From 1ef550081db3c695f320cd789ff44a053f2eaf71 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:25:25 +0100 Subject: [PATCH 01/53] first command test --- pyquantms/diann/create_diann_cfg.py | 16 ++-------------- pyquantms/pyquantms.py | 0 pyquantms/pyquantmsc.py | 16 ++++++++++++++++ tests/test_future.py | 14 +++++++++++++- 4 files changed, 31 insertions(+), 15 deletions(-) delete mode 100644 pyquantms/pyquantms.py create mode 100644 pyquantms/pyquantmsc.py diff --git a/pyquantms/diann/create_diann_cfg.py b/pyquantms/diann/create_diann_cfg.py index 7be83e6..5c8d540 100644 --- a/pyquantms/diann/create_diann_cfg.py +++ b/pyquantms/diann/create_diann_cfg.py @@ -12,20 +12,13 @@ import click from sdrf_pipelines.openms.unimod import UnimodDatabase -CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) - -@click.group(context_settings=CONTEXT_SETTINGS) -def cli(): - pass - - -@click.command("generate") +@click.command("create_diann_cfg") @click.option("--enzyme", "-e", help="") @click.option("--fix_mod", "-f", help="") @click.option("--var_mod", "-v", help="") @click.pass_context -def generate_cfg(ctx, enzyme, fix_mod, var_mod): +def create_diann_cfg(ctx, enzyme, fix_mod, var_mod): cut = enzyme_cut(enzyme) unimod_database = UnimodDatabase() fix_ptm, var_ptm = convert_mod(unimod_database, fix_mod, var_mod) @@ -108,8 +101,3 @@ def convert_mod(unimod_database, fix_mod: str, var_mod: str) -> Tuple[List, List def enzyme_cut(enzyme: str) -> str: return _ENZYME_SPECIFICITY.get(enzyme) or "--cut" - -cli.add_command(generate_cfg) - -if __name__ == "__main__": - cli() diff --git a/pyquantms/pyquantms.py b/pyquantms/pyquantms.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py new file mode 100644 index 0000000..ffc9d3f --- /dev/null +++ b/pyquantms/pyquantmsc.py @@ -0,0 +1,16 @@ +import click + +from pyquantms.diann.create_diann_cfg import create_diann_cfg + +CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) + + +@click.group(context_settings=CONTEXT_SETTINGS) +def cli(): + pass + + +cli.add_command(create_diann_cfg) + +if __name__ == "__main__": + cli() diff --git a/tests/test_future.py b/tests/test_future.py index a09425e..d40506e 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -1,4 +1,16 @@ import pytest +from click.testing import CliRunner + +from pyquantms.pyquantmsc import cli + def test_future_test(): - assert 1 == 1 \ No newline at end of file + assert 1 == 1 + +# test for the create_diann_cfg command in cli +def test_create_diann_cfg(): + runner = CliRunner() + result = runner.invoke(cli, + ['create_diann_cfg','--help']) + + assert result.exit_code == 0 \ No newline at end of file From 5cb8dcf2ae02a05c1ef7712aa227de282ce30529 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:28:43 +0100 Subject: [PATCH 02/53] first command test --- .github/workflows/python-app.yml | 1 + .github/workflows/python-package.yml | 1 + 2 files changed, 2 insertions(+) diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml index 1168bd9..d1a2d67 100644 --- a/.github/workflows/python-app.yml +++ b/.github/workflows/python-app.yml @@ -28,6 +28,7 @@ jobs: python -m pip install --upgrade pip pip install flake8 pytest if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + python setup.py install - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index e56abb6..853daa2 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -29,6 +29,7 @@ jobs: python -m pip install --upgrade pip python -m pip install flake8 pytest if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + python setup.py install - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names From 56c4d46be5b702054f20d077da93cc8503d42e59 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:30:23 +0100 Subject: [PATCH 03/53] first command test --- pyquantms/pyquantmsc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index ffc9d3f..423517e 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -1,6 +1,6 @@ import click -from pyquantms.diann.create_diann_cfg import create_diann_cfg +from diann.create_diann_cfg import create_diann_cfg CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) From e931a0dd7f5f54ef3a23ace8a0dc41249ee6bba0 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:37:00 +0100 Subject: [PATCH 04/53] first command test --- pyquantms/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 pyquantms/__init__.py diff --git a/pyquantms/__init__.py b/pyquantms/__init__.py deleted file mode 100644 index e69de29..0000000 From b9bab3c59bcabacf0d00e7509fed6e83a8b7c3f5 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:40:44 +0100 Subject: [PATCH 05/53] first command test --- pyquantms/__init__.py | 0 setup.py | 4 ++-- tests/test_future.py | 3 +-- 3 files changed, 3 insertions(+), 4 deletions(-) create mode 100644 pyquantms/__init__.py diff --git a/pyquantms/__init__.py b/pyquantms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/setup.py b/setup.py index 0006625..809e711 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,4 @@ -from setuptools import setup +from setuptools import setup, find_packages VERSION = "0.0.1" @@ -47,7 +47,7 @@ project_urls=PROJECT_URLS, keywords=KEYWORDS, classifiers=CLASSIFIERS, - packages=["pyquantms"], + packages=find_packages(), include_package_data=True, entry_points={ "console_scripts": [ diff --git a/tests/test_future.py b/tests/test_future.py index d40506e..94de4e6 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -10,7 +10,6 @@ def test_future_test(): # test for the create_diann_cfg command in cli def test_create_diann_cfg(): runner = CliRunner() - result = runner.invoke(cli, - ['create_diann_cfg','--help']) + result = runner.invoke(cli,['create_diann_cfg','--help']) assert result.exit_code == 0 \ No newline at end of file From 83bbc7ff3acfb1e6544d11de63e480febac9b5f6 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:42:12 +0100 Subject: [PATCH 06/53] first command test --- pyquantms/pyquantmsc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index 423517e..ffc9d3f 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -1,6 +1,6 @@ import click -from diann.create_diann_cfg import create_diann_cfg +from pyquantms.diann.create_diann_cfg import create_diann_cfg CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) From 15e0de019934ca277b30ccc3b36b437f2fd096e0 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:50:31 +0100 Subject: [PATCH 07/53] add sage features --- pyquantms/features/__init__.py | 0 pyquantms/features/add_sage_feature.py | 40 ++++++++++++++++++++++++++ pyquantms/pyquantmsc.py | 2 ++ tests/test_future.py | 7 +++++ 4 files changed, 49 insertions(+) create mode 100644 pyquantms/features/__init__.py create mode 100644 pyquantms/features/add_sage_feature.py diff --git a/pyquantms/features/__init__.py b/pyquantms/features/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pyquantms/features/add_sage_feature.py b/pyquantms/features/add_sage_feature.py new file mode 100644 index 0000000..d8616a3 --- /dev/null +++ b/pyquantms/features/add_sage_feature.py @@ -0,0 +1,40 @@ +import click +import pyopenms as oms +import pandas as pd + + +@click.command("add_sage_feature") +@click.option("--idx_file", "-i", help="Input idXML file") +@click.option("--output_file", "-o", help="Output idXML file") +@click.option("--feat_file", "-f", help="Input feature table file") +def add_feature(idx_file: str, output_file: str, feat_file: str): + """ + Add extra features in features idXML. Adding extra feature in Sage isn't known input for PSMFeatureExtractor + + :param idx_file: Original idXML file + :param output_file: Outpuf file with the extra feature + :param feat_file: Feature file from Sage + :return: None + """ + extra_feat = [] + feat = pd.read_csv(feat_file, sep='\t') + for _, row in feat.iterrows(): + if row["feature_generator"] == 'psm_file': + continue + else: + extra_feat.append(row["feature_name"]) + print("Adding extra feature: {}".format(extra_feat)) + protein_ids = [] + peptide_ids = [] + oms.IdXMLFile().load(idx_file, protein_ids, peptide_ids) + SearchParameters = protein_ids[0].getSearchParameters() + features = SearchParameters.getMetaValue("extra_features") + extra_features = features + "," + ",".join(extra_feat) + SearchParameters.setMetaValue("extra_features", extra_features) + protein_ids[0].setSearchParameters(SearchParameters) + oms.IdXMLFile().store(output_file, protein_ids, peptide_ids) + print("Done") + + + + diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index ffc9d3f..6c99550 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -1,6 +1,7 @@ import click from pyquantms.diann.create_diann_cfg import create_diann_cfg +from pyquantms.features import add_sage_feature CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) @@ -11,6 +12,7 @@ def cli(): cli.add_command(create_diann_cfg) +cli.add_command(add_sage_feature) if __name__ == "__main__": cli() diff --git a/tests/test_future.py b/tests/test_future.py index 94de4e6..b4b21dd 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -12,4 +12,11 @@ def test_create_diann_cfg(): runner = CliRunner() result = runner.invoke(cli,['create_diann_cfg','--help']) + assert result.exit_code == 0 + +# test for the add_sage_feature command in cli +def test_add_sage_feature(): + runner = CliRunner() + result = runner.invoke(cli,['add_sage_feature','--help']) + assert result.exit_code == 0 \ No newline at end of file From 6e40867dc2dc107698c3c6d9d8ebdcbba995b63d Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:53:20 +0100 Subject: [PATCH 08/53] add sage features --- pyproject.toml | 1 + requirements.txt | 3 ++- setup.py | 2 +- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d088e2b..3456b7c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,7 @@ click = ">=7.0" pydantic = ">=1.10,<2" python = "^3.7" sdrf_pipelines = ">=0.0.26" +pyopenms = ">=2.6.0" [tool.poetry.dev-dependencies] pytest = "*" diff --git a/requirements.txt b/requirements.txt index c221a2f..65dc967 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,3 @@ click -sdrf_pipelines \ No newline at end of file +sdrf_pipelines +pyopenms \ No newline at end of file diff --git a/setup.py b/setup.py index 809e711..2b2e903 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ "Development Status :: 5 - Production/Stable", ] -INSTALL_REQUIRES = ["click", "sdrf_pipelines"] +INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms"] PYTHON_REQUIRES = ">=3.7,<4" with open("README.md", "r") as fh: From 3e9546e85a1671ee3046a287b3ccb32458e08019 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:55:06 +0100 Subject: [PATCH 09/53] add sage features --- pyquantms/features/add_sage_feature.