[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ECME0KKJA9GP8D0XPD7XJB":3,"skill-same:01M6ECME0KKJA9GP8D0XPD7XJB":62},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":25,"publishedAt":29,"linkOnly":30,"markdownContent":31,"markdownBody":31,"frontMatter":32,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":38,"ownerHtmlUrl":39,"ownerBlogUrl":6,"ownerDescription":40,"updatedAt":41,"install":42,"resources":55},"01M6ECME0KKJA9GP8D0XPD7XJB","string-database",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Fstring-database\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[26,27,28],"database","string database","STRING","2026-11-10 10:12:30",false,"# STRING Database\n\n## Overview\n\nSTRING is a comprehensive database of known and predicted protein-protein interactions covering 59M proteins and 20B+ interactions across 5000+ organisms. Query interaction networks, perform functional enrichment, discover partners via REST API for systems biology and pathway analysis.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Retrieving protein-protein interaction networks for single or multiple proteins\n- Performing functional enrichment analysis (GO, KEGG, Pfam) on protein lists\n- Discovering interaction partners and expanding protein networks\n- Testing if proteins form significantly enriched functional modules\n- Generating network visualizations with evidence-based coloring\n- Analyzing homology and protein family relationships\n- Conducting cross-species protein interaction comparisons\n- Identifying hub proteins and network connectivity patterns\n\n## Quick Start\n\nThe skill provides:\n1. Python helper functions (`scripts\u002Fstring_api.py`) for all STRING REST API operations\n2. Comprehensive reference documentation (`references\u002Fstring_reference.md`) with detailed API specifications\n\nWhen users request STRING data, determine which operation is needed and use the appropriate function from `scripts\u002Fstring_api.py`.\n\n## Core Operations\n\n### 1. Identifier Mapping (`string_map_ids`)\n\nConvert gene names, protein names, and external IDs to STRING identifiers.\n\n**When to use**: Starting any STRING analysis, validating protein names, finding canonical identifiers.\n\n**Usage**:\n```python\nfrom scripts.string_api import string_map_ids\n\n# Map single protein\nresult = string_map_ids('TP53', species=9606)\n\n# Map multiple proteins\nresult = string_map_ids(['TP53', 'BRCA1', 'EGFR', 'MDM2'], species=9606)\n\n# Map with multiple matches per query\nresult = string_map_ids('p53', species=9606, limit=5)\n```\n\n**Parameters**:\n- `species`: NCBI taxon ID (9606 = human, 10090 = mouse, 7227 = fly)\n- `limit`: Number of matches per identifier (default: 1)\n- `echo_query`: Include query term in output (default: 1)\n\n**Best practice**: Always map identifiers first for faster subsequent queries.\n\n### 2. Network Retrieval (`string_network`)\n\nGet protein-protein interaction network data in tabular format.\n\n**When to use**: Building interaction networks, analyzing connectivity, retrieving interaction evidence.\n\n**Usage**:\n```python\nfrom scripts.string_api import string_network\n\n# Get network for single protein\nnetwork = string_network('9606.ENSP00000269305', species=9606)\n\n# Get network with multiple proteins\nproteins = ['9606.ENSP00000269305', '9606.ENSP00000275493']\nnetwork = string_network(proteins, required_score=700)\n\n# Expand network with additional interactors\nnetwork = string_network('TP53', species=9606, add_nodes=10, required_score=400)\n\n# Physical interactions only\nnetwork = string_network('TP53', species=9606, network_type='physical')\n```\n\n**Parameters**:\n- `required_score`: Confidence threshold (0-1000)\n  - 150: low confidence (exploratory)\n  - 400: medium confidence (default, standard analysis)\n  - 700: high confidence (conservative)\n  - 900: highest confidence (very stringent)\n- `network_type`: `'functional'` (all evidence, default) or `'physical'` (direct binding only)\n- `add_nodes`: Add N most connected proteins (0-10)\n\n**Output columns**: Interaction pairs, confidence scores, and individual evidence scores (neighborhood, fusion, coexpression, experimental, database, text-mining).