string-database

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STRING Database

STRING数据库

Overview

概述

STRING 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.

STRING是一个涵盖已知和预测蛋白质-蛋白质相互作用的综合数据库，覆盖5000+种生物的5900万种蛋白质和200亿+条相互作用关系。可通过REST API查询相互作用网络、执行功能富集分析、发现相互作用伙伴，用于系统生物学和通路分析。

When to Use This Skill

何时使用该技能

This skill should be used when:

Retrieving protein-protein interaction networks for single or multiple proteins
Performing functional enrichment analysis (GO, KEGG, Pfam) on protein lists
Discovering interaction partners and expanding protein networks
Testing if proteins form significantly enriched functional modules
Generating network visualizations with evidence-based coloring
Analyzing homology and protein family relationships
Conducting cross-species protein interaction comparisons
Identifying hub proteins and network connectivity patterns

在以下场景中应使用本技能：

检索单个或多个蛋白质的蛋白质-蛋白质相互作用网络
对蛋白质列表执行功能富集分析（GO、KEGG、Pfam）
发现相互作用伙伴并扩展蛋白质网络
测试蛋白质是否形成显著富集的功能模块
生成带有证据着色的网络可视化图
分析同源性和蛋白质家族关系
进行跨物种蛋白质相互作用比较
识别枢纽蛋白质和网络连接模式

Quick Start

快速开始

The skill provides:

Python helper functions (
```
scripts/string_api.py
```
) for all STRING REST API operations
Comprehensive reference documentation (
```
references/string_reference.md
```
) with detailed API specifications

When users request STRING data, determine which operation is needed and use the appropriate function from

scripts/string_api.py

本技能提供：

用于所有STRING REST API操作的Python辅助函数（
```
scripts/string_api.py
```
）
包含详细API规范的综合参考文档（
```
references/string_reference.md
```
）

当用户请求STRING数据时，确定所需操作并使用

scripts/string_api.py

中的对应函数。

Core Operations

核心操作

1. Identifier Mapping (

string_map_ids

)

1. 标识符映射（

string_map_ids

）

Convert gene names, protein names, and external IDs to STRING identifiers.

When to use: Starting any STRING analysis, validating protein names, finding canonical identifiers.

Usage:

python

from scripts.string_api import string_map_ids

将基因名称、蛋白质名称和外部ID转换为STRING标识符。

适用场景：启动任何STRING分析、验证蛋白质名称、查找标准标识符。

使用示例：

python

from scripts.string_api import string_map_ids

Map single protein

映射单个蛋白质

result = string_map_ids('TP53', species=9606)

Map multiple proteins

映射多个蛋白质

result = string_map_ids(['TP53', 'BRCA1', 'EGFR', 'MDM2'], species=9606)

Map with multiple matches per query

为每个查询返回多个匹配结果

result = string_map_ids('p53', species=9606, limit=5)


**Parameters**:
- `species`: NCBI taxon ID (9606 = human, 10090 = mouse, 7227 = fly)
- `limit`: Number of matches per identifier (default: 1)
- `echo_query`: Include query term in output (default: 1)

**Best practice**: Always map identifiers first for faster subsequent queries.

result = string_map_ids('p53', species=9606, limit=5)


**参数**：
- `species`：NCBI分类ID（9606 = 人类，10090 = 小鼠，7227 = 果蝇）
- `limit`：每个标识符的匹配结果数量（默认值：1）
- `echo_query`：在输出中包含查询术语（默认值：1）

**最佳实践**：在后续查询前始终先映射标识符，以提高查询速度。

2. Network Retrieval (

string_network

)

2. 网络检索（

string_network

）

Get protein-protein interaction network data in tabular format.

When to use: Building interaction networks, analyzing connectivity, retrieving interaction evidence.

