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---
name: geo-database
description: "Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis."
---
# GEO Database
## Overview
The Gene Expression Omnibus (GEO) is NCBI's public repository for high-throughput gene expression and functional genomics data. GEO contains over 264,000 studies with more than 8 million samples from both array-based and sequence-based experiments.
## When to Use This Skill
This skill should be used when searching for gene expression datasets, retrieving experimental data, downloading raw and processed files, querying expression profiles, or integrating GEO data into computational analysis workflows.
## Core Capabilities
### 1. Understanding GEO Data Organization
GEO organizes data hierarchically using different accession types:
**Series (GSE):** A complete experiment with a set of related samples
- Example: GSE123456
- Contains experimental design, samples, and overall study information
- Largest organizational unit in GEO
- Current count: 264,928+ series
**Sample (GSM):** A single experimental sample or biological replicate
- Example: GSM987654
- Contains individual sample data, protocols, and metadata
- Linked to platforms and series
- Current count: 8,068,632+ samples
**Platform (GPL):** The microarray or sequencing platform used
- Example: GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array)
- Describes the technology and probe/feature annotations
- Shared across multiple experiments
- Current count: 27,739+ platforms
**DataSet (GDS):** Curated collections with consistent formatting
- Example: GDS5678
- Experimentally-comparable samples organized by study design
- Processed for differential analysis
- Subset of GEO data (4,348 curated datasets)
- Ideal for quick comparative analyses
**Profiles:** Gene-specific expression data linked to sequence features
- Queryable by gene name or annotation
- Cross-references to Entrez Gene
- Enables gene-centric searches across all studies
### 2. Searching GEO Data
**GEO DataSets Search:**
Search for studies by keywords, organism, or experimental conditions:
```python
from Bio import Entrez
# Configure Entrez (required)
Entrez.email = "your.email@example.com"
# Search for datasets
def search_geo_datasets(query, retmax=20):
"""Search GEO DataSets database"""
handle = Entrez.esearch(
db="gds",
term=query,
retmax=retmax,
usehistory="y"
)
results = Entrez.read(handle)
handle.close()
return results
# Example searches
results = search_geo_datasets("breast cancer[MeSH] AND Homo sapiens[Organism]")
print(f"Found {results['Count']} datasets")
# Search by specific platform
results = search_geo_datasets("GPL570[Accession]")
# Search by study type
results = search_geo_datasets("expression profiling by array[DataSet Type]")
```
**GEO Profiles Search:**
Find gene-specific expression patterns:
```python
# Search for gene expression profiles
def search_geo_profiles(gene_name, organism="Homo sapiens", retmax=100):
"""Search GEO Profiles for a specific gene"""
query = f"{gene_name}[Gene Name] AND {organism}[Organism]"
handle = Entrez.esearch(
db="geoprofiles",
term=query,
retmax=retmax
)
results = Entrez.read(handle)
handle.close()
return results
# Find TP53 expression across studies
tp53_results = search_geo_profiles("TP53", organism="Homo sapiens")
print(f"Found {tp53_results['Count']} expression profiles for TP53")
```
**Advanced Search Patterns:**
```python
# Combine multiple search terms
def advanced_geo_search(terms, operator="AND"):
"""Build complex search queries"""
query = f" {operator} ".join(terms)
return search_geo_datasets(query)
# Find recent high-throughput studies
search_terms = [
"RNA-seq[DataSet Type]",
"Homo sapiens[Organism]",
"2024[Publication Date]"
]
results = advanced_geo_search(search_terms)
# Search by author and condition
search_terms = [
"Smith[Author]",
"diabetes[Disease]"
]
results = advanced_geo_search(search_terms)
```
### 3. Retrieving GEO Data with GEOparse (Recommended)
**GEOparse** is the primary Python library for accessing GEO data:
**Installation:**
```bash
uv pip install GEOparse
```
**Basic Usage:**
```python
import GEOparse
# Download and parse a GEO Series
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Access series metadata
print(gse.metadata['title'])
print(gse.metadata['summary'])
print(gse.metadata['overall_design'])
# Access sample information
for gsm_name, gsm in gse.gsms.items():
print(f"Sample: {gsm_name}")
print(f" Title: {gsm.metadata['title'][0]}")
print(f" Source: {gsm.metadata['source_name_ch1'][0]}")
print(f" Characteristics: {gsm.metadata.get('characteristics_ch1', [])}")
# Access platform information
for gpl_name, gpl in gse.gpls.items():
print(f"Platform: {gpl_name}")
print(f" Title: {gpl.metadata['title'][0]}")
print(f" Organism: {gpl.metadata['organism'][0]}")
```
**Working with Expression Data:**
```python
import GEOparse
import pandas as pd
# Get expression data from series
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Extract expression matrix
# Method 1: From series matrix file (fastest)
if hasattr(gse, 'pivot_samples'):
expression_df = gse.pivot_samples('VALUE')
print(expression_df.shape) # genes x samples
# Method 2: From individual samples
expression_data = {}
for gsm_name, gsm in gse.gsms.items():
if hasattr(gsm, 'table'):
expression_data[gsm_name] = gsm.table['VALUE']
expression_df = pd.DataFrame(expression_data)
print(f"Expression matrix: {expression_df.shape}")
```
**Accessing Supplementary Files:**
```python
import GEOparse
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Download supplementary files
gse.download_supplementary_files(
