hotwa
5883e13c56
- 新增 scripts/bttoxin_shoter.py：从 BPPRC 正样本 CSV 构建 name/亚家族/家族特异性索引， 解析 BtToxin_Digger All_Toxins.txt，计算 per-hit 权重并以 noisy-OR 合成菌株×目标目/物种分数， 输出 TSV/JSON；含 HMM 加成与配对毒素规则（Vip1/Vip2，Vpa/Vpb），other/unknown 桶。 - 新增端到端工具链： - scripts/run_single_fna_pipeline.py：Digger → Shotter → Plot → 打包 - scripts/plot_shotter.py：绘制热图并生成论文式/摘要式报告 - scripts/bttoxin_api.py 与 bttoxin/api.py：纯 Python API；bttoxin/cli.py 暴露 bttoxin-run - pyproject.toml：项目打包与 CLI 入口 - docs(README): 增加输入文件格式与结果解读，补充单目录写入方案 - chore(gitignore): 忽略 runs/ 与 tests/output - ci: 移除 .woodpecker/test.yml
300 lines
12 KiB
Python
300 lines
12 KiB
Python
#!/usr/bin/env python3
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from __future__ import annotations
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import logging
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import os
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import shutil
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import tarfile
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Dict, Any, Optional
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import sys as _sys
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# Ensure repo-relative imports for backend and scripts when running from installed package
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_REPO_ROOT = Path(__file__).resolve().parents[1]
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_BACKEND_DIR = _REPO_ROOT / "backend"
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_SCRIPTS_DIR = _REPO_ROOT / "scripts"
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for _p in (str(_BACKEND_DIR), str(_SCRIPTS_DIR)):
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if _p not in _sys.path:
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_sys.path.append(_p)
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# Import DockerContainerManager from backend
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from app.utils.docker_client import DockerContainerManager # type: ignore
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logger = logging.getLogger(__name__)
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def _lazy_import_shoter():
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try:
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import bttoxin_shoter as shoter # type: ignore
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return shoter
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except Exception as e:
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raise ImportError(
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f"Failed to import bttoxin_shoter from {_SCRIPTS_DIR}. Ensure repo is present in the image.\n{e}"
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)
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def _lazy_import_plotter():
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try:
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import plot_shotter as plotter # type: ignore
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return plotter
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except Exception as e:
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raise ImportError(
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f"Failed to import plot_shotter from {_SCRIPTS_DIR}. Ensure repo is present in the image.\n{e}"
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)
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class BtToxinRunner:
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"""Wrap BtToxin_Digger docker invocation for a single FNA."""
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def __init__(
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self,
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image: str = "quay.io/biocontainers/bttoxin_digger:1.0.10--hdfd78af_0",
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platform: str = "linux/amd64",
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base_workdir: Optional[Path] = None,
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) -> None:
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self.image = image
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self.platform = platform
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if base_workdir is None:
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base_workdir = _REPO_ROOT / "runs" / "bttoxin"
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self.base_workdir = base_workdir
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self.base_workdir.mkdir(parents=True, exist_ok=True)
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self.mgr = DockerContainerManager(image=self.image, platform=self.platform)
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def _prepare_layout(self, fna_path: Path) -> tuple[Path, Path, Path, Path, str]:
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if not fna_path.exists():
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raise FileNotFoundError(f"FNA file not found: {fna_path}")
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sample_name = fna_path.stem
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run_root = self.base_workdir / sample_name
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input_dir = run_root / "input"
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digger_out = run_root / "output" / "digger"
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log_dir = run_root / "logs"
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for d in (input_dir, digger_out, log_dir):
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d.mkdir(parents=True, exist_ok=True)
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target = input_dir / fna_path.name
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if target.exists():
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target.unlink()
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try:
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os.link(fna_path, target)
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logger.info("Hard-linked FNA: %s → %s", fna_path, target)
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except OSError:
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shutil.copy2(fna_path, target)
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logger.info("Copied FNA: %s → %s", fna_path, target)
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return input_dir, digger_out, log_dir, run_root, sample_name
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def run_single_fna(self, fna_path: Path | str, sequence_type: str = "nucl", threads: int = 4) -> Dict[str, Any]:
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fna_path = Path(fna_path)
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input_dir, digger_out, log_dir, run_root, sample_name = self._prepare_layout(fna_path)
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logger.info("Start BtToxin_Digger: %s (sample=%s)", fna_path, sample_name)
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result = self.mgr.run_bttoxin_digger(
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input_dir=input_dir,
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output_dir=digger_out,
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log_dir=log_dir,
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sequence_type=sequence_type,
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scaf_suffix=fna_path.suffix or ".fna",
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threads=threads,
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)
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toxins_dir = digger_out / "Results" / "Toxins"
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files = {
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"list": toxins_dir / f"{sample_name}.list",
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"gbk": toxins_dir / f"{sample_name}.gbk",
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"all_genes": toxins_dir / "Bt_all_genes.table",
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"all_toxins": toxins_dir / "All_Toxins.txt",
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}
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ok = bool(result.get("success")) and files["all_toxins"].exists()
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return {
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"success": ok,
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"sample": sample_name,
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"run_root": run_root,
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"input_dir": input_dir,
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"digger_out": digger_out,
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"log_dir": log_dir,
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"toxins_dir": toxins_dir,
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"files": files,
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"raw_result": result,
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}
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class ShotterAPI:
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"""Pure Python Shotter scoring and saving (no subprocess)."""