\n\n### 3. Network Visualization (`string_network_image`)\n\nGenerate network visualization as PNG image.\n\n**When to use**: Creating figures, visual exploration, presentations.\n\n**Usage**:\n```python\nfrom scripts.string_api import string_network_image\n\n# Get network image\nproteins = ['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1']\nimg_data = string_network_image(proteins, species=9606, required_score=700)\n\n# Save image\nwith open('network.png', 'wb') as f:\n    f.write(img_data)\n\n# Evidence-colored network\nimg = string_network_image(proteins, species=9606, network_flavor='evidence')\n\n# Confidence-based visualization\nimg = string_network_image(proteins, species=9606, network_flavor='confidence')\n\n# Actions network (activation\u002Finhibition)\nimg = string_network_image(proteins, species=9606, network_flavor='actions')\n```\n\n**Network flavors**:\n- `'evidence'`: Colored lines show evidence types (default)\n- `'confidence'`: Line thickness represents confidence\n- `'actions'`: Shows activating\u002Finhibiting relationships\n\n### 4. Interaction Partners (`string_interaction_partners`)\n\nFind all proteins that interact with given protein(s).\n\n**When to use**: Discovering novel interactions, finding hub proteins, expanding networks.\n\n**Usage**:\n```python\nfrom scripts.string_api import string_interaction_partners\n\n# Get top 10 interactors of TP53\npartners = string_interaction_partners('TP53', species=9606, limit=10)\n\n# Get high-confidence interactors\npartners = string_interaction_partners('TP53', species=9606,\n                                      limit=20, required_score=700)\n\n# Find interactors for multiple proteins\npartners = string_interaction_partners(['TP53', 'MDM2'],\n                                      species=9606, limit=15)\n```\n\n**Parameters**:\n- `limit`: Maximum number of partners to return (default: 10)\n- `required_score`: Confidence threshold (0-1000)\n\n**Use cases**:\n- Hub protein identification\n- Network expansion from seed proteins\n- Discovering indirect connections\n\n### 5. Functional Enrichment (`string_enrichment`)\n\nPerform enrichment analysis across Gene Ontology, KEGG pathways, Pfam domains, and more.\n\n**When to use**: Interpreting protein lists, pathway analysis, functional characterization, understanding biological processes.\n\n**Usage**:\n```python\nfrom scripts.string_enrichment import string_enrichment\n\n# Enrichment for a protein list\nproteins = ['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1', 'ATR', 'TP73']\nenrichment = string_enrichment(proteins, species=9606)\n\n# Parse results to find significant terms\nimport pandas as pd\ndf = pd.read_csv(io.StringIO(enrichment), sep='\\t')\nsignificant = df[df['fdr'] \u003C 0.05]\n```\n\n**Enrichment categories**:\n- **Gene Ontology**: Biological Process, Molecular Function, Cellular Component\n- **KEGG Pathways**: Metabolic and signaling pathways\n- **Pfam**: Protein domains\n- **InterPro**: Protein families and domains\n- **SMART**: Domain architecture\n- **UniProt Keywords**: Curated functional keywords\n\n**Output columns**:\n- `category`: Annotation database (e.g., \"KEGG Pathways\", \"GO Biological Process\")\n- `term`: Term identifier\n- `description`: Human-readable term description\n- `number_of_genes`: Input proteins with this annotation\n- `p_value`: Uncorrected enrichment p-value\n- `fdr`: False discovery rate (corrected p-value)\n\n**Statistical method**: Fisher's exact test with Benjamini-Hochberg FDR correction.