directory="./data/GSE123456_suppl",
download_sra=False # Set to True to download SRA files
)
# List available supplementary files
for gsm_name, gsm in gse.gsms.items():
if hasattr(gsm, 'supplementary_files'):
print(f"Sample {gsm_name}:")
for file_url in gsm.metadata.get('supplementary_file', []):
print(f" {file_url}")
```
**Filtering and Subsetting Data:**
```python
import GEOparse
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Filter samples by metadata
control_samples = [
gsm_name for gsm_name, gsm in gse.gsms.items()
if 'control' in gsm.metadata.get('title', [''])[0].lower()
]
treatment_samples = [
gsm_name for gsm_name, gsm in gse.gsms.items()
if 'treatment' in gsm.metadata.get('title', [''])[0].lower()
]
print(f"Control samples: {len(control_samples)}")
print(f"Treatment samples: {len(treatment_samples)}")
# Extract subset expression matrix
expression_df = gse.pivot_samples('VALUE')
control_expr = expression_df[control_samples]
treatment_expr = expression_df[treatment_samples]
```
### 4. Using NCBI E-utilities for GEO Access
**E-utilities** provide lower-level programmatic access to GEO metadata:
**Basic E-utilities Workflow:**
```python
from Bio import Entrez
import time
Entrez.email = "your.email@example.com"
# Step 1: Search for GEO entries
def search_geo(query, db="gds", retmax=100):
"""Search GEO using E-utilities"""
handle = Entrez.esearch(
db=db,
term=query,
retmax=retmax,
usehistory="y"
)
results = Entrez.read(handle)
handle.close()
return results
# Step 2: Fetch summaries
def fetch_geo_summaries(id_list, db="gds"):
"""Fetch document summaries for GEO entries"""
ids = ",".join(id_list)
handle = Entrez.esummary(db=db, id=ids)
summaries = Entrez.read(handle)
handle.close()
return summaries
# Step 3: Fetch full records
def fetch_geo_records(id_list, db="gds"):
"""Fetch full GEO records"""
ids = ",".join(id_list)
handle = Entrez.efetch(db=db, id=ids, retmode="xml")
records = Entrez.read(handle)
handle.close()
return records
# Example workflow
search_results = search_geo("breast cancer AND Homo sapiens")
id_list = search_results['IdList'][:5]
summaries = fetch_geo_summaries(id_list)
for summary in summaries:
print(f"GDS: {summary.get('Accession', 'N/A')}")
print(f"Title: {summary.get('title', 'N/A')}")
print(f"Samples: {summary.get('n_samples', 'N/A')}")
print()
```
**Batch Processing with E-utilities:**
```python
from Bio import Entrez
import time
Entrez.email = "your.email@example.com"
def batch_fetch_geo_metadata(accessions, batch_size=100):
"""Fetch metadata for multiple GEO accessions"""
results = {}
for i in range(0, len(accessions), batch_size):
batch = accessions[i:i + batch_size]
# Search for each accession
for accession in batch:
try:
query = f"{accession}[Accession]"
search_handle = Entrez.esearch(db="gds", term=query)
search_results = Entrez.read(search_handle)
search_handle.close()
if search_results['IdList']:
# Fetch summary
summary_handle = Entrez.esummary(
db="gds",
id=search_results['IdList'][0]
)
summary = Entrez.read(summary_handle)
summary_handle.close()
results[accession] = summary[0]
# Be polite to NCBI servers
time.sleep(0.34) # Max 3 requests per second
except Exception as e:
print(f"Error fetching {accession}: {e}")
return results
# Fetch metadata for multiple datasets
gse_list = ["GSE100001", "GSE100002", "GSE100003"]
metadata = batch_fetch_geo_metadata(gse_list)
```
### 5. Direct FTP Access for Data Files
**FTP URLs for GEO Data:**
GEO data can be downloaded directly via FTP:
```python
import ftplib
import os
def download_geo_ftp(accession, file_type="matrix", dest_dir="./data"):
"""Download GEO files via FTP"""
# Construct FTP path based on accession type
if accession.startswith("GSE"):
# Series files
gse_num = accession[3:]
base_num = gse_num[:-3] + "nnn"
ftp_path = f"/geo/series/GSE{base_num}/{accession}/"
if file_type == "matrix":
filename = f"{accession}_series_matrix.txt.gz"
elif file_type == "soft":
filename = f"{accession}_family.soft.gz"
elif file_type == "miniml":
filename = f"{accession}_family.xml.tgz"
# Connect to FTP server
ftp = ftplib.FTP("ftp.ncbi.nlm.nih.gov")
ftp.login()
ftp.cwd(ftp_path)
# Download file
os.makedirs(dest_dir, exist_ok=True)
local_file = os.path.join(dest_dir, filename)
with open(local_file, 'wb') as f:
ftp.retrbinary(f'RETR {filename}', f.write)
ftp.quit()
print(f"Downloaded: {local_file}")
return local_file
# Download series matrix file
download_geo_ftp("GSE123456", file_type="matrix")
# Download SOFT format file
download_geo_ftp("GSE123456", file_type="soft")
```
**Using wget or curl for Downloads:**
```bash
# Download series matrix file
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz
# Download all supplementary files for a series
wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/
# Download SOFT format family file
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz
```
### 6. Analyzing GEO Data
**Quality Control and Preprocessing:**
```python
import GEOparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load dataset
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
expression_df = gse.pivot_samples('VALUE')
# Check for missing values
print(f"Missing values: {expression_df.isnull().sum().sum()}")
# Log transformation (if needed)
if expression_df.min().min() > 0: # Check if already log-transformed
if expression_df.max().max() > 100:
expression_df = np.log2(expression_df + 1)
print("Applied log2 transformation")
# Distribution plots
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
expression_df.plot.box(ax=plt.gca())
plt.title("Expression Distribution per Sample")
plt.xticks(rotation=90)
plt.subplot(1, 2, 2)