Usage:

python

from scripts.string_api import string_network

以表格格式获取蛋白质-蛋白质相互作用网络数据。

适用场景：构建相互作用网络、分析连接性、检索相互作用证据。

使用示例：

python

from scripts.string_api import string_network

Get network for single protein

获取单个蛋白质的网络

network = string_network('9606.ENSP00000269305', species=9606)

Get network with multiple proteins

获取多个蛋白质的网络

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

Expand network with additional interactors

添加额外的相互作用蛋白以扩展网络

network = string_network('TP53', species=9606, add_nodes=10, required_score=400)

Physical interactions only

仅获取物理相互作用

network = string_network('TP53', species=9606, network_type='physical')


**Parameters**:
- `required_score`: Confidence threshold (0-1000)
  - 150: low confidence (exploratory)
  - 400: medium confidence (default, standard analysis)
  - 700: high confidence (conservative)
  - 900: highest confidence (very stringent)
- `network_type`: `'functional'` (all evidence, default) or `'physical'` (direct binding only)
- `add_nodes`: Add N most connected proteins (0-10)

**Output columns**: Interaction pairs, confidence scores, and individual evidence scores (neighborhood, fusion, coexpression, experimental, database, text-mining).

network = string_network('TP53', species=9606, network_type='physical')


**参数**：
- `required_score`：置信度阈值（0-1000）
  - 150：低置信度（探索性分析）
  - 400：中等置信度（默认值，标准分析）
  - 700：高置信度（保守性分析）
  - 900：最高置信度（非常严格）
- `network_type`：`'functional'`（所有证据类型，默认值）或`'physical'`（仅直接结合）
- `add_nodes`：添加N个连接最紧密的蛋白质（0-10）

**输出列**：相互作用对、置信度得分、各证据类型得分（邻域、融合、共表达、实验、数据库、文本挖掘）。

3. Network Visualization (

string_network_image

)

3. 网络可视化（

string_network_image

）

Generate network visualization as PNG image.

When to use: Creating figures, visual exploration, presentations.

Usage:

python

from scripts.string_api import string_network_image

生成PNG格式的网络可视化图。

适用场景：创建图表、可视化探索、演示汇报。

使用示例：

python

from scripts.string_api import string_network_image

Get network image

获取网络图片

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

Save image

保存图片

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

Evidence-colored network

带证据着色的网络

img = string_network_image(proteins, species=9606, network_flavor='evidence')

Confidence-based visualization

基于置信度的可视化

img = string_network_image(proteins, species=9606, network_flavor='confidence')

Actions network (activation/inhibition)

作用网络（激活/抑制）

img = string_network_image(proteins, species=9606, network_flavor='actions')


**Network flavors**:
- `'evidence'`: Colored lines show evidence types (default)
- `'confidence'`: Line thickness represents confidence
- `'actions'`: Shows activating/inhibiting relationships

img = string_network_image(proteins, species=9606, network_flavor='actions')


**网络风格**：
- `'evidence'`：彩色线条表示证据类型（默认值）
- `'confidence'`：线条粗细代表置信度
- `'actions'`：显示激活/抑制关系

4. Interaction Partners (

string_interaction_partners

)

4. 相互作用伙伴（

string_interaction_partners

）

Find all proteins that interact with given protein(s).

When to use: Discovering novel interactions, finding hub proteins, expanding networks.

Usage:

python

from scripts.string_api import string_interaction_partners

查找与给定蛋白质相互作用的所有蛋白质。

适用场景：发现新型相互作用、查找枢纽蛋白质、扩展网络。

使用示例：

python

from scripts.string_api import string_interaction_partners

Get top 10 interactors of TP53

获取TP53的前10个相互作用伙伴

partners = string_interaction_partners('TP53', species=9606, limit=10)

Get high-confidence interactors

获取高置信度的相互作用伙伴

partners = string_interaction_partners('TP53', species=9606, limit=20, required_score=700)

Find interactors for multiple proteins

查找多个蛋白质的相互作用伙伴

partners = string_interaction_partners(['TP53', 'MDM2'], species=9606, limit=15)


**Parameters**:
- `limit`: Maximum number of partners to return (default: 10)
- `required_score`: Confidence threshold (0-1000)

**Use cases**:
- Hub protein identification
- Network expansion from seed proteins
- Discovering indirect connections

partners = string_interaction_partners(['TP53', 'MDM2'], species=9606, limit=15)


**参数**：
- `limit`：返回的最大伙伴数量（默认值：10）
- `required_score`：置信度阈值（0-1000）

**应用场景**：
- 枢纽蛋白质识别
- 从种子蛋白质扩展网络
- 发现间接连接

5. Functional Enrichment (

string_enrichment

)

5. 功能富集分析（

string_enrichment

）

Perform enrichment analysis across Gene Ontology, KEGG pathways, Pfam domains, and more.

