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 NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
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 Gene Expression Omnibus for public expression datasets.
from Bio import Entrez
import json
Entrez.email = "bioclaw@example.com"
# 1. Search GEO datasets
def search_geo(query, max_results=10, db="gds"):
handle = Entrez.esearch(db=db, term=query, retmax=max_results, sort="relevance")
record = Entrez.read(handle)
handle.close()
return record
# 2. Get dataset summaries
def geo_summary(id_list, db="gds"):
ids = ",".join(str(i) for i in id_list)
handle = Entrez.esummary(db=db, id=ids, retmode="json")
result = json.loads(handle.read())
handle.close()
return result
# 3. Search for Series (GSE)
def search_gse(keyword, organism="Homo sapiens", max_results=10):
query = f'"{keyword}" AND "{organism}"[Organism] AND gse[ETYP]'
return search_geo(query, max_results)
# Example: Find breast cancer RNA-seq datasets
search = search_gse("breast cancer RNA-seq", max_results=5)
print(f"Found {search['Count']} datasets")
if search['IdList']:
summaries = geo_summary(search['IdList'])
for uid in search['IdList']:
info = summaries['result'].get(str(uid), {})
title = info.get('title', 'N/A')
gse = info.get('accession', 'N/A')
gpl = info.get('gpl', 'N/A')
n_samples = info.get('n_samples', 'N/A')
summary = info.get('summary', 'N/A')[:200]
print(f"\n{gse}: {title}")
print(f" Platform: {gpl}, Samples: {n_samples}")
print(f" Summary: {summary}...")
print(f" URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={gse}")
"CRISPR" AND gse[ETYP]"Homo sapiens"[Organism]"Illumina"[Platform]"2024/01:2026/12"[PDAT]"breast cancer" AND "RNA-seq" AND "Homo sapiens"[Organism] AND gse[ETYP]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。