scientific-plant-biology - SKILL.md Agent Skill

name: scientific-plant-biology description: | 植物バイオロジー統合スキル。Plant Reactome 代謝パスウェイ・ TAIR Arabidopsis ゲノム情報・Phytozome 比較ゲノミクス・ Ensembl Plants 種間オーソログ解析。 tu_tools:

key: tair name: TAIR description: シロイヌナズナゲノム・植物データ検索

Scientific Plant Biology

Plant Reactome / TAIR / Phytozome / Ensembl Plants を活用した植物ゲノム・代謝パスウェイ統合解析パイプラインを提供する。

When to Use

植物代謝パスウェイ解析 (Plant Reactome) を実行するとき
Arabidopsis thaliana の遺伝子・タンパク質情報を取得するとき
植物種間の比較ゲノミクス解析を行うとき
植物オーソログ・パラログを同定するとき
作物改良のための候補遺伝子を探索するとき
植物表現型データと遺伝子型を統合するとき

Quick Start

1. Plant Reactome パスウェイ検索

import requests
import pandas as pd
import json

PLANT_REACTOME = "https://plantreactome.gramene.org/ContentService"


def plant_reactome_search(query, species="Oryza sativa"):
    """
    Plant Reactome — 植物代謝/シグナルパスウェイ検索。

    Parameters:
        query: str — 検索クエリ (例: "photosynthesis")
        species: str — 種名
    """
    url = f"{PLANT_REACTOME}/search/query"
    params = {"query": query, "species": species, "cluster": True}
    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for group in data.get("results", []):
        for entry in group.get("entries", []):
            results.append({
                "stId": entry.get("stId", ""),
                "name": entry.get("name", ""),
                "species": entry.get("species", ""),
                "type": entry.get("exactType", ""),
                "compartment": entry.get("compartmentNames", []),
            })

    df = pd.DataFrame(results)
    print(f"Plant Reactome: '{query}' → {len(df)} entries ({species})")
    return df


def plant_reactome_pathway_detail(pathway_id):
    """
    Plant Reactome パスウェイ詳細取得。

    Parameters:
        pathway_id: str — パスウェイ ID (例: "R-OSA-1119616")
    """
    url = f"{PLANT_REACTOME}/data/pathway/{pathway_id}/containedEvents"
    resp = requests.get(url, timeout=30)
    resp.raise_for_status()
    events = resp.json()

    steps = []
    for event in events:
        steps.append({
            "stId": event.get("stId", ""),
            "name": event.get("displayName", ""),
            "type": event.get("className", ""),
            "input_count": len(event.get("input", [])),
            "output_count": len(event.get("output", [])),
            "catalyst": event.get("catalystActivity", [{}])[0].get(
                "displayName", "") if event.get("catalystActivity") else "",
        })

    df = pd.DataFrame(steps)
    print(f"Pathway {pathway_id}: {len(df)} reaction steps")
    return df

2. TAIR Arabidopsis 遺伝子情報

TAIR_BASE = "https://www.arabidopsis.org/api"


def tair_gene_search(gene_id=None, gene_name=None, keyword=None):
    """
    TAIR — Arabidopsis thaliana 遺伝子情報取得。

    Parameters:
        gene_id: str — AGI ID (例: "AT1G01010")
        gene_name: str — 遺伝子名 (例: "FLC")
        keyword: str — キーワード検索
    """
    if gene_id:
        url = f"{TAIR_BASE}/gene/{gene_id}"
        resp = requests.get(url, timeout=30)
        resp.raise_for_status()
        data = resp.json()
        return pd.DataFrame([{
            "agi_id": data.get("locus", ""),
            "name": data.get("name", ""),
            "description": data.get("description", ""),
            "chromosome": data.get("chromosome", ""),
            "start": data.get("start", ""),
            "end": data.get("end", ""),
            "strand": data.get("strand", ""),
            "gene_model_type": data.get("gene_model_type", ""),
        }])

    # キーワード検索
    search_term = gene_name or keyword or ""
    url = f"{TAIR_BASE}/search/gene"
    params = {"query": search_term, "limit": 50}
    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for gene in data.get("results", []):
        results.append({
            "agi_id": gene.get("locus", ""),
            "name": gene.get("name", ""),
            "description": gene.get("description", ""),
            "chromosome": gene.get("chromosome", ""),
        })

    df = pd.DataFrame(results)
    print(f"TAIR: '{search_term}' → {len(df)} genes")
    return df


def tair_gene_expression(gene_id):
    """
    TAIR — 遺伝子発現パターン取得。

    Parameters:
        gene_id: str — AGI ID
    """
    url = f"{TAIR_BASE}/gene/{gene_id}/expression"
    resp = requests.get(url, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    tissues = []
    for expr in data.get("expression", []):
        tissues.append({
            "tissue": expr.get("tissue", ""),
            "stage": expr.get("developmental_stage", ""),
            "level": expr.get("expression_level", ""),
            "source": expr.get("source", ""),
        })

    df = pd.DataFrame(tissues)
    print(f"TAIR expression: {gene_id} → {len(df)} tissue records")
    return df