expression_df.mean(axis=1).hist(bins=50)
plt.title("Gene Expression Distribution")
plt.xlabel("Average Expression")
plt.tight_layout()
plt.savefig("geo_qc.png", dpi=300, bbox_inches='tight')
```
**Differential Expression Analysis:**
```python
import GEOparse
import pandas as pd
import numpy as np
from scipy import stats
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
expression_df = gse.pivot_samples('VALUE')
# Define sample groups
control_samples = ["GSM1", "GSM2", "GSM3"]
treatment_samples = ["GSM4", "GSM5", "GSM6"]
# Calculate fold changes and p-values
results = []
for gene in expression_df.index:
control_expr = expression_df.loc[gene, control_samples]
treatment_expr = expression_df.loc[gene, treatment_samples]
# Calculate statistics
fold_change = treatment_expr.mean() - control_expr.mean()
t_stat, p_value = stats.ttest_ind(treatment_expr, control_expr)
results.append({
'gene': gene,
'log2_fold_change': fold_change,
'p_value': p_value,
'control_mean': control_expr.mean(),
'treatment_mean': treatment_expr.mean()
})
# Create results DataFrame
de_results = pd.DataFrame(results)
# Multiple testing correction (Benjamini-Hochberg)
from statsmodels.stats.multitest import multipletests
_, de_results['q_value'], _, _ = multipletests(
de_results['p_value'],
method='fdr_bh'
)
# Filter significant genes
significant_genes = de_results[
(de_results['q_value'] < 0.05) &
(abs(de_results['log2_fold_change']) > 1)
]
print(f"Significant genes: {len(significant_genes)}")
significant_genes.to_csv("de_results.csv", index=False)
```
**Correlation and Clustering Analysis:**
```python
import GEOparse
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.cluster import hierarchy
from scipy.spatial.distance import pdist
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
expression_df = gse.pivot_samples('VALUE')
# Sample correlation heatmap
sample_corr = expression_df.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(sample_corr, cmap='coolwarm', center=0,
square=True, linewidths=0.5)
plt.title("Sample Correlation Matrix")
plt.tight_layout()
plt.savefig("sample_correlation.png", dpi=300, bbox_inches='tight')
# Hierarchical clustering
distances = pdist(expression_df.T, metric='correlation')
linkage = hierarchy.linkage(distances, method='average')
plt.figure(figsize=(12, 6))
hierarchy.dendrogram(linkage, labels=expression_df.columns)
plt.title("Hierarchical Clustering of Samples")
plt.xlabel("Samples")
plt.ylabel("Distance")
plt.xticks(rotation=90)
plt.tight_layout()
plt.savefig("sample_clustering.png", dpi=300, bbox_inches='tight')
```
### 7. Batch Processing Multiple Datasets
**Download and Process Multiple Series:**
```python
import GEOparse
import pandas as pd
import os
def batch_download_geo(gse_list, destdir="./geo_data"):
"""Download multiple GEO series"""
results = {}
for gse_id in gse_list:
try:
print(f"Processing {gse_id}...")
gse = GEOparse.get_GEO(geo=gse_id, destdir=destdir)
# Extract key information
results[gse_id] = {
'title': gse.metadata.get('title', ['N/A'])[0],
'organism': gse.metadata.get('organism', ['N/A'])[0],
'platform': list(gse.gpls.keys())[0] if gse.gpls else 'N/A',
'num_samples': len(gse.gsms),
'submission_date': gse.metadata.get('submission_date', ['N/A'])[0]
}
# Save expression data
if hasattr(gse, 'pivot_samples'):
expr_df = gse.pivot_samples('VALUE')
expr_df.to_csv(f"{destdir}/{gse_id}_expression.csv")
results[gse_id]['num_genes'] = len(expr_df)
except Exception as e:
print(f"Error processing {gse_id}: {e}")
results[gse_id] = {'error': str(e)}
# Save summary
summary_df = pd.DataFrame(results).T
summary_df.to_csv(f"{destdir}/batch_summary.csv")
return results
# Process multiple datasets
gse_list = ["GSE100001", "GSE100002", "GSE100003"]
results = batch_download_geo(gse_list)
```
**Meta-Analysis Across Studies:**
```python
import GEOparse
import pandas as pd
import numpy as np
def meta_analysis_geo(gse_list, gene_of_interest):
"""Perform meta-analysis of gene expression across studies"""
results = []
for gse_id in gse_list:
try:
gse = GEOparse.get_GEO(geo=gse_id, destdir="./data")
# Get platform annotation
gpl = list(gse.gpls.values())[0]
# Find gene in platform
if hasattr(gpl, 'table'):
gene_probes = gpl.table[
gpl.table['Gene Symbol'].str.contains(
gene_of_interest,
case=False,
na=False
)
]
if not gene_probes.empty:
expr_df = gse.pivot_samples('VALUE')
for probe_id in gene_probes['ID']:
if probe_id in expr_df.index:
expr_values = expr_df.loc[probe_id]
results.append({
'study': gse_id,
'probe': probe_id,
'mean_expression': expr_values.mean(),
'std_expression': expr_values.std(),
'num_samples': len(expr_values)
})
except Exception as e:
print(f"Error in {gse_id}: {e}")
return pd.DataFrame(results)
# Meta-analysis for TP53
gse_studies = ["GSE100001", "GSE100002", "GSE100003"]
meta_results = meta_analysis_geo(gse_studies, "TP53")
print(meta_results)
```
## Installation and Setup
### Python Libraries
```bash
# Primary GEO access library (recommended)
uv pip install GEOparse
# For E-utilities and programmatic NCBI access
uv pip install biopython
# For data analysis
uv pip install pandas numpy scipy
# For visualization
uv pip install matplotlib seaborn
# For statistical analysis
uv pip install statsmodels scikit-learn
```
### Configuration
Set up NCBI E-utilities access:
```python
from Bio import Entrez
# Always set your email (required by NCBI)
Entrez.email = "your.email@example.com"
# Optional: Set API key for increased rate limits
# Get your API key from: https://www.ncbi.nlm.nih.gov/account/
Entrez.api_key = "your_api_key_here"
# With API key: 10 requests/second
# Without API key: 3 requests/second
```