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def score(
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self,
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toxicity_csv: Path,
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all_toxins: Path,
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out_dir: Path,
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min_identity: float = 0.0,
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min_coverage: float = 0.0,
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allow_unknown_families: bool = True,
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require_index_hit: bool = False,
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) -> Dict[str, Any]:
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shoter = _lazy_import_shoter()
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out_dir.mkdir(parents=True, exist_ok=True)
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index = shoter.SpecificityIndex.from_csv(toxicity_csv)
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df = shoter.parse_all_toxins(all_toxins)
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if min_identity > 0:
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df = df[df["identity01"].astype(float) >= float(min_identity)]
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if min_coverage > 0:
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df = df[df["coverage"].astype(float) >= float(min_coverage)]
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if not allow_unknown_families:
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df = df[df["family_key"].astype(str) != "unknown"]
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if require_index_hit:
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def _has_index_orders(row) -> bool:
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name_key = str(row.get("Hit_id_norm", ""))
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fam = str(row.get("family_key", ""))
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d = index.orders_for_name_or_backoff(name_key)
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if not d:
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d = index.orders_for_name_or_backoff(fam)
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return bool(d)
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df = df[df.apply(_has_index_orders, axis=1)]
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strains = sorted(df["Strain"].astype(str).unique().tolist())
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all_hits: list[shoter.ToxinHit] = []
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all_strain_scores: list[shoter.StrainScores] = []
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all_species_scores: list[shoter.StrainSpeciesScores] = []
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for strain in strains:
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sdf = df[df["Strain"].astype(str).eq(strain)].copy()
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per_hit, sscore, sspecies = shoter.score_strain(strain, sdf, index)
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all_hits.extend(per_hit)
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all_strain_scores.append(sscore)
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if sspecies is not None:
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all_species_scores.append(sspecies)
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order_columns = sorted({*index.all_orders, "other", "unknown"}) or ["unknown"]
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species_columns = sorted(index.all_species)
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shoter.ToxinHit.save_list_tsv(out_dir / "toxin_support.tsv", all_hits, order_columns)
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shoter.StrainScores.save_list_tsv(out_dir / "strain_target_scores.tsv", all_strain_scores, order_columns)
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shoter.StrainScores.save_list_json(out_dir / "strain_scores.json", all_strain_scores)
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if species_columns and all_species_scores:
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shoter.StrainSpeciesScores.save_list_tsv(out_dir / "strain_target_species_scores.tsv", all_species_scores, species_columns)
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shoter.StrainSpeciesScores.save_list_json(out_dir / "strain_species_scores.json", all_species_scores)
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return {
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"orders": order_columns,
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"species": species_columns,
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"strain_scores": out_dir / "strain_target_scores.tsv",
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"toxin_support": out_dir / "toxin_support.tsv",
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"strain_scores_json": out_dir / "strain_scores.json",
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"species_scores": out_dir / "strain_target_species_scores.tsv",
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"species_scores_json": out_dir / "strain_species_scores.json",
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}
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class PlotAPI:
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"""Plot heatmaps and write Markdown report (no subprocess)."""