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyquantms/features/add_sage_feature.py b/pyquantms/features/add_sage_feature.py index d8616a3..8ed1b81 100644 --- a/pyquantms/features/add_sage_feature.py +++ b/pyquantms/features/add_sage_feature.py @@ -7,7 +7,8 @@ @click.option("--idx_file", "-i", help="Input idXML file") @click.option("--output_file", "-o", help="Output idXML file") @click.option("--feat_file", "-f", help="Input feature table file") -def add_feature(idx_file: str, output_file: str, feat_file: str): +@click.pass_context +def add_feature(ctx, idx_file: str, output_file: str, feat_file: str): """ Add extra features in features idXML. Adding extra feature in Sage isn't known input for PSMFeatureExtractor From a12f322e035c519a956f0f5dc2084baeeb3a6dd4 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:56:18 +0100 Subject: [PATCH 10/53] add sage features --- pyquantms/features/add_sage_feature.py | 2 +- pyquantms/pyquantmsc.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyquantms/features/add_sage_feature.py b/pyquantms/features/add_sage_feature.py index 8ed1b81..5d8adf9 100644 --- a/pyquantms/features/add_sage_feature.py +++ b/pyquantms/features/add_sage_feature.py @@ -8,7 +8,7 @@ @click.option("--output_file", "-o", help="Output idXML file") @click.option("--feat_file", "-f", help="Input feature table file") @click.pass_context -def add_feature(ctx, idx_file: str, output_file: str, feat_file: str): +def add_sage_feature(ctx, idx_file: str, output_file: str, feat_file: str): """ Add extra features in features idXML. Adding extra feature in Sage isn't known input for PSMFeatureExtractor diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index 6c99550..6cc6192 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -1,7 +1,7 @@ import click from pyquantms.diann.create_diann_cfg import create_diann_cfg -from pyquantms.features import add_sage_feature +from pyquantms.features.add_sage_feature import add_sage_feature CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) @@ -12,7 +12,7 @@ def cli(): cli.add_command(create_diann_cfg) -cli.add_command(add_sage_feature) +cli.add_command(add_sage_feature()) if __name__ == "__main__": cli() From d2d27533f507af1a87d24aa386b818f920025b7d Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 10:57:25 +0100 Subject: [PATCH 11/53] add sage features --- pyquantms/pyquantmsc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index 6cc6192..c6e9941 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -12,7 +12,7 @@ def cli(): cli.add_command(create_diann_cfg) -cli.add_command(add_sage_feature()) +cli.add_command(add_sage_feature) if __name__ == "__main__": cli() From 2f246967d0f82101f1bc27ae8a517e61b5eb3505 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 11:45:19 +0100 Subject: [PATCH 12/53] mzml statistics --- pyproject.toml | 3 + pyquantms/mzml/__init__py.py | 0 pyquantms/mzml/mzml_statistics.py | 175 ++++++++++++++++++++++++++++++ pyquantms/pyquantmsc.py | 2 + requirements.txt | 3 +- setup.py | 2 +- 6 files changed, 183 insertions(+), 2 deletions(-) create mode 100644 pyquantms/mzml/__init__py.py create mode 100644 pyquantms/mzml/mzml_statistics.py diff --git a/pyproject.toml b/pyproject.toml index 3456b7c..71229c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,9 @@ pydantic = ">=1.10,<2" python = "^3.7" sdrf_pipelines = ">=0.0.26" pyopenms = ">=2.6.0" +ms2rescore = ">=3.0.2" +psm-utils = ">=0.8.0" +pydantic = ">=1.10" [tool.poetry.dev-dependencies] pytest = "*" diff --git a/pyquantms/mzml/__init__py.py b/pyquantms/mzml/__init__py.py new file mode 100644 index 0000000..e69de29 diff --git a/pyquantms/mzml/mzml_statistics.py b/pyquantms/mzml/mzml_statistics.py new file mode 100644 index 0000000..67ba86d --- /dev/null +++ b/pyquantms/mzml/mzml_statistics.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python + +from pathlib import Path +import sqlite3 +import re + +import click +import pandas as pd +from pyopenms import MSExperiment, MzMLFile + + +@click.command("mzml_statistics") +@click.argument("ms_path", type=click.Path(exists=True)) +@click.option("--id_only", is_flag=True, help="Generate a csv with the spectrum id and the peaks") +@click.pass_context +def mzml_statistics(ctx, ms_path: str, id_only: bool = False) -> None: + """ + Parse the mass spectrum file and generates a set of statistics about the file. + + :param ms_path: Path to the mass spectrum file + :param id_only: Generate a csv with the spectrum id and the peaks + + """ + import sys + file_columns = [ + "SpectrumID", + "MSLevel", + "Charge", + "MS_peaks", + "Base_Peak_Intensity", + "Summed_Peak_Intensities", + "Retention_Time", + "Exp_Mass_To_Charge", + "AcquisitionDateTime", + ] + + def parse_mzml(file_name: str, file_columns: list, id_only: bool = False): + info = [] + psm_part_info = [] + exp = MSExperiment() + acquisition_datetime = exp.getDateTime().get() + MzMLFile().load(file_name, exp) + for spectrum in exp: + id_ = spectrum.getNativeID() + MSLevel = spectrum.getMSLevel() + rt = spectrum.getRT() if spectrum.getRT() else None + + peaks_tuple = spectrum.get_peaks() + peak_per_ms = len(peaks_tuple[0]) + + if not spectrum.metaValueExists("base peak intensity"): + bpc = max(peaks_tuple[1]) if len(peaks_tuple[1]) > 0 else None + else: + bpc = spectrum.getMetaValue("base peak intensity") + + if not spectrum.metaValueExists("total ion current"): + tic = sum(peaks_tuple[1]) if len(peaks_tuple[1]) > 0 else None + else: + tic = spectrum.getMetaValue("total ion current") + + if MSLevel == 1: + info_list = [id_, MSLevel, None, peak_per_ms, bpc, tic, rt, None, acquisition_datetime] + elif MSLevel == 2: + charge_state = spectrum.getPrecursors()[0].getCharge() + emz = spectrum.getPrecursors()[0].getMZ() if spectrum.getPrecursors()[0].getMZ() else None + info_list = [id_, MSLevel, charge_state, peak_per_ms, bpc, tic, rt, emz, acquisition_datetime] + mz_array = peaks_tuple[0] + intensity_array = peaks_tuple[1] + else: + info_list = [id_, MSLevel, None, None, None, None, rt, None, acquisition_datetime] + + if id_only and MSLevel == 2: + psm_part_info.append([re.findall(r"[scan|spectrum]=(\d+)", id_)[0], MSLevel, mz_array, intensity_array]) + info.append(info_list) + + if id_only and len(psm_part_info) > 0: + pd.DataFrame(psm_part_info, columns=["scan", "ms_level", "mz", "intensity"]).to_csv( + f"{Path(ms_path).stem}_spectrum_df.csv", + mode="w", + index=False, + header=True, + ) + + return pd.DataFrame(info, columns=file_columns) + + def parse_bruker_d(file_name: str, file_columns: list): + sql_filepath = f"{file_name}/analysis.tdf" + if not Path(sql_filepath).exists(): + msg = f"File '{sql_filepath}' not found" + raise FileNotFoundError(msg) + conn = sqlite3.connect(sql_filepath) + c = conn.cursor() + + datetime_cmd = "SELECT Value FROM GlobalMetadata WHERE key='AcquisitionDateTime'" + AcquisitionDateTime = c.execute(datetime_cmd).fetchall()[0][0] + + df = pd.read_sql_query("SELECT Id, MsMsType, NumPeaks, MaxIntensity, SummedIntensities, Time FROM frames", conn) + df["AcquisitionDateTime"] = AcquisitionDateTime + + # {8:'DDA-PASEF', 9:'DIA-PASEF'} + if 8 in df["MsMsType"].values: + mslevel_map = {0: 1, 8: 2} + elif 9 in df["MsMsType"].values: + mslevel_map = {0: 1, 9: 2} + else: + msg = f"Unrecognized ms type '{df['MsMsType'].values}'" + raise ValueError(msg) + df["MsMsType"] = df["MsMsType"].map(mslevel_map) + + try: + # This line raises an sqlite error if the table does not exist + _ = conn.execute("SELECT * from Precursors LIMIT 1").fetchall() + precursor_df = pd.read_sql_query("SELECT * from Precursors", conn) + except sqlite3.OperationalError as e: + if "no such table: Precursors" in str(e): + print(f"No precursers recorded in {file_name}, This is normal for DIA data.") + precursor_df = pd.DataFrame() + else: + raise + + if len(df) == len(precursor_df): + df = pd.concat([df, precursor_df["Charge", "MonoisotopicMz"]], axis=1) + df["Charge"] = df["Charge"].fillna(0) + else: + df[["Charge", "Exp_Mass_To_Charge"]] = None, None + + df = df[ + [ + "Id", + "MsMsType", + "Charge", + "NumPeaks", + "MaxIntensity", + "SummedIntensities", + "Time", + "Exp_Mass_To_Charge", + "AcquisitionDateTime", + ] + ] + df.columns = pd.Index(file_columns) + + return df + + if not (Path(ms_path).exists()): + print(f"Not found '{ms_path}', trying to find alias") + ms_path_path = Path(ms_path) + path_stem = str(ms_path_path.stem) + candidates = ( + list(ms_path_path.parent.glob("*.d")) + + list(ms_path_path.parent.glob("*.mzml")) + + list(ms_path_path.parent.glob("*.mzML")) + ) + + candidates = [c for c in candidates if path_stem in str(c)] + + if len(candidates) == 1: + ms_path = str(candidates[0].resolve()) + else: + raise