Metagenome Analysis - Graduate Course Materials

Beginner-Level Practical Training

Welcome to the beginner-friendly companion to the 10-Day Metagenome Analysis Series!

This repository contains materials for a graduate-level metagenome analysis course, co-taught as part of a university graduate program. It’s designed for students with little to no prior bioinformatics experience who want hands-on practice with real workflows using manageable toy datasets.

Why this course?

• ✅ Quick results - Complete workflows in 1-2 hours (not days!)
• ✅ Laptop-friendly - Runs on 4-8 GB RAM
• ✅ Realistic expectations - Learn with toy data, understand limitations
• ✅ Foundation for advanced work - Bridge to the full 10-Day Series

📚 Course Overview

A hands-on introduction to metagenomics covering the complete workflow from raw sequencing reads to taxonomic classification, genome recovery, and phylogenetic analysis.

Level: Beginner
Prerequisites: Basic command-line knowledge
Duration: Full semester practical course
Instructors: Co-taught graduate course

📁 Repository Contents

metagenome-analysis-class/
├── README.md                                    # This file
├── metagenome_practical_full_version_jojy.md   # Complete course guide
├── data.md                                      # Data download instructions
├── metawrap_micromamba.md                       # MetaWRAP setup guide
│
├── Toy Data (< 100 MB - Included)
│   ├── toy-R1.fastq.gz                         # Forward reads
│   ├── toy-R2.fastq.gz                         # Reverse reads
│   └── assembly.fasta                          # Pre-assembled contigs
│
└── Phylogenetic Analysis
    ├── class_practice_tree/                    # Tree files
    └── tree_annotation_data.xlsx               # Annotation metadata
    └──Metagenome_M8130_Feb16_JJ.pdf            # full class materials

🎯 Learning Objectives

After completing this course, students will be able to:

✅ Perform quality control on raw sequencing data
✅ Conduct taxonomic profiling of microbial communities
✅ Assemble metagenome reads into contigs
✅ Recover individual genomes (MAGs) through binning
✅ Classify recovered genomes taxonomically
✅ Annotate and visualize phylogenetic relationships

🧬 Toy Dataset (< 100 MB)

What’s Included:

• toy-R1.fastq.gz - Forward paired-end reads
• toy-R2.fastq.gz - Reverse paired-end reads
• assembly.fasta - Pre-assembled contigs for binning practice

What This Dataset is Good For:

✅ Quality Control

• FastQC analysis
• Adapter trimming (fastp / Trimmomatic)
• Quality score visualization

✅ Taxonomic Profiling

• Kraken2 / Bracken classification
• Kaiju protein-level profiling
• K-mer profiling (Mash/sourmash)

✅ Read Mapping

• Bowtie2 / BWA alignment
• Coverage calculation
• Mapping to reference genomes

✅ Assembly Demonstration

• MEGAHIT or metaSPAdes assembly
• Note: Results will be fragmented (toy-scale data) but demonstrate the workflow

✅ Binning Practice

• Use provided assembly.fasta for binning exercises
• MetaBAT2, MaxBin2, or CONCOCT
• Bin refinement and quality assessment

What to Expect:

⚠️ Important Notes:

• Dataset is intentionally small for quick processing
• Assembly will be fragmented - this is expected for toy data
• Binning will produce ~2 bins only - sufficient for learning
• Bins are from contaminated genomes - for educational purposes only
• Results are not publication-quality - this is for learning workflows

🧪 Pre-Assembled Data

assembly.fasta

A toy assembly file provided for students to practice:

• Genome binning
• Bin quality assessment (CheckM)
• Dereplication (dRep)
• Taxonomic classification (GTDB-Tk)
• Functional annotation

Expected Output: ~2 bins (intentionally limited for faster processing)

Use Case:

• Practice binning workflows without waiting hours for assembly
• Learn downstream analysis steps quickly
• Understand MAG quality metrics

🌳 Phylogenetic Tree Materials

For learning phylogenetic tree construction and annotation:

Files:

• class_practice_tree/ - Tree files in various formats
• tree_annotation_data.xlsx - Metadata for tree annotation

Purpose:

• Visualize evolutionary relationships
• Annotate trees with metadata
• Practice using iTOL or similar tools

Note: Use these tree files for annotation practice, as toy data binning produces limited genomes.

📊 Real Dataset (Optional)

For students wanting to work with real-world data:

NCBI BioProject: PRJNA432171

Download Instructions:

See data.md for detailed download instructions.

Important:

• Real data is significantly larger (several GB)
• Processing time: hours to days depending on sample
• Requires more computational resources
• Provides publication-quality results

🚀 Getting Started

1. Prerequisites

Software Requirements:

• FastQC
• fastp or Trimmomatic
• MEGAHIT or metaSPAdes
• MetaBAT2
• CheckM
• (Optional) MetaWRAP for integrated workflow

See metawrap_micromamba.md for complete setup instructions.

