String Database

from scripts.string_api import string_map_ids

# Map single protein
result = string_map_ids('TP53', species=9606)

# Map multiple proteins
result = string_map_ids(['TP53', 'BRCA1', 'EGFR', 'MDM2'], species=9606)

# Map with multiple matches per query
result = string_map_ids('p53', species=9606, limit=5)

from scripts.string_api import string_network

# Get network for single protein
network = string_network('9606.ENSP00000269305', species=9606)

# Get network with multiple proteins
proteins = ['9606.ENSP00000269305', '9606.ENSP00000275493']
network = string_network(proteins, required_score=700)

# Expand network with additional interactors
network = string_network('TP53', species=9606, add_nodes=10, required_score=400)

# Physical interactions only
network = string_network('TP53', species=9606, network_type='physical')

from scripts.string_api import string_network_image

# Get network image
proteins = ['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1']
img_data = string_network_image(proteins, species=9606, required_score=700)

# Save image
with open('network.png', 'wb') as f:
    f.write(img_data)

# Evidence-colored network
img = string_network_image(proteins, species=9606, network_flavor='evidence')

# Confidence-based visualization
img = string_network_image(proteins, species=9606, network_flavor='confidence')

# Actions network (activation/inhibition)
img = string_network_image(proteins, species=9606, network_flavor='actions')

from scripts.string_api import string_interaction_partners

# Get top 10 interactors of TP53
partners = string_interaction_partners('TP53', species=9606, limit=10)

# Get high-confidence interactors
partners = string_interaction_partners('TP53', species=9606,
                                      limit=20, required_score=700)

# Find interactors for multiple proteins
partners = string_interaction_partners(['TP53', 'MDM2'],
                                      species=9606, limit=15)

from scripts.string_enrichment import string_enrichment

# Enrichment for a protein list
proteins = ['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1', 'ATR', 'TP73']
enrichment = string_enrichment(proteins, species=9606)

# Parse results to find significant terms
import pandas as pd
df = pd.read_csv(io.StringIO(enrichment), sep='\t')
significant = df[df['fdr'] < 0.05]

from scripts.string_api import string_ppi_enrichment
import json

# Test network connectivity
proteins = ['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1']
result = string_ppi_enrichment(proteins, species=9606, required_score=400)

# Parse JSON result
data = json.loads(result)
print(f"Observed edges: {data['number_of_edges']}")
print(f"Expected edges: {data['expected_number_of_edges']}")
print(f"P-value: {data['p_value']}")

from scripts.string_api import string_homology

# Get homology between proteins
proteins = ['TP53', 'TP63', 'TP73']  # p53 family
homology = string_homology(proteins, species=9606)

from scripts.string_api import string_version

version = string_version()
print(f"STRING version: {version}")

from scripts.string_api import (string_map_ids, string_network,
                                string_enrichment, string_ppi_enrichment,
                                string_network_image)

# Step 1: Map gene names to STRING IDs
gene_list = ['TP53', 'BRCA1', 'ATM', 'CHEK2', 'MDM2', 'ATR', 'BRCA2']
mapping = string_map_ids(gene_list, species=9606)

# Step 2: Get interaction network
network = string_network(gene_list, species=9606, required_score=400)

# Step 3: Test if network is enriched
ppi_result = string_ppi_enrichment(gene_list, species=9606)

# Step 4: Perform functional enrichment
enrichment = string_enrichment(gene_list, species=9606)

# Step 5: Generate network visualization
img = string_network_image(gene_list, species=9606,
                          network_flavor='evidence', required_score=400)
with open('protein_network.png', 'wb') as f:
    f.write(img)

# Step 6: Parse and interpret results

from scripts.string_api import (string_map_ids, string_interaction_partners,
                                string_network_image)

# Step 1: Map protein name
protein = 'TP53'
mapping = string_map_ids(protein, species=9606)

# Step 2: Get all interaction partners
partners = string_interaction_partners(protein, species=9606,
                                      limit=20, required_score=700)

# Step 3: Visualize expanded network
img = string_network_image(protein, species=9606, add_nodes=15,
                          network_flavor='confidence', required_score=700)
with open('tp53_network.png', 'wb') as f:
    f.write(img)

from scripts.string_api import string_enrichment, string_network

# Step 1: Start with known pathway proteins
dna_repair_proteins = ['TP53', 'ATM', 'ATR', 'CHEK1', 'CHEK2',
                       'BRCA1', 'BRCA2', 'RAD51', 'XRCC1']

# Step 2: Get network
network = string_network(dna_repair_proteins, species=9606,
                        required_score=700, add_nodes=5)

# Step 3: Enrichment to confirm pathway annotation
enrichment = string_enrichment(dna_repair_proteins, species=9606)

# Step 4: Parse enrichment for DNA repair pathways
import pandas as pd
import io
df = pd.read_csv(io.StringIO(enrichment), sep='\t')
dna_repair = df[df['description'].str.contains('DNA repair', case=False)]

from scripts.string_api import string_network

# Human network
human_network = string_network('TP53', species=9606, required_score=700)

# Mouse network
mouse_network = string_network('Trp53', species=10090, required_score=700)

# Yeast network (if ortholog exists)
yeast_network = string_network('gene_name', species=4932, required_score=700)

from scripts.string_api import (string_interaction_partners, string_network,
                                string_enrichment)

# Step 1: Start with seed protein(s)
seed_proteins = ['TP53']

# Step 2: Get first-degree interactors
partners = string_interaction_partners(seed_proteins, species=9606,
                                      limit=30, required_score=700)

# Step 3: Parse partners to get protein list
import pandas as pd
import io
df = pd.read_csv(io.StringIO(partners), sep='\t')
all_proteins = list(set(df['preferredName_A'].tolist() +
                       df['preferredName_B'].tolist()))

# Step 4: Perform enrichment on expanded network
enrichment = string_enrichment(all_proteins[:50], species=9606)

# Step 5: Filter for interesting functional modules
enrichment_df = pd.read_csv(io.StringIO(enrichment), sep='\t')
modules = enrichment_df[enrichment_df['fdr'] < 0.001]