When to use: Interpreting protein lists, pathway analysis, functional characterization, understanding biological processes.

Usage:

python

from scripts.string_enrichment import string_enrichment

对基因本体（Gene Ontology）、KEGG通路、Pfam结构域等执行富集分析。

适用场景：解释蛋白质列表、通路分析、功能表征、理解生物学过程。

使用示例：

python

from scripts.string_enrichment import string_enrichment

Enrichment for a protein list

对蛋白质列表进行富集分析

proteins = ['TP53', 'MDM2', 'ATM', 'CHEK2', 'BRCA1', 'ATR', 'TP73'] enrichment = string_enrichment(proteins, species=9606)

Parse results to find significant terms

解析结果以找到显著术语

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


**Enrichment categories**:
- **Gene Ontology**: Biological Process, Molecular Function, Cellular Component
- **KEGG Pathways**: Metabolic and signaling pathways
- **Pfam**: Protein domains
- **InterPro**: Protein families and domains
- **SMART**: Domain architecture
- **UniProt Keywords**: Curated functional keywords

**Output columns**:
- `category`: Annotation database (e.g., "KEGG Pathways", "GO Biological Process")
- `term`: Term identifier
- `description`: Human-readable term description
- `number_of_genes`: Input proteins with this annotation
- `p_value`: Uncorrected enrichment p-value
- `fdr`: False discovery rate (corrected p-value)

**Statistical method**: Fisher's exact test with Benjamini-Hochberg FDR correction.

**Interpretation**: FDR < 0.05 indicates statistically significant enrichment.

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


**富集类别**：
- **基因本体（Gene Ontology）**：生物过程、分子功能、细胞组分
- **KEGG通路**：代谢和信号通路
- **Pfam**：蛋白质结构域
- **InterPro**：蛋白质家族和结构域
- **SMART**：结构域架构
- **UniProt关键词**：人工整理的功能关键词

**输出列**：
- `category`：注释数据库（例如"KEGG Pathways"、"GO Biological Process"）
- `term`：术语标识符
- `description`：人类可读的术语描述
- `number_of_genes`：带有该注释的输入蛋白质数量
- `p_value`：未校正的富集p值
- `fdr`：错误发现率（校正后的p值）

**统计方法**：采用Benjamini-Hochberg FDR校正的Fisher精确检验。

**解读**：FDR < 0.05表示具有统计学意义的富集。

6. PPI Enrichment (

string_ppi_enrichment

)

6. PPI富集分析（

string_ppi_enrichment

）

Test if a protein network has significantly more interactions than expected by chance.

When to use: Validating if proteins form functional module, testing network connectivity.

Usage:

python

from scripts.string_api import string_ppi_enrichment
import json

测试蛋白质网络的相互作用数量是否显著高于随机预期。

适用场景：验证蛋白质是否形成功能模块、测试网络连接性。

使用示例：

python

from scripts.string_api import string_ppi_enrichment
import json

Test network connectivity

测试网络连接性

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

Parse JSON result

解析JSON结果

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']}")


**Output fields**:
- `number_of_nodes`: Proteins in network
- `number_of_edges`: Observed interactions
- `expected_number_of_edges`: Expected in random network
- `p_value`: Statistical significance

**Interpretation**:
- p-value < 0.05: Network is significantly enriched (proteins likely form functional module)
- p-value ≥ 0.05: No significant enrichment (proteins may be unrelated)

data = json.loads(result) print(f"观测到的边数: {data['number_of_edges']}") print(f"预期边数: {data['expected_number_of_edges']}") print(f"P值: {data['p_value']}")


**输出字段**：
- `number_of_nodes`：网络中的蛋白质数量
- `number_of_edges`：观测到的相互作用数量
- `expected_number_of_edges`：随机网络中的预期边数
- `p_value`：统计学显著性

**解读**：
- p值 < 0.05：网络显著富集（蛋白质可能形成功能模块）
- p值 ≥ 0.05：无显著富集（蛋白质可能不相关）

7. Homology Scores (

string_homology

)

7. 同源性得分（

string_homology

）

Retrieve protein similarity and homology information.

