NIST/count.py

# count_mgf_peptides.py
# -*- coding: utf-8 -*-

import re
import sys
from pathlib import Path

import pandas as pd


SEQ_PATTERNS = [
    re.compile(r"^SEQ\s*=\s*(.+)$", re.IGNORECASE),
    re.compile(r"^PEPTIDE\s*=\s*(.+)$", re.IGNORECASE),
    re.compile(r"\bSEQ\s*=\s*([^;\s]+)", re.IGNORECASE),           # inside TITLE/COMMENT
    re.compile(r"\bSEQUENCE\s*=\s*([^;\s]+)", re.IGNORECASE),      # inside TITLE/COMMENT
    re.compile(r"\bPep(?:tide)?\s*=\s*([^;\s]+)", re.IGNORECASE),  # Pep= / Peptide=
]

def normalize_raw(seq: str) -> str:
    s = seq.strip().strip('"').strip("'")
    # 截断在第一个空白处（有些 TITLE 里会把很多字段拼一起）
    s = s.split()[0]
    return s

def strip_modifications(seq: str) -> str:
    """
    将修饰等非字母字符去掉，仅保留 A-Z 作为“纯肽段序列”口径。
    例如: "M(ox)PEP[+16]TIDE" -> "MPEPTIDE"
    """
    s = normalize_raw(seq).upper()
    s = re.sub(r"[^A-Z]", "", s)
    return s

def extract_peptide_from_line(line: str):
    line = line.strip()
    for pat in SEQ_PATTERNS[:2]:
        m = pat.match(line)
        if m:
            return m.group(1).strip()
    # 其他模式一般在 TITLE/COMMENT 这种行里
    for pat in SEQ_PATTERNS[2:]:
        m = pat.search(line)
        if m:
            return m.group(1).strip()
    return None

def parse_mgf_file(mgf_path: Path):
    """
    返回:
      peptides_raw: set[str]      (原始提取到的序列/字段值)
      peptides_stripped: set[str] (去修饰后仅A-Z)
      spectra_cnt: int            (BEGIN IONS ... END IONS 块数)
    """
    peptides_raw = set()
    peptides_stripped = set()
    spectra_cnt = 0

    in_block = False
    current_seq = None

    with mgf_path.open("r", encoding="utf-8", errors="ignore") as f:
        for line in f:
            s = line.strip()
            if not s:
                continue

            up = s.upper()
            if up.startswith("BEGIN IONS"):
                in_block = True
                current_seq = None
                continue

            if up.startswith("END IONS"):
                if in_block:
                    spectra_cnt += 1
                    if current_seq:
                        raw = normalize_raw(current_seq)
                        stripped = strip_modifications(current_seq)
                        if raw:
                            peptides_raw.add(raw)
                        if stripped:
                            peptides_stripped.add(stripped)
                in_block = False
                current_seq = None
                continue

            if in_block:
                seq_candidate = extract_peptide_from_line(s)
                if seq_candidate and (current_seq is None):
                    current_seq = seq_candidate

    return peptides_raw, peptides_stripped, spectra_cnt

def main():
    # 默认：脚本同级目录下的 Data/
    base_dir = Path(sys.argv[1]).expanduser().resolve() if len(sys.argv) > 1 else (Path(__file__).resolve().parent)
    if not base_dir.exists():
        print(f"[ERROR] Data directory not found: {base_dir}")
        print("用法: python count_mgf_peptides.py /path/to/Data")
        sys.exit(1)

    mgf_files = sorted(base_dir.rglob("*.mgf"))
    if not mgf_files:
        print(f"[WARN] No .mgf files found under: {base_dir}")
        sys.exit(0)

    rows = []
    global_raw = set()
    global_stripped = set()

    for p in mgf_files:
        peptides_raw, peptides_stripped, spectra_cnt = parse_mgf_file(p)
        global_raw |= peptides_raw
        global_stripped |= peptides_stripped

        rel = p.relative_to(base_dir)
        organism = rel.parts[0] if len(rel.parts) >= 2 else ""
        rows.append({
            "organism_folder": organism,
            "file_name": p.name,
            "relative_path": str(rel),
            "spectra_blocks": spectra_cnt,
            "unique_peptides_stripped(A-Z)": len(peptides_stripped),
            "unique_peptides_raw": len(peptides_raw),
        })

    df = pd.DataFrame(rows).sort_values(["organism_folder", "file_name"]).reset_index(drop=True)

    # 汇总行
    summary = pd.DataFrame([{
        "organism_folder": "TOTAL",
        "file_name": "",
        "relative_path": "",
        "spectra_blocks": int(df["spectra_blocks"].sum()),
        "unique_peptides_stripped(A-Z)": len(global_stripped),
        "unique_peptides_raw": len(global_raw),
    }])

    out_xlsx = "/Users/guanmumu/Desktop/Data/mgf_unique_peptides_summary.xlsx"
    with pd.ExcelWriter(out_xlsx, engine="openpyxl") as writer:
        df.to_excel(writer, index=False, sheet_name="per_file")
        summary.to_excel(writer, index=False, sheet_name="summary")

    # 可选：把全局unique peptide列表也落盘，方便你核对
    (Path.cwd() / "global_unique_peptides_stripped.txt").write_text(
        "\n".join(sorted(global_stripped)) + "\n", encoding="utf-8"
    )
    (Path.cwd() / "global_unique_peptides_raw.txt").write_text(
        "\n".join(sorted(global_raw)) + "\n", encoding="utf-8"
    )

    print(f"[OK] Found {len(mgf_files)} MGF files under: {base_dir}")
    print(f"[OK] Excel written to: {out_xlsx}")
    print(f"[OK] Global unique peptides (stripped) = {len(global_stripped)}")
    print(f"[OK] Global unique peptides (raw)      = {len(global_raw)}")

if __name__ == "__main__":
    main()