ncbi-geo-pubmed-search 1.0.0

A powerful Python package for searching NCBI GEO and PubMed databases with advanced filtering

ncbi-geo-pubmed-search

A powerful Python package for searching NCBI GEO and PubMed databases with advanced filtering

Features

• 🔍 Unified Search Interface: Search both PubMed and GEO databases with a single command
• 🏥 PubMed Search: Find scientific articles with advanced filtering by date, field, and more
• 🧬 GEO Search: Discover gene expression datasets with organism and dataset type filtering
• 🛡️ Robust Error Handling: Built-in retry logic with exponential backoff for API rate limits
• 📊 Flexible Output: Export results to Excel or CSV formats
• 🔧 Highly Configurable: Customize API delays, retries, and search parameters
• 🌍 Environment Variable Support: Secure credential management
• 📈 Result Statistics: Get summary statistics for your search results

Installation

Install from PyPI:

pip install ncbi-geo-pubmed-search

Or install the latest development version:

pip install git+https://github.com/yourusername/ncbi-geo-pubmed-search.git

Quick Start

Basic Usage

from ncbi_geo_pubmed import NCBISearcher

# Initialize with your email (required by NCBI)
searcher = NCBISearcher(email="[e-mail]")

# Search both PubMed and GEO
results = searcher.search(
    search_terms=["cancer", "immunotherapy"],
    start_year=2020,
    end_year=2024
)

# Access results
pubmed_df = results["pubmed"]
geo_df = results["geo_all"]

print(f"Found {len(pubmed_df)} PubMed articles")
print(f"Found {len(geo_df)} GEO datasets")

Using Environment Variables

Set your credentials as environment variables for security:

export NCBI_EMAIL="[e-mail]"
export NCBI_API_KEY="your_optional_api_key"

Then use without explicit credentials:

from ncbi_geo_pubmed import NCBISearcher

searcher = NCBISearcher()
results = searcher.search(["aging", "senescence"], 2022, 2024)

Search Only PubMed

# Search PubMed with specific field
pubmed_results = searcher.search_pubmed(
    search_terms=["CRISPR", "gene editing"],
    start_year=2023,
    end_year=2024,
    field="Title/Abstract",  # Search in title and abstract
    retmax=500
)

# Save results
pubmed_results = searcher.search_pubmed(
    search_terms=["diabetes", "metabolism"],
    start_year=2020,
    end_year=2024,
    output_folder="./results",
    save_format="excel"
)

Search Only GEO

# Search GEO with organism filter
geo_results = searcher.search_geo(
    search_terms=["RNA-seq", "single cell"],
    organisms=["Homo sapiens", "Mus musculus"],
    dataset_type="expression profiling by high throughput sequencing",
    retmax=1000
)

# Access organism-specific results
human_datasets = geo_results["geo_homo_sapiens"]
mouse_datasets = geo_results["geo_mus_musculus"]

Advanced Usage

Custom Configuration

# Initialize with custom settings
searcher = NCBISearcher(
    email="[e-mail]",
    api_key="your_api_key",  # Optional, provides higher rate limits
    request_delay=0.5,       # Delay between requests (seconds)
    max_retries=5,          # Maximum retry attempts
    backoff_factor=2        # Exponential backoff multiplier
)

Combined Search with All Options

results = searcher.search(
    search_terms=["alzheimer", "neurodegeneration", "tau"],
    start_year=2020,
    end_year=2024,
    databases=["pubmed", "geo"],  # Which databases to search
    organisms=["Homo sapiens"],    # GEO organism filter
    retmax=2000,                   # Max results per database
    pubmed_field="Title/Abstract", # PubMed search field
    geo_dataset_type="expression profiling by array",
    output_folder="./alzheimer_results",
    save_format="excel",
    combine_results=True  # Save all results in one file
)

# Get summary statistics
stats = searcher.get_stats(results)
print(stats)

Batch Processing

# Process multiple search topics
topics = [
    {"terms": ["COVID-19", "long COVID"], "years": (2021, 2024)},
    {"terms": ["cancer", "immunotherapy"], "years": (2022, 2024)},
    {"terms": ["CRISPR", "base editing"], "years": (2020, 2024)}
]

all_results = {}
for topic in topics:
    key = "_".join(topic["terms"])
    results = searcher.search(
        search_terms=topic["terms"],
        start_year=topic["years"][0],
        end_year=topic["years"][1],
        retmax=100
    )
    all_results[key] = results

Output Examples

PubMed Results DataFrame

PMID	Title	Authors	Journal	Year	DOI	Citation
12345678	Cancer immunotherapy...	Smith J, et al.	Nature	2023	10.1038/...	Nature. 123(45):678-90 (2023)

GEO Results DataFrame

GEO_ID	Accession	Title	Organism	Platform	Samples	DatasetType
200012345	GSE12345	Single-cell RNA-seq...	Homo sapiens	GPL20301	10000	expression profiling...

Error Handling

The package includes robust error handling:

from ncbi_geo_pubmed import NCBISearcher, NCBISearchError, RateLimitError

try:
    searcher = NCBISearcher(email="[e-mail]")
    results = searcher.search(["cancer"], 2020, 2024)
except RateLimitError:
    print("Rate limit exceeded. Try again later or use an API key.")
except NCBISearchError as e:
    print(f"Search failed: {e}")

Command Line Interface (CLI)

The package also provides a command-line interface:

# Basic search
ncbi-search --email [e-mail] --terms "cancer,immunotherapy" --start 2020 --end 2024

# Search with all options
ncbi-search \
    --email [e-mail] \
    --api-key your_api_key \
    --terms "aging,senescence" \
    --start 2020 \
    --end 2024 \
    --databases pubmed geo \
    --organisms "Homo sapiens" "Mus musculus" \
    --output ./results \
    --format excel

Requirements

• Python >= 3.7
• biopython >= 1.79
• pandas >= 1.3.0
• openpyxl >= 3.0.9
• requests >= 2.25.0

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Support

If you encounter any problems or have questions:

1. Check the FAQ
2. Look through existing issues
3. Open a new issue with a detailed description

Citation

If you use this package in your research, please cite:

@software{ncbi-geo-pubmed-search,
  author = {{ Your Name }},
  title = {{ ncbi-geo-pubmed-search: A powerful Python package for searching NCBI GEO and PubMed databases with advanced filtering }},
  year = {2024},
  url = {https://github.com/yourusername/ncbi-geo-pubmed-search}
}

Acknowledgments

• NCBI for providing the E-utilities API
• The BioPython community for the excellent Bio.Entrez module
• All contributors and users of this package

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Made with ❤️ for the bioinformatics community