When to use: Identifying protein families, paralog analysis, cross-species comparisons.

Usage:

python

from scripts.string_api import string_homology

检索蛋白质相似性和同源性信息。

适用场景：识别蛋白质家族、旁系同源物分析、跨物种比较。

使用示例：

python

from scripts.string_api import string_homology

Get homology between proteins

获取蛋白质间的同源性

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


**Use cases**:
- Protein family identification
- Paralog discovery
- Evolutionary analysis

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


**应用场景**：
- 蛋白质家族识别
- 旁系同源物发现
- 进化分析

8. Version Information (

string_version

)

8. 版本信息（

string_version

）

Get current STRING database version.

When to use: Ensuring reproducibility, documenting methods.

Usage:

python

from scripts.string_api import string_version

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

获取当前STRING数据库版本。

适用场景：确保可重复性、记录方法。

使用示例：

python

from scripts.string_api import string_version

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

Common Analysis Workflows

常见分析工作流

Workflow 1: Protein List Analysis (Standard Workflow)

工作流1：蛋白质列表分析（标准工作流）

Use case: Analyze a list of proteins from experiment (e.g., differential expression, proteomics).

python

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

应用场景：分析实验得到的蛋白质列表（例如差异表达、蛋白质组学）。

python

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

步骤1：将基因名称映射为STRING ID

gene_list = ['TP53', 'BRCA1', 'ATM', 'CHEK2', 'MDM2', 'ATR', 'BRCA2'] mapping = string_map_ids(gene_list, species=9606)

Step 2: Get interaction network

步骤2：获取相互作用网络

network = string_network(gene_list, species=9606, required_score=400)

Step 3: Test if network is enriched

步骤3：测试网络是否富集

ppi_result = string_ppi_enrichment(gene_list, species=9606)

Step 4: Perform functional enrichment

步骤4：执行功能富集分析

enrichment = string_enrichment(gene_list, species=9606)

Step 5: Generate network visualization

步骤5：生成网络可视化图

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

步骤6：解析并解读结果

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Workflow 2: Single Protein Investigation

工作流2：单个蛋白质研究

Use case: Deep dive into one protein's interactions and partners.

python

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

应用场景：深入研究单个蛋白质的相互作用和伙伴。

python

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

Step 1: Map protein name

步骤1：映射蛋白质名称

protein = 'TP53' mapping = string_map_ids(protein, species=9606)

Step 2: Get all interaction partners

步骤2：获取所有相互作用伙伴

partners = string_interaction_partners(protein, species=9606, limit=20, required_score=700)

Step 3: Visualize expanded network

步骤3：可视化扩展后的网络

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)

undefined

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)

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Workflow 3: Pathway-Centric Analysis

工作流3：通路中心分析

Use case: Identify and visualize proteins in a specific biological pathway.

python

from scripts.string_api import string_enrichment, string_network

应用场景：识别并可视化特定生物学通路中的蛋白质。

python

from scripts.string_api import string_enrichment, string_network

Step 1: Start with known pathway proteins

步骤1：从已知通路蛋白质开始

dna_repair_proteins = ['TP53', 'ATM', 'ATR', 'CHEK1', 'CHEK2', 'BRCA1', 'BRCA2', 'RAD51', 'XRCC1']

Step 2: Get network

步骤2：获取网络

network = string_network(dna_repair_proteins, species=9606, required_score=700, add_nodes=5)

Step 3: Enrichment to confirm pathway annotation

步骤3：富集分析以确认通路注释

enrichment = string_enrichment(dna_repair_proteins, species=9606)

Step 4: Parse enrichment for DNA repair pathways

步骤4：解析富集结果以获取DNA修复通路

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)]

undefined

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)]

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Workflow 4: Cross-Species Analysis