FileNotFoundError() + + if Path(ms_path).suffix == ".d" and Path(ms_path).is_dir(): + ms_df = parse_bruker_d(ms_path, file_columns) + elif Path(ms_path).suffix in [".mzML", ".mzml"]: + ms_df = parse_mzml(ms_path, file_columns, id_only) + else: + msg = f"Unrecognized or inexistent mass spec file '{ms_path}'" + raise RuntimeError(msg) + + ms_df.to_csv( + f"{Path(ms_path).stem}_ms_info.tsv", + mode="w", + sep="\t", + index=False, + header=True, + ) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index c6e9941..600c229 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -2,6 +2,7 @@ from pyquantms.diann.create_diann_cfg import create_diann_cfg from pyquantms.features.add_sage_feature import add_sage_feature +from pyquantms.mzml.mzml_statistics import mzml_statistics CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) @@ -13,6 +14,7 @@ def cli(): cli.add_command(create_diann_cfg) cli.add_command(add_sage_feature) +cli.add_command(mzml_statistics) if __name__ == "__main__": cli() diff --git a/requirements.txt b/requirements.txt index 65dc967..023502a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ click sdrf_pipelines -pyopenms \ No newline at end of file +pyopenms +ms2rescore \ No newline at end of file diff --git a/setup.py b/setup.py index 2b2e903..2d08098 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ "Development Status :: 5 - Production/Stable", ] -INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms"] +INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms", "ms2rescore=>3.0.2", "psm-utils=>0.8.0" "pydantic=>1.10"] PYTHON_REQUIRES = ">=3.7,<4" with open("README.md", "r") as fh: From 14478c70fd346e4c72a8155adc9e32b4c54ab327 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 16:52:59 +0100 Subject: [PATCH 13/53] mzml statistics --- pyquantms/diann/diann_convert.py | 1377 ++++++++++++++++++++++++ pyquantms/ms2rescore/__init__py.py | 0 pyquantms/ms2rescore/ms2rescore_cli.py | 178 +++ pyquantms/psm/__init__py.py | 0 pyquantms/psm/psm_conversion.py | 121 +++ pyquantms/sdrf/__init__.py | 0 pyquantms/sdrf/check_samplesheet.py | 126 +++ pyquantms/sdrf/extract_sample.py | 49 + requirements.txt | 4 +- tests/test_future.py | 7 + 10 files changed, 1861 insertions(+), 1 deletion(-) create mode 100644 pyquantms/diann/diann_convert.py create mode 100644 pyquantms/ms2rescore/__init__py.py create mode 100644 pyquantms/ms2rescore/ms2rescore_cli.py create mode 100644 pyquantms/psm/__init__py.py create mode 100644 pyquantms/psm/psm_conversion.py create mode 100644 pyquantms/sdrf/__init__.py create mode 100644 pyquantms/sdrf/check_samplesheet.py create mode 100644 pyquantms/sdrf/extract_sample.py diff --git a/pyquantms/diann/diann_convert.py b/pyquantms/diann/diann_convert.py new file mode 100644 index 0000000..fb2bdd2 --- /dev/null +++ b/pyquantms/diann/diann_convert.py @@ -0,0 +1,1377 @@ +#!/usr/bin/env python +""" +This script converts the output from DIA-NN into three standard formats: MSstats, Triqler and mzTab. +License: Apache 2.0 +Authors: Hong Wong, Yasset Perez-Riverol +Revisions: + 2023-Aug-05: J. Sebastian Paez +""" +import logging +import os +import re +import warnings +from pathlib import Path +from typing import Any, List, Tuple, Dict, Set, Union + +import click +import numpy as np +import pandas as pd +from pyopenms import AASequence, FASTAFile, ModificationsDB +from pyopenms.Constants import PROTON_MASS_U + +pd.set_option("display.max_rows", 500) +pd.set_option("display.max_columns", 500) +pd.set_option("display.width", 1000) + +CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) +REVISION = "0.1.1" + +logging.basicConfig(format="%(asctime)s [%(funcName)s] - %(message)s", level=logging.DEBUG) +logger = logging.getLogger(__name__) + + +@click.group(context_settings=CONTEXT_SETTINGS) +def cli(): + pass + + +@click.command("convert") +@click.option("--folder", "-f") +@click.option("--exp_design", "-d") +@click.option("--diann_version", "-v") +@click.option("--dia_params", "-p") +@click.option("--charge", "-c") +@click.option("--missed_cleavages", "-m") +@click.option("--qvalue_threshold", "-q", type=float) +@click.pass_context +def convert(ctx, folder, exp_design, dia_params, diann_version, charge, missed_cleavages, qvalue_threshold): + """ + Convert DIA-NN output to MSstats, Triqler or mzTab. + The output formats are used for quality control and downstream analysis. + + :param folder: DiannConvert specifies the folder where the required file resides. The folder contains + the DiaNN main report, protein matrix, precursor matrix, experimental design file, protein sequence + FASTA file, version file of DiaNN and ms_info TSVs + :type folder: str + :param dia_params: A list contains DIA parameters + :type dia_params: list + :param diann_version: Path to a version file of DIA-NN + :type diann_version: str + :param charge: The charge assigned by DIA-NN(max_precursor_charge) + :type charge: int + :param missed_cleavages: Allowed missed cleavages assigned by DIA-NN + :type missed_cleavages: int + :param qvalue_threshold: Threshold for filtering q value + :type qvalue_threshold: float + """ + logger.debug(f"Revision {REVISION}") + logger.debug("Reading input files...") + diann_directory = DiannDirectory(folder, diann_version_file=diann_version) + report = diann_directory.main_report_df(qvalue_threshold=qvalue_threshold) + s_DataFrame, f_table = get_exp_design_dfs(exp_design) + + # Convert to MSstats + msstats_columns_keep = [ + "Protein.Names", + "Modified.Sequence", + "Precursor.Charge", + "Precursor.Quantity", + "File.Name", + "Run", + ] + + logger.debug("Converting to MSstats format...") + out_msstats = report[msstats_columns_keep] + out_msstats.columns = ["ProteinName", "PeptideSequence", "PrecursorCharge", "Intensity", "Reference", "Run"] + out_msstats = out_msstats[out_msstats["Intensity"] != 0] + + # Q: What is this line doing? + out_msstats.loc[:, "PeptideSequence"] = out_msstats.apply( + lambda x: AASequence.fromString(x["PeptideSequence"]).toString(), axis=1 + ) + out_msstats["FragmentIon"] = "NA" + out_msstats["ProductCharge"] = "0" + out_msstats["IsotopeLabelType"] = "L" + unique_reference_map = {k: os.path.basename(k) for k in out_msstats["Reference"].unique()} + out_msstats["Reference"] = out_msstats["Reference"].map(unique_reference_map) + del unique_reference_map + + logger.debug("\n\nReference Column >>>") + logger.debug(out_msstats["Reference"]) + + logger.debug(f"\n\nout_msstats ({out_msstats.shape}) >>>") + logger.debug(out_msstats.head(5)) + + logger.debug(f"\n\nf_table ({f_table.shape})>>>") + logger.debug(f_table.head(5)) + + logger.debug(f"\n\ns_DataFrame ({s_DataFrame.shape})>>>") + logger.debug(s_DataFrame.head(5)) + + logger.debug("Adding Fraction, BioReplicate, Condition columns") + # Changing implementation from apply to merge went from several minutes to + # ~50ms + out_msstats = out_msstats.merge( + ( + s_DataFrame[["Sample", "MSstats_Condition", "MSstats_BioReplicate"]] + .merge(f_table[["Fraction", "Sample", "run"]], on="Sample") + .rename(columns={"run": "Run", "MSstats_BioReplicate": "BioReplicate", "MSstats_Condition": "Condition"}) + .drop(columns=["Sample"]) + ), + on="Run", + validate="many_to_one", + ) + exp_out_prefix = Path(exp_design).stem + out_msstats.to_csv(exp_out_prefix + "_msstats_in.csv", sep=",", index=False) + logger.info(f"MSstats input file is saved as {exp_out_prefix}_msstats_in.csv") + + # Convert to Triqler + triqler_cols = ["ProteinName", "PeptideSequence", "PrecursorCharge", "Intensity", "Run", "Condition"] + out_triqler = out_msstats[triqler_cols] + del out_msstats + out_triqler.columns = ["proteins", "peptide", "charge", "intensity", "run", "condition"] + out_triqler = out_triqler[out_triqler["intensity"] != 0] + + out_triqler.loc[:, "searchScore"] = report["Q.Value"] + out_triqler.loc[:, "searchScore"] = 1 - out_triqler["searchScore"] + out_triqler.to_csv(exp_out_prefix + "_triqler_in.tsv", sep="\t", index=False) + logger.info(f"Triqler input file is saved as {exp_out_prefix}_triqler_in.tsv") + del out_triqler + + mztab_out = f"{Path(exp_design).stem}_out.mzTab" + # Convert to mzTab + diann_directory.convert_to_mztab( + report=report, + f_table=f_table, + charge=charge, + missed_cleavages=missed_cleavages, + dia_params=dia_params, + out=mztab_out, + ) + + +def _true_stem(x): + """ + Return the true stem of a file name, i.e. the + file name without the extension. + + :param x: The file name + :type x: str + :return: The true stem of the file name + :rtype: str + + Examples: + >>> _true_stem("foo.mzML") + 'foo' + >>> _true_stem("foo.d.tar") + 'foo' + + These examples can be tested with pytest: + $ pytest -v --doctest-modules + """ + split = os.path.basename(x).split(".") + stem = split[0] + + # Should I check here that the extensions are + # allowed? I can see how this would break if the + # file name contains a period. + return stem + + +def get_exp_design_dfs(exp_design_file): + logger.info(f"Reading experimental design file: {exp_design_file}") + with open(exp_design_file, "r") as f: + data = f.readlines() + empty_row = data.index("\n") + f_table = [i.replace("\n", "").split("\t") for i in data[1:empty_row]] + f_header = data[0].replace("\n", "").split("\t") + f_table = pd.DataFrame(f_table, columns=f_header) + f_table.loc[:, "run"] = f_table.apply(lambda x: _true_stem(x["Spectra_Filepath"]), axis=1) + + s_table = [i.replace("\n", "").split("\t") for i in data[empty_row + 1 :]][1:] + s_header = data[empty_row + 1].replace("\n", "").split("\t") + s_DataFrame = pd.DataFrame(s_table, columns=s_header) + + return s_DataFrame, f_table + +def compute_mass_modified_peptide(peptide_seq: str) -> float: + """ + Function that takes a peptide sequence including modifications and compute the mass using the AASequence class from + pyopenms. The notation of a peptidoform for pyopenms is the following: + + if not modifications is present: + AVQVHQDTLRTMYFAXR -> AVQVHQDTLRTMYFAX[178.995499]R + if modification is present in Methionine: + AVQVHQDTLRTM(Oxidation)YFAXR -> AVQVHQDTLRTM(Oxidation)YFAX[178.995499]R + + @param peptide_seq: str, peptide sequence + @return: float, mass of the peptide + """ + peptide_parts: List[str] = [] + not_mod = True + aa_mass = { + "X": "X[178.98493453312]", # 196.995499 - 17.003288 - 1.00727646688 + "U": "X[132.94306553312]", # 150.95363 - 17.003288 - 1.00727646688 + "O": "X[237.14773053312]", # 255.158295 - 17.003288 - 1.00727646688 + } + for aa in peptide_seq: + # Check if the letter is in aminoacid + if aa == "(": + not_mod = False + elif aa == ")": + not_mod = True + # Check aminoacid letter + if aa in aa_mass and not_mod: + aa = aa_mass[aa] + elif aa not in ['G','A','V','L','I','F','M','P','W','S','C','T','Y','N','Q','D','E','K','R','H'] and not_mod and aa != ")": + logger.info(f"Unknown amino acid with mass not known:{aa}") + peptide_parts.append(aa) + new_peptide_seq = ''.join(peptide_parts) + mass = AASequence.fromString(new_peptide_seq).getMonoWeight() + logger.debug(new_peptide_seq + ":" + str(mass)) + return mass + +class DiannDirectory: + def __init__(self, base_path, diann_version_file): + self.base_path = Path(base_path) + if not self.base_path.exists() and not self.base_path.is_dir(): + raise NotADirectoryError(f"Path {self.base_path} does not exist") + self.diann_version_file = Path(diann_version_file) + if not self.diann_version_file.is_file(): + raise FileNotFoundError(f"Path {self.diann_version_file} does not exist") + + def find_first_file_with_suffix(self, suffix: str) -> os.PathLike: + """Finds a file with a given suffix in the directory. + + :param suffix: The suffix to search for + :type suffix: str + + :raises FileNotFoundError: If no file with the given suffix is found + """ + try: + return next(self.base_path.glob(f"**/*{suffix}")) + except StopIteration: + raise FileNotFoundError(f"Could not find file with suffix {suffix}") + + @property + def report(self) -> os.PathLike: + return self.find_first_file_with_suffix("report.tsv") + + @property + def pg_matrix(self) -> os.PathLike: + return self.find_first_file_with_suffix("pg_matrix.tsv") + + @property + def pr_matrix(self) -> os.PathLike: + return self.find_first_file_with_suffix("pr_matrix.tsv") + + @property + def fasta(self) -> os.PathLike: + try: + return self.find_first_file_with_suffix(".fasta") + except FileNotFoundError: + return self.find_first_file_with_suffix(".fa") + + @property + def ms_info(self) -> os.PathLike: + return self.find_first_file_with_suffix("ms_info.tsv") + + @property + def diann_version(self) -> str: + logger.debug("Validating DIANN version") + diann_version_id = None + with open(self.diann_version_file) as f: + for line in f: + if "DIA-NN" in line: + logger.debug(f"Found DIA-NN version: {line}") + diann_version_id = line.rstrip("\n").split(": ")[1] + + if diann_version_id is None: + raise ValueError(f"Could not find DIA-NN version in file {self.diann_version_file}") + + return diann_version_id + + def validate_diann_version(self) -> None: + supported_diann_versions = ["1.8.1"] + if self.diann_version not in supported_diann_versions: + raise ValueError(f"Unsupported DIANN version {self.diann_version}") + + def convert_to_mztab( + self, report, f_table, charge: int, missed_cleavages: int, dia_params: List[Any], out: os.PathLike + ) -> None: + logger.info("Converting to mzTab") + self.validate_diann_version() + + # This could be a branching point if we want to support other versions + # of DIA-NN, maybe something like this: + # if diann_version_id == "1.8.1": + # self.convert_to_mztab_1_8_1(report, f_table, charge, missed_cleavages, dia_params) + # else: + # raise ValueError(f"Unsupported DIANN version {diann_version_id}, supported versions are 1.8.1 ...") + + logger.info(f"Reading fasta file: {self.fasta}") + entries: list = [] + f = FASTAFile() + f.load(str(self.fasta), entries) + fasta_entries = [(e.identifier, e.sequence, len(e.sequence)) for e in entries] + fasta_df = pd.DataFrame(fasta_entries, columns=["id", "seq", "len"]) + + logger.info("Mapping run information to report") + index_ref = f_table.copy() + index_ref.rename(columns={"Fraction_Group": "ms_run", "Sample": "study_variable", "run": "Run"}, inplace=True) + index_ref["ms_run"] = index_ref["ms_run"].astype("int") + index_ref["study_variable"] = index_ref["study_variable"].astype("int") + report = report.merge(index_ref[["ms_run", "Run", "study_variable"]], on="Run", validate="many_to_one") + + MTD, database = mztab_MTD(index_ref, dia_params, str(self.fasta), charge, missed_cleavages) + pg = pd.read_csv( + self.pg_matrix, + sep="\t", + header=0, + ) + PRH = mztab_PRH(report, pg, index_ref, database, fasta_df) + del pg + pr = pd.read_csv( + self.pr_matrix, + sep="\t", + header=0, + ) + precursor_list = list(report["Precursor.Id"].unique()) + PEH = mztab_PEH(report, pr, precursor_list, index_ref, database) + del pr + PSH = mztab_PSH(report, str(self.base_path), database) + del report + MTD.loc["", :] = "" + PRH.loc[len(PRH) + 1, :] = "" + PEH.loc[len(PEH) + 1, :] = "" + with open(out, "w", newline="") as f: + MTD.to_csv(f, mode="w", sep="\t", index=False, header=False) + PRH.to_csv(f, mode="w", sep="\t", index=False, header=True) + PEH.to_csv(f, mode="w", sep="\t", index=False, header=True) + PSH.to_csv(f, mode="w", sep="\t", index=False, header=True) + + logger.info(f"mzTab file generated successfully! at {out}_out.mzTab") + + def main_report_df(self, qvalue_threshold: float) -> pd.DataFrame: + remain_cols = [ + "File.Name", + "Run", + "Protein.Group", + "Protein.Names", + "Protein.Ids", + "First.Protein.Description", + "PG.MaxLFQ", + "RT.Start", + "Global.Q.Value", + "Lib.Q.Value", + "PEP", + "Precursor.Normalised", + "Precursor.Id", + "Q.Value", + "Modified.Sequence", + "Stripped.Sequence", + "Precursor.Charge", + "Precursor.Quantity", + "Global.PG.Q.Value", + ] + report = pd.read_csv(self.report, sep="\t", header=0, usecols=remain_cols) + + # filter based on qvalue parameter for downstream analysiss + logger.debug(f"Filtering report based on qvalue threshold: {qvalue_threshold}, {len(report)} rows") + report = report[report["Q.Value"] < qvalue_threshold] + logger.debug(f"Report filtered, {len(report)} rows remaining") + + logger.debug("Calculating Precursor.Mz") + # Making the map is 10x faster, and includes the mass of + # the modification. with respect to the previous implementation. + uniq_masses = {k: compute_mass_modified_peptide(k) for k in report["Modified.Sequence"].unique()} + mass_vector = report["Modified.Sequence"].map(uniq_masses) + report["Calculate.Precursor.Mz"] = (mass_vector + (PROTON_MASS_U * report["Precursor.Charge"])) / report[ + "Precursor.Charge" + ] + + logger.debug("Indexing Precursors") + # Making the map is 1500x faster + precursor_index_map = {k: i for i, k in enumerate(report["Precursor.Id"].unique())} + report["precursor.Index"] = report["Precursor.Id"].map(precursor_index_map) + + logger.debug(f"Shape of main report {report.shape}") + logger.debug(str(report.head())) + + return report + + +def MTD_mod_info(fix_mod, var_mod): + """ + Convert fixed and variable modifications to the format required by the MTD sub-table. + + :param fix_mod: Fixed modifications from DIA parameter list + :type fix_mod: str + :param var_mod: Variable modifications from DIA parameter list + :type var_mod: str + :return: A tuple contains fixed and variable modifications, and flags indicating whether they are null + :rtype: tuple + """ + var_ptm = [] + fix_ptm = [] + mods_db = ModificationsDB() + + if fix_mod != "null": + fix_flag = 1 + for mod in fix_mod.split(","): + mod_obj = mods_db.getModification(mod) + mod_name = mod_obj.getId() + mod_accession = mod_obj.getUniModAccession() + site = mod_obj.getOrigin() + fix_ptm.append(("[UNIMOD, " + mod_accession.upper() + ", " + mod_name + ", ]", site)) + else: + fix_flag = 0 + fix_ptm.append("[MS, MS:1002453, No fixed modifications searched, ]") + + if var_mod != "null": + var_flag = 1 + for mod in var_mod.split(","): + mod_obj = mods_db.getModification(mod) + mod_name = mod_obj.getId() + mod_accession = mod_obj.getUniModAccession() + site = mod_obj.getOrigin() + var_ptm.append(("[UNIMOD, " + mod_accession.upper() + ", " + mod_name + ", ]", site)) + else: + var_flag = 0 + var_ptm.append("[MS, MS:1002454, No variable modifications searched, ]") + + return fix_ptm, var_ptm, fix_flag, var_flag + + +def mztab_MTD(index_ref, dia_params, fasta, charge, missed_cleavages): + """ + Construct MTD sub-table. + + :param index_ref: On the basis of f_table, two columns "MS_run" and "study_variable" are added for matching + :type index_ref: pandas.core.frame.DataFrame + :param dia_params: A list contains DIA parameters + :type dia_params: list + :param fasta: Fasta file path + :type fasta: str + :param charge: Charges set by Dia-NN + :type charge: int + :param missed_cleavages: Missed cleavages set by Dia-NN + :type missed_cleavages: int + :return: MTD sub-table + :rtype: pandas.core.frame.DataFrame + """ + logger.info("Constructing MTD sub-table...") + dia_params_list = dia_params.split(";") + dia_params_list = ["null" if i == "" else i for i in dia_params_list] + FragmentMassTolerance = dia_params_list[0] + FragmentMassToleranceUnit = dia_params_list[1] + PrecursorMassTolerance = dia_params_list[2] + PrecursorMassToleranceUnit = dia_params_list[3] + Enzyme = dia_params_list[4] + FixedModifications = dia_params_list[5] + VariableModifications = dia_params_list[6] + out_mztab_MTD = pd.DataFrame() + out_mztab_MTD.loc[1, "mzTab-version"] = "1.0.0" + out_mztab_MTD.loc[1, "mzTab-mode"] = "Summary" + out_mztab_MTD.loc[1, "mzTab-type"] = "Quantification" + out_mztab_MTD.loc[1, "title"] = "ConsensusMap export from OpenMS" + out_mztab_MTD.loc[1, "description"] = "OpenMS export from consensusXML" + out_mztab_MTD.loc[1, "protein_search_engine_score[1]"] = "[, , DIA-NN Global.PG.Q.Value, ]" + out_mztab_MTD.loc[1, "peptide_search_engine_score[1]"] = ( + "[, , DIA-NN Q.Value (minimum of the respective precursor q-values), ]" + ) + out_mztab_MTD.loc[1, "psm_search_engine_score[1]"] = "[MS, MS:MS:1001869, protein-level q-value, ]" + out_mztab_MTD.loc[1, "software[1]"] = "[MS, MS:1003253, DIA-NN, Release (v1.8.1)]" + out_mztab_MTD.loc[1, "software[1]-setting[1]"] = fasta + out_mztab_MTD.loc[1, "software[1]-setting[2]"] = "db_version:null" + out_mztab_MTD.loc[1, "software[1]-setting[3]"] = "fragment_mass_tolerance:" + FragmentMassTolerance + out_mztab_MTD.loc[1, "software[1]-setting[4]"] = "fragment_mass_tolerance_unit:" + FragmentMassToleranceUnit + out_mztab_MTD.loc[1, "software[1]-setting[5]"] = "precursor_mass_tolerance:" + PrecursorMassTolerance + out_mztab_MTD.loc[1, "software[1]-setting[6]"] = "precursor_mass_tolerance_unit:" + PrecursorMassToleranceUnit + out_mztab_MTD.loc[1, "software[1]-setting[7]"] = "enzyme:" + Enzyme + out_mztab_MTD.loc[1, "software[1]-setting[8]"] = "enzyme_term_specificity:full" + out_mztab_MTD.loc[1, "software[1]-setting[9]"] = "charges:" + str(charge) + out_mztab_MTD.loc[1, "software[1]-setting[10]"] = "missed_cleavages:" + str(missed_cleavages) + out_mztab_MTD.loc[1, "software[1]-setting[11]"] = "fixed_modifications:" + FixedModifications + out_mztab_MTD.loc[1, "software[1]-setting[12]"] = "variable_modifications:" + VariableModifications + + (fixed_mods, variable_mods, fix_flag, var_flag) = MTD_mod_info(FixedModifications, VariableModifications) + if fix_flag == 1: + for i in range(1, len(fixed_mods) + 1): + out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]"] = fixed_mods[i - 1][0] + out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]-site"] = fixed_mods[i - 1][1] + out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]-position"] = "Anywhere" + else: + out_mztab_MTD.loc[1, "fixed_mod[1]"] = fixed_mods[0] + + if var_flag == 1: + for i in range(1, len(variable_mods) + 1): + out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]"] = variable_mods[i - 1][0] + out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]-site"] = variable_mods[i - 1][1] + out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]-position"] = "Anywhere" + else: + out_mztab_MTD.loc[1, "variable_mod[1]"] = variable_mods[0] + + out_mztab_MTD.loc[1, "quantification_method"] = "[MS, MS:1001834, LC-MS label-free quantitation analysis, ]" + out_mztab_MTD.loc[1, "protein-quantification_unit"] = "[, , Abundance, ]" + out_mztab_MTD.loc[1, "peptide-quantification_unit"] = "[, , Abundance, ]" + + for i in range(1, max(index_ref["ms_run"]) + 1): + out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-format"] = "[MS, MS:1000584, mzML file, ]" + out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-location"] = ( + "file://" + index_ref[index_ref["ms_run"] == i]["Spectra_Filepath"].values[0] + ) + out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-id_format"] = ( + "[MS, MS:1000777, spectrum identifier nativeID format, ]" + ) + out_mztab_MTD.loc[1, "assay[" + str(i) + "]-quantification_reagent"] = "[MS, MS:1002038, unlabeled sample, ]" + out_mztab_MTD.loc[1, "assay[" + str(i) + "]-ms_run_ref"] = "ms_run[" + str(i) + "]" + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + # This is used here in order to ignore performance warnings from pandas. + for i in range(1, max(index_ref["study_variable"]) + 1): + study_variable = [] + for j in list(index_ref[index_ref["study_variable"] == i]["ms_run"].values): + study_variable.append("assay[" + str(j) + "]") + out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-assay_refs"] = ",".join(study_variable) + out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-description"] = "no description given" + + # The former loop makes a very sharded frame, this + # makes the frame more compact in memory. + out_mztab_MTD = out_mztab_MTD.copy() + out_mztab_MTD.loc[2, :] = "MTD" + + # Transpose out_mztab_MTD + col = list(out_mztab_MTD.columns) + row = list(out_mztab_MTD.index) + out_mztab_MTD_T = pd.DataFrame(out_mztab_MTD.values.T, index=col, columns=row) + out_mztab_MTD_T.columns = ["inf", "index"] + out_mztab_MTD_T.insert(0, "title", out_mztab_MTD_T.index) + index = out_mztab_MTD_T.loc[:, "index"] + out_mztab_MTD_T.drop(labels=["index"], axis=1, inplace=True) + out_mztab_MTD_T.insert(0, "index", index) + database = os.path.basename(fasta.split(".")[-2]) + + return out_mztab_MTD_T, database + + +def mztab_PRH(report, pg, index_ref, database, fasta_df): + """ + Construct PRH sub-table. + + :param report: Dataframe for Dia-NN main report + :type report: pandas.core.frame.DataFrame + :param pg: Dataframe for Dia-NN protein groups matrix + :type pg: pandas.core.frame.DataFrame + :param index_ref: On the basis of f_table, two columns "ms_run" and "study_variable" are added for matching + :type index_ref: pandas.core.frame.DataFrame + :param database: Path to fasta file + :type database: str + :param fasta_df: A dataframe contains protein IDs, sequences and lengths + :type fasta_df: pandas.core.frame.DataFrame + :return: PRH sub-table + :rtype: pandas.core.frame.DataFrame + """ + logger.info("Constructing PRH sub-table...") + logger.debug( + f"Input report shape: {report.shape}," + f" input pg shape: {pg.shape}," + f" input index_ref shape: {index_ref.shape}," + f" input fasta_df shape: {fasta_df.shape}" + ) + file = list(pg.columns[5:]) + col = {} + for i in file: + col[i] = ( + "protein_abundance_assay[" + str(index_ref[index_ref["Run"] == _true_stem(i)]["ms_run"].values[0]) + "]" + ) + + pg.rename(columns=col, inplace=True) + + logger.debug("Classifying results type ...") + pg["opt_global_result_type"] = "single_protein" + pg.loc[pg["Protein.Ids"].str.contains(";"), "opt_global_result_type"] = "indistinguishable_protein_group" + + out_mztab_PRH = pg + del pg + out_mztab_PRH = out_mztab_PRH.drop(["Protein.Names"], axis=1) + out_mztab_PRH.rename( + columns={"Protein.Group": "accession", "First.Protein.Description": "description"}, inplace=True + ) + out_mztab_PRH.loc[:, "database"] = database + + null_col = [ + "taxid", + "species", + "database_version", + "search_engine", + "opt_global_Posterior_Probability_score", + "opt_global_nr_found_peptides", + "opt_global_cv_PRIDE:0000303_decoy_hit", + ] + for i in null_col: + out_mztab_PRH.loc[:, i] = "null" + + logger.debug("Extracting accession values (keeping first)...") + out_mztab_PRH.loc[:, "accession"] = out_mztab_PRH.apply(lambda x: x["accession"].split(";")[0], axis=1) + + protein_details_df = out_mztab_PRH[out_mztab_PRH["opt_global_result_type"] == "indistinguishable_protein_group"] + prh_series = protein_details_df["Protein.Ids"].str.split(";", expand=True).stack().reset_index(level=1, drop=True) + prh_series.name = "accession" + protein_details_df = ( + protein_details_df.drop("accession", axis=1).join(prh_series).reset_index().drop(columns="index") + ) + if len(protein_details_df) > 0: + logger.info(f"Found {len(protein_details_df)} indistinguishable protein groups") + # The Following line fails if there are no indistinguishable protein groups + protein_details_df.loc[:, "col"] = "protein_details" + # protein_details_df = protein_details_df[-protein_details_df["accession"].str.contains("-")] + out_mztab_PRH = pd.concat([out_mztab_PRH, protein_details_df]).reset_index(drop=True) + else: + logger.info("No indistinguishable protein groups found") + + logger.debug("Calculating protein coverage (bottleneck)...") + # This is a bottleneck + # reimplementation runs in 67s vs 137s (old) in my data + out_mztab_PRH.loc[:, "protein_coverage"] = calculate_protein_coverages( + report=report, out_mztab_PRH=out_mztab_PRH, fasta_df=fasta_df + ) + + logger.debug("Getting ambiguity members...") + # IN THEORY this should be the same as + # out_mztab_PRH["ambiguity_members"] = out_mztab_PRH["Protein.Ids"] + # out_mztab_PRH.loc[out_mztab_PRH["opt_global_result_type"] == "single_protein", "ambiguity_members"] = "null" + # or out_mztab_PRH.loc[out_mztab_PRH["Protein.Ids"] == out_mztab_PRH["accession"], "ambiguity_members"] = "null" + out_mztab_PRH.loc[:, "ambiguity_members"] = out_mztab_PRH.apply( + lambda x: x["Protein.Ids"] if x["opt_global_result_type"] == "indistinguishable_protein_group" else "null", + axis=1, + ) + + logger.debug("Matching PRH to best search engine score...") + score_looker = ModScoreLooker(report) + out_mztab_PRH[["modifiedSequence", "best_search_engine_score[1]"]] = out_mztab_PRH.apply( + lambda x: score_looker.get_score(x["Protein.Ids"]), axis=1, result_type="expand" + ) + + logger.debug("Matching PRH to modifications...") + out_mztab_PRH.loc[:, "modifications"] = out_mztab_PRH.apply( + lambda x: find_modification(x["modifiedSequence"]), axis=1, result_type="expand" + ) + + logger.debug("Matching PRH to protein quantification...") + ## quantity at protein level: PG.MaxLFQ + # This used to be a bottleneck in performance + # This implementation drops the run time from 57s to 25ms + protein_agg_report = ( + report[["PG.MaxLFQ", "Protein.Ids", "study_variable"]] + .groupby(["study_variable", "Protein.Ids"]) + .agg({"PG.MaxLFQ": ["mean", "std", "sem"]}) + .reset_index() + .pivot(columns=["study_variable"], index="Protein.Ids") + .reset_index() + ) + protein_agg_report.columns = ["::".join([str(s) for s in col]).strip() for col in protein_agg_report.columns.values] + subname_mapper = { + "Protein.Ids::::": "Protein.Ids", + "PG.MaxLFQ::mean": "protein_abundance_study_variable", + "PG.MaxLFQ::std": "protein_abundance_stdev_study_variable", + "PG.MaxLFQ::sem": "protein_abundance_std_error_study_variable", + } + name_mapper = name_mapper_builder(subname_mapper) + protein_agg_report.rename(columns=name_mapper, inplace=True) + # out_mztab_PRH has columns accession and Protein.Ids; 'Q9NZJ9', 'A0A024RBG1;Q9NZJ9;Q9NZJ9-2'] + # the report table has 'Protein.Group' and 'Protein.Ids': 'Q9NZJ9', 'A0A024RBG1;Q9NZJ9;Q9NZJ9-2' + # Oddly enough the last implementation mapped the the accession (Q9NZJ9) in the mztab + # to the Protein.Ids (A0A024RBG1;Q9NZJ9;Q9NZJ9-2), leading to A LOT of missing values. + out_mztab_PRH = out_mztab_PRH.merge( + protein_agg_report, on="Protein.Ids", how="left", validate="many_to_one", copy=True + ) + del name_mapper + del subname_mapper + del protein_agg_report + # end of (former) bottleneck + + out_mztab_PRH.loc[:, "PRH"] = "PRT" + index = out_mztab_PRH.loc[:, "PRH"] + out_mztab_PRH.drop(["PRH", "Genes", "modifiedSequence", "Protein.Ids"], axis=1, inplace=True) + out_mztab_PRH.insert(0, "PRH", index) + out_mztab_PRH.fillna("null", inplace=True) + out_mztab_PRH.loc[:, "database"] = database + new_cols = [col for col in out_mztab_PRH.columns if not col.startswith("opt_")] + [ + col for col in out_mztab_PRH.columns if col.startswith("opt_") + ] + out_mztab_PRH = out_mztab_PRH[new_cols] + return out_mztab_PRH + + +def mztab_PEH( + report: pd.DataFrame, pr: pd.DataFrame, precursor_list: List[str], index_ref: pd.DataFrame, database: os.PathLike +) -> pd.DataFrame: + """ + Construct PEH sub-table. + + :param report: Dataframe for Dia-NN main report + :type report: pandas.core.frame.DataFrame + :param pr: Dataframe for Dia-NN precursors matrix + :type pr: pandas.core.frame.DataFrame + :param precursor_list: A list contains all precursor IDs + :type precursor_list: list + :param index_ref: On the basis of f_table, two columns "ms_run" and "study_variable" are added for matching + :type index_ref: pandas.core.frame.DataFrame + :param database: Path to fasta file + :type database: str + :return: PEH sub-table + :rtype: pandas.core.frame.DataFrame + """ + logger.info("Constructing PEH sub-table...") + logger.debug( + f"report.shape: {report.shape}, " + f" pr.shape: {pr.shape}," + f" len(precursor_list): {len(precursor_list)}," + f" index_ref.shape: {index_ref.shape}" + ) + out_mztab_PEH = pd.DataFrame() + out_mztab_PEH = pr.iloc[:, 0:10] + out_mztab_PEH.drop( + ["Protein.Group", "Protein.Names", "First.Protein.Description", "Proteotypic"], axis=1, inplace=True + ) + out_mztab_PEH.rename( + columns={ + "Stripped.Sequence": "sequence", + "Protein.Ids": "accession", + "Modified.Sequence": "opt_global_cv_MS:1000889_peptidoform_sequence", + "Precursor.Charge": "charge", + }, + inplace=True, + ) + + logger.debug("Finding modifications...") + out_mztab_PEH.loc[:, "modifications"] = out_mztab_PEH.apply( + lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), axis=1, result_type="expand" + ) + + logger.debug("Extracting sequence...") + out_mztab_PEH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = out_mztab_PEH.apply( + lambda x: AASequence.fromString(x["opt_global_cv_MS:1000889_peptidoform_sequence"]).toString(), axis=1 + ) + + logger.debug("Checking accession uniqueness...") + out_mztab_PEH.loc[:, "unique"] = out_mztab_PEH.apply( + lambda x: "0" if ";" in str(x["accession"]) else "1", axis=1, result_type="expand" + ) + + null_col = ["database_version", "search_engine", "retention_time_window", "mass_to_charge", "opt_global_feature_id"] + for i in null_col: + out_mztab_PEH.loc[:, i] = "null" + out_mztab_PEH.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" + + logger.debug("Matching precursor IDs...") + # Pre-calculating the indices and using a lookup table drops run time from + # ~6.5s to 11ms + precursor_indices = {k: i for i, k in enumerate(precursor_list)} + pr_ids = out_mztab_PEH["Precursor.Id"].map(precursor_indices) + out_mztab_PEH["pr_id"] = pr_ids + del precursor_indices + + logger.debug("Getting scores per run") + # This implementation is 422-700x faster than the apply-based one + tmp = ( + report.groupby(["precursor.Index", "ms_run"]) + .agg({"Q.Value": ["min"]}) + .reset_index() + .pivot(columns=["ms_run"], index="precursor.Index") + .reset_index() + ) + tmp.columns = pd.Index(["::".join([str(s) for s in col]).strip() for col in tmp.columns.values]) + subname_mapper = { + "precursor.Index::::": "precursor.Index", + "Q.Value::min": "search_engine_score[1]_ms_run", + } + name_mapper = name_mapper_builder(subname_mapper) + tmp.rename(columns=name_mapper, inplace=True) + out_mztab_PEH = out_mztab_PEH.merge( + tmp.rename(columns={"precursor.Index": "pr_id"}), on="pr_id", validate="one_to_one" + ) + del tmp + del subname_mapper + del name_mapper + + logger.debug("Getting peptide abundances per study variable") + pep_study_report = per_peptide_study_report(report) + out_mztab_PEH = out_mztab_PEH.merge(pep_study_report, on="pr_id", how="left", validate="one_to_one", copy=True) + del pep_study_report + + logger.debug("Getting peptide properties...") + # Re-implementing this section from apply -> assign to groupby->agg + # speeds up the process from 11s to 25ms in my data (~440x faster) + # Notably, this changes slightly... + # "opt_global_q-value" was the FIRST "Global.Q.Value", now its the min + # "opt_global_SpecEValue_score" was the FIRST "Lib.Q.Value" now its the min + # I believe picking the first is inconsistent because no sorting is checked + # and the first is arbitrary. + + aggtable = ( + report.groupby(["precursor.Index"]) + .agg( + { + "Q.Value": "min", + "RT.Start": "mean", + "Global.Q.Value": "min", + "Lib.Q.Value": "min", + "Calculate.Precursor.Mz": "mean", + } + ) + .reset_index() + .rename( + columns={ + "precursor.Index": "pr_id", + "Q.Value": "best_search_engine_score[1]", + "RT.Start": "retention_time", + "Global.Q.Value": "opt_global_q-value", + "Lib.Q.Value": "opt_global_SpecEValue_score", + "Calculate.Precursor.Mz": "mass_to_charge", + } + ) + ) + del out_mztab_PEH["mass_to_charge"] + out_mztab_PEH = out_mztab_PEH.merge(aggtable, on="pr_id", validate="one_to_one") + + logger.debug("Re-ordering columns...") + out_mztab_PEH.loc[:, "PEH"] = "PEP" + out_mztab_PEH.loc[:, "database"] = str(database) + index = out_mztab_PEH.loc[:, "PEH"] + out_mztab_PEH.drop(["PEH", "Precursor.Id", "Genes", "pr_id"], axis=1, inplace=True) + out_mztab_PEH.insert(0, "PEH", index) + out_mztab_PEH.fillna("null", inplace=True) + new_cols = [col for col in out_mztab_PEH.columns if not col.startswith("opt_")] + [ + col for col in out_mztab_PEH.columns if col.startswith("opt_") + ] + out_mztab_PEH = out_mztab_PEH[new_cols] + + return out_mztab_PEH + + +def mztab_PSH(report, folder, database): + """ + Construct PSH sub-table. + + :param report: Dataframe for Dia-NN main report + :type report: pandas.core.frame.DataFrame + :param folder: DiannConvert specifies the folder where the required file resides. The folder contains + the DiaNN main report, protein matrix, precursor matrix, experimental design file, protein sequence + FASTA file, version file of DiaNN and ms_info TSVs + :type folder: str + :param database: Path to fasta file + :type database: str + :return: PSH sub-table + :rtype: pandas.core.frame.DataFrame + """ + logger.info("Constructing PSH sub-table") + + def __find_info(directory, n): + # This line matches n="220101_myfile", folder="." to + # "myfolder/220101_myfile_ms_info.tsv" + files = list(Path(directory).rglob(f"{n}_ms_info.tsv")) + # Check that it matches one and only one file + if not files: + raise ValueError(f"Could not find {n} info file in {directory}") + if len(files) > 1: + raise ValueError(f"Found multiple {n} info files in {directory}: {files}") + + return files[0] + + out_mztab_PSH = pd.DataFrame() + for n, group in report.groupby(["Run"]): + if isinstance(n, tuple) and len(n) == 1: + # This is here only to support versions of pandas where the groupby + # key is a tuple. + # related: https://github.com/pandas-dev/pandas/pull/51817 + n = n[0] + + file = __find_info(folder, n) + target = pd.read_csv(file, sep="\t") + group.sort_values(by="RT.Start", inplace=True) + target = target[["Retention_Time", "SpectrumID", "Exp_Mass_To_Charge"]] + target.columns = ["RT.Start", "opt_global_spectrum_reference", "exp_mass_to_charge"] + # Standardize spectrum identifier format for bruker data + if type(target.loc[0, "opt_global_spectrum_reference"]) != str: + target.loc[:, "opt_global_spectrum_reference"] = "scan=" + target.loc[ + :, "opt_global_spectrum_reference" + ].astype(str) + + # TODO seconds returned from precursor.getRT() + target.loc[:, "RT.Start"] = target.apply(lambda x: x["RT.Start"] / 60, axis=1) + out_mztab_PSH = pd.concat([out_mztab_PSH, pd.merge_asof(group, target, on="RT.Start", direction="nearest")]) + del report + + ## Score at PSM level: Q.Value + out_mztab_PSH = out_mztab_PSH[ + [ + "Stripped.Sequence", + "Protein.Ids", + "Q.Value", + "RT.Start", + "Precursor.Charge", + "Calculate.Precursor.Mz", + "exp_mass_to_charge", + "Modified.Sequence", + "PEP", + "Global.Q.Value", + "Global.Q.Value", + "opt_global_spectrum_reference", + "ms_run", + ] + ] + out_mztab_PSH.columns = [ + "sequence", + "accession", + "search_engine_score[1]", + "retention_time", + "charge", + "calc_mass_to_charge", + "exp_mass_to_charge", + "opt_global_cv_MS:1000889_peptidoform_sequence", + "opt_global_SpecEValue_score", + "opt_global_q-value", + "opt_global_q-value_score", + "opt_global_spectrum_reference", + "ms_run", + ] + + out_mztab_PSH.