RNA-seq Analysis Pipeline (Nextflow)

This repository contains a RNA-seq analysis pipeline I developed using Nextflow. It takes paired-end FASTQ files as input and processes them through quality control, trimming, alignment, and statistical analysis.

I created this as part of a project to better understand RNA-seq data analysis and workflow management with Nextflow. This can be used as a base or educational reference for similar projects. I also used some gtf and fa files from the GENCODE those can be changed the parts that I marked as #EDIT.

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Pipeline Steps

The workflow consists of the following main steps:

1. FastQC – run before trimming and after trimming to check read quality.
2. Trim Galore – detects low-quality reads and adapter sequences to remove them.
3. STAR Indexing – indexes the reference genome.
4. STAR Alignment – aligns trimmed reads to the genome.
5. R Analysis – an R script (counts_and_tests.R) that:
   • Generates count tables
   • Performs differential expression with edgeR
   • Uses clusterProfiler for enrichment analysis
   • Works with GTF annotations using rtracklayer and org.Hs.eg.db
   • Generates volcano and heatmap plots

The workflow is written in Nextflow DSL2 and uses Conda for reproducible environments.

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Installation and Setup

Before running the pipeline:

1. Download the Repository

git clone https://github.com/uzay-citimoglu/RNAseq-Analysis.git
cd RNAseq-Analysis

2. Install Nextflow & Conda
   Follow the Nextflow installation guide:

curl -s https://get.nextflow.io | bash
mv nextflow ~/bin/   # or another directory in your PATH

If you don't have Conda, install Miniconda:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

3. Set Up the Conda Environment
   Edit envsetup.slurm and update the .yml file path. Then run:

sbatch envsetup.slurm
# or
bash envsetup.slurm

4. Prepare Input Files

• Paired-end FASTQs (*_1.fastq.gz, *_2.fastq.gz) they need to be in this format
• Reference genome FASTA
• Annotation GTF

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Running the Pipeline

1. Edit nfrun.slurm

Open the file and update the four parameters in the nextflow run command.

#!/bin/bash -l
#SBATCH --job-name=testnf
#SBATCH --output=testnf_%j.out
#SBATCH --error=testnf_%j.err
#SBATCH --cpus-per-task=16
#SBATCH --mem=72G
#SBATCH -p your_partition_name       # EDIT

# --- USER INPUTS (EDIT BELOW) ---
nextflow run mainuzay.nf \
  --reads "/PATH/TO/FASTQ/*_{1,2}.fastq.gz" \                               # EDIT
  --fasta "/PATH/TO/REFERENCE/GENOME.fa" \                                  # EDIT
  --gtf "/PATH/TO/ANNOTATION/GENCODE.gtf" \                                 # EDIT
  --outdir "/PATH/TO/OUTPUT/DIRECTORY" \                                    # EDIT
  -with-report "/PATH/TO/OUTPUT/DIRECTORY/summary_$(date +%F_%H-%M-%S).html" \
  -resume

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2. Nextflow Script (mainuzay.nf)

The Nextflow workflow defines the full RNA-seq analysis process.
You must provide the same four required parameters (--reads, --fasta, --gtf, --outdir) either:

• In the SLURM script (nfrun.slurm)
• Or directly on the Nextflow CLI

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Parameters in the Script

These are the defaults inside mainuzay.nf — you can edit them here or override via CLI.

// --- USER INPUTS (EDIT THESE PATHS OR OVERRIDE VIA CLI) ---
params.reads  = "/PATH/TO/FASTQ/*_{1,2}.fastq.gz"                            // EDIT
params.fasta  = "/PATH/TO/REFERENCE/GENOME.fa"                               // EDIT
params.gtf    = "/PATH/TO/ANNOTATION/GENCODE.gtf"                            // EDIT
params.outdir = "/PATH/TO/OUTPUT/DIRECTORY"                                  // EDIT

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3. R Analysis Script (counts_and_tests.R)

This R script performs the downstream RNA-seq analysis after alignment and counting.

User Inputs to Edit

Section / Variable	Description	Example
bams <- c(...)	Paths to sorted BAM files	/path/to/sample1.sorted.bam
annot.ext in featureCounts	Path to GTF annotation	/refs/gencode.v48.annotation.gtf
Output file paths	Where counts and FPKM TSVs are written	/project/results/counts.tsv
nthreads	Number of CPU threads for counting	20
group <- factor(...)	Experimental groups for DE analysis	c("Control", "Control", "Treatment", "Treatment")
Filtering thresholds	Expression cutoffs and DE cutoffs	Adjust as needed
PCA color mapping	Colors assigned to samples in PCA plot	"Sample1" = "red", "Sample2" = "blue"

Main Analysis Steps

1. Counting & FPKM calculation
2. Annotation merge with GTF
3. Filtering low-expression genes
4. PCA plot
5. Differential expression analysis
6. Volcano plots
7. Heatmap
8. KEGG & GO enrichment analysis

Output Files

File	Description
counts.tsv	Raw gene counts
fpkm_values.tsv	FPKM-normalized values
edgeR_glm_DEG.tsv	DE results from glmLRT
edgeR_exact_DEG.tsv	DE results from exactTest
kegg_enrichment.tsv	KEGG enrichment results
go_enrichment.tsv	GO enrichment results
kegg_plots.pdf	KEGG enrichment plots
go_plots.pdf	GO enrichment plots

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File Structure

• scripts/: Contains a Rscript for future usage in statistical analysis.
• envsetup.slurm: Contains the bash code for environment setup.
• mainuzay.nf: The pipeline.
• nextflow.config: Additional setting for the pipeline.
• nfrun.slurm: Contains the code and inputs for running the pipeline.
• nfuzay.yml: Contains needed packages and tools for pipeline.