Scientific Data Submission
GenBank / SRA / ENA / GEO / BioProject を活用した科学データの 登録・アーカイブパイプラインを提供する。FAIR 原則に準拠した 配列データ・発現データ・メタデータの公開準備。
When to Use
- •配列データを GenBank/DDBJ/ENA に登録するとき
- •RNA-seq/WGS データを SRA にアーカイブするとき
- •GEO にマイクロアレイ/RNA-seq 発現データを登録するとき
- •BioProject/BioSample でメタデータを構造化するとき
- •論文投稿時にデータアクセッション番号が必要なとき
- •FAIR 原則 (Findable, Accessible, Interoperable, Reusable) に準拠するとき
Quick Start
1. BioProject/BioSample メタデータ作成
python
import json
import pandas as pd
from pathlib import Path
from datetime import date
def create_bioproject_metadata(title, description, organism,
data_type="Genome Sequencing",
relevance="Medical"):
"""
BioProject メタデータ XML/JSON 生成。
Parameters:
title: str — プロジェクトタイトル
description: str — プロジェクト説明
organism: str — 生物種名
data_type: str — データ種別
relevance: str — 関連分野
"""
bioproject = {
"Project": {
"ProjectID": {"ArchiveID": {"accession": "PRJNA_PENDING"}},
"Descriptor": {
"Title": title,
"Description": description,
"Relevance": relevance,
},
"ProjectType": {
"ProjectTypeSubmission": {
"Target": {
"Organism": {"OrganismName": organism},
},
"Method": {"MethodType": data_type},
"Objectives": {"Data": {"DataType": data_type}},
}
},
}
}
print(f"BioProject metadata created:")
print(f" Title: {title}")
print(f" Organism: {organism}")
print(f" Data type: {data_type}")
return bioproject
def create_biosample_table(samples, organism, package="Generic"):
"""
BioSample TSV テンプレート生成。
Parameters:
samples: list[dict] — サンプル情報
organism: str — 生物種名
package: str — BioSample パッケージ
"""
required_fields = [
"sample_name", "organism", "collection_date",
"geo_loc_name", "tissue", "description",
]
rows = []
for s in samples:
row = {
"sample_name": s.get("name", ""),
"organism": organism,
"collection_date": s.get("date", str(date.today())),
"geo_loc_name": s.get("location", "not collected"),
"tissue": s.get("tissue", "not applicable"),
"description": s.get("description", ""),
}
row.update({k: v for k, v in s.items()
if k not in ["name", "date", "location"]})
rows.append(row)
df = pd.DataFrame(rows)
print(f"BioSample table: {len(df)} samples, package='{package}'")
return df
2. GenBank 配列登録準備
python
def prepare_genbank_submission(sequences, annotations, output_dir="submission"):
"""
GenBank 配列登録用 .sqn ファイル準備。
Parameters:
sequences: dict — {seq_id: sequence_string}
annotations: dict — {seq_id: {gene, product, organism, ...}}
output_dir: str — 出力ディレクトリ
"""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# FASTA 生成
fasta_path = output_dir / "sequences.fsa"
with open(fasta_path, "w") as f:
for seq_id, seq in sequences.items():
ann = annotations.get(seq_id, {})
organism = ann.get("organism", "Unknown organism")
f.write(f">{seq_id} [organism={organism}]\n")
# 80文字折返し
for i in range(0, len(seq), 80):
f.write(seq[i:i+80] + "\n")
# Feature Table 生成
tbl_path = output_dir / "sequences.tbl"
with open(tbl_path, "w") as f:
for seq_id, ann in annotations.items():
f.write(f">Feature {seq_id}\n")
if "gene" in ann:
f.write(f"1\t{len(sequences[seq_id])}\tgene\n")
f.write(f"\t\t\tgene\t{ann['gene']}\n")
if "product" in ann:
f.write(f"1\t{len(sequences[seq_id])}\tCDS\n")
f.write(f"\t\t\tproduct\t{ann['product']}\n")
# Template 生成
template = {
"source": {
"organism": list(annotations.values())[0].get("organism", ""),
"mol_type": "genomic DNA",
},
"submitter": {
"name": "AutoSubmission",
},
}
template_path = output_dir / "template.json"
with open(template_path, "w") as f:
json.dump(template, f, indent=2)
print(f"GenBank submission prepared: {len(sequences)} sequences")
print(f" FASTA: {fasta_path}")
print(f" Feature Table: {tbl_path}")
return {"fasta": str(fasta_path), "tbl": str(tbl_path)}
3. SRA メタデータ & アップロード
python
def prepare_sra_metadata(samples, library_strategy="WGS",
library_source="GENOMIC",
platform="ILLUMINA",
instrument_model="Illumina NovaSeq 6000"):
"""
SRA メタデータ TSV 生成。
Parameters:
samples: list[dict] — {biosample, title, file_r1, file_r2}
library_strategy: str — WGS/RNA-Seq/AMPLICON/etc.
library_source: str — GENOMIC/TRANSCRIPTOMIC/etc.
platform: str — ILLUMINA/OXFORD_NANOPORE/etc.
