Metadata-Version: 2.4
Name: biomeai
Version: 1.0.0
Summary: A package for easy integration with NVIDIA AI capabilities
Home-page: https://github.com/TheRaj71
Author: The Raj
Author-email: theraj05@duck.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: httpx>=0.24.0
Requires-Dist: typing-extensions>=4.0.0
Requires-Dist: python-dotenv>=0.19.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-asyncio>=0.20.0; extra == "dev"
Requires-Dist: pytest-cov>=4.0.0; extra == "dev"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
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# BiomeAI

Simple Python package for Multiple Sequence Alignment search using NVIDIA's ColabFold.

## Install
```bash
pip install biomeai
```

## Quick Usage

```python
from biomeai import ColabFoldMSA

# Initialize with API key
msa = ColabFoldMSA(api_key="your-api-key")
# Or set NVCF_RUN_KEY environment variable

# Your protein sequence
sequence = "MVPSAGQLALFALGIVLAACQALENSTSPLSADPPVAAAVVSHFNDCPDSHTQFCFHGTCRFL"

# 1. Use a single database
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302"]
)

# 2. Use multiple databases
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302", "PDB70_220313"]
)

# 3. Use all databases (Cascaded Search)
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302", "PDB70_220313", "colabfold_envdb_202108"]
)

# Save results (optional)
msa.save_results(results, "results.json")
```

## Available Databases

- `Uniref30_2302`: Universal Reference Database
- `PDB70_220313`: Protein Data Bank
- `colabfold_envdb_202108`: Environmental Database

## Additional Options

```python
results = msa.search_sync(
    sequence=sequence,
    databases=["Uniref30_2302"],
    e_value=0.0001,        # E-value threshold
    iterations=1,          # Number of iterations
    output_formats=["a3m", "fasta"]  # Output formats
)
```

## Async Support

```python
import asyncio

async def search_msa():
    results = await msa.search(
        sequence=sequence,
        databases=["Uniref30_2302"]
    )
    return results

results = asyncio.run(search_msa())
```

## Development

Install with development dependencies:
```bash
pip install biomeai[dev]
```

Run tests:
```bash
pytest
```

## License

MIT License - see the [LICENSE](LICENSE) file for details.
