A collection of bioinformatics projects and analyses targeting real-world problems in genomics, drug discovery, and molecular biology. Built with Python and Jupyter Notebooks.
| # | Topic | Tools | Status |
|---|---|---|---|
| 01 | RNA-seq Differential Expression Analysis | Python, scipy, seaborn, statsmodels | ✅ Complete |
| 02 | ML-Based Drug Activity Prediction (QSAR) | scikit-learn, pandas | ✅ Complete |
| 03 | GWAS Manhattan & QQ Plots | Python, matplotlib, statsmodels | ✅ Complete |
| 04 | Protein Sequence Feature Extraction | Biopython, pandas, scikit-learn | ✅ Complete |
| 05 | Single-Cell RNA-seq Clustering | scanpy, UMAP, Leiden | 🔧 In Progress |
| RNA-seq Volcano | GWAS Manhattan | Protein PCA |
|---|---|---|
 |
 |
 |
- Genomics: RNA-seq count matrix processing, normalization, differential expression (fold change, p-values, volcano plots)
- Cheminformatics: Molecular fingerprinting (Morgan/ECFP), QSAR modeling, ROC-AUC evaluation
- Population Genetics: GWAS summary statistics, Manhattan plots, QQ plots, genomic inflation factor (λ)
- Data Visualization: Publication-quality figures with matplotlib / seaborn
- ML Pipeline Design: Feature engineering, cross-validation, model comparison, interpretability
# Clone
git clone https://github.com/lukaszplk/biogenetics-portfolio.git
cd biogenetics-portfolio
# Create conda environment
conda env create -f environment.yml
conda activate biogenetics
# Or using pip
pip install -r requirements.txtbiogenetics-portfolio/
├── 01_rna_seq/ # RNA-seq differential expression analysis
├── 02_drug_discovery/ # ML drug activity prediction (QSAR)
├── 03_population_genetics/ # GWAS visualization tools
├── 04_protein_analysis/ # Protein sequence feature extraction
├── 05_single_cell/ # scRNA-seq clustering & annotation
├── utils/ # Shared helper functions
├── requirements.txt
└── environment.yml
Open to opportunities in computational biology, bioinformatics engineering, and data science roles in biotech/pharma.