multiomics-integration

# Multi-Omics Integration **Discipline**: Multi-Omics | **Tools Used**: 4 | **Servers**: 4 ## Description For a given gene (e.g. TP53), this skill integrates four layers of omics data: 1. **Transcriptomics** — gene expression across cancer types (TCGA) 2. **Proteomics** — protein structure, function, and annotations (UniProt) 3. **Pathway Enrichment** — functional enrichment analysis (STRING) 4. **Metabolic Pathway** — detailed KEGG pathway information The final output is a structured JSON report containing all four layers. ## Tool Descriptions ### Tool 1: `get_gene_expression_across_cancers` (Origene-TCGA) ``` Analyze tissue-specific expression of a gene across cancer types. Server: https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA Args: gene (str, required): Gene symbol (e.g., "TP53", "BRCA1", "EGFR") Returns: High/low expression cancer types with z-scores, mean expression values, and sample counts per cancer type. ``` ### Tool 2: `get_uniprotkb_entry_by_accession` (Origene-UniProt) ``` Retrieve all data associated with a UniProtKB entry by accession ID. Server: https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt Args: accession (str, required): UniProtKB accession ID (e.g., "P04637" for TP53) Returns: Complete protein entry including: protein names, gene names, organism, sequence, function annotations, subcellular location, post-translational modifications, disease associations, cross-references. ``` ### Tool 3: `get_functional_enrichment` (Origene-STRING) ``` Retrieve functional enrichment (GO, KEGG, Pfam, InterPro) for a protein set. Server: https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING Args: identifiers (array of str, required): Gene/protein identifiers (e.g., ["TP53", "MDM2"]) species (int, required): NCBI taxonomy ID (e.g., 9606 for human) background_string_identifiers (str, required): Background protein set for enrichment statistics. Use empty string "" for whole-genome background. Returns: List of enriched terms with: term name, category (GO/KEGG/Pfam/etc.), p-value, FDR, description, and which input proteins match. ``` ### Tool 4: `kegg_get` (Origene-KEGG) ``` Retrieve KEGG database entries in flat file format. Server: https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG Args: dbentries (str, required): KEGG entry identifier(s). Examples: - "hsa04115" (p53 signaling pathway) - "hsa:7157" (TP53 gene entry) - Multiple entries separated by "+" option (str, required): Output format. Use "" for default flat file, or: "aaseq" (amino acid), "ntseq" (nucleotide), "mol", "kcf", "image", "kgml" (XML), "json" Returns: KEGG entry data in the specified format. Flat file includes: pathway name, description, gene members, compounds, references. ``` ## Workflow 1. **Get transcriptomic data** — Query TCGA for gene expression across cancers 2. **Get proteomic data** — Query UniProt for protein annotations 3. **Run pathway enrichment** — Use STRING to find enriched functional terms for the gene 4. **Get metabolic pathway details** — Retrieve the relevant KEGG pathway entry **Data Flow:** - Steps 1 & 2 provide foundational omics data for the target gene/protein - Step 3 uses the same gene identifier to find enriched pathways - Step 4 retrieves detailed information for the target pathway (e.g., hsa04115 = p53 signaling) ## Test Case ### Input ```json { "gene": "TP53", "accession": "P04637", "pathway": "hsa04115" } ``` ### Expected Output A JSON report file `TP53_multiomics_report.json` containing: - `transcriptomics`: Expression data across cancer types (non-empty) - `proteomics`: UniProt protein entry with sequence and annotations - `enrichment`: List of enriched functional terms (GO, KEGG, etc.) - `kegg_pathway`: p53 signaling pathway details ### Success Criteria 1. All four tools return non-empty, parseable data 2. Step 1 result relates to the queried gene (TP53) 3. Step 2 result contains the queried accession (P04637) 4. Step 3 returns at least one enriched term 5. Step 4 returns pathway information containing "p53" ## Agent Instructions > **Important for AI agents executing this skill:** > > - **Do NOT copy or modify this script.** Call the MCP tools directly with the parameters shown in Tool Descriptions. > - If a tool returns an error, **check the parameter names and types** against the Tool Descriptions above — do not rewrite the workflow. > - The workflow is **complete** when all four steps return non-empty data and the JSON report is saved. Stop execution at that point. > - If a tool is temporarily unavailable (network error), retry up to 2 times before reporting failure. ## Usage Example > **Note:** Replace `<YOUR_SCP_HUB_API_KEY>` 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 datetime import datetime from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client # ══════════════════════════════════════════════════════════════ # Configuration # ══════════════════════════════════════════════════════════════ API_KEY = "<YOUR_SCP_HUB_API_KEY>" SERVERS = { "tcga": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "uniprot": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", "string": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", "kegg": "https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG", } # Input parameters GENE = "TP53" ACCESSION = "P04637" PATHWAY = "hsa04115" # ══════════════════════════════════════════════════════════════ # MCP Client # ══════════════════════════════════════════════════════════════ class OrigeneClient: def __init__(self, server_url: str, api_key: str): self.server_url = server_url self.api_key = api_key self.session = None async def connect(self): try: self.transport = streamablehttp_client( url=self.server_url, headers={"SCP-HUB-API-KEY": self.api_key} ) self.read, self.write, _ = await self.transport.