工作流4：跨物种分析

Use case: Compare protein interactions across different organisms.

python

from scripts.string_api import string_network

应用场景：比较不同生物间的蛋白质相互作用。

python

from scripts.string_api import string_network

Human network

人类网络

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

Mouse network

小鼠网络

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

Yeast network (if ortholog exists)

酵母网络（如果存在同源物）

yeast_network = string_network('gene_name', species=4932, required_score=700)

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yeast_network = string_network('gene_name', species=4932, required_score=700)

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Workflow 5: Network Expansion and Discovery

工作流5：网络扩展与发现

Use case: Start with seed proteins and discover connected functional modules.

python

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

应用场景：从种子蛋白质开始，发现相连的功能模块。

python

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

Step 1: Start with seed protein(s)

步骤1：从种子蛋白质开始

seed_proteins = ['TP53']

Step 2: Get first-degree interactors

步骤2：获取一级相互作用伙伴

partners = string_interaction_partners(seed_proteins, species=9606, limit=30, required_score=700)

Step 3: Parse partners to get protein list

步骤3：解析伙伴以获取蛋白质列表

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

步骤4：对扩展后的网络执行富集分析

enrichment = string_enrichment(all_proteins[:50], species=9606)

Step 5: Filter for interesting functional modules

步骤5：筛选有趣的功能模块

enrichment_df = pd.read_csv(io.StringIO(enrichment), sep='\t') modules = enrichment_df[enrichment_df['fdr'] < 0.001]

undefined

enrichment_df = pd.read_csv(io.StringIO(enrichment), sep='\t') modules = enrichment_df[enrichment_df['fdr'] < 0.001]

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Common Species

常见物种

When specifying species, use NCBI taxon IDs:

Organism	Common Name	Taxon ID
Homo sapiens	Human	9606
Mus musculus	Mouse	10090
Rattus norvegicus	Rat	10116
Drosophila melanogaster	Fruit fly	7227
Caenorhabditis elegans	C. elegans	6239
Saccharomyces cerevisiae	Yeast	4932
Arabidopsis thaliana	Thale cress	3702
Escherichia coli	E. coli	511145
Danio rerio	Zebrafish	7955

Full list available at: https://string-db.org/cgi/input?input_page_active_form=organisms

指定物种时，请使用NCBI分类ID：

生物	通用名称	分类ID
Homo sapiens	人类	9606
Mus musculus	小鼠	10090
Rattus norvegicus	大鼠	10116
Drosophila melanogaster	果蝇	7227
Caenorhabditis elegans	秀丽隐杆线虫	6239
Saccharomyces cerevisiae	酵母	4932
Arabidopsis thaliana	拟南芥	3702
Escherichia coli	大肠杆菌	511145
Danio rerio	斑马鱼	7955

完整列表请访问：https://string-db.org/cgi/input?input_page_active_form=organisms

Understanding Confidence Scores

置信度得分说明

STRING provides combined confidence scores (0-1000) integrating multiple evidence types:

STRING提供整合多种证据类型的综合置信度得分（0-1000）：

Evidence Channels

证据渠道

Neighborhood (nscore): Conserved genomic neighborhood across species
Fusion (fscore): Gene fusion events
Phylogenetic Profile (pscore): Co-occurrence patterns across species
Coexpression (ascore): Correlated RNA expression
Experimental (escore): Biochemical and genetic experiments
Database (dscore): Curated pathway and complex databases
Text-mining (tscore): Literature co-occurrence and NLP extraction

邻域（nscore）：跨物种保守的基因组邻域
融合（fscore）：基因融合事件
系统发育谱（pscore）：跨物种共现模式
共表达（ascore）：相关的RNA表达
实验（escore）：生化和遗传实验
数据库（dscore）：人工整理的通路和复合物数据库
文本挖掘（tscore）：文献共现和NLP提取

Recommended Thresholds

Network Types

网络类型

Functional Networks (Default)

功能网络（默认）

Includes all evidence types (experimental, computational, text-mining). Represents proteins that are functionally associated, even without direct physical binding.

