Resources

Software

Tool 1: DeepBio

Description: A deep learning framework for biological sequence analysis
GitHub: https://github.com/yourlab/deepbio
Documentation: https://deepbio.readthedocs.io
Paper: Doe et al., Nature Methods 2023

Tool 2: SingleCellAnalyzer

Description: Comprehensive toolkit for single-cell RNA-seq analysis
GitHub: https://github.com/yourlab/singlecell
Tutorial: Getting Started Guide

Tool 3: DrugTargetPredictor

Description: Machine learning models for drug-target interaction prediction
Web Server: https://drugtarget.yourlab.edu
API: Available for academic use upon request

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Datasets

Dataset 1: Human Cell Atlas Integration

Description: Integrated single-cell dataset across 50 human tissues
Size: 10 million cells
Download: Zenodo
Citation: Smith et al., Cell 2023

Dataset 2: Drug-Target Interaction Database

Description: Curated database of 100,000 drug-target interactions
Format: CSV, JSON
Access: Download

Dataset 3: Protein Structure Predictions

Description: AlphaFold predictions for 10,000 human proteins with functional annotations
Format: PDB, CSV
Access: FTP Server

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Protocols

Computational Protocols

• Single-cell RNA-seq analysis pipeline
• Drug screening data analysis
• Machine learning model training guide

Experimental Protocols

• CRISPR screen analysis
• High-content imaging analysis

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Educational Materials

Tutorials

• Introduction to Machine Learning for Biology
• Python for Bioinformatics
• Deep Learning with PyTorch

Workshops

We regularly conduct workshops on computational biology topics:

• Annual Summer School: 2-week intensive course (July)
• Monthly Seminars: First Friday of each month
• Online Courses: Available on Coursera

Lecture Slides

• Computational Biology 101
• Machine Learning in Drug Discovery
• Single-Cell Analysis Methods

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External Links

Databases

• PubMed
• UniProt
• Gene Expression Omnibus
• ChEMBL

Tools We Recommend

• Scanpy - Single-cell analysis
• PyMOL - Molecular visualization
• RDKit - Cheminformatics
• Cytoscape - Network analysis

Collaborator Resources

• Collaborator Lab 1
• Collaborator Lab 2
• Consortium Website

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How to Cite

If you use our resources in your research, please cite:

@article{yourlab2024,
  title={YourLab Resources: Tools and Data for Computational Biology},
  author={Doe, John and Smith, Jane},
  journal={Bioinformatics},
  year={2024},
  volume={40},
  pages={1234-1245}
}

License

All software is released under the MIT License unless otherwise specified.
Datasets are available under CC-BY 4.0 International License.

Contact

For questions about resources, please email: resources@yourlab.edu