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@@ -89,3 +89,11 @@ drosophila_consensus_final_true_lib.mgf filter=lfs diff=lfs merge=lfs -text
 drosophila_consensus_final_true_lib.msp filter=lfs diff=lfs merge=lfs -text
 e_coli_consensus_final_true_lib.mgf filter=lfs diff=lfs merge=lfs -text
 e_coli_consensus_final_true_lib.msp filter=lfs diff=lfs merge=lfs -text
+extracted/cptac2_mouse_hcd_itraq_phospho_selected.msp filter=lfs diff=lfs merge=lfs -text
+extracted/cptac2_mouse_hcd_itraq_selected.msp filter=lfs diff=lfs merge=lfs -text
+extracted/cptac2_mouse_hcd_selected.msp filter=lfs diff=lfs merge=lfs -text
+extracted/mouse_consensus_final_true_lib.msp filter=lfs diff=lfs merge=lfs -text
+mgf/cptac2_mouse_hcd_itraq_phospho_selected.mgf filter=lfs diff=lfs merge=lfs -text
+mgf/cptac2_mouse_hcd_itraq_selected.mgf filter=lfs diff=lfs merge=lfs -text
+mgf/cptac2_mouse_hcd_selected.mgf filter=lfs diff=lfs merge=lfs -text
+mgf/mouse_consensus_final_true_lib.mgf filter=lfs diff=lfs merge=lfs -text

download_list.txt

@@ -1,11 +1,4 @@
-https://chemdata.nist.gov/download/peptide_library/libraries/human/ION_TRAP/2014_05_29/2014_05_29_human_consensus_final_true_lib.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/human/HCD/2020_05_19/human_hcd_tryp_best.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/human/HCD/2020_05_19/human_hcd_tryp_good.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/human/HCD/2020_05_19/human_hcd_semitryp.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/cptaclib/2015/cptac2_human_hcd_itraq_selected_part1.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/cptaclib/2015/cptac2_human_hcd_itraq_selected_part2.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/cptaclib/2015/cptac2_human_hcd_itraq_phospho_selected.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/human/HCD/2017_05_30/human_synthetic_hcd_selected.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/human/HCD/2019_02_14/human_hcd_labelfree_phospho_selected_passed.msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/human/HCD/hair/human_hair_selected_with_GVPs_passed_msp.tar.gz
-https://chemdata.nist.gov/download/peptide_library/libraries/human/HCD/2022_03_16/cptac3_tmt_selected_passed_best.msp.tar.gz
+https://chemdata.nist.gov/download/peptide_library/libraries/mouse/ION_TRAP/2013_05_20/2013_05_20_mouse_consensus_final_true_lib.tar.gz
+https://chemdata.nist.gov/download/peptide_library/libraries/cptaclib/2015/cptac2_mouse_hcd_selected.msp.tar.gz
+https://chemdata.nist.gov/download/peptide_library/libraries/cptaclib/2015/cptac2_mouse_hcd_itraq_selected.msp.tar.gz
+https://chemdata.nist.gov/download/peptide_library/libraries/cptaclib/2015/cptac2_mouse_hcd_itraq_phospho_selected.msp.tar.gz

downloads/2013_05_20_mouse_consensus_final_true_lib.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9f88234d90684bd7bdbca31d9f9742ef107685d3791d0ec2570e91f2c66b82ba
+size 227578902

downloads/cptac2_mouse_hcd_itraq_phospho_selected.msp.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6cd24b2fc9d15a2269c872c8f9ce5822a76dfa361787efc9ecf7693b0ee98de3
+size 74644121

downloads/cptac2_mouse_hcd_itraq_selected.msp.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e9b1c8865fa859279b294407a6993a5c22c76631db9331373d86b1232441249
+size 360659920

downloads/cptac2_mouse_hcd_selected.msp.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:347c0d14aa3b673ea51089924b22a528e20db2290e165703814085a554aff254
+size 61660818

extracted/cptac2_mouse_hcd_itraq_phospho_selected.msp

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1569974c2c0af3d9f26695fa33f95d2161ab4af85ba5defb1bb498d62319e105
+size 217718178

extracted/cptac2_mouse_hcd_itraq_selected.msp

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b477dd318c085b146915b48f3fa7c6b543c6b53ce80735579580678c296e8e2
+size 1044198105

extracted/cptac2_mouse_hcd_selected.msp

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+version https://git-lfs.github.com/spec/v1
+oid sha256:943a663f11127b98a0bd7cf4343b460f9a109630f59c04e18938eaa3a353080d
+size 181845806

