region-gene-elements
Query IGVF Catalog for regulatory element–gene associations within a genomic region, including association scores, element types, and biosample context.
Best use case
region-gene-elements is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Query IGVF Catalog for regulatory element–gene associations within a genomic region, including association scores, element types, and biosample context.
Teams using region-gene-elements should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/region-gene-elements/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How region-gene-elements Compares
| Feature / Agent | region-gene-elements | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Query IGVF Catalog for regulatory element–gene associations within a genomic region, including association scores, element types, and biosample context.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# IGVF Catalog — Regulatory Element–Gene Associations by Region
## Usage
### Tool Description
```tex
Query IGVF Catalog API to find regulatory-element-to-gene associations within a genomic region.
Database: IGVF Catalog (https://api.catalogkg.igvf.org/)
API: GET https://api.catalogkg.igvf.org/api/genomic-elements/genes
Args:
region (str): Required. Genomic region, e.g. "chr1:903900-904900"
organism (str): Default "Homo sapiens"
source_annotation (str, optional): e.g. "enhancer", "intergenic"
region_type (str, optional): e.g. "accessible dna elements", "tested elements"
source (str, optional): e.g. "ENCODE_EpiRaction"
method (str, optional): e.g. "CRISPR FACS screen"
biosample_name (str, optional): e.g. "placenta"
verbose (bool): true=返回完整信息, false=精简 (default false)
page (int): 0-based page
limit (int): 每页条目数, 最大 500
Return (list of dicts), each item contains:
- score (float): 调控元件与基因的关联分数
- source (str): 数据来源 (e.g. "ENCODE")
- source_url (str): 来源链接
- genomic_element (dict):
- chr, start, end: 调控元件基因组坐标
- source_annotation: 元件注释类型 (intergenic, enhancer, promoter 等)
- type: 元件类型 (accessible dna elements, tested elements 等)
- gene (dict):
- _id: Ensembl Gene ID (e.g. "ENSG00000187634")
- name: 基因名 (e.g. "SAMD11")
- chr, start, end, strand: 基因坐标
- gene_type: 基因类型 (protein_coding, lncRNA 等)
- hgnc: HGNC ID (e.g. "HGNC:28706")
- entrez: Entrez ID (e.g. "ENTREZ:148398")
- biosample (str): 来源生物样本 (e.g. "natural killer cell...")
- method (str): 实验方法 (e.g. "CRISPR FACS screen")
```
### Query Example
```python
import requests
region = "chr1:903900-904900"
url = "https://api.catalogkg.igvf.org/api/genomic-elements/genes"
params = {
"region": region,
"organism": "Homo sapiens",
"verbose": "true",
"page": 0,
}
resp = requests.get(url, params=params, timeout=30).json()
print(f"[IGVF] 区域 {region} 内调控元件-基因关联: {len(resp)} 条")
for i, item in enumerate(resp[:10]):
gene = item.get("gene", {})
elem = item.get("genomic_element", {})
print(f"\n [{i+1}] 基因: {gene.get('name', 'N/A')} ({gene.get('_id', '')})")
print(f" 基因坐标: {gene.get('chr')}:{gene.get('start')}-{gene.get('end')} ({gene.get('strand')})")
print(f" 基因类型: {gene.get('gene_type', '')}")
print(f" 调控元件: {elem.get('chr')}:{elem.get('start')}-{elem.get('end')}")
print(f" 元件类型: {elem.get('type', '')} ({elem.get('source_annotation', '')})")
print(f" 关联分数: {item.get('score', 'N/A')}")
print(f" 来源: {item.get('source', '')}, 方法: {item.get('method', '')}")
print(f" 样本: {item.get('biosample', '')[:60]}")
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