--- name: regulatory-region-analysis-c22c7808cd44 description: "Regulatory Region Analysis - Analyze regulatory regions: get overlapping features, binding matrix, sequence, and phenotype associations. Use this skill for epigenomics tasks involving get overlap region get species binding matrix get sequence get phenotype region. Combines 4 tools from 2 SCP server(s)." --- # Regulatory Region Analysis **Discipline**: Epigenomics | **Tools Used**: 4 | **Servers**: 2 ## Description Analyze regulatory regions: get overlapping features, binding matrix, sequence, and phenotype associations. ## Tools Used - **`get_overlap_region`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_species_binding_matrix`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_sequence`** from `ucsc-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC` - **`get_phenotype_region`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` ## Workflow 1. Get overlapping regulatory features 2. Get transcription factor binding 3. Retrieve DNA sequence 4. Check phenotype associations ## Test Case ### Input ```json { "region": "7:140753336-140753436", "species": "homo_sapiens", "genome": "hg38" } ``` ### Expected Steps 1. Get overlapping regulatory features 2. Get transcription factor binding 3. Retrieve DNA sequence 4. Check phenotype associations ## Usage Example > **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```python import asyncio import json from contextlib import AsyncExitStack from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client from mcp.client.sse import sse_client SERVERS = { "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "ucsc-server": "https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC" } async def connect(url, stack): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"}) read, write, _ = await stack.enter_async_context(transport) ctx = ClientSession(read, write) session = await stack.enter_async_context(ctx) await session.initialize() return session def parse(result): try: if hasattr(result, 'content') and result.content: c = result.content[0] if hasattr(c, 'text'): try: return json.loads(c.text) except: return c.text return str(result) except: return str(result) async def main(): async with AsyncExitStack() as stack: # Connect to required servers sessions = {} sessions["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack) sessions["ucsc-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC", stack) # Execute workflow steps # Step 1: Get overlapping regulatory features result_1 = await sessions["ensembl-server"].call_tool("get_overlap_region", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Get transcription factor binding result_2 = await sessions["ensembl-server"].call_tool("get_species_binding_matrix", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Retrieve DNA sequence result_3 = await sessions["ucsc-server"].call_tool("get_sequence", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Check phenotype associations result_4 = await sessions["ensembl-server"].call_tool("get_phenotype_region", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ``` --- Source attribution: SpectrAI-Initiative Original project: https://github.com/SpectrAI-Initiative/InnoClaw License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: SpectrAI-Initiative Original project: https://github.com/SpectrAI-Initiative/InnoClaw Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences Roles: Clinical Research, Data Scientist, Researcher Specialties: API Tools: Ensembl Server, Ucsc Server Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 1d7bc75f560f472f7d5d8ae07880889fac949226554788014823f9dd83de3725