PyNCBI 0.2.0

PyNCBI

Simple API for Python Integration with NCBI GEO .
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Table of Contents

• About the Project
• Installation
• Quick Start
• Usage
  • GSM (Gene Sample Microarray)
  • GSE (Gene Series Expression)
  • GEOReader (Low-Level API)
• Configuration
• Logging
• Exception Handling
• Features
• Roadmap
• Contributing
• License
• Contact

About The Project

PyNCBI provides a clean Python API for accessing DNA methylation data from NCBI's Gene Expression Omnibus (GEO) database.

Why PyNCBI?

• Simple API: Fetch methylation data with just a few lines of code
• Automatic Caching: Downloaded data is cached locally for fast subsequent access
• Type-Safe: Full type hints and modern Python 3.11+ support
• Robust Error Handling: Descriptive exceptions with helpful hints
• Configurable Logging: Control verbosity with colored output
• Multiple Fetch Modes: Choose between per-sample or supplementary file downloads

Installation

pip install PyNCBI

Requirements

• Python 3.11+
• pandas
• numpy
• requests
• tqdm
• methylprep (for IDAT file processing)

Quick Start

from PyNCBI import GSM, GSE

# Fetch a single sample
gsm = GSM('GSM1518180')
print(gsm.info)
print(gsm.data)

# Fetch an entire series
gse = GSE('GSE85506', mode='supp')
print(f"Found {len(gse)} samples")

Usage

GSM (Gene Sample Microarray)

The GSM class represents a single sample with its methylation data and metadata.

Attributes

Attribute	Type	Description
info	pd.Series	Complete sample metadata
data	pd.DataFrame	Probe IDs and beta values
characteristics	dict	Parsed sample characteristics
array_type	str	Methylation array platform
gse	str	Parent series ID

Basic Usage

from PyNCBI import GSM

# Fetch and cache a sample
gsm = GSM('GSM1518180')
print(gsm)

GSM: GSM1518180 | GSE: GSE62003
tissue:  Whole blood
Sex:  Male
age:  77

Accessing Data

# Get methylation beta values
beta_values = gsm.data
print(f"Probes: {len(beta_values)}")

# Get sample metadata
print(gsm.info['!Sample_source_name_ch1'])

# Get parsed characteristics
print(gsm.characteristics)
# {'tissue': 'Whole blood', 'Sex': 'Male', 'age': '77'}

Metadata Only (No Data Download)

# Fetch only metadata without downloading methylation data
gsm = GSM('GSM1518180', shell_only=True)
print(gsm.info)  # Available
print(gsm.data)  # None - no data downloaded

GSE (Gene Series Expression)

The GSE class represents a collection of samples from an experiment.

Attributes

Attribute	Type	Description
info	pd.Series	Series metadata
gsms	dict[str, GSM]	Dictionary of GSM objects

Fetch Modes

Mode	Description	Best For
'per_gsm'	Fetch each sample individually	Small datasets, reliability
'supp'	Use supplementary tar file	Large datasets, speed

Using Supplementary Files (Recommended for Large Datasets)

from PyNCBI import GSE

# Fetch using supplementary file - faster for large datasets
gse = GSE('GSE85506', mode='supp', file_index=0)  # Use first supplementary file
print(gse)

GSE: GSE85506
Array Type: GPL13534 (450k)
Number of Samples: 47
Title: DNA methylation analysis in women with fibromyalgia

The file_index parameter selects which supplementary file to use (0-indexed). This avoids interactive prompts for batch processing.

Using Per-GSM Mode

# Fetch each sample individually - more reliable but slower
gse = GSE('GSE62003', mode='per_gsm')

Accessing Samples

# Access a specific sample
gsm = gse['GSM2267972']
print(gsm.data)

# Iterate over all samples
for gsm_id, gsm in gse.items():
    print(f"{gsm_id}: {len(gsm.data)} probes")

# Get all sample IDs
print(list(gse.keys()))

Using FetchMode Enum (Type-Safe)

from PyNCBI import GSE, FetchMode

# Use enum instead of string for type safety
gse = GSE('GSE85506', mode=FetchMode.SUPPLEMENTARY, file_index=0)

# Equivalent to mode='supp'

GEOReader (Low-Level API)

For direct interaction with NCBI GEO without caching.

from PyNCBI import GEOReader

reader = GEOReader()

# Extract metadata for a single sample
gsm_info = reader.extract_gsm_info('GSM1518180')

# Extract metadata for all samples in a series
gse_info = reader.extract_gse_sample_info('GSE62003')

# List samples in a series
gsm_ids = reader.list_gse_samples('GSE62003')
print(f"Found {len(gsm_ids)} samples")

# Check data availability
status = reader.get_gsm_data_status('GSM1518180')
# 0 = data on page, 1 = IDAT files, -1 = no data

Configuration

PyNCBI can be configured via environment variables or programmatically.