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" + out_mztab_PSH.loc[:, "PSM_ID"] = out_mztab_PSH.index + out_mztab_PSH.loc[:, "unique"] = out_mztab_PSH.apply( + lambda x: "0" if ";" in str(x["accession"]) else "1", axis=1, result_type="expand" + ) + out_mztab_PSH.loc[:, "database"] = database + + null_col = [ + "database_version", + "search_engine", + "pre", + "post", + "start", + "end", + "opt_global_feature_id", + "opt_global_map_index", + ] + for i in null_col: + out_mztab_PSH.loc[:, i] = "null" + + logger.info("Finding Modifications ...") + out_mztab_PSH.loc[:, "modifications"] = out_mztab_PSH.apply( + lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), axis=1, result_type="expand" + ) + + out_mztab_PSH.loc[:, "spectra_ref"] = out_mztab_PSH.apply( + lambda x: "ms_run[{}]:".format(x["ms_run"]) + x["opt_global_spectrum_reference"], axis=1, result_type="expand" + ) + + out_mztab_PSH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = out_mztab_PSH.apply( + lambda x: AASequence.fromString(x["opt_global_cv_MS:1000889_peptidoform_sequence"]).toString(), + axis=1, + result_type="expand", + ) + + out_mztab_PSH.loc[:, "PSH"] = "PSM" + index = out_mztab_PSH.loc[:, "PSH"] + out_mztab_PSH.drop(["PSH", "ms_run"], axis=1, inplace=True) + out_mztab_PSH.insert(0, "PSH", index) + out_mztab_PSH.fillna("null", inplace=True) + new_cols = [col for col in out_mztab_PSH.columns if not col.startswith("opt_")] + [ + col for col in out_mztab_PSH.columns if col.startswith("opt_") + ] + out_mztab_PSH = out_mztab_PSH[new_cols] + # out_mztab_PSH.to_csv("./out_psms.mztab", sep=",", index=False) + + return out_mztab_PSH + + +def add_info(target, index_ref): + """ + On the basis of f_table, two columns "ms_run" and "study_variable" are added for matching. + + :param target: The value of "Run" column in f_table + :type target: str + :param index_ref: A dataframe on the basis of f_table + :type index_ref: pandas.core.frame.DataFrame + :return: A tuple contains ms_run and study_variable + :rtype: tuple + """ + match = index_ref[index_ref["Run"] == target] + ms_run = match["ms_run"].values[0] + study_variable = match["study_variable"].values[0] + + return ms_run, study_variable + + +def classify_result_type(target): + """Classify proteins + + :param target: The target dataframe contains "Protein.Group" and "Protein.Ids" + :type target: pandas.core.frame.DataFrame + :return: A string implys protein type + :rtype: str + """ + if ";" in target["Protein.Ids"]: + return "indistinguishable_protein_group" + return "single_protein" + + +def match_in_report(report, target, max_, flag, level): + """ + This function is used to match the columns "ms_run" and "study_variable" from the report and + get the corresponding information for the mztab ms_run and study_values metadata values. + + :param report: Dataframe for Dia-NN main report + :type report: pandas.core.frame.DataFrame + :param target: The value of "pr_id" column in out_mztab_PEH(level="peptide") or the "accession" column in out_mztab_PRH(level="protein") + :type target: str + :param max_: max_assay or max_study_variable + :type max_: int + :param flag: Match the "study_variable" column(flag=1) or the "ms_run" column(flag=0) in the filter result + :type flag: int + :param level: "pep" or "protein" + :type level: str + :return: A tuple contains multiple messages + :rtype: tuple + """ # noqa + if flag == 1 and level == "pep": + result = report[report["precursor.Index"] == target] + PEH_params = [] + for i in range(1, max_ + 1): + match = result[result["study_variable"] == i] + PEH_params.extend([match["Precursor.Normalised"].mean(), "null", "null", "null", match["RT.Start"].mean()]) + + return tuple(PEH_params) + + if flag == 0 and level == "pep": + result = report[report["precursor.Index"] == target] + q_value = [] + for i in range(1, max_ + 1): + match = result[result["ms_run"] == i] + q_value.append(match["Q.Value"].values[0] if match["Q.Value"].values.size > 0 else np.nan) + + return tuple(q_value) + + if flag == 1 and level == "protein": + result = report[report["Protein.Ids"] == target] + PRH_params = [] + for i in range(1, max_ + 1): + match = result[result["study_variable"] == i] + PRH_params.extend([match["PG.MaxLFQ"].mean(), "null", "null"]) + + return tuple(PRH_params) + + +class ModScoreLooker: + """ + Class used to cache the lookup table of accessions to best scores and their + respective mod sequences. + + Pre-computing the lookup table leverages a lot of speedum and vectortized + operations from pandas, and is much faster than doing the lookup on the fly + in a loop. + + :param report: Dataframe for Dia-NN main report + :type report: pandas.core.frame.DataFrame + """ + + def __init__(self, report: pd.DataFrame) -> None: + self.lookup_dict = self.make_lookup_dict(report) + + def make_lookup_dict(self, report) -> Dict[str, Tuple[str, float]]: + grouped_df = ( + report[["Modified.Sequence", "Protein.Ids", "Global.PG.Q.Value"]] + .sort_values("Global.PG.Q.Value", ascending=True) + .groupby(["Protein.Ids"]) + .head(1) + ) + # Modified.Sequence Protein.Ids Global.PG.Q.Value + # 78265 LFNEQNFFQR Q8IV63;Q8IV63-2;Q8IV63-3 0.000252 + # 103585 NPTIVNFPITNVDLR Q53GS9;Q53GS9-2 0.000252 + # 103586 NPTWKPLIR Q7Z4Q2;Q7Z4Q2-2 0.000252 + # 103588 NPVGYPLAWQFLR Q9NZ08;Q9NZ08-2 0.000252 + + out = { + row["Protein.Ids"]: (row["Modified.Sequence"], row["Global.PG.Q.Value"]) for _, row in grouped_df.iterrows() + } + return out + + def get_score(self, protein_id: str) -> Tuple[Union[str, float], float]: + """Returns a tuple contains modified sequences and the score at protein level. + + Gets the best score and corresponding peptide for a given protein_id + + Note that protein id can be something like 'Q8IV63;Q8IV63-2;Q8IV63-3' + + Note2: This implementation also fixes a bug where the function would + return the first peptide in the report, not the best one. (but with the + score of the best one for that accession) + + :param protein_id: The value of "accession" column in report + :type target: str + :return: A tuple that contains (best modified sequence, best score) + if the accession is not found, (np.nan, np.nan) is returned. + :rtype: tuple + """ + # Q: in what cases can the accession not exist in the table? + # or an accession not have peptides? + val = self.lookup_dict.get(protein_id, (np.nan, np.nan)) + return val + + +# Pre-compiling the regex makes the next function 2x faster +# in my benchmarking - JSPP +MODIFICATION_PATTERN = re.compile(r"\((.*?)\)") + + +def find_modification(peptide): + """ + Identify the modification site based on the peptide containing modifications. + + :param peptide: Sequences of peptides + :type peptide: str + :return: Modification sites + :rtype: str + + Examples: + >>> find_modification("PEPM(UNIMOD:35)IDE") + '4-UNIMOD:35' + >>> find_modification("SM(UNIMOD:35)EWEIRDS(UNIMOD:21)EPTIDEK") + '2-UNIMOD:35,9-UNIMOD:21' + """ + peptide = str(peptide) + original_mods = MODIFICATION_PATTERN.findall(peptide) + peptide = MODIFICATION_PATTERN.sub(".", peptide) + position = [i for i, x in enumerate(peptide) if x == "."] + for j in range(1, len(position)): + position[j] -= j + + for k in range(0, len(original_mods)): + original_mods[k] = str(position[k]) + "-" + original_mods[k].upper() + + original_mods = ",".join(str(i) for i in original_mods) if len(original_mods) > 0 else "null" + + return original_mods + + +def name_mapper_builder(subname_mapper): + """Returns a function that renames the columns of the grouped table to match the ones + in the final table. + + Examples: + >>> mapping_dict = { + ... "precursor.Index::::": "pr_id", + ... "Precursor.Normalised::mean": "peptide_abundance_study_variable" + ... } + >>> name_mapper = name_mapper_builder(mapping_dict) + >>> name_mapper("precursor.Index::::") + "pr_id" + >>> name_mapper("Precursor.Normalised::mean::1") + "peptide_abundance_study_variable[1]" + """ + num_regex = re.compile(r"(.*)::(\d+)$") + + def name_mapper(x): + """Renames the columns of the grouped table to match the ones + in the final table. + + Examples: + >>> name_mapper("precursor.Index::::") + "pr_id" + >>> name_mapper("Precursor.Normalised::mean::1") + "peptide_abundance_study_variable[1]" + """ + orig_x = x + for k, v in subname_mapper.items(): + if k in x: + x = x.replace(k, v) + out = num_regex.sub(r"\1[\2]", x) + if out == orig_x: + # This should never happen but I am adding it here + # to prevent myself from shoting myself in the foot in the future. + raise ValueError(f"Column name {x} not found in subname_mapper") + return out + + return name_mapper + + +def per_peptide_study_report(report: pd.DataFrame) -> pd.DataFrame: + """Summarizes the report at peptide/study level and flattens the columns. + + This function was implemented to replace an 'apply -> filter' approach. + In my benchmarking it went from 35.23 seconds for 4 samples, 4 conditions to + 0.007 seconds. + + This implementation differs in several aspects in the output values: + 1. in the fact that it actually gets values for the m/z + 2. always returns a float, whilst the apply version returns an 'object' dtype. + 3. The original implementation, missing values had the string 'null', here they have the value np.nan. + 4. The order of the final output is different; the original orders columns by + study variables > calculated value, this one is calculated value > study variables. + + Calculates the mean, standard deviation