Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Query KEGG for biological pathways and gene info. Use when user asks about metabolic pathways, signaling pathways, pathway genes, or KEGG IDs. Triggers on "kegg", "pathway", "metabolic pathway", "signaling pathway", "pathway genes".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Query the KEGG REST API for biological pathways, genes, and compounds.
import requests
BASE_URL = "https://rest.kegg.jp"
# 1. Find pathways by keyword
def find_pathways(keyword, organism="hsa"):
url = f"{BASE_URL}/find/pathway/{keyword}"
r = requests.get(url)
lines = r.text.strip().split('\n')
results = []
for line in lines:
if line:
parts = line.split('\t')
pid = parts[0].replace("map", organism) if organism else parts[0]
results.append({"id": pid, "name": parts[1] if len(parts) > 1 else ""})
return results
# 2. Get pathway details
def get_pathway(pathway_id):
url = f"{BASE_URL}/get/{pathway_id}"
r = requests.get(url)
return r.text
# 3. Get genes in a pathway
def get_pathway_genes(pathway_id):
url = f"{BASE_URL}/link/genes/{pathway_id}"
r = requests.get(url)
genes = []
for line in r.text.strip().split('\n'):
if line:
parts = line.split('\t')
if len(parts) >= 2:
genes.append(parts[1])
return genes
# 4. Get gene info
def get_gene(kegg_gene_id):
url = f"{BASE_URL}/get/{kegg_gene_id}"
r = requests.get(url)
return r.text
# 5. Find genes by name
def find_gene(gene_name, organism="hsa"):
url = f"{BASE_URL}/find/{organism}/{gene_name}"
r = requests.get(url)
return r.text
# 6. List all human pathways
def list_pathways(organism="hsa"):
url = f"{BASE_URL}/list/pathway/{organism}"
r = requests.get(url)
return r.text
# Example
pathways = find_pathways("apoptosis")
for p in pathways[:5]:
print(f"{p['id']}: {p['name']}")
https://rest.kegg.jp/<operation>/<argument>
| Operation | Example | Use |
|---|---|---|
list | /list/pathway/hsa | List all human pathways |
find | /find/pathway/cancer | Search by keyword |
get | /get/hsa:672 | Get BRCA1 gene info |
link | /link/genes/hsa00010 | Get genes in pathway |
conv | /conv/genes/ncbi-geneid:672 | Convert IDs |
hsa = Human, mmu = Mouse, rno = Rat, dme = Fly, sce = Yeast, eco = E. coli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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