Environment Variables

Variable	Default	Description
PYNCBI_CACHE_FOLDER	~/.pyncbi/cache	Cache directory
PYNCBI_REQUEST_TIMEOUT	30	HTTP timeout in seconds
PYNCBI_LOG_LEVEL	INFO	Logging level

Programmatic Configuration

from PyNCBI import get_config, set_config, Config

# View current configuration
config = get_config()
print(config.cache_folder)
print(config.request_timeout)

# Update configuration
set_config(Config(
    cache_folder='/custom/cache/path',
    request_timeout=60.0
))

Logging

PyNCBI includes a configurable logging system with colored output.

Quick Setup

from PyNCBI import configure_logging, silence, verbose, LogLevel

# Enable verbose output
verbose()

# Silence all output
silence()

# Set specific level
configure_logging(level=LogLevel.DEBUG)

Temporary Log Level

from PyNCBI import log_level, LogLevel, GSM

# Temporarily change log level
with log_level(LogLevel.DEBUG):
    gsm = GSM('GSM1518180')  # Verbose output

# Back to normal level
gsm2 = GSM('GSM1518181')  # Normal output

Log Levels

Level	Description
LogLevel.DEBUG	Detailed debugging information
LogLevel.INFO	General progress information
LogLevel.WARNING	Warnings about potential issues
LogLevel.ERROR	Error messages
LogLevel.CRITICAL	Critical errors
LogLevel.SILENT	Suppress all output

Environment Variable

# Set log level via environment
export PYNCBI_LOG_LEVEL=DEBUG

Exception Handling

PyNCBI provides a comprehensive exception hierarchy for robust error handling.

Exception Hierarchy

PyNCBIError (base)
├── NetworkError
│   ├── ConnectionFailedError
│   ├── RequestTimeoutError
│   ├── HTTPError
│   └── DownloadError
├── DataError
│   ├── ParseError / SOFTParseError
│   ├── NoDataAvailableError
│   ├── InvalidAccessionError
│   └── DataProcessingError
├── CacheError
│   ├── CacheCorruptedError
│   ├── CacheNotFoundError
│   └── CacheWriteError
└── ConfigurationError
    ├── InvalidModeError
    └── UnsupportedArrayTypeError

Basic Error Handling

from PyNCBI import GSM
from PyNCBI.exceptions import PyNCBIError, NoDataAvailableError, NetworkError

try:
    gsm = GSM('GSM123456')
except NoDataAvailableError as e:
    print(f"No data available for {e.accession}")
    print(f"Hint: {e.hint}")
except NetworkError as e:
    print(f"Network error: {e.message}")
    print(f"URL: {e.url}")
except PyNCBIError as e:
    print(f"PyNCBI error: {e.message}")

Handling Transient Errors with Retry

from PyNCBI import GSM
from PyNCBI.exceptions import TRANSIENT_ERRORS
import time

def fetch_with_retry(gsm_id, max_retries=3):
    for attempt in range(max_retries):
        try:
            return GSM(gsm_id)
        except TRANSIENT_ERRORS as e:
            if attempt < max_retries - 1:
                print(f"Attempt {attempt + 1} failed, retrying...")
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise

Exception Groups

Group	Exceptions	Use Case
TRANSIENT_ERRORS	Connection, Timeout, HTTP	Worth retrying
USER_ERRORS	InvalidAccession, InvalidMode	Fix user input
DATA_ERRORS	NoData, ParseError, Processing	Data issues

Features

Currently Supported

• GSE and GSM card information extraction
• Methylation beta value download and parsing
• Multiple fetch modes (per_gsm, supplementary)
• IDAT file processing via methylprep
• Automatic caching with inspection API
• Configurable logging with colors
• Comprehensive exception hierarchy
• Full type hints (py.typed)

Supported Platforms

Platform	Array Type	Description
GPL8490	27k	Illumina HumanMethylation27
GPL13534	450k	Illumina HumanMethylation450
GPL16304	450k	(Alternative)
GPL21145	EPIC	Illumina MethylationEPIC
GPL23976	EPIC+	Illumina MethylationEPIC v2.0

Roadmap

See the open issues for a list of proposed features and known issues.

Planned Features

• Async/concurrent downloads
• Additional array platform support
• Data quality metrics
• Export to various formats

Contributing

Contributions are what make the open-source community such a powerful place to create new ideas, inspire, and make progress. Any contributions you make are greatly appreciated.

1. Fork the Project
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

Development Setup

# Clone the repository
git clone https://github.com/MuteJester/PyNCBI.git
cd PyNCBI

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/

# Run type checking
mypy src/PyNCBI/

# Run linting
ruff check src/PyNCBI/

License

Distributed under the MIT license. See LICENSE for more information.

Contact

Thomas Konstantinovsky - thomaskon90@gmail.com

Project Link: https://github.com/MuteJester/PyNCBI