"""
rows = []
for s in samples:
rows.append({
"biosample_accession": s.get("biosample", "SAMN_PENDING"),
"library_ID": s.get("library_id", s.get("title", "")),
"title": s.get("title", ""),
"library_strategy": library_strategy,
"library_source": library_source,
"library_selection": s.get("selection", "RANDOM"),
"library_layout": "paired" if s.get("file_r2") else "single",
"platform": platform,
"instrument_model": instrument_model,
"filetype": "fastq",
"filename": s.get("file_r1", ""),
"filename2": s.get("file_r2", ""),
})
df = pd.DataFrame(rows)
print(f"SRA metadata: {len(df)} runs, strategy={library_strategy}")
return df
def sra_upload_ascp(files, destination, ascp_key=None):
"""
Aspera (ascp) による SRA データ高速アップロード。
Parameters:
files: list — アップロードファイルリスト
destination: str — SRA アップロード先
ascp_key: str — Aspera SSH キーパス
"""
import subprocess
if ascp_key is None:
ascp_key = Path.home() / ".aspera/connect/etc/asperaweb_id_dsa.openssh"
for f in files:
cmd = [
"ascp", "-i", str(ascp_key),
"-QT", "-l", "300m", "-k", "1",
str(f), destination,
]
print(f"Uploading: {f}")
subprocess.run(cmd, check=True)
print(f"SRA upload complete: {len(files)} files")
4. GEO 発現データ登録
python
def prepare_geo_submission(expression_matrix, sample_metadata,
platform="GPL16791", output_dir="geo_submission"):
"""
GEO SOFT 形式サブミッション準備。
Parameters:
expression_matrix: pd.DataFrame — 遺伝子 × サンプルマトリクス
sample_metadata: pd.DataFrame — サンプルメタデータ
platform: str — GEO プラットフォーム ID
output_dir: str — 出力ディレクトリ
"""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# SOFT テンプレート
soft_path = output_dir / "submission.soft"
with open(soft_path, "w") as f:
# Series section
f.write("^SERIES\n")
f.write("!Series_title = \n")
f.write("!Series_summary = \n")
f.write(f"!Series_platform_id = {platform}\n")
# Sample sections
for col in expression_matrix.columns:
meta = sample_metadata[sample_metadata["sample_id"] == col]
f.write(f"\n^SAMPLE = {col}\n")
f.write(f"!Sample_title = {col}\n")
if len(meta) > 0:
for key, val in meta.iloc[0].items():
if key != "sample_id":
f.write(f"!Sample_characteristics_ch1 = {key}: {val}\n")
# Matrix file
matrix_path = output_dir / "expression_matrix.txt"
expression_matrix.to_csv(matrix_path, sep="\t")
# Raw data files list
raw_files_path = output_dir / "raw_files.txt"
with open(raw_files_path, "w") as f:
for col in expression_matrix.columns:
f.write(f"{col}.fastq.gz\n")
print(f"GEO submission: {expression_matrix.shape[1]} samples, "
f"{expression_matrix.shape[0]} genes")
return {"soft": str(soft_path), "matrix": str(matrix_path)}
5. FAIR データ検証チェックリスト
python
def fair_checklist(submission_package):
"""
FAIR 原則準拠チェックリスト。
Parameters:
submission_package: dict — 登録パッケージ情報
"""
checks = {
"Findable": {
"F1_persistent_id": bool(submission_package.get("accession")),
"F2_metadata_rich": bool(submission_package.get("metadata")),
"F3_id_in_metadata": True,
"F4_searchable_registry": bool(submission_package.get("repository")),
},
"Accessible": {
"A1_retrievable_protocol": bool(submission_package.get("access_url")),
"A1_1_open_protocol": True,
"A2_metadata_persists": True,
},
"Interoperable": {
"I1_formal_language": bool(submission_package.get("format")),
"I2_fair_vocabularies": bool(submission_package.get("ontology_terms")),
"I3_qualified_references": bool(submission_package.get("references")),
},
"Reusable": {
"R1_usage_license": bool(submission_package.get("license")),
"R1_1_community_standards": bool(submission_package.get("standard")),
"R1_2_provenance": bool(submission_package.get("methods")),
},
}
total = 0
passed = 0
for principle, items in checks.items():
for check, status in items.items():
total += 1
if status:
passed += 1
score = passed / total * 100 if total > 0 else 0
print(f"FAIR checklist: {passed}/{total} ({score:.0f}%)")
for principle, items in checks.items():
n_pass = sum(items.values())
print(f" {principle}: {n_pass}/{len(items)}")
return checks
パイプライン統合
code
bioinformatics → data-submission → literature-search
(解析完了) (データ登録) (論文投稿時)
│ │ ↓
lab-data-management ───┘ academic-writing
(Benchling/OMERO) │ (論文執筆)
↓
ebi-databases
(ENA/BioStudies 連携)
パイプライン出力
| ファイル | 説明 | 次スキル |
|---|---|---|
submission/sequences.fsa | GenBank 登録用 FASTA | → bioinformatics |
submission/sra_metadata.tsv | SRA メタデータ | → ebi-databases |
geo_submission/submission.soft | GEO SOFT テンプレート | → gene-expression |
submission/fair_report.json | FAIR チェックリスト結果 | → academic-writing |