3192408,1,s3://nf-core-awsmegatests/rnaseq/input_data/SRX1603392_T1_1.fastq.gz,s3://nf-core-awsmegatests/rnaseq/input_data/SRX1603392_T1_2.fastq.gz,hg38
...

This file should be named as sample_sheet.csv and move into the data folder

sample_sheet: "data/sample_sheet.csv"

Comparisons File

Provide a CSV file defining the comparisons for differential expression analysis. (If your file is in Excel format, convert it to CSV or adjust the Snakefile to use pd.read_excel.)

The comparisons file should include the following columns:

comparison_number: A unique identifier for the comparison treatment: Name of the treatment group control: Name of the control group

comparison_number,treatment,control
1,GM12878,K562
2,MCF7,H1

Place this file in the data/ directory (e.g., as data/comparison.csv), or update the Snakefile accordingly.

🚀 Running the Pipeline

1️⃣ Test Pipeline Execution (Dry Run)

snakemake --use-conda -np

2️⃣ Run the Full Pipeline

snakemake --use-conda --rerun-triggers mtime --cores 30

💡 Adjust --cores based on your system's available CPUs.

Run the Docker image

To run the pipeline using the Docker image, mount your current directory into the container:

docker run --rm -v $(pwd):/app umranyaman/bulk-rna-seq --use-conda --rerun-triggers mtime --cores 30

License

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