## Common Use Cases
### Transcriptomics Research
- Download gene expression data for specific conditions
- Compare expression profiles across studies
- Identify differentially expressed genes
- Perform meta-analyses across multiple datasets
### Drug Response Studies
- Analyze gene expression changes after drug treatment
- Identify biomarkers for drug response
- Compare drug effects across cell lines or patients
- Build predictive models for drug sensitivity
### Disease Biology
- Study gene expression in disease vs. normal tissues
- Identify disease-associated expression signatures
- Compare patient subgroups and disease stages
- Correlate expression with clinical outcomes
### Biomarker Discovery
- Screen for diagnostic or prognostic markers
- Validate biomarkers across independent cohorts
- Compare marker performance across platforms
- Integrate expression with clinical data
## Key Concepts
**SOFT (Simple Omnibus Format in Text):** GEO's primary text-based format containing metadata and data tables. Easily parsed by GEOparse.
**MINiML (MIAME Notation in Markup Language):** XML format for GEO data, used for programmatic access and data exchange.
**Series Matrix:** Tab-delimited expression matrix with samples as columns and genes/probes as rows. Fastest format for getting expression data.
**MIAME Compliance:** Minimum Information About a Microarray Experiment - standardized annotation that GEO enforces for all submissions.
**Expression Value Types:** Different types of expression measurements (raw signal, normalized, log-transformed). Always check platform and processing methods.
**Platform Annotation:** Maps probe/feature IDs to genes. Essential for biological interpretation of expression data.
## GEO2R Web Tool
For quick analysis without coding, use GEO2R:
- Web-based statistical analysis tool integrated into GEO
- Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx
- Performs differential expression analysis
- Generates R scripts for reproducibility
- Useful for exploratory analysis before downloading data
## Rate Limiting and Best Practices
**NCBI E-utilities Rate Limits:**
- Without API key: 3 requests per second
- With API key: 10 requests per second
- Implement delays between requests: `time.sleep(0.34)` (no API key) or `time.sleep(0.1)` (with API key)
**FTP Access:**
- No rate limits for FTP downloads
- Preferred method for bulk downloads
- Can download entire directories with wget -r
**GEOparse Caching:**
- GEOparse automatically caches downloaded files in destdir
- Subsequent calls use cached data
- Clean cache periodically to save disk space
**Optimal Practices:**
- Use GEOparse for series-level access (easiest)
- Use E-utilities for metadata searching and batch queries
- Use FTP for direct file downloads and bulk operations
- Cache data locally to avoid repeated downloads
- Always set Entrez.email when using Biopython
## Resources
### references/geo_reference.md
Comprehensive reference documentation covering:
- Detailed E-utilities API specifications and endpoints
- Complete SOFT and MINiML file format documentation
- Advanced GEOparse usage patterns and examples
- FTP directory structure and file naming conventions
- Data processing pipelines and normalization methods
- Troubleshooting common issues and error handling
- Platform-specific considerations and quirks
Consult this reference for in-depth technical details, complex query patterns, or when working with uncommon data formats.
## Important Notes
### Data Quality Considerations
- GEO accepts user-submitted data with varying quality standards
- Always check platform annotation and processing methods
- Verify sample metadata and experimental design
- Be cautious with batch effects across studies
- Consider reprocessing raw data for consistency
### File Size Warnings
- Series matrix files can be large (>1 GB for large studies)
- Supplementary files (e.g., CEL files) can be very large
- Plan for adequate disk space before downloading
- Consider downloading samples incrementally
### Data Usage and Citation
- GEO data is freely available for research use
- Always cite original studies when using GEO data
- Cite GEO database: Barrett et al. (2013) Nucleic Acids Research
- Check individual dataset usage restrictions (if any)
- Follow NCBI guidelines for programmatic access
### Common Pitfalls
- Different platforms use different probe IDs (requires annotation mapping)
- Expression values may be raw, normalized, or log-transformed (check metadata)
- Sample metadata can be inconsistently formatted across studies
- Not all series have series matrix files (older submissions)
- Platform annotations may be outdated (genes renamed, IDs deprecated)
## Additional Resources
- **GEO Website:** https://www.ncbi.nlm.nih.gov/geo/
- **GEO Submission Guidelines:** https://www.ncbi.nlm.nih.gov/geo/info/submission.html
- **GEOparse Documentation:** https://geoparse.readthedocs.io/
- **E-utilities Documentation:** https://www.ncbi.nlm.nih.gov/books/NBK25501/
- **GEO FTP Site:** ftp://ftp.ncbi.nlm.nih.gov/geo/
- **GEO2R Tool:** https://www.ncbi.nlm.nih.gov/geo/geo2r/
- **NCBI API Keys:** https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/
- **Biopython Tutorial:** https://biopython.org/DIST/docs/tutorial/Tutorial.html

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# GEO Database Reference Documentation
## Complete E-utilities API Specifications
### Overview
The NCBI Entrez Programming Utilities (E-utilities) provide programmatic access to GEO metadata through a set of nine server-side programs. All E-utilities return results in XML format by default.
