A comprehensive bioinformatics platform for molecular biology research and analysis, integrating tools for protein structure visualization, sequence analysis, and gene ontology exploration.
Explore BioCore directly through these live site deployments:
- Streamlit Cloud: biocore-suite-nnilayy.streamlit.app
- Hugging Face Spaces: huggingface.co/spaces/nnilayy/BioCore
BioCore is a modern, integrated platform that combines three powerful modules for bioinformatics research:
Core Features:
I. PDB Integration & Structure Fetching
- Retrieve protein structures in real-time from the Protein Data Bank directly using their unique PDB IDs
II. Interactive 3D Visualization & Styling
- Dynamic 3D visualization of bio-molecular structures with adjustable viewing styles and interactive controls for detailed exploration of the proteins
III. Detailed Protein Structural Information
- Displays comprehensive structural metadata (classification, deposition date, title, R-values) including experimental methods (X-ray diffraction, NMR) and resolution metrics (resolution, R-free, R-work)
- Provides detailed molecular information about protein chains and their source organisms
- Includes bibliographic references with complete citation details for research attribution
IV. Export Features
- Facilitates direct PDB file download for offline access and further analysis
Core Features:
I. Analysis of Proteins from FASTA Files
- Process protein sequences to generate general file-level statistics and specific protein analysis using interactive Plotly visualizations
II. General File-Level Analysis
- Statistical exploration of sequence properties including length metrics and distributions, coupled with metadata insights on organism diversity and protein evidence levels, alongside global amino acid composition patterns
III. Specific Protein Analysis
- Deep dive into individual proteins through biochemical characterization (molecular weight, pI, stability metrics), structural element predictions, and detailed amino acid compositional breakdown
Core Features:
I. Interactive Hierarchical DAGs for Genomic Ontologies
- Process OBO files to create interactive networks of related Gene Ontology terms, exploring their hierarchical relationships across biological processes, molecular functions, and cellular components
II. GO Term Metadata Analysis
- Extract and analyze metadata of GO terms (IDs, names, definitions, relationship types) from OBO files
- Clone the repository:
git clone https://github.com/nnilayy/biocore.git
cd biocore- Set up environment using
uv:
uv venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies:
uv pip install -r requirements.txt- Launch the application:
streamlit run home.pystreamlit: Web application frameworkpy3Dmol: Molecular visualizationbiopython: Biological computationpandas: Data manipulationplotly: Interactive plottingnetworkx: Network analysisobonet: Ontology parsingseaborn: Statistical visualization
We welcome contributions! Here's how you can help:
-
Fork the repository (Click here to fork BioCore)
-
Clone your fork:
git clone https://github.com/nnilayy/biocore.git
cd biocore- Create your feature branch:
git checkout -b feature/AmazingFeature- Make your changes and commit them:
git add .
git commit -m 'Add AmazingFeature'- Push to your branch:
git push origin feature/AmazingFeatureThen open a Pull Request from your fork to our main repository.
If you've found a bug or have a suggestion, feel free to open an issue.
To create a new issue:
- Go to the Issues tab
- Click the New Issue button
- Choose the appropriate template if available
- Fill in the required information
- Submit the issue
This project is licensed under the MIT License - see the LICENSE file for details.
- PDB Database for structural data
- Gene Ontology Consortium
- BioPython community
- Streamlit team
Have questions or suggestions? Feel free to reach out!
- Author: Nilay Kumar Bhatnagar
- Email: nnilayy.work@gmail.com