\n\n**Interpretation**: FDR \u003C 0.05 indicates statistically significant enrichment.\n\n### 6. PPI Enrichment (`string_ppi_enrichment`)\n\nTest if a protein network has significantly more interactions than expected by chance.\n\n**When to use**: Validating if proteins form functional module, testing network connectivity.\n\n**Usage**:\n```python\nfrom scripts.string_api import string_ppi_enrichment\nimport json\n\n# Test network connectivity\nproteins = ['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1']\nresult = string_ppi_enrichment(proteins, species=9606, required_score=400)\n\n# Parse JSON result\ndata = json.loads(result)\nprint(f\"Observed edges: {data['number_of_edges']}\")\nprint(f\"Expected edges: {data['expected_number_of_edges']}\")\nprint(f\"P-value: {data['p_value']}\")\n```\n\n**Output fields**:\n- `number_of_nodes`: Proteins in network\n- `number_of_edges`: Observed interactions\n- `expected_number_of_edges`: Expected in random network\n- `p_value`: Statistical significance\n\n**Interpretation**:\n- p-value \u003C 0.05: Network is significantly enriched (proteins likely form functional module)\n- p-value ≥ 0.05: No significant enrichment (proteins may be unrelated)\n\n### 7. Homology Scores (`string_homology`)\n\nRetrieve protein similarity and homology information.\n\n**When to use**: Identifying protein families, paralog analysis, cross-species comparisons.\n\n**Usage**:\n```python\nfrom scripts.string_api import string_homology\n\n# Get homology between proteins\nproteins = ['TP53', 'TP63', 'TP73']  # p53 family\nhomology = string_homology(proteins, species=9606)\n```\n\n**Use cases**:\n- Protein family identification\n- Paralog discovery\n- Evolutionary analysis\n\n### 8. Version Information (`string_version`)\n\nGet current STRING database version.\n\n**When to use**: Ensuring reproducibility, documenting methods.\n\n**Usage**:\n```python\nfrom scripts.string_api import string_version\n\nversion = string_version()\nprint(f\"STRING version: {version}\")\n```\n\n## Common Analysis Workflows\n\n### Workflow 1: Protein List Analysis (Standard Workflow)\n\n**Use case**: Analyze a list of proteins from experiment (e.g., differential expression, proteomics).\n\n```python\nfrom scripts.string_api import (string_map_ids, string_network,\n                                string_enrichment, string_ppi_enrichment,\n                                string_network_image)\n\n# Step 1: Map gene names to STRING IDs\ngene_list = ['TP53', 'BRCA1', 'ATM', 'CHEK2', 'MDM2', 'ATR', 'BRCA2']\nmapping = string_map_ids(gene_list, species=9606)\n\n# Step 2: Get interaction network\nnetwork = string_network(gene_list, species=9606, required_score=400)\n\n# Step 3: Test if network is enriched\nppi_result = string_ppi_enrichment(gene_list, species=9606)\n\n# Step 4: Perform functional enrichment\nenrichment = string_enrichment(gene_list, species=9606)\n\n# Step 5: Generate network visualization\nimg = string_network_image(gene_list, species=9606,\n                          network_flavor='evidence', required_score=400)\nwith open('protein_network.png', 'wb') as f:\n    f.write(img)\n\n# Step 6: Parse and interpret results\n```\n\n### Workflow 2: Single Protein Investigation\n\n**Use case**: Deep dive into one protein's interactions and partners.\n\n```python\nfrom scripts.string_api import (string_map_ids, string_interaction_partners,\n                                string_network_image)\n\n# Step 1: Map protein name\nprotein = 'TP53'\nmapping = string_map_ids(protein, species=9606)\n\n# Step 2: Get all interaction partners\npartners = string_interaction_partners(protein, species=9606,\n                                      limit=20, required_score=700)\n\n# Step 3: Visualize expanded network\nimg = string_network_image(protein, species=9606, add_nodes=15,\n                          network_flavor='confidence', required_score=700)\nwith open('tp53_network.png', 'wb') as f:\n    f.write(img)\n```\n\n### Workflow 3: Pathway-Centric Analysis\n\n**Use case**: Identify and visualize proteins in a specific biological pathway.