### Base URL
```
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/
```
### Core E-utility Programs
#### eSearch - Text Query to ID List
**Purpose:** Search a database and return a list of UIDs matching the query.
**URL Pattern:**
```
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi
```
**Parameters:**
- `db` (required): Database to search (e.g., "gds", "geoprofiles")
- `term` (required): Search query string
- `retmax`: Maximum number of UIDs to return (default: 20, max: 10000)
- `retstart`: Starting position in result set (for pagination)
- `usehistory`: Set to "y" to store results on history server
- `sort`: Sort order (e.g., "relevance", "pub_date")
- `field`: Limit search to specific field
- `datetype`: Type of date to limit by
- `reldate`: Limit to items within N days of today
- `mindate`, `maxdate`: Date range limits (YYYY/MM/DD)
**Example:**
```python
from Bio import Entrez
Entrez.email = "your@email.com"
# Basic search
handle = Entrez.esearch(
db="gds",
term="breast cancer AND Homo sapiens",
retmax=100,
usehistory="y"
)
results = Entrez.read(handle)
handle.close()
# Results contain:
# - Count: Total number of matches
# - RetMax: Number of UIDs returned
# - RetStart: Starting position
# - IdList: List of UIDs
# - QueryKey: Key for history server (if usehistory="y")
# - WebEnv: Web environment string (if usehistory="y")
```
#### eSummary - Document Summaries
**Purpose:** Retrieve document summaries for a list of UIDs.
**URL Pattern:**
```
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi
```
**Parameters:**
- `db` (required): Database
- `id` (required): Comma-separated list of UIDs or query_key+WebEnv
- `retmode`: Return format ("xml" or "json")
- `version`: Summary version ("2.0" recommended)
**Example:**
```python
from Bio import Entrez
Entrez.email = "your@email.com"
# Get summaries for multiple IDs
handle = Entrez.esummary(
db="gds",
id="200000001,200000002",
retmode="xml",
version="2.0"
)
summaries = Entrez.read(handle)
handle.close()
# Summary fields for GEO DataSets:
# - Accession: GDS accession
# - title: Dataset title
# - summary: Dataset description
# - PDAT: Publication date
# - n_samples: Number of samples
# - Organism: Source organism
# - PubMedIds: Associated PubMed IDs
```
#### eFetch - Full Records
**Purpose:** Retrieve full records for a list of UIDs.
**URL Pattern:**
```
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi
```
**Parameters:**
- `db` (required): Database
- `id` (required): Comma-separated list of UIDs
- `retmode`: Return format ("xml", "text")
- `rettype`: Record type (database-specific)
**Example:**
```python
from Bio import Entrez
Entrez.email = "your@email.com"
# Fetch full records
handle = Entrez.efetch(
db="gds",
id="200000001",
retmode="xml"
)
records = Entrez.read(handle)
handle.close()
```
#### eLink - Cross-Database Linking
**Purpose:** Find related records in same or different databases.
**URL Pattern:**
```
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi
```
**Parameters:**
- `dbfrom` (required): Source database
- `db` (required): Target database
- `id` (required): UID from source database
- `cmd`: Link command type
- "neighbor": Return linked UIDs (default)
- "neighbor_score": Return scored links
- "acheck": Check for links
- "ncheck": Count links
- "llinks": Return URLs to LinkOut resources
**Example:**
```python
from Bio import Entrez
Entrez.email = "your@email.com"
# Find PubMed articles linked to a GEO dataset
handle = Entrez.elink(
dbfrom="gds",
db="pubmed",
id="200000001"
)
links = Entrez.read(handle)
handle.close()
```
#### ePost - Upload UID List
**Purpose:** Upload a list of UIDs to the history server for use in subsequent requests.
**URL Pattern:**
```
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/epost.fcgi
```
**Parameters:**
- `db` (required): Database
- `id` (required): Comma-separated list of UIDs
**Example:**
```python
from Bio import Entrez
Entrez.email = "your@email.com"
# Post large list of IDs
large_id_list = [str(i) for i in range(200000001, 200000101)]
handle = Entrez.epost(db="gds", id=",".join(large_id_list))
result = Entrez.read(handle)
handle.close()
# Use returned QueryKey and WebEnv in subsequent calls
query_key = result["QueryKey"]
webenv = result["WebEnv"]
