Access protein-protein interaction networks from STRING database covering 59M proteins and 20B interactions. Perform functional enrichment analysis, discover interaction partners, and generate network visualizations for systems biology research.
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正在使用「string-database」。 Get interaction partners for TP53
預期結果:
- Top TP53 interactors: MDM2 (score 999), MDM4 (score 995), ATM (score 957)
- MDM2 is the strongest interactor with experimental evidence
正在使用「string-database」。 Enrich BRCA1 gene list
預期結果:
- DNA repair (GO:0006281) FDR: 1.2e-15
- Cell cycle (GO:0007049) FDR: 3.4e-12
- Homologous recombination (GO:0000724) FDR: 8.9e-10
正在使用「string-database」。 Create network image
預期結果:
- Generated network image showing TP53, ATM, CHEK2, BRCA1 connections
- Edges colored by evidence type, thickness by confidence score
安全審計
安全All static findings are false positives. External command detections are markdown code fences, network access is to legitimate STRING database API, filesystem operations save visualization images, and heuristic alerts are standard API client patterns. No malicious intent detected.
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Protein interaction analysis
Analyze protein networks for gene lists from proteomics or RNA-seq experiments
Pathway enrichment
Identify enriched GO terms and KEGG pathways in protein sets
Interaction partner discovery
Find and visualize proteins interacting with a protein of interest
試試這些提示
Use string-database to get the interaction network for TP53 and MDM2 at high confidence (score 700)
Perform GO and KEGG enrichment analysis on these proteins: TP53, BRCA1, ATM, CHEK2, MDM2
Create a confidence-colored network image for these cancer genes and save as png
Check if these proteins form a significantly connected module: BRCA1, BRCA2, PALB2, RAD51, ATM
最佳實務
- Map gene names to STRING IDs first for faster queries
- Use confidence score threshold appropriate for your analysis goals
- Wait 1 second between API calls to respect rate limits
避免
- Do not use low confidence thresholds for publication figures
- Do not omit species parameter for large protein lists
- Do not skip error checking when parsing API responses