When to use:

Pathway analysis
Functional enrichment studies
Systems biology
Most general analyses

包含所有证据类型（实验、计算、文本挖掘）。代表功能相关的蛋白质，即使没有直接物理结合。

适用场景：

通路分析
功能富集研究
系统生物学
大多数常规分析

Physical Networks

物理网络

Only includes evidence for direct physical binding (experimental data and database annotations for physical interactions).

When to use:

Structural biology studies
Protein complex analysis
Direct binding validation
When physical contact is required

仅包含直接物理结合的证据（实验数据和数据库注释的物理相互作用）。

适用场景：

结构生物学研究
蛋白质复合物分析
直接结合验证
需要物理接触的场景

API Best Practices

API最佳实践

Always map identifiers first: Use
```
string_map_ids()
```
before other operations for faster queries
Use STRING IDs when possible: Use format
```
9606.ENSP00000269305
```
instead of gene names
Specify species for networks >10 proteins: Required for accurate results
Respect rate limits: Wait 1 second between API calls
Use versioned URLs for reproducibility: Available in reference documentation
Handle errors gracefully: Check for "Error:" prefix in returned strings
Choose appropriate confidence thresholds: Match threshold to analysis goals

始终先映射标识符：在执行其他操作前使用
```
string_map_ids()
```
，以提高查询速度
尽可能使用STRING ID：使用
```
9606.ENSP00000269305
```
格式而非基因名称
蛋白质数量>10时指定物种：为了结果准确必须指定
遵守速率限制：API调用间隔至少1秒
使用带版本的URL以确保可重复性：参考文档中提供
优雅处理错误：检查返回字符串中的"Error:"前缀
选择合适的置信度阈值：匹配阈值与分析目标

Detailed Reference

详细参考

For comprehensive API documentation, complete parameter lists, output formats, and advanced usage, refer to

references/string_reference.md

. This includes:

Complete API endpoint specifications
All supported output formats (TSV, JSON, XML, PSI-MI)
Advanced features (bulk upload, values/ranks enrichment)
Error handling and troubleshooting
Integration with other tools (Cytoscape, R, Python libraries)
Data license and citation information

如需完整的API文档、参数列表、输出格式和高级用法，请参考

references/string_reference.md

。其中包括：

完整的API端点规范
所有支持的输出格式（TSV、JSON、XML、PSI-MI）
高级功能（批量上传、值/排名富集）
错误处理和故障排除
与其他工具的集成（Cytoscape、R、Python库）
数据许可和引用信息

Troubleshooting

故障排除

No proteins found:

Verify species parameter matches identifiers
Try mapping identifiers first with
```
string_map_ids()
```
Check for typos in protein names

Empty network results:

Lower confidence threshold (
```
required_score
```
)
Check if proteins actually interact
Verify species is correct

Timeout or slow queries:

Reduce number of input proteins
Use STRING IDs instead of gene names
Split large queries into batches

"Species required" error:

Add
```
species
```
parameter for networks with >10 proteins
Always include species for consistency

Results look unexpected:

Check STRING version with
```
string_version()
```
Verify network_type is appropriate (functional vs physical)
Review confidence threshold selection

未找到蛋白质：

验证物种参数与标识符匹配
先尝试使用
```
string_map_ids()
```
映射标识符
检查蛋白质名称是否有拼写错误

网络结果为空：

降低置信度阈值（
```
required_score
```
）
检查蛋白质是否真的存在相互作用
验证物种是否正确

超时或查询缓慢：

减少输入蛋白质的数量
使用STRING ID而非基因名称
将大型查询拆分为多个批次

"Species required"错误：

蛋白质数量>10时添加
```
species
```
参数
为了一致性始终包含物种参数

结果不符合预期：

使用
```
string_version()
```
检查STRING版本
验证
```
network_type
```
是否合适（功能型vs物理型）
重新审视置信度阈值的选择

Additional Resources

其他资源

For proteome-scale analysis or complete species network upload:

Visit https://string-db.org
Use "Upload proteome" feature
STRING will generate complete interaction network and predict functions

For bulk downloads of complete datasets:

Download page: https://string-db.org/cgi/download
Includes complete interaction files, protein annotations, and pathway mappings