```
#### eInfo - Database Information
**Purpose:** Get information about available databases and their fields.
**URL Pattern:**
```
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/einfo.fcgi
```
**Parameters:**
- `db`: Database name (omit to get list of all databases)
- `version`: Set to "2.0" for detailed field information
**Example:**
```python
from Bio import Entrez
Entrez.email = "your@email.com"
# Get information about gds database
handle = Entrez.einfo(db="gds", version="2.0")
info = Entrez.read(handle)
handle.close()
# Returns:
# - Database description
# - Last update date
# - Record count
# - Available search fields
# - Link information
```
### Search Field Qualifiers for GEO
Common search fields for building targeted queries:
**General Fields:**
- `[Accession]`: GEO accession number
- `[Title]`: Dataset title
- `[Author]`: Author name
- `[Organism]`: Source organism
- `[Entry Type]`: Type of entry (e.g., "Expression profiling by array")
- `[Platform]`: Platform accession or name
- `[PubMed ID]`: Associated PubMed ID
**Date Fields:**
- `[Publication Date]`: Publication date (YYYY or YYYY/MM/DD)
- `[Submission Date]`: Submission date
- `[Modification Date]`: Last modification date
**MeSH Terms:**
- `[MeSH Terms]`: Medical Subject Headings
- `[MeSH Major Topic]`: Major MeSH topics
**Study Type Fields:**
- `[DataSet Type]`: Type of study (e.g., "RNA-seq", "ChIP-seq")
- `[Sample Type]`: Sample type
**Example Complex Query:**
```python
query = """
(breast cancer[MeSH] OR breast neoplasms[Title]) AND
Homo sapiens[Organism] AND
expression profiling by array[Entry Type] AND
2020:2024[Publication Date] AND
GPL570[Platform]
"""
```
## SOFT File Format Specification
### Overview
SOFT (Simple Omnibus Format in Text) is GEO's primary data exchange format. Files are structured as key-value pairs with data tables.
### File Types
**Family SOFT Files:**
- Filename: `GSExxxxx_family.soft.gz`
- Contains: Complete series with all samples and platforms
- Size: Can be very large (100s of MB compressed)
- Use: Complete data extraction
**Series Matrix Files:**
- Filename: `GSExxxxx_series_matrix.txt.gz`
- Contains: Expression matrix with minimal metadata
- Size: Smaller than family files
- Use: Quick access to expression data
**Platform SOFT Files:**
- Filename: `GPLxxxxx.soft`
- Contains: Platform annotation and probe information
- Use: Mapping probes to genes
### SOFT File Structure
```
^DATABASE = GeoMiame
!Database_name = Gene Expression Omnibus (GEO)
!Database_institute = NCBI NLM NIH
!Database_web_link = http://www.ncbi.nlm.nih.gov/geo
!Database_email = geo@ncbi.nlm.nih.gov
^SERIES = GSExxxxx
!Series_title = Study Title Here
!Series_summary = Study description and background...
!Series_overall_design = Experimental design...
!Series_type = Expression profiling by array
!Series_pubmed_id = 12345678
!Series_submission_date = Jan 01 2024
!Series_last_update_date = Jan 15 2024
!Series_contributor = John,Doe
!Series_contributor = Jane,Smith
!Series_sample_id = GSMxxxxxx
!Series_sample_id = GSMxxxxxx
^PLATFORM = GPLxxxxx
!Platform_title = Platform Name
!Platform_distribution = commercial or custom
!Platform_organism = Homo sapiens
!Platform_manufacturer = Affymetrix
!Platform_technology = in situ oligonucleotide
!Platform_data_row_count = 54675
#ID = Probe ID
#GB_ACC = GenBank accession
#SPOT_ID = Spot identifier
#Gene Symbol = Gene symbol
#Gene Title = Gene title
!platform_table_begin
ID GB_ACC SPOT_ID Gene Symbol Gene Title
1007_s_at U48705 - DDR1 discoidin domain receptor...
1053_at M87338 - RFC2 replication factor C...
!platform_table_end
^SAMPLE = GSMxxxxxx
!Sample_title = Sample name
!Sample_source_name_ch1 = cell line XYZ
!Sample_organism_ch1 = Homo sapiens
!Sample_characteristics_ch1 = cell type: epithelial
!Sample_characteristics_ch1 = treatment: control
!Sample_molecule_ch1 = total RNA
!Sample_label_ch1 = biotin
!Sample_platform_id = GPLxxxxx
!Sample_data_processing = normalization method
#ID_REF = Probe identifier
#VALUE = Expression value
!sample_table_begin
ID_REF VALUE
1007_s_at 8.456
1053_at 7.234
!sample_table_end
```
### Parsing SOFT Files
**With GEOparse:**
```python
import GEOparse
# Parse series
gse = GEOparse.get_GEO(filepath="GSE123456_family.soft.gz")
# Access metadata
metadata = gse.metadata
phenotype_data = gse.phenotype_data
# Access samples
for gsm_name, gsm in gse.gsms.items():
sample_data = gsm.table
sample_metadata = gsm.metadata
# Access platforms
for gpl_name, gpl in gse.gpls.items():
platform_table = gpl.table
platform_metadata = gpl.metadata
```
**Manual Parsing:**
```python
import gzip
def parse_soft_file(filename):
"""Basic SOFT file parser"""
sections = {}
current_section = None
current_metadata = {}
current_table = []
in_table = False
with gzip.open(filename, 'rt') as f:
for line in f:
line = line.strip()
# New section
if line.startswith('^'):
if current_section:
sections[current_section] = {
'metadata': current_metadata,
'table': current_table
}
parts = line[1:].split(' = ')
current_section = parts[1] if len(parts) > 1 else parts[0]
current_metadata = {}
current_table = []
in_table = False
# Metadata
elif line.startswith('!'):
if in_table:
in_table = False
key_value = line[1:].split(' = ', 1)
if len(key_value) == 2:
key, value = key_value
if key in current_metadata:
if isinstance(current_metadata[key], list):
current_metadata[key].append(value)
else:
current_metadata[key] = [current_metadata[key], value]
else:
current_metadata[key] = value
# Table data
elif line.startswith('#') or in_table:
in_table = True
current_table.append(line)
return sections
```
## MINiML File Format
### Overview
MINiML (MIAME Notation in Markup Language) is GEO's XML-based format for data exchange.