extracted/mouse_consensus_final_true_lib.msp

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+version https://git-lfs.github.com/spec/v1
+oid sha256:557685a16568d90e5f31473de35ca98561c2da2e6154443d4b09be185c691ac6
+size 685987106

mgf/cptac2_mouse_hcd_itraq_phospho_selected.mgf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f52662493c6be02447cac538bf5321bacff84f806986502a47887fceab9c441f
+size 83274964

mgf/cptac2_mouse_hcd_itraq_selected.mgf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e4a94b27d2cd8464431ee771e35c0cd8323a61e3577d2d59a56df0f81ddb221
+size 403687841

mgf/cptac2_mouse_hcd_selected.mgf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a77cb8ae41ed596864cda104364e0c893219f4afddff95a2761a7af0e4c73c0f
+size 67648441

mgf/mouse_consensus_final_true_lib.mgf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ecafa0686d7aac09a3705432d5906ebd6e9909c7984c1c8ff5bc5e080fe43c37
+size 238390604

msp_to_mgf.py

@@ -1,25 +1,29 @@
 # -*- coding: utf-8 -*-
-import os
 import re
+from pathlib import Path
+from openpyxl import Workbook
 
 # =========================================================
-# 1) 读取路径与保存路径（请你按自己的电脑路径修改）
+# 1) 路径配置：改这里即可
+#    INPUT_DIR : 你截图里的 extracted 目录（存放 .msp）
+#    OUTPUT_DIR: 输出 .mgf 的目录（会自动创建）
+#    EXCEL_PATH: 输出统计表
 # =========================================================
-# 示例（macOS）:
-MSP_PATH = "/Users/guanmumu/Desktop/Data/E. coli/e_coli_consensus_final_true_lib.msp"
-MGF_PATH = "/Users/guanmumu/Desktop/Data/E. coli/e_coli_consensus_final_true_lib.mgf"
+INPUT_DIR = Path("/Users/guanmumu/Desktop/Data/Mouse/extracted")
+OUTPUT_DIR = Path("/Users/guanmumu/Desktop/Data/Mouse/mgf")
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
 
-# 如果你想让脚本“放哪儿都能跑”，可以用脚本所在目录：
-# BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-# MSP_PATH = os.path.join(BASE_DIR, "bsa_consensus_final_true_lib.msp")
-# MGF_PATH = os.path.join(BASE_DIR, "bsa_consensus_final_true_lib.pepmass_is_mw.mgf")
+EXCEL_PATH = OUTPUT_DIR / "msp_to_mgf_summary.xlsx"
+
+# 进度打印（大文件可调）
+PROGRESS_EVERY_SPECTRA = 50000
 
 # =========================================================
-# 2) MSP 字段解析函数
+# 2) MSP 字段解析函数（保持和你单文件脚本一致）
 # =========================================================
 def parse_sequence_from_name(name_val: str) -> str:
     # Name: SEQUENCE/charge_...
-    return name_val.split("/")[0].strip()
+    return name_val.split("/", 1)[0].strip()
 
 def parse_charge_from_name(name_val: str):
     m = re.search(r"/(\d+)", name_val)
@@ -31,14 +35,16 @@ def parse_mz_exact_from_comment(line: str):
     return float(m.group(1)) if m else None
 
 # =========================================================
-# 3) 主逻辑：流式读取 MSP，写 MGF
+# 3) 转换单个 MSP -> MGF，并返回统计信息
+#    - Spectra count: Num peaks 次数
+#    - Unique peptides: 对 Name: 的 seq 去重（与你单文件脚本一致）
 # =========================================================
-def main():
+def convert_one_msp(msp_path: Path, mgf_path: Path):
     spectra_count = 0
     unique_peptides = set()
 
-    with open(MSP_PATH, "r", encoding="utf-8", errors="replace") as fin, \
-         open(MGF_PATH, "w", encoding="utf-8") as fout:
+    with msp_path.open("r", encoding="utf-8", errors="replace") as fin, \
+         mgf_path.open("w", encoding="utf-8") as fout:
 
         name_val = None
         seq = None
@@ -65,15 +71,12 @@ def main():
                 precursor_mz = parse_mz_exact_from_comment(line)
 
             elif line.startswith("Num peaks:"):
-                # 进入峰列表块
                 num_peaks = int(line.split("Num peaks:", 1)[1].strip())
-
                 spectra_count += 1
 
