RNA-seq NGS Pipeline

A Python-based RNA-seq preprocessing pipeline for quality control, read trimming, genome alignment, read quantification, and quality reporting.

The pipeline automates the main preprocessing steps required before downstream RNA-seq analyses such as differential gene expression analysis.

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Features

• Quality assessment with FastQC
• Read trimming using fastp
• Genome alignment using STAR
• BAM processing using SAMtools
• Gene-level read counting using featureCounts
• Quality summary report generated with MultiQC
• Configuration through a YAML file
• Modular Python implementation

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Workflow

Paired-end FASTQ files
            │
            ▼
        FastQC
            │
            ▼
         fastp
            │
            ▼
          STAR
            │
            ▼
   SAMtools Index
            │
            ▼
     featureCounts
            │
            ▼
         MultiQC

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Project Structure

02_NGS_Pipeline_Python/
│
├── config.yaml
├── pipeline.py
├── requirements.txt
├── README.md
│
├── data/
│   ├── fastq/
│   ├── raw/
│   └── trimmed/
│
├── genome/
│   ├── annotation/
│   ├── reference/
│   └── star_index/
│
├── results/
│   ├── fastqc/
│   ├── fastp/
│   ├── bam/
│   ├── counts/
│   └── multiqc/
│
├── logs/
├── scripts/
└── tests/

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Requirements

• Python 3.11+
• Ubuntu / Linux (tested using WSL2)
• FastQC
• fastp
• STAR
• SAMtools
• featureCounts (Subread)
• MultiQC

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Installation

Clone the repository:

git clone <repository_url>
cd 02_NGS_Pipeline_Python

Install Python dependencies:

pip install -r requirements.txt

Install the required bioinformatics tools:

• FastQC
• fastp
• STAR
• SAMtools
• featureCounts
• MultiQC

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Configuration

Edit the configuration file:

threads: 8

genomeDir: genome/star_index

gtf: genome/annotation/gencode.v49.primary_assembly.annotation.gtf

samples:
  sample1:
    R1: data/fastq/SRR11365252_1.fastq
    R2: data/fastq/SRR11365252_2.fastq

The pipeline supports multiple paired-end samples defined in config.yaml.

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Running the Pipeline

Run the complete workflow:

python3 pipeline.py --config config.yaml

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Pipeline Steps

The pipeline performs the following operations:

1. FastQC quality assessment
2. Read trimming with fastp
3. Genome alignment using STAR
4. BAM indexing using SAMtools
5. Gene quantification using featureCounts
6. Quality summary with MultiQC

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Output

The pipeline generates:

• FastQC reports
• Trimmed FASTQ files
• BAM alignment files
• Gene count table
• MultiQC report

Example output directories:

results/
├── fastqc/
├── fastp/
├── bam/
├── counts/
└── multiqc/

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Tools Used

Tool	Purpose
FastQC	Read quality assessment
fastp	Adapter trimming and filtering
STAR	RNA-seq alignment
SAMtools	BAM processing
featureCounts	Gene quantification
MultiQC	Summary quality report

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Future Improvements

Planned improvements include:

• Command-line options for individual pipeline steps
• Automatic checkpoint detection
• Improved logging
• Docker support
• Snakemake / Nextflow implementation
• Support for multiple reference genomes

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Author

Katarzyna Zielińska

Bioinformatics Portfolio

2026

Created as part of a Bioinformatics Portfolio project focused on RNA-seq data analysis using Python and Linux.