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def render(
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self,
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shotter_dir: Path,
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lang: str = "zh",
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merge_unresolved: bool = True,
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per_hit_strain: Optional[str] = None,
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cmap: str = "viridis",
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vmin: float = 0.0,
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vmax: float = 1.0,
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) -> Dict[str, Any]:
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plotter = _lazy_import_plotter()
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strain_scores = shotter_dir / "strain_target_scores.tsv"
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toxin_support = shotter_dir / "toxin_support.tsv"
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species_scores = shotter_dir / "strain_target_species_scores.tsv"
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out1 = shotter_dir / "strain_target_scores.png"
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plotter.plot_strain_scores(strain_scores, out1, cmap, vmin, vmax, None, merge_unresolved)
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out2 = None
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if per_hit_strain and toxin_support.exists():
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out2 = shotter_dir / f"per_hit_{per_hit_strain}.png"
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plotter.plot_per_hit_for_strain(toxin_support, per_hit_strain, out2, cmap, vmin, vmax, None, merge_unresolved)
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species_png = None
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if species_scores.exists():
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species_png = shotter_dir / "strain_target_species_scores.png"
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plotter.plot_species_scores(species_scores, species_png, cmap, vmin, vmax, None)
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args_ns = SimpleNamespace(
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allow_unknown_families=None,
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require_index_hit=None,
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min_identity=None,
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min_coverage=None,
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lang=lang,
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)
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report_path = shotter_dir / "shotter_report_paper.md"
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plotter.write_report_md(
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out_path=report_path,
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mode="paper",
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lang=lang,
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strain_scores_path=strain_scores,
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toxin_support_path=toxin_support if toxin_support.exists() else None,
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species_scores_path=species_scores if species_scores.exists() else None,
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strain_heatmap_path=out1,
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per_hit_heatmap_path=out2,
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species_heatmap_path=species_png,
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merge_unresolved=merge_unresolved,
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args_namespace=args_ns,
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)
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return {
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"strain_orders_png": out1,
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"per_hit_png": out2,
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"species_png": species_png,
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"report_md": report_path,
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}
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class BtSingleFnaPipeline:
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"""End-to-end single-FNA pipeline: Digger → Shotter → Plot → Bundle."""
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def __init__(
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self,
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image: str = "quay.io/biocontainers/bttoxin_digger:1.0.10--hdfd78af_0",
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platform: str = "linux/amd64",
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base_workdir: Optional[Path] = None,
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) -> None:
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self.digger = BtToxinRunner(image=image, platform=platform, base_workdir=base_workdir)
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self.shotter = ShotterAPI()
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self.plotter = PlotAPI()
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def run(
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self,
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fna: Path | str,
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toxicity_csv: Path | str = Path("Data/toxicity-data.csv"),
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min_identity: float = 0.0,
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min_coverage: float = 0.0,
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allow_unknown_families: bool = True,
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require_index_hit: bool = False,
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lang: str = "zh",
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threads: int = 4,
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) -> Dict[str, Any]:
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dig = self.digger.run_single_fna(fna_path=fna, sequence_type="nucl", threads=threads)
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if not dig.get("success"):
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return {"ok": False, "stage": "digger", "detail": dig}
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run_root: Path = dig["run_root"]
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shotter_dir = run_root / "output" / "shotter"
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shot = self.shotter.score(
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toxicity_csv=Path(toxicity_csv),
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all_toxins=Path(dig["files"]["all_toxins"]),
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out_dir=shotter_dir,
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min_identity=min_identity,
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min_coverage=min_coverage,
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allow_unknown_families=allow_unknown_families,
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require_index_hit=require_index_hit,
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)
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strain_for_plot = None
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try:
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import pandas as pd
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df = pd.read_csv(shot["strain_scores"], sep="\t")
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if len(df):
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strain_for_plot = str(df.iloc[0]["Strain"])
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except Exception:
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pass
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_ = self.plotter.render(
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shotter_dir=shotter_dir,
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lang=lang,
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merge_unresolved=True,
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per_hit_strain=strain_for_plot,
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)
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bundle = run_root / "pipeline_results.tar.gz"
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with tarfile.open(bundle, "w:gz") as tar:
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tar.add(run_root / "output" / "digger", arcname="digger")
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tar.add(run_root / "output" / "shotter", arcname="shotter")
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return {
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"ok": True,
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"run_root": str(run_root),
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"digger_dir": str(run_root / "output" / "digger"),
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"shotter_dir": str(shotter_dir),
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"bundle": str(bundle),
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"strain": strain_for_plot or "",
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}
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