-                # ===================== 写一个 MGF block =====================
+                # ===== 写一个 MGF block =====
                 fout.write("BEGIN IONS\n")
 
-                # TITLE：建议写得可追溯
                 if seq is not None and charge is not None:
                     fout.write(f"TITLE={seq}/z{charge}\n")
                 elif seq is not None:
@@ -82,49 +85,94 @@ def main():
                     fout.write(f"TITLE=Spectrum_{spectra_count}\n")
 
                 # 你要求：PEPMASS = MW（不做换算）
-                # 注意：如果 mw 缺失，会跳过 PEPMASS 行
                 if mw is not None:
                     fout.write(f"PEPMASS={mw:.6f}\n")
 
-                # 电荷
                 if charge is not None:
                     fout.write(f"CHARGE={charge}+\n")
 
-                # 你要的 sequence 列：这里写在 MGF 自定义字段中
+                # 自定义字段
                 if seq is not None:
                     fout.write(f"SEQ={seq}\n")
 
-                # 辅助字段：把 Comment 里 Mz_exact 也记录下来（不影响你要求）
+                # 可选：记录 Mz_exact（不影响你要求）
                 if precursor_mz is not None:
                     fout.write(f"PRECURSOR_MZ={precursor_mz:.6f}\n")
 
-                # 写峰：每行 "m/z intensity"
+                # 峰：每行 "m/z intensity"
                 for _ in range(num_peaks):
                     peak_line = next(fin).strip()
                     if not peak_line:
                         continue
                     parts = peak_line.split()
                     if len(parts) >= 2:
-                        mz = parts[0]
-                        inten = parts[1]
-                        fout.write(f"{mz} {inten}\n")
+                        fout.write(f"{parts[0]} {parts[1]}\n")
 
                 fout.write("END IONS\n\n")
 
-                # reset 当前谱状态
+                # reset 当前谱状态（与你单文件脚本一致）
                 name_val = None
                 seq = None
                 charge = None
                 mw = None
                 precursor_mz = None
 
-    # =========================================================
-    # 4) 最终统计输出
-    # =========================================================
-    print(f"[DONE] MSP input : {MSP_PATH}")
-    print(f"[DONE] MGF output: {MGF_PATH}")
-    print(f"Spectra count      : {spectra_count}")
-    print(f"Unique peptides    : {len(unique_peptides)}")
+                if spectra_count % PROGRESS_EVERY_SPECTRA == 0:
+                    print(f"  ... {msp_path.name}: spectra={spectra_count}")
+
+    return spectra_count, len(unique_peptides)
+
+# =========================================================
+# 4) 写 Excel（3列：文件名 / Spectra count / Unique peptides）
+# =========================================================
+def write_excel(excel_path: Path, rows):
+    wb = Workbook()
+    ws = wb.active
+    ws.title = "summary"
+
+    for r in rows:
+        ws.append(list(r))
+
+    ws.column_dimensions["A"].width = 55
+    ws.column_dimensions["B"].width = 18
+    ws.column_dimensions["C"].width = 20
+
+    wb.save(excel_path)
+
+# =========================================================
+# 5) 批量处理
+# =========================================================
+def main():
+    if not INPUT_DIR.exists():
+        raise FileNotFoundError(f"INPUT_DIR not found: {INPUT_DIR}")
+
+    msp_files = sorted(INPUT_DIR.glob("*.msp"))
+    if not msp_files:
+        print(f"No .msp files found in: {INPUT_DIR}")
+        return
+
+    print(f"Input dir : {INPUT_DIR}")
+    print(f"Output dir: {OUTPUT_DIR}")
+    print(f"Found {len(msp_files)} MSP files.")
+
+    rows = [("file_name", "spectra_count", "unique_peptides")]
+
+    for idx, msp_path in enumerate(msp_files, 1):
+        mgf_path = OUTPUT_DIR / (msp_path.stem + ".mgf")
+
+        print(f"\n[{idx}/{len(msp_files)}] Converting: {msp_path.name}")
+        spectra_count, unique_pep = convert_one_msp(msp_path, mgf_path)
+
+        print(f"  -> MGF: {mgf_path.name}")
+        print(f"  Spectra count   : {spectra_count}")
+        print(f"  Unique peptides : {unique_pep}")
+
+        rows.append((msp_path.name, spectra_count, unique_pep))
+
+    write_excel(EXCEL_PATH, rows)
+
+    print("\n================= DONE =================")
+    print(f"Excel summary: {EXCEL_PATH}")
 
 if __name__ == "__main__":
     main()