The internet is filled with bioinformatics resources - tools, pipelines, and tutorials for studying, practicing, and utilizing computational biology. But here's the problem: most of these valuable resources have a critical flaw - the data is missing.
Why are datasets often unavailable?
- The data is too big - Multi-terabyte genomic datasets exceed typical storage
- The data is too private - Clinical and sensitive biological information
- They just don't want to share it - Proprietary or competitive restrictions
So here, I'm gathering and re-creating bioinformatics hands-on practices with public datasets that you can actually download, run, and analyze. This project bridges the gap between theoretical knowledge and practical implementation, boosting your real-world bioinformatics experience.
| Project Name | Data Size | Technology Stack | Status | Documentation |
|---|---|---|---|---|
| Fetal Health Multiple Classification | 223KB | Python, TensorFlow/PyTorch | ✅ Complete | README |
| BrainOmics2024 | Variable | Python, scikit-learn | 🚧 Active | README |
| Nextflow RNA-seq STAR Feature Count | Variable | Nextflow, STAR, featureCounts | ✅ Complete | README |
- Actually runnable: Every workflow includes complete, accessible data
- End-to-end implementation: From raw data to final analysis
- Production-ready code: Professional-grade implementations
- Educational focus: Designed to enhance learning and skill development