\n\n```python\nfrom scripts.string_api import string_enrichment, string_network\n\n# Step 1: Start with known pathway proteins\ndna_repair_proteins = ['TP53', 'ATM', 'ATR', 'CHEK1', 'CHEK2',\n                       'BRCA1', 'BRCA2', 'RAD51', 'XRCC1']\n\n# Step 2: Get network\nnetwork = string_network(dna_repair_proteins, species=9606,\n                        required_score=700, add_nodes=5)\n\n# Step 3: Enrichment to confirm pathway annotation\nenrichment = string_enrichment(dna_repair_proteins, species=9606)\n\n# Step 4: Parse enrichment for DNA repair pathways\nimport pandas as pd\nimport io\ndf = pd.read_csv(io.StringIO(enrichment), sep='\\t')\ndna_repair = df[df['description'].str.contains('DNA repair', case=False)]\n```\n\n### Workflow 4: Cross-Species Analysis\n\n**Use case**: Compare protein interactions across different organisms.\n\n```python\nfrom scripts.string_api import string_network\n\n# Human network\nhuman_network = string_network('TP53', species=9606, required_score=700)\n\n# Mouse network\nmouse_network = string_network('Trp53', species=10090, required_score=700)\n\n# Yeast network (if ortholog exists)\nyeast_network = string_network('gene_name', species=4932, required_score=700)\n```\n\n### Workflow 5: Network Expansion and Discovery\n\n**Use case**: Start with seed proteins and discover connected functional modules.\n\n```python\nfrom scripts.string_api import (string_interaction_partners, string_network,\n                                string_enrichment)\n\n# Step 1: Start with seed protein(s)\nseed_proteins = ['TP53']\n\n# Step 2: Get first-degree interactors\npartners = string_interaction_partners(seed_proteins, species=9606,\n                                      limit=30, required_score=700)\n\n# Step 3: Parse partners to get protein list\nimport pandas as pd\nimport io\ndf = pd.read_csv(io.StringIO(partners), sep='\\t')\nall_proteins = list(set(df['preferredName_A'].tolist() +\n                       df['preferredName_B'].tolist()))\n\n# Step 4: Perform enrichment on expanded network\nenrichment = string_enrichment(all_proteins[:50], species=9606)\n\n# Step 5: Filter for interesting functional modules\nenrichment_df = pd.read_csv(io.StringIO(enrichment), sep='\\t')\nmodules = enrichment_df[enrichment_df['fdr'] \u003C 0.001]\n```\n\n## Common Species\n\nWhen specifying species, use NCBI taxon IDs:\n\n| Organism | Common Name | Taxon ID |\n|----------|-------------|----------|\n| Homo sapiens | Human | 9606 |\n| Mus musculus | Mouse | 10090 |\n| Rattus norvegicus | Rat | 10116 |\n| Drosophila melanogaster | Fruit fly | 7227 |\n| Caenorhabditis elegans | C. elegans | 6239 |\n| Saccharomyces cerevisiae | Yeast | 4932 |\n| Arabidopsis thaliana | Thale cress | 3702 |\n| Escherichia coli | E. coli | 511145 |\n| Danio rerio | Zebrafish | 7955 |\n\nFull list available at: https:\u002F\u002Fstring-db.org\u002Fcgi\u002Finput?input_page_active_form=organisms\n\n## Understanding Confidence Scores\n\nSTRING provides combined confidence scores (0-1000) integrating multiple evidence types:\n\n### Evidence Channels\n\n1. **Neighborhood (nscore)**: Conserved genomic neighborhood across species\n2. **Fusion (fscore)**: Gene fusion events\n3. **Phylogenetic Profile (pscore)**: Co-occurrence patterns across species\n4. **Coexpression (ascore)**: Correlated RNA expression\n5. **Experimental (escore)**: Biochemical and genetic experiments\n6. **Database (dscore)**: Curated pathway and complex databases\n7. **Text-mining (tscore)**: Literature co-occurrence and NLP extraction\n\n### Recommended Thresholds\n\nChoose threshold based on analysis