Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Query OpenTargets for drug targets, disease associations, and therapeutic evidence. Use when user asks about drug targets, disease mechanisms, target validation, or drug-disease associations. Triggers on "opentarget", "drug target", "target validation", "disease association", "therapeutic target", "drug for disease".
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See how to installReview the instructions the agent will receive before you install.
Query the OpenTargets Platform GraphQL API for drug-target-disease associations.
import requests
import json
OPENTARGETS_URL = "https://api.platform.opentargets.org/api/v4/graphql"
def query_opentargets(graphql_query, variables=None):
payload = {"query": graphql_query, "variables": variables or {}}
r = requests.post(OPENTARGETS_URL, json=payload, headers={"Content-Type": "application/json"})
r.raise_for_status()
return r.json()
# 1. Search for a target (gene)
def search_target(gene_name):
query = '''
query searchTarget($name: String!) {
search(queryString: $name, entityNames: ["target"], page: {index: 0, size: 5}) {
hits { id name description entity }
}
}'''
return query_opentargets(query, {"name": gene_name})
# 2. Get diseases associated with a target
def target_diseases(ensembl_id, size=10):
query = '''
query targetDiseases($ensemblId: String!, $size: Int!) {
target(ensemblId: $ensemblId) {
id approvedSymbol approvedName
associatedDiseases(page: {index: 0, size: $size}) {
count
rows { disease { id name } score
datasourceScores { componentId score } }
}
}
}'''
return query_opentargets(query, {"ensemblId": ensembl_id, "size": size})
# 3. Get targets for a disease
def disease_targets(disease_id, size=10):
query = '''
query diseaseTargets($diseaseId: String!, $size: Int!) {
disease(efoId: $diseaseId) {
id name
associatedTargets(page: {index: 0, size: $size}) {
count
rows { target { id approvedSymbol approvedName } score }
}
}
}'''
return query_opentargets(query, {"diseaseId": disease_id, "size": size})
# 4. Get drugs for a target
def target_drugs(ensembl_id):
query = '''
query targetDrugs($ensemblId: String!) {
target(ensemblId: $ensemblId) {
id approvedSymbol
knownDrugs(size: 10) {
count
rows { drug { id name } mechanismOfAction phase status
disease { id name } }
}
}
}'''
return query_opentargets(query, {"ensemblId": ensembl_id})
# 5. Search diseases
def search_disease(disease_name):
query = '''
query searchDisease($name: String!) {
search(queryString: $name, entityNames: ["disease"], page: {index: 0, size: 5}) {
hits { id name description entity }
}
}'''
return query_opentargets(query, {"name": disease_name})
# Example: Find top drug targets for Alzheimer's
result = search_disease("Alzheimer")
hits = result.get("data", {}).get("search", {}).get("hits", [])
if hits:
disease_id = hits[0]["id"]
targets = disease_targets(disease_id, size=5)
disease = targets.get("data", {}).get("disease", {})
print(f"Disease: {disease.get('name')} ({disease.get('id')})")
for row in disease.get("associatedTargets", {}).get("rows", []):
t = row["target"]
print(f" {t['approvedSymbol']} ({t['approvedName']}) — score: {row['score']:.3f}")
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
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AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
The English summary is being prepared. Showing the original description.