Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Query Reactome for biological pathways and reactions. Use when user asks about signaling cascades, biological processes, pathway diagrams, or reaction details. Triggers on "reactome", "signaling cascade", "biological pathway", "pathway diagram", "reaction mechanism".
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Query the Reactome ContentService and AnalysisService APIs.
import requests
import json
CONTENT_URL = "https://reactome.org/ContentService"
ANALYSIS_URL = "https://reactome.org/AnalysisService"
# 1. Search pathways by keyword
def search_pathways(keyword, species="Homo sapiens"):
url = f"{CONTENT_URL}/search/query"
params = {"query": keyword, "species": species, "types": "Pathway", "cluster": True}
r = requests.get(url, params=params)
r.raise_for_status()
return r.json()
# 2. Get pathway details
def get_pathway(pathway_id):
url = f"{CONTENT_URL}/data/query/{pathway_id}"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 3. Get genes/proteins in a pathway
def get_pathway_participants(pathway_id):
url = f"{CONTENT_URL}/data/participants/{pathway_id}"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 4. Gene list pathway enrichment
def pathway_enrichment(gene_list):
url = f"{ANALYSIS_URL}/identifiers/projection"
genes_text = "\n".join(gene_list)
headers = {"Content-Type": "text/plain"}
r = requests.post(url, data=genes_text, headers=headers)
r.raise_for_status()
return r.json()
# 5. Look up a gene in Reactome
def query_gene(gene_symbol):
url = f"{CONTENT_URL}/data/query/{gene_symbol}"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# Example: DNA repair pathways
results = search_pathways("DNA repair")
entries = results.get("results", [])
for entry in entries[:5]:
for e in entry.get("entries", []):
print(f"{e.get('stId', 'N/A')}: {e.get('name', 'N/A')}")
# Pathway enrichment
enrichment = pathway_enrichment(["BRCA1", "BRCA2", "TP53", "ATM", "CHEK2"])
for p in enrichment.get("pathways", [])[:5]:
name = p.get("name", "N/A")
pval = p.get("entities", {}).get("pValue", "N/A")
found = p.get("entities", {}).get("found", 0)
print(f"{name} — p={pval:.2e}, {found} genes found")
R-HSA-73894R-HSA-109581R-HSA-1640170R-HSA-168256まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。