2. Quick Start with Toy Data

# 1. Clone this repository
git clone https://github.com/jojyjohn28/metagenome-analysis-class.git
cd metagenome-analysis-class

# 2. Quality control
fastqc toy-R1.fastq.gz toy-R2.fastq.gz

# 3. Trim adapters
fastp -i toy-R1.fastq.gz -I toy-R2.fastq.gz \
      -o toy-R1.clean.fastq.gz -O toy-R2.clean.fastq.gz

# 4. Taxonomic profiling
kraken2 --db /path/to/db --paired \
        toy-R1.clean.fastq.gz toy-R2.clean.fastq.gz \
        --output toy.kraken --report toy.report

# 5. Binning (using provided assembly)
metabat2 -i assembly.fasta -o bins/bin

3. Follow the Full Tutorial

Open metagenome_practical_full_version_jojy.md for complete step-by-step instructions.

📖 Course Materials

Main Tutorial

metagenome_practical_full_version_jojy.md

• Complete workflow from QC to phylogenetic analysis
• Code examples for each step
• Troubleshooting tips
• Expected outputs

Setup Guides

metawrap_micromamba.md

• MetaWRAP installation with micromamba
• Database setup
• Configuration instructions

data.md

• Download instructions for real data
• File organization
• Storage requirements

💡 Teaching Tips

For Instructors:

1. Start with Toy Data
   • Fast processing for demonstrations
   • Students see results quickly
   • Reduces frustration with long wait times
2. Use Pre-Assembled Contigs
   • Skip assembly for initial binning lessons
   • Focus on binning concepts first
   • Students can assemble later if interested
3. Provide Tree Files
   • Binning produces limited results
   • Use provided trees for annotation practice
   • Demonstrates publication-quality phylogenies
4. Optional Real Data
   • Advanced students can download real data
   • Compare toy vs. real results
   • Understand computational requirements

For Students:

1. Master the Workflow First
   • Use toy data to understand each step
   • Don’t worry about fragmented assembly
   • Focus on learning the process
2. Understand Limitations
   • Toy data = toy results
   • Real data requires more time/resources
   • Concepts are the same at any scale
3. Practice Makes Perfect
   • Run the workflow multiple times
   • Experiment with parameters
   • Try real data when confident

⚠️ Important Disclaimers

About Toy Data:

• Not for publication - Educational purposes only
• Contaminated genomes - Intentionally included for learning
• Limited results - Expect ~2 bins maximum
• Fragmented assembly - Expected behavior for small dataset
• Simplified workflow - Real projects are more complex

About Computational Resources:

• Toy data: Runs on laptops (4-8 GB RAM)
• Real data: Requires HPC or workstation (64+ GB RAM)
• Processing time: Toy=minutes, Real=hours to days

📚 Additional Resources

Recommended Reading:

• MetaWRAP paper
• GTDB-Tk documentation
• CheckM2 guide

Online Tutorials:

• Galaxy Metagenomics
• Anvi’o Tutorials

Databases:

• GTDB: https://gtdb.ecogenomic.org/
• NCBI Taxonomy: https://www.ncbi.nlm.nih.gov/taxonomy

🤝 Contributing

This is a course repository. If you’re a student or instructor using these materials:

• Students: Report issues or ask questions via GitHub Issues
• Instructors: Feel free to adapt materials for your courses
• Improvements: Pull requests welcome for corrections or enhancements

📧 Contact

Course Instructor: Jojy John
GitHub: jojyjohn28
Website: jojyjohn28.github.io

For course-related questions, open an issue in this repository.

📝 Citation

If you use these materials in your course, please cite:

Jojy John. (2026). Metagenome Analysis Graduate Course Materials.
GitHub repository: https://github.com/jojyjohn28/metagenome-analysis-class
Part of the 10-Day Metagenome Analysis Series.

Related work:

• 10-Day Series: https://github.com/jojyjohn28/metagenome-analysis-series
• Blog: https://jojyjohn28.github.io/blog/

📜 License

Educational materials provided for academic use.

• Course materials: Free to use with attribution
• Toy data: Educational purposes only
• Code examples: MIT License

🎉 Acknowledgments

This course was developed as part of a graduate-level metagenomics training program.

Special Thanks:

• Students who provided feedback
• Co-instructors for collaboration
• Metagenomics community for tool development

Last Updated: February 2026
Version: 1.0 - Beginner Level

Quick Reference Card

Total workflow time with toy data: ~1-2 hours

🔗 Related Resources

10-Day Metagenome Analysis Series

For comprehensive production-level workflows, check out the complete series:

📖 Day 1: QC & Taxonomic Profiling
📖 Day 2: Genome Assembly
📖 Day 3: Genome Binning
📖 Day 4: Dereplication & Taxonomy
📖 Day 5: Genome Annotation
📖 Day 6: Specialized Functions
📖 Day 7: Comparative Genomics
📖 Day 8: Workflow Platforms
📖 Day 9: Visualization
📖 Day 10: Multi-Omics Integration

Repository

🔗 Course Materials GitHub
🔗 10-Day Series GitHub 🔗 10-Day Series blog

Last updated: February 2026

Happy Learning! 🧬