goals:\n\n- **150 (low confidence)**: Exploratory analysis, hypothesis generation\n- **400 (medium confidence)**: Standard analysis, balanced sensitivity\u002Fspecificity\n- **700 (high confidence)**: Conservative analysis, high-confidence interactions\n- **900 (highest confidence)**: Very stringent, experimental evidence preferred\n\n**Trade-offs**:\n- Lower thresholds: More interactions (higher recall, more false positives)\n- Higher thresholds: Fewer interactions (higher precision, more false negatives)\n\n## Network Types\n\n### Functional Networks (Default)\n\nIncludes all evidence types (experimental, computational, text-mining). Represents proteins that are functionally associated, even without direct physical binding.\n\n**When to use**:\n- Pathway analysis\n- Functional enrichment studies\n- Systems biology\n- Most general analyses\n\n### Physical Networks\n\nOnly includes evidence for direct physical binding (experimental data and database annotations for physical interactions).\n\n**When to use**:\n- Structural biology studies\n- Protein complex analysis\n- Direct binding validation\n- When physical contact is required\n\n## API Best Practices\n\n1. **Always map identifiers first**: Use `string_map_ids()` before other operations for faster queries\n2. **Use STRING IDs when possible**: Use format `9606.ENSP00000269305` instead of gene names\n3. **Specify species for networks >10 proteins**: Required for accurate results\n4. **Respect rate limits**: Wait 1 second between API calls\n5. **Use versioned URLs for reproducibility**: Available in reference documentation\n6. **Handle errors gracefully**: Check for \"Error:\" prefix in returned strings\n7. **Choose appropriate confidence thresholds**: Match threshold to analysis goals\n\n## Detailed Reference\n\nFor comprehensive API documentation, complete parameter lists, output formats, and advanced usage, refer to `references\u002Fstring_reference.md`. This includes:\n\n- Complete API endpoint specifications\n- All supported output formats (TSV, JSON, XML, PSI-MI)\n- Advanced features (bulk upload, values\u002Franks enrichment)\n- Error handling and troubleshooting\n- Integration with other tools (Cytoscape, R, Python libraries)\n- Data license and citation information\n\n## Troubleshooting\n\n**No proteins found**:\n- Verify species parameter matches identifiers\n- Try mapping identifiers first with `string_map_ids()`\n- Check for typos in protein names\n\n**Empty network results**:\n- Lower confidence threshold (`required_score`)\n- Check if proteins actually interact\n- Verify species is correct\n\n**Timeout or slow queries**:\n- Reduce number of input proteins\n- Use STRING IDs instead of gene names\n- Split large queries into batches\n\n**\"Species required\" error**:\n- Add `species` parameter for networks with >10 proteins\n- Always include species for consistency\n\n**Results look unexpected**:\n- Check STRING version with `string_version()`\n- Verify network_type is appropriate (functional vs physical)\n- Review confidence threshold selection\n\n## Additional Resources\n\nFor proteome-scale analysis or complete species network upload:\n- Visit https:\u002F\u002Fstring-db.org\n- Use \"Upload proteome\" feature\n- STRING will generate complete interaction network and predict functions\n\nFor bulk downloads of complete datasets:\n- Download page: https:\u002F\u002Fstring-db.org\u002Fcgi\u002Fdownload\n- Includes complete interaction files, protein annotations, and pathway mappings\n\n## Data License\n\nSTRING data is freely available under **Creative Commons BY 4.0** license:\n- Free for academic and commercial use\n- Attribution required when publishing\n- Cite latest STRING publication\n\n## Citation\n\nWhen using STRING in publications, cite the most recent publication from: 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