__aenter__() self.session_ctx = ClientSession(self.read, self.write) self.session = await self.session_ctx.__aenter__() await self.session.initialize() return True except Exception as e: print(f"[ERROR] Connection failed for {self.server_url}: {e}") return False async def disconnect(self): try: if self.session: await self.session_ctx.__aexit__(None, None, None) if hasattr(self, 'transport'): await self.transport.__aexit__(None, None, None) except Exception: pass def parse_result(self, result): """Parse MCP tool result into Python object.""" if isinstance(result, dict): content_list = result.get("content") or [] else: content_list = getattr(result, "content", []) or [] texts = [] for item in content_list: if isinstance(item, dict): if item.get("type") == "text": texts.append(item.get("text") or "") else: if getattr(item, "type", None) == "text": texts.append(getattr(item, "text", "") or "") raw = "".join(texts) try: return json.loads(raw) except (json.JSONDecodeError, TypeError): return raw # ══════════════════════════════════════════════════════════════ # Workflow # ══════════════════════════════════════════════════════════════ async def main(): report = { "query": {"gene": GENE, "accession": ACCESSION, "pathway": PATHWAY}, "timestamp": datetime.now().isoformat(), "steps": {} } # ── Step 1: Transcriptomics (TCGA) ────────────────────── print(f"[Step 1] Querying TCGA for {GENE} expression across cancers...") tcga = OrigeneClient(SERVERS["tcga"], API_KEY) if not await tcga.connect(): report["steps"]["transcriptomics"] = {"error": "Connection failed"} else: result = await tcga.session.call_tool( "get_gene_expression_across_cancers", arguments={"gene": GENE} ) data = tcga.parse_result(result) report["steps"]["transcriptomics"] = data await tcga.disconnect() if data and not isinstance(data, str): print(f" [OK] Received expression data for {GENE}") else: print(f" [WARN] Unexpected response format: {str(data)[:200]}") # ── Step 2: Proteomics (UniProt) ──────────────────────── print(f"[Step 2] Querying UniProt for accession {ACCESSION}...") uniprot = OrigeneClient(SERVERS["uniprot"], API_KEY) if not await uniprot.connect(): report["steps"]["proteomics"] = {"error": "Connection failed"} else: result = await uniprot.session.call_tool( "get_uniprotkb_entry_by_accession", arguments={"accession": ACCESSION} ) data = uniprot.parse_result(result) report["steps"]["proteomics"] = data await uniprot.disconnect() if data and not isinstance(data, str): print(f" [OK] Retrieved protein entry for {ACCESSION}") else: print(f" [WARN] Unexpected response format: {str(data)[:200]}") # ── Step 3: Pathway Enrichment (STRING) ───────────────── # Uses the same gene identifier from input print(f"[Step 3] Running STRING functional enrichment for [{GENE}]...") string = OrigeneClient(SERVERS["string"], API_KEY) if not await string.connect(): report["steps"]["enrichment"] = {"error": "Connection failed"} else: result = await string.session.call_tool( "get_functional_enrichment", arguments={ "identifiers": [GENE], "species": 9606, "background_string_identifiers": "" } ) data = string.parse_result(result) report["steps"]["enrichment"] = data await string.disconnect() if isinstance(data, list) and len(data) > 0: print(f" [OK] Found {len(data)} enriched terms") elif isinstance(data, dict) and data: print(f" [OK] Enrichment data received") else: print(f" [WARN] Unexpected response: {str(data)[:200]}") # ── Step 4: KEGG Pathway Details ──────────────────────── print(f"[Step 4] Retrieving KEGG pathway {PATHWAY}...") kegg = OrigeneClient(SERVERS["kegg"], API_KEY) if not await kegg.connect(): report["steps"]["kegg_pathway"] = {"error": "Connection failed"} else: result = await kegg.session.call_tool( "kegg_get", arguments={"dbentries": PATHWAY, "option": ""} ) data = kegg.parse_result(result) report["steps"]["kegg_pathway"] = data await kegg.disconnect() if data and "p53" in str(data).lower(): print(f" [OK] Retrieved p53 signaling pathway") elif data: print(f" [OK] Retrieved pathway data") else: print(f" [WARN] Empty response for {PATHWAY}") # ── Save Report ───────────────────────────────────────── output_file = f"{GENE}_multiomics_report.json" with open(output_file, "w", encoding="utf-8") as f: json.dump(report, f, indent=2, ensure_ascii=False, default=str) print(f"\n{'='*60}") print(f"Multi-omics integration complete!") print(f"Report saved: {output_file}") print(f"{'='*60}") # ── Final Validation ──────────────────────────────────── steps = report["steps"] success = all([ steps.get("transcriptomics") and "error" not in str(steps.get("transcriptomics", "")), steps.get("proteomics") and "error" not in str(steps.get("proteomics", "")), steps.get("enrichment") and "error" not in str(steps.get("enrichment", "")), steps.get("kegg_pathway") and "error" not in str(steps.get("kegg_pathway", "")), ]) if success: print("[PASS] All 4 omics layers successfully integrated.") else: print("[FAIL] Some steps encountered errors. Check the report.") return success if __name__ == "__main__": asyncio.run(main()) ```