### File Structure
```xml
<?xml version="1.0" encoding="UTF-8"?>
<MINiML xmlns="http://www.ncbi.nlm.nih.gov/geo/info/MINiML"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<Series iid="GDS123">
<Status>
<Submission-Date>2024-01-01</Submission-Date>
<Release-Date>2024-01-15</Release-Date>
<Last-Update-Date>2024-01-15</Last-Update-Date>
</Status>
<Title>Study Title</Title>
<Summary>Study description...</Summary>
<Overall-Design>Experimental design...</Overall-Design>
<Type>Expression profiling by array</Type>
<Contributor>
<Person>
<First>John</First>
<Last>Doe</Last>
</Person>
</Contributor>
</Series>
<Platform iid="GPL123">
<Title>Platform Name</Title>
<Distribution>commercial</Distribution>
<Technology>in situ oligonucleotide</Technology>
<Organism taxid="9606">Homo sapiens</Organism>
<Data-Table>
<Column position="1">
<Name>ID</Name>
<Description>Probe identifier</Description>
</Column>
<Data>
<Row>
<Cell column="1">1007_s_at</Cell>
<Cell column="2">U48705</Cell>
</Row>
</Data>
</Data-Table>
</Platform>
<Sample iid="GSM123">
<Title>Sample name</Title>
<Source>cell line XYZ</Source>
<Organism taxid="9606">Homo sapiens</Organism>
<Characteristics tag="cell type">epithelial</Characteristics>
<Characteristics tag="treatment">control</Characteristics>
<Platform-Ref ref="GPL123"/>
<Data-Table>
<Column position="1">
<Name>ID_REF</Name>
</Column>
<Column position="2">
<Name>VALUE</Name>
</Column>
<Data>
<Row>
<Cell column="1">1007_s_at</Cell>
<Cell column="2">8.456</Cell>
</Row>
</Data>
</Data-Table>
</Sample>
</MINiML>
```
## FTP Directory Structure
### Series Files
**Pattern:**
```
ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE{nnn}nnn/GSE{xxxxx}/
```
Where `{nnn}` represents replacing last 3 digits with "nnn" and `{xxxxx}` is the full accession.
**Example:**
- GSE123456 → `/geo/series/GSE123nnn/GSE123456/`
- GSE1234 → `/geo/series/GSE1nnn/GSE1234/`
- GSE100001 → `/geo/series/GSE100nnn/GSE100001/`
**Subdirectories:**
- `/matrix/` - Series matrix files
- `/soft/` - Family SOFT files
- `/miniml/` - MINiML XML files
- `/suppl/` - Supplementary files
**File Types:**
```
matrix/
└── GSE123456_series_matrix.txt.gz
soft/
└── GSE123456_family.soft.gz
miniml/
└── GSE123456_family.xml.tgz
suppl/
├── GSE123456_RAW.tar
├── filelist.txt
└── [various supplementary files]
```
### Sample Files
**Pattern:**
```
ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM{nnn}nnn/GSM{xxxxx}/
```
**Subdirectories:**
- `/suppl/` - Sample-specific supplementary files
### Platform Files
**Pattern:**
```
ftp://ftp.ncbi.nlm.nih.gov/geo/platforms/GPL{nnn}nnn/GPL{xxxxx}/
```
**File Types:**
```
soft/
└── GPL570.soft.gz
miniml/
└── GPL570.xml
annot/
└── GPL570.annot.gz # Enhanced annotation (if available)
```
## Advanced GEOparse Usage
### Custom Parsing Options
```python
import GEOparse
# Parse with custom options
gse = GEOparse.get_GEO(
geo="GSE123456",
destdir="./data",
silent=False, # Show progress
how="full", # Parse mode: "full", "quick", "brief"
annotate_gpl=True, # Include platform annotation
geotype="GSE" # Explicit type
)
# Access specific sample
gsm = gse.gsms['GSM1234567']
# Get expression values for specific probe
probe_id = "1007_s_at"
if hasattr(gsm, 'table'):
probe_data = gsm.table[gsm.table['ID_REF'] == probe_id]
# Get all characteristics
characteristics = {}
for key, values in gsm.metadata.items():
if key.startswith('characteristics'):
for value in (values if isinstance(values, list) else [values]):
if ':' in value:
char_key, char_value = value.split(':', 1)
characteristics[char_key.strip()] = char_value.strip()
```
### Working with Platform Annotations
```python
import GEOparse
import pandas as pd
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Get platform
gpl = list(gse.gpls.values())[0]
# Extract annotation table
if hasattr(gpl, 'table'):
annotation = gpl.table
# Common annotation columns:
# - ID: Probe identifier
# - Gene Symbol: Gene symbol
# - Gene Title: Gene description
# - GB_ACC: GenBank accession
# - Gene ID: Entrez Gene ID
# - RefSeq: RefSeq accession
# - UniGene: UniGene cluster
# Map probes to genes
probe_to_gene = dict(zip(
annotation['ID'],
annotation['Gene Symbol']
))
# Handle multiple probes per gene
gene_to_probes = {}
for probe, gene in probe_to_gene.items():
if gene and gene != '---':
if gene not in gene_to_probes:
gene_to_probes[gene] = []
gene_to_probes[gene].append(probe)
```
### Handling Large Datasets
```python
import GEOparse
import pandas as pd
import numpy as np
def process_large_gse(gse_id, chunk_size=1000):