3. Ensembl Plants 種間比較

ENSEMBL_PLANTS = "https://rest.ensembl.org"


def ensembl_plants_orthologs(gene_id, source_species="arabidopsis_thaliana",
                              target_species=None):
    """
    Ensembl Plants — 植物種間オーソログ検索。

    Parameters:
        gene_id: str — Ensembl Gene ID or symbol
        source_species: str — 起源種
        target_species: str — ターゲット種 (None = 全種)
    """
    url = f"{ENSEMBL_PLANTS}/homology/id/{gene_id}"
    params = {
        "type": "orthologues",
        "content-type": "application/json",
        "compara": "plants",
    }
    if target_species:
        params["target_species"] = target_species

    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    orthologs = []
    for homology in data.get("data", [{}])[0].get("homologies", []):
        target = homology.get("target", {})
        orthologs.append({
            "source_gene": gene_id,
            "source_species": source_species,
            "target_gene": target.get("id", ""),
            "target_species": target.get("species", ""),
            "target_protein": target.get("protein_id", ""),
            "identity": target.get("perc_id", 0),
            "dn_ds": homology.get("dn_ds", None),
            "type": homology.get("type", ""),
        })

    df = pd.DataFrame(orthologs)
    print(f"Ensembl Plants orthologs: {gene_id} → {len(df)} homologs")
    return df

4. Phytozome 比較ゲノミクス

PHYTOZOME_BASE = "https://phytozome-next.jgi.doe.gov/api"


def phytozome_gene_family(gene_id, species="Athaliana"):
    """
    Phytozome — 遺伝子ファミリー・比較ゲノミクス。

    Parameters:
        gene_id: str — 遺伝子 ID
        species: str — 種略称
    """
    url = f"{PHYTOZOME_BASE}/search"
    params = {"query": gene_id, "organism": species}
    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    families = []
    for hit in data.get("hits", []):
        families.append({
            "gene_id": hit.get("gene_id", ""),
            "family_id": hit.get("family_id", ""),
            "family_name": hit.get("family_name", ""),
            "species": hit.get("organism", ""),
            "annotation": hit.get("annotation", ""),
            "pfam_domains": hit.get("pfam", []),
        })

    df = pd.DataFrame(families)
    print(f"Phytozome: {gene_id} → {len(df)} family members")
    return df

5. 植物バイオロジー統合パイプライン

def plant_biology_pipeline(gene_query, species="Oryza sativa",
                            output_dir="results"):
    """
    植物バイオロジー統合パイプライン。

    Parameters:
        gene_query: str — 遺伝子/パスウェイクエリ
        species: str — 対象種
        output_dir: str — 出力ディレクトリ
    """
    from pathlib import Path
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    # 1) Plant Reactome パスウェイ
    pathways = plant_reactome_search(gene_query, species=species)
    pathways.to_csv(output_dir / "plant_pathways.csv", index=False)

    # 2) TAIR (Arabidopsis ならば)
    tair_genes = tair_gene_search(keyword=gene_query)
    tair_genes.to_csv(output_dir / "tair_genes.csv", index=False)

    # 3) Ensembl Plants オーソログ
    if len(tair_genes) > 0:
        top_gene = tair_genes.iloc[0]["agi_id"]
        orthologs = ensembl_plants_orthologs(top_gene)
        orthologs.to_csv(output_dir / "orthologs.csv", index=False)
    else:
        orthologs = pd.DataFrame()

    print(f"Plant biology pipeline: {output_dir}")
    return {
        "pathways": pathways,
        "tair_genes": tair_genes,
        "orthologs": orthologs,
    }

パイプライン統合

pathway-enrichment → plant-biology → environmental-ecology
  (KEGG/Reactome)   (PlantReactome)   (生態学/環境)
       │                   │                ↓
  gene-annotation ────────┘         marine-ecology
  (GO/InterPro)      │              (OBIS/WoRMS)
                      ↓
               comparative-genomics
               (Ensembl 比較)

パイプライン出力

ファイル	説明	次スキル
`results/plant_pathways.csv`	Plant Reactome パスウェイ	→ pathway-enrichment
`results/tair_genes.csv`	TAIR Arabidopsis 遺伝子	→ gene-annotation
`results/orthologs.csv`	種間オーソログ	→ comparative-genomics

ToolUniverse 連携

TU Key	ツール名	連携内容
`tair`	TAIR	シロイヌナズナゲノム・植物データ検索