如需蛋白质组规模的分析或完整物种网络上传：

访问https://string-db.org
使用"Upload proteome"功能
STRING将生成完整的相互作用网络并预测功能

如需批量下载完整数据集：

下载页面：https://string-db.org/cgi/download
包含完整的相互作用文件、蛋白质注释和通路映射

Data License

数据许可

STRING data is freely available under Creative Commons BY 4.0 license:

Free for academic and commercial use
Attribution required when publishing
Cite latest STRING publication

STRING数据根据Creative Commons BY 4.0许可免费提供：

学术和商业用途均免费
发表时需注明来源
引用最新的STRING出版物

Citation

引用

When using STRING in publications, cite the most recent publication from: https://string-db.org/cgi/about

在出版物中使用STRING时，请引用最新的STRING出版物，地址：https://string-db.org/cgi/about

string-database

Original

Translation

STRING Database

STRING数据库

Overview

概述

When to Use This Skill

何时使用该技能

Quick Start

快速开始

Core Operations

核心操作

1. Identifier Mapping (string_map_ids)

1. 标识符映射（string_map_ids）

Map single protein

映射单个蛋白质

Map multiple proteins

映射多个蛋白质

Map with multiple matches per query

为每个查询返回多个匹配结果

2. Network Retrieval (string_network)

2. 网络检索（string_network）

Get network for single protein

获取单个蛋白质的网络

Get network with multiple proteins

获取多个蛋白质的网络

Expand network with additional interactors

添加额外的相互作用蛋白以扩展网络

Physical interactions only

仅获取物理相互作用

3. Network Visualization (string_network_image)

3. 网络可视化（string_network_image）

Get network image

获取网络图片

Save image

保存图片

Evidence-colored network

带证据着色的网络

Confidence-based visualization

基于置信度的可视化

Actions network (activation/inhibition)

作用网络（激活/抑制）

4. Interaction Partners (string_interaction_partners)

4. 相互作用伙伴（string_interaction_partners）

Get top 10 interactors of TP53

获取TP53的前10个相互作用伙伴

Get high-confidence interactors

获取高置信度的相互作用伙伴

Find interactors for multiple proteins

查找多个蛋白质的相互作用伙伴

5. Functional Enrichment (string_enrichment)

5. 功能富集分析（string_enrichment）

Enrichment for a protein list

对蛋白质列表进行富集分析

Parse results to find significant terms

解析结果以找到显著术语

6. PPI Enrichment (string_ppi_enrichment)

6. PPI富集分析（string_ppi_enrichment）

Test network connectivity

测试网络连接性

Parse JSON result

解析JSON结果

7. Homology Scores (string_homology)

7. 同源性得分（string_homology）

Get homology between proteins

获取蛋白质间的同源性

8. Version Information (string_version)

8. 版本信息（string_version）

Common Analysis Workflows

常见分析工作流

Workflow 1: Protein List Analysis (Standard Workflow)

工作流1：蛋白质列表分析（标准工作流）

Step 1: Map gene names to STRING IDs

步骤1：将基因名称映射为STRING ID

Step 2: Get interaction network

步骤2：获取相互作用网络

Step 3: Test if network is enriched

步骤3：测试网络是否富集

Step 4: Perform functional enrichment

1. Identifier Mapping (
`string_map_ids`
)

1. 标识符映射（
`string_map_ids`
）

2. Network Retrieval (
`string_network`
)

2. 网络检索（
`string_network`
）

3. Network Visualization (
`string_network_image`
)

3. 网络可视化（
`string_network_image`
）

4. Interaction Partners (
`string_interaction_partners`
)

4. 相互作用伙伴（
`string_interaction_partners`
）

5. Functional Enrichment (
`string_enrichment`
)

5. 功能富集分析（
`string_enrichment`
）

6. PPI Enrichment (
`string_ppi_enrichment`
)

6. PPI富集分析（
`string_ppi_enrichment`
）

7. Homology Scores (
`string_homology`
)

7. 同源性得分（
`string_homology`
）

8. Version Information (
`string_version`
)

8. 版本信息（
`string_version`
）