Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Query ClinVar for clinical variant significance. Use when user asks about variant pathogenicity, genetic variants, clinical significance, or disease-causing mutations. Triggers on "clinvar", "pathogenic", "variant significance", "clinical significance", "disease variant", "mutation pathogenicity".
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Query NCBI ClinVar for clinical significance of genetic variants.
from Bio import Entrez
import json
Entrez.email = "bioclaw@example.com"
# 1. Search ClinVar
def search_clinvar(query, max_results=10):
handle = Entrez.esearch(db="clinvar", term=query, retmax=max_results)
record = Entrez.read(handle)
handle.close()
return record
# 2. Fetch variant details
def fetch_clinvar(id_list):
ids = ",".join(str(i) for i in id_list)
handle = Entrez.efetch(db="clinvar", id=ids, rettype="vcv", retmode="xml")
result = handle.read()
handle.close()
return result
# 3. Summary for ClinVar IDs
def clinvar_summary(id_list):
ids = ",".join(str(i) for i in id_list)
handle = Entrez.esummary(db="clinvar", id=ids, retmode="json")
result = json.loads(handle.read())
handle.close()
return result
# Example: Find pathogenic BRCA1 variants
search = search_clinvar("BRCA1[gene] AND clinsig_pathogenic[prop]", max_results=5)
print(f"Total pathogenic BRCA1 variants: {search['Count']}")
if search['IdList']:
summaries = clinvar_summary(search['IdList'])
for uid in search['IdList']:
info = summaries['result'].get(str(uid), {})
title = info.get('title', 'N/A')
clinical_sig = info.get('clinical_significance', {}).get('description', 'N/A')
genes = info.get('genes', [{}])
gene = genes[0].get('symbol', 'N/A') if genes else 'N/A'
print(f"\nVariant: {title}")
print(f"Gene: {gene}")
print(f"Clinical significance: {clinical_sig}")
BRCA1[gene] AND clinsig_pathogenic[prop]rs6025[rsid]"breast cancer"[dis] AND clinsig_pathogenic[prop]17[chr] AND 43000000:44000000[chrpos37]BRCA1[gene] AND origin_germline[prop]还没有评价。欢迎分享你的使用感受。
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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.
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