🧬 Bioinformatics & Data Science Repository

A curated collection of data science projects, scripts, and analyses with a focus on bioinformatics.
This repository serves as a portfolio and resource for exploring computational biology, genomics, proteomics, and related fields through the lens of data science.

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📁 Repository Structure

Data-Science/
│
├── datasets/               # Metadata or processed data (no large raw data)
├── notebooks/              # Jupyter notebooks for analysis and visualization
├── scripts/
│   ├── data_download/      # Automated data download & preprocessing scripts
│   ├── analysis/           # Python/R scripts for bioinformatics analyses
│   └── ml_models/          # Machine learning pipelines and model training
├── results/                # Output figures, reports, and tables
├── environment.yml         # Conda environment specification
├── requirements.txt        # Python dependencies (for pip users)
└── README.md               # This file

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🚀 Quick Start

Prerequisites

• Python 3.8+
• conda (recommended) or pip

Installation

1. Clone the repository:

   git clone https://github.com/prabathjayatissa/Data-Science.git
   cd Data-Science

2. Set up the environment (using Conda - Recommended):

   conda env create -f environment.yml
   conda activate bioinfo_ds_env  # Or whatever you name your environment in the YAML file

   Alternatively, using pip:

   pip install -r requirements.txt

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📊 Datasets

This repository works with the following types of bioinformatics data.
Due to size constraints, raw data is often not stored in Git.

Dataset	Description	Source	Size	Link
TCGA-BRCA	RNA-Seq and clinical data for Breast Cancer	The Cancer Genome Atlas	~2 GB	GDC Portal
Human Genome (GRCh38)	Reference genome sequence	GENCODE	~1 GB	GENCODE
Example Microbiome	16S rRNA sequencing data from a mock community	[Cite Source]	~50 MB

Pro Tip:

• Use tables for clarity.
• Always provide a source and link (DOI if available).
• Mention the size to set expectations.
• For large datasets, provide scripts in scripts/data_download/ to automate fetching.

Data Download Scripts

Run the provided scripts from the repository root to fetch data:

# Example: Download and preprocess TCGA data
bash scripts/data_download/fetch_tcga_data.sh

# Example: Download a reference genome
python scripts/data_download/get_reference_genome.py

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🧪 Projects & Analyses

Here are the key analyses and projects contained in this repository:

1. Differential Gene Expression Analysis

Description: Identifies significantly up/down-regulated genes between two biological conditions (e.g., tumor vs. normal) using RNA-Seq data.
Tools: DESeq2 (R) / limma-voom / Scanpy (for scRNA-Seq)
Key Output: Lists of DEGs, volcano plots, MA plots.

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2. Variant Calling Pipeline

Description: A Snakemake/Nextflow pipeline for calling genetic variants from NGS data (e.g., WES, WGS).
Tools: FastQC, BWA-MEM, GATK, Samtools, Snakemake
Key Output: VCF files containing SNP and Indel calls.

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3. Machine Learning for Protein Function Prediction

Description: Predicts protein function from amino acid sequences using machine learning.
Tools: scikit-learn, Biopython, PyTorch
Features: Amino acid composition, PSSM profiles, protein embeddings (ESM, etc.)
Models: Random Forest, XGBoost, Simple Neural Network.

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🛠️ Tools & Technologies

🧰 Programming Languages

• Python
• R
• Bash

🧬 Core Bioinformatics Libraries

• Biopython, Pysam, PyVCF
• DESeq2, limma, Bioconductor (R)
• Scanpy, Scikit-bio

📈 Data Science & ML

• pandas, numpy, scikit-learn, XGBoost
• matplotlib, seaborn, plotly

⚙️ Workflow Management

• Snakemake, Nextflow

🐳 Containers & Reproducibility

• Conda, Docker

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🤝 Contributing

Contributions are welcome!
If you have a new analysis, script, or suggestion for improvement, please:

1. Fork the repo
2. Create a feature branch

   git checkout -b feature/AmazingFeature

3. Commit your changes

   git commit -m "Add some AmazingFeature"

4. Push to your branch

   git push origin feature/AmazingFeature

5. Open a Pull Request

Please ensure your code is well-documented and follows the existing style.

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📜 License

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

Note: The code in this repository is licensed under MIT.
However, datasets may have their own licensing terms — please check each source for compliance.

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📚 References & Useful Links

• Biostars Handbook — A fantastic resource for bioinformatics.
• ROSALIND — A platform for learning bioinformatics through problem-solving.
• GATK Best Practices — For variant discovery pipelines.
• GEO Database — Functional genomics data repository.

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