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Enhanced MetaWorks Pipeline: An improved and extended version of the original MetaWorks multi-marker metabarcode processing pipeline
This enhanced version of MetaWorks builds upon the excellent foundation established by Porter & Hajibabaei (2022), adding significant improvements:
- 🐳 Docker Containerization: Complete containerized environment eliminates dependency conflicts and ensures reproducible results across systems
- ⚡ Parallel RDP Classifier: Multi-threaded taxonomic assignment dramatically reduces processing time for large datasets
- 🧩 Modular Architecture: Rules split into logical modules (preprocessing, denoising, pseudogene filtering) for easier maintenance and customization
- 🔧 Flexible Sample Input: Support for both CSV-based sample sheets and folder-based sample discovery
- 🐍 Enhanced Python Scripts: Modernized Python 3 scripts with improved error handling and performance
- 📊 Advanced Statistics: Comprehensive quality metrics and processing statistics at each pipeline step
| Section | Description |
|---|---|
| Installation Guide | Step-by-step installation instructions |
| Quick Start | Get running in 10 minutes |
| Supported Markers | Complete list of supported metabarcoding markers |
| Workflows | Detailed workflow documentation |
| Configuration | Pipeline configuration options |
| Troubleshooting | Common issues and solutions |
| API Reference | Command-line interface documentation |
| Examples | Real-world usage examples |
| Contributing | How to contribute to this project |
| Marker | Target Taxa | Classifier Available |
|---|---|---|
| COI | Animals, Eukaryotes | ✅ |
| 16S rRNA | Bacteria, Archaea | ✅ |
| ITS | Fungi | ✅ |
| rbcL | Plants, Diatoms | ✅ |
| 12S | Fish, Vertebrates | ✅ |
| 18S | Eukaryotes | ✅ |
| 28S | Fungi | ✅ |
Process multiple metabarcoding markers in a single, harmonized workflow using consistent bioinformatic approaches across all supported markers.
Generate high-resolution ESVs or traditional OTUs with taxonomic assignments and confidence scores using the RDP Classifier.
Built with Snakemake for reproducible, scalable processing on everything from laptops to high-performance computing clusters.
- ITS markers: Automatic removal of flanking conserved rRNA regions
- Protein-coding markers: Pseudogene filtering using profile HMMs
- Quality control: Comprehensive read quality assessment and filtering
Research Applications
- Biodiversity assessments
- Environmental monitoring
- Ecological studies
- Taxonomic surveys
Operational Applications
- Biomonitoring programs
- Environmental impact assessments
- Water quality monitoring
- Conservation projects
If you use this enhanced pipeline, please cite the original MetaWorks paper:
Porter, T. M., & Hajibabaei, M. (2022). MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments. PLOS ONE, 17(9), e0274260. [doi:10.1371/journal.pone.0274260](https://doi.org/10.1371/journal.pone.0274260)
Additional Citations:
- COI classifier: Porter, T. M., & Hajibabaei, M. (2018). Scientific Reports, 8, 4226.
- Pseudogene filtering: Porter, T.M., & Hajibabaei, M. (2021). BMC Bioinformatics, 22: 256.
- RDP classifier: Wang, Q., et al. (2007). Applied and Environmental Microbiology, 73(16), 5261–5267.
- Issues: [Report bugs or request features](https://github.com/Hajibabaei-Lab/MetaWorks-2.0/issues)
- Discussions: [Join community discussions](https://github.com/Hajibabaei-Lab/MetaWorks-2.0/discussions)
- Contributing: See our Contributing Guidelines
- [Original MetaWorks Repository](https://github.com/terrimporter/MetaWorks)
- [Original MetaWorks Website](https://terrimporter.github.io/MetaWorksSite)
- [Hajibabaei Lab](https://github.com/Hajibabaei-Lab)
Getting Started: Ready to dive in? Head to our Installation Guide or Quick Start to begin processing your metabarcoding data.