A comprehensive, modular, and user-friendly pipeline for de novo genome assembly and annotation that runs seamlessly on Jupyter Notebook and Google Colab

# Clone the repository
git clone https://github.com/engkinandatama/genoflow.git
cd genoflow

# Launch Jupyter Notebook
jupyter notebook GenoFlow_Complete_Pipeline.ipynb

GenoFlow is a modular bioinformatics pipeline designed to democratize genome assembly and annotation analysis. Whether you're a beginner researcher or an experienced bioinformatician, GenoFlow provides:

• 🔧 Modular Design: Mix and match tools based on your needs
• 🌐 Cloud-Ready: Run on Google Colab without local installation
• 📊 Interactive Visualizations: Rich plots and reports
• 🤖 Smart Automation: Auto-detection of optimal parameters
• 📚 Educational: Detailed explanations for learning
• 🔄 Reproducible: Complete provenance tracking

• Manual file upload (drag & drop)
• Direct download from NCBI SRA/ENA
• API integration for batch processing
• Cloud storage integration (Google Drive, AWS S3)

• Quality Control: FastQC, fastp, MultiQC
• Assembly: SPAdes, Flye, Unicycler, Canu
• Annotation: Prokka, bakta, PGAP, antiSMASH
• Comparative Genomics: Pan-genome, phylogeny, ANI
• Visualization: Interactive plots, genome browsers

• Bacterial/Archaeal genomes
• Fungal genomes
• Plasmid analysis
• Metagenome assembly
• Comparative genomics studies

# Minimal setup for standard bacterial genome
pipeline = GenoFlow()
pipeline.set_mode("bacterial_quick")
pipeline.input_data("SRR12345678")  # SRA accession
pipeline.run_assembly("spades")
pipeline.annotate("prokka")
pipeline.generate_report()

# Full pipeline with comparative analysis
pipeline = GenoFlow()
pipeline.set_mode("comprehensive")
pipeline.input_data(["genome1.fastq", "genome2.fastq"])
pipeline.run_qc(tools=["fastqc", "fastp"])
pipeline.run_assembly("unicycler", polish=True)
pipeline.annotate(["prokka", "antismash"])
pipeline.comparative_analysis()
pipeline.generate_interactive_report()

# Specialized for metagenomic data
pipeline = GenoFlow()
pipeline.set_mode("metagenome")
pipeline.input_data("metagenome_reads.fastq")
pipeline.run_assembly("metaspades")
pipeline.run_binning("metabat2")
pipeline.taxonomic_classification()

genoflow/
├── 📁 notebooks/
│   ├── GenoFlow_Complete_Pipeline.ipynb    # Main pipeline notebook
│   ├── GenoFlow_Quick_Start.ipynb          # Beginner-friendly version
│   ├── GenoFlow_Advanced.ipynb             # Advanced features
│   └── GenoFlow_Examples/                  # Example analyses
├── 📁 src/
│   ├── genoflow/                           # Core pipeline modules
│   ├── utils/                              # Utility functions
│   └── visualization/                      # Plotting functions
├── 📁 data/
│   ├── examples/                           # Example datasets
│   └── references/                         # Reference databases
├── 📁 docs/
│   ├── user_guide.md                       # Detailed user guide
│   ├── api_reference.md                    # API documentation
│   └── tutorials/                          # Step-by-step tutorials
├── 📁 tests/                               # Unit tests
├── requirements.txt                        # Python dependencies
└── environment.yml                         # Conda environment

No installation required! Just click the Colab badge above.

# Clone repository
git clone https://github.com/engkinandatama/genoflow.git
cd genoflow

# Create conda environment
conda env create -f environment.yml
conda activate genoflow

# Install Python packages
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook

docker pull engkinandatama/genoflow:latest
docker run -p 8888:8888 engkinandatama/genoflow:latest

• Python 3.8+
• 4 GB RAM
• 10 GB free disk space
• Internet connection (for tool downloads)

• 16+ GB RAM
• 100+ GB free disk space
• Multi-core CPU (4+ cores)
• High-speed internet

• Google Colab Pro (for large datasets)
• AWS/Azure instances (for production use)

• 📚 User Guide: Comprehensive usage instructions
• 🔧 API Reference: Function documentation
• 🎓 Tutorials: Step-by-step learning materials
• ❓ FAQ: Frequently asked questions
• 🐛 Troubleshooting: Common issues and solutions

• N50: 2.5 Mbp
• Total length: 4.2 Mbp
• Contigs: 25
• GC content: 42.3%

• Protein-coding genes: 3,847
• rRNA genes: 12
• tRNA genes: 67
• CRISPR arrays: 2

• Interactive genome browser
• Assembly quality plots
• Phylogenetic trees
• Functional category pie charts

We welcome contributions! Please see our Contributing Guidelines.

• 🐛 Report bugs and issues
• 💡 Suggest new features
• 📝 Improve documentation
• 🔧 Add new tools/modules
• 🧪 Contribute example datasets

If you use GenoFlow in your research, please cite:

@software{genoflow2025,
  title={GenoFlow: Modular De Novo Genome Assembly and Annotation Pipeline},
  author={Nandatama, Engki},
  year={2025},
  url={https://github.com/engkinandatama/genoflow},
  version={1.0.0}
}

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

• Tools Integration: SPAdes, Prokka, QUAST, and all incorporated tools
• Communities: Bioinformatics Stack Exchange, Galaxy Project
• Contributors: See CONTRIBUTORS.md

• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

• Prokka - Rapid prokaryotic genome annotation
• SPAdes - Genome assembly toolkit
• QUAST - Quality assessment tool
• Roary - Pan genome pipeline