"""Process large GEO series in chunks"""
gse = GEOparse.get_GEO(geo=gse_id, destdir="./data")
# Get sample list
sample_list = list(gse.gsms.keys())
# Process in chunks
for i in range(0, len(sample_list), chunk_size):
chunk_samples = sample_list[i:i+chunk_size]
# Extract data for chunk
chunk_data = {}
for gsm_id in chunk_samples:
gsm = gse.gsms[gsm_id]
if hasattr(gsm, 'table'):
chunk_data[gsm_id] = gsm.table['VALUE']
# Process chunk
chunk_df = pd.DataFrame(chunk_data)
# Save chunk results
chunk_df.to_csv(f"chunk_{i//chunk_size}.csv")
print(f"Processed {i+len(chunk_samples)}/{len(sample_list)} samples")
```
## Troubleshooting Common Issues
### Issue: GEOparse Fails to Download
**Symptoms:** Timeout errors, connection failures
**Solutions:**
1. Check internet connection
2. Try downloading directly via FTP first
3. Parse local files:
```python
gse = GEOparse.get_GEO(filepath="./local/GSE123456_family.soft.gz")
```
4. Increase timeout (modify GEOparse source if needed)
### Issue: Missing Expression Data
**Symptoms:** `pivot_samples()` fails or returns empty
**Cause:** Not all series have series matrix files (older submissions)
**Solution:** Parse individual sample tables:
```python
expression_data = {}
for gsm_name, gsm in gse.gsms.items():
if hasattr(gsm, 'table') and 'VALUE' in gsm.table.columns:
expression_data[gsm_name] = gsm.table.set_index('ID_REF')['VALUE']
expression_df = pd.DataFrame(expression_data)
```
### Issue: Inconsistent Probe IDs
**Symptoms:** Probe IDs don't match between samples
**Cause:** Different platform versions or sample processing
**Solution:** Standardize using platform annotation:
```python
# Get common probe set
all_probes = set()
for gsm in gse.gsms.values():
if hasattr(gsm, 'table'):
all_probes.update(gsm.table['ID_REF'].values)
# Create standardized matrix
standardized_data = {}
for gsm_name, gsm in gse.gsms.items():
if hasattr(gsm, 'table'):
sample_data = gsm.table.set_index('ID_REF')['VALUE']
standardized_data[gsm_name] = sample_data.reindex(all_probes)
expression_df = pd.DataFrame(standardized_data)
```
### Issue: E-utilities Rate Limiting
**Symptoms:** HTTP 429 errors, slow responses
**Solution:**
1. Get an API key from NCBI
2. Implement rate limiting:
```python
import time
from functools import wraps
def rate_limit(calls_per_second=3):
min_interval = 1.0 / calls_per_second
def decorator(func):
last_called = [0.0]
@wraps(func)
def wrapper(*args, **kwargs):
elapsed = time.time() - last_called[0]
wait_time = min_interval - elapsed
if wait_time > 0:
time.sleep(wait_time)
result = func(*args, **kwargs)
last_called[0] = time.time()
return result
return wrapper
return decorator
@rate_limit(calls_per_second=3)
def safe_esearch(query):
handle = Entrez.esearch(db="gds", term=query)
results = Entrez.read(handle)
handle.close()
return results
```
### Issue: Memory Errors with Large Datasets
**Symptoms:** MemoryError, system slowdown
**Solution:**
1. Process data in chunks
2. Use sparse matrices for expression data
3. Load only necessary columns
4. Use memory-efficient data types:
```python
import pandas as pd
# Read with specific dtypes
expression_df = pd.read_csv(
"expression_matrix.csv",
dtype={'ID': str, 'GSM1': np.float32} # Use float32 instead of float64
)
# Or use sparse format for mostly-zero data
import scipy.sparse as sp
sparse_matrix = sp.csr_matrix(expression_df.values)
```
## Platform-Specific Considerations
### Affymetrix Arrays
- Probe IDs format: `1007_s_at`, `1053_at`
- Multiple probe sets per gene common
- Check for `_at`, `_s_at`, `_x_at` suffixes
- May need RMA or MAS5 normalization
### Illumina Arrays
- Probe IDs format: `ILMN_1234567`
- Watch for duplicate probes
- BeadChip-specific processing may be needed
### RNA-seq
- May not have traditional "probes"
- Check for gene IDs (Ensembl, Entrez)
- Counts vs. FPKM/TPM values
- May need separate count files
### Two-Channel Arrays
- Look for `_ch1` and `_ch2` suffixes in metadata
- VALUE_ch1, VALUE_ch2 columns
- May need ratio or intensity values
- Check dye-swap experiments
## Best Practices Summary
1. **Always set Entrez.email** before using E-utilities
2. **Use API key** for better rate limits
3. **Cache downloaded files** locally
4. **Check data quality** before analysis
5. **Verify platform annotations** are current
6. **Document data processing** steps
7. **Cite original studies** when using data
8. **Check for batch effects** in meta-analyses
9. **Validate results** with independent datasets
10. **Follow NCBI usage guidelines**