and std error of the precursor + abundances, as well as the mean retention time and m/z. + + The names in the end are called "peptide" but thechnically the are at the + precursor level. (peptide+charge combinations). + + The columns will look like this in the end: + [ + 'pr_id', + 'peptide_abundance_study_variable[1]', + ... + 'peptide_abundance_stdev_study_variable[1]', + ... + 'peptide_abundance_std_error_study_variable[1]', + ... + 'opt_global_retention_time_study_variable[1]', + ... + 'opt_global_mass_to_charge_study_variable[1]', + ... + ] + """ + pep_study_grouped = ( + report.groupby(["study_variable", "precursor.Index"]) + .agg({"Precursor.Normalised": ["mean", "std", "sem"], "RT.Start": ["mean"], "Calculate.Precursor.Mz": ["mean"]}) + .reset_index() + .pivot(columns=["study_variable"], index="precursor.Index") + .reset_index() + ) + pep_study_grouped.columns = pd.Index( + ["::".join([str(s) for s in col]).strip() for col in pep_study_grouped.columns.values] + ) + # Columns here would be like: + # [ + # "precursor.Index::::", + # "Precursor.Normalised::mean::1", + # "Precursor.Normalised::mean::2", + # "Precursor.Normalised::std::1", + # "Precursor.Normalised::std::2", + # "Precursor.Normalised::sem::1", + # "Precursor.Normalised::sem::2", + # "RT.Start::mean::1", + # "RT.Start::mean::2", + # ] + # So the right names need to be given and the table can be joined with the other one + subname_mapper = { + "precursor.Index::::": "pr_id", + "Precursor.Normalised::mean": "peptide_abundance_study_variable", + "Precursor.Normalised::std": "peptide_abundance_stdev_study_variable", + "Precursor.Normalised::sem": "peptide_abundance_std_error_study_variable", + "Calculate.Precursor.Mz::mean": "opt_global_mass_to_charge_study_variable", + "RT.Start::mean": "opt_global_retention_time_study_variable", + } + name_mapper = name_mapper_builder(subname_mapper) + + pep_study_grouped.rename( + columns=name_mapper, + inplace=True, + ) + + return pep_study_grouped + + +def calculate_coverage(ref_sequence: str, sequences: Set[str]): + """ + Calculates the coverage of the reference sequence by the given sequences. + + Examples: + >>> calculate_coverage("WATEROVERTHEDUCKSBACK", {"WATER", "DUCK"}) + 0.42857142857142855 + >>> calculate_coverage("DUCKDUCKDUCK", {"DUCK"}) + 1.0 + >>> calculate_coverage("WATEROVERTHEDUCK", {"DUCK"}) + 0.25 + >>> calculate_coverage("WATER", {"WAT", "TER"}) + 1.0 + >>> calculate_coverage("WATERGLASS", {"WAT", "TER"}) + 0.5 + """ + starts = [] + lengths = [] + for sequence in sequences: + local_start = 0 + while True: + local_start = ref_sequence.find(sequence, local_start) + if local_start == -1: + break + starts.append(local_start) + lengths.append(len(sequence)) + local_start += 1 + + # merge overlapping intervals + merged_starts: list = [] + merged_lengths: list = [] + for start, length in sorted(zip(starts, lengths)): + if merged_starts and merged_starts[-1] + merged_lengths[-1] >= start: + merged_lengths[-1] = max(merged_starts[-1] + merged_lengths[-1], start + length) - merged_starts[-1] + else: + merged_starts.append(start) + merged_lengths.append(length) + + # calculate coverage + coverage = sum(merged_lengths) / len(ref_sequence) + return coverage + + +def calculate_protein_coverages(report: pd.DataFrame, out_mztab_PRH: pd.DataFrame, fasta_df: pd.DataFrame) -> List[str]: + """Calculates protein coverages for the PRH table. + + The protein coverage is calculated as the fraction of the protein sequence + in the fasta df, covered by the peptides in the report table, for every + protein in the PRH table (defined by accession, not protein.ids). + """ + nested_df = ( + report[["Protein.Ids", "Stripped.Sequence"]] + .groupby("Protein.Ids") + .agg({"Stripped.Sequence": set}) + .reset_index() + ) + # Protein.Ids Stripped.Sequence + # 0 A0A024RBG1;Q9NZJ9;Q9NZJ9-2 {SEQEDEVLLVSSSR} + # 1 A0A096LP49;A0A096LP49-2 {SPWAMTERKHSSLER} + # 2 A0AVT1;A0AVT1-2 {EDFTLLDFINAVK, KPDHVPISSEDER, QDVIITALDNVEAR,... + ids_to_seqs = dict(zip(nested_df["Protein.Ids"], nested_df["Stripped.Sequence"])) + acc_to_ids = dict(zip(out_mztab_PRH["accession"], out_mztab_PRH["Protein.Ids"])) + fasta_id_to_seqs = dict(zip(fasta_df["id"], fasta_df["seq"])) + acc_to_fasta_ids: dict = {} + + # Since fasta ids are something like sp|P51451|BLK_HUMAN but + # accessions are something like Q9Y6V7-2, we need to find a + # partial string match between the two (the best one) + for acc in acc_to_ids: + # I am pretty sure this is the slowest part of the code + matches = fasta_df[fasta_df["id"].str.contains(acc)]["id"] + if len(matches) == 0: + logger.warning(f"Could not find fasta id for accession {acc} in the fasta file.") + acc_to_fasta_ids[acc] = None + elif len(matches) == 1: + acc_to_fasta_ids[acc] = matches.iloc[0] + else: + # If multiple, find best match. ej. Pick Q9Y6V7 over Q9Y6V7-2 + # This can be acquired by finding the shortest string, since + # it entails more un-matched characters. + acc_to_fasta_ids[acc] = min(matches, key=len) + + out: List[str] = [""] * len(out_mztab_PRH["accession"]) + + for i, acc in enumerate(out_mztab_PRH["accession"]): + f_id = acc_to_fasta_ids[acc] + if f_id is None: + out_cov = "null" + else: + cov = calculate_coverage(fasta_id_to_seqs[f_id], ids_to_seqs[acc_to_ids[acc]]) + out_cov = format(cov, ".03f") + + out[i] = out_cov + + return out + + +cli.add_command(convert) + +if __name__ == "__main__": + cli() diff --git a/pyquantms/ms2rescore/__init__py.py b/pyquantms/ms2rescore/__init__py.py new file mode 100644 index 0000000..e69de29 diff --git a/pyquantms/ms2rescore/ms2rescore_cli.py b/pyquantms/ms2rescore/ms2rescore_cli.py new file mode 100644 index 0000000..4c5cd7f --- /dev/null +++ b/pyquantms/ms2rescore/ms2rescore_cli.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python +# Written by Jonas Scheid under the MIT license + + +import sys +import click +import importlib.resources +import json +import logging +from typing import List + +import pandas as pd + +from ms2rescore import rescore, package_data +from psm_utils.io.idxml import IdXMLReader, IdXMLWriter +from psm_utils import PSMList +import pyopenms as oms + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") + + +def parse_cli_arguments_to_config(**kwargs): + """Update default MS²Rescore config with CLI arguments""" + config = json.load(importlib.resources.open_text(package_data, "config_default.json")) + + for key, value in kwargs.items(): + # Skip these arguments since they need to set in a nested dict of feature_generators + if key in ["ms2pip_model", "ms2_tolerance", "rng", "calibration_set_size"]: + continue + + elif key == "feature_generators": + feature_generators = value.split(",") + # Reset feature generator dict since there might be default generators we don't want + config["ms2rescore"]["feature_generators"] = {} + if "basic" in feature_generators: + config["ms2rescore"]["feature_generators"]["basic"] = {} + if "ms2pip" in feature_generators: + config["ms2rescore"]["feature_generators"]["ms2pip"] = { + "model": kwargs["ms2pip_model"], + "ms2_tolerance": kwargs["ms2_tolerance"], + } + if "deeplc" in feature_generators: + config["ms2rescore"]["feature_generators"]["deeplc"] = { + "deeplc_retrain": False, + "calibration_set_size": kwargs["calibration_set_size"], + } + if "maxquant" in feature_generators: + config["ms2rescore"]["feature_generators"]["maxquant"] = {} + if "ionmob" in feature_generators: + config["ms2rescore"]["feature_generators"]["ionmob"] = {} + + elif key == "rescoring_engine": + # Reset rescoring engine dict we want to allow only computing features + config["ms2rescore"]["rescoring_engine"] = {} + if value == "mokapot": + config["ms2rescore"]["rescoring_engine"]["mokapot"] = { + "write_weights": True, + "write_txt": False, + "write_flashlfq": False, + "rng": kwargs["rng"], + "test_fdr": kwargs["test_fdr"], + "max_workers": kwargs["processes"], + } + if value == "percolator": + logging.info( + "Percolator rescoring engine has been specified. Use the idXML containing rescoring features and run Percolator in a separate step." + ) + continue + + else: + config["ms2rescore"][key] = value + + return config + + +def rescore_idxml(input_file, output_file, config) -> None: + """Rescore PSMs in an idXML file and keep other information unchanged.""" + # Read PSMs + reader = IdXMLReader(input_file) + psm_list = reader.read_file() + + # Rescore + rescore(config, psm_list) + + # Filter out PeptideHits within PeptideIdentification(s) that could not be processed by all feature generators + peptide_ids_filtered = filter_out_artifact_psms(psm_list, reader.peptide_ids) + + # Write + writer = IdXMLWriter(output_file, reader.protein_ids, peptide_ids_filtered) + writer.write_file(psm_list) + + +def filter_out_artifact_psms( + psm_list: PSMList, peptide_ids: List[oms.PeptideIdentification] +) -> List[oms.PeptideIdentification]: + """Filter out PeptideHits that could not be processed by all feature generators""" + num_mandatory_features = max([len(psm.rescoring_features) for psm in psm_list]) + new_psm_list = PSMList(psm_list=[psm for psm in psm_list if len(psm.rescoring_features) == num_mandatory_features]) + + # get differing peptidoforms of both psm lists + psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in psm_list]) + new_psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in new_psm_list]) + not_supported_peptides = psm_list_peptides - new_psm_list_peptides + + # no need to filter if all peptides are supported + if len(not_supported_peptides) == 0: + return peptide_ids + # Create new peptide ids and filter out not supported peptides + new_peptide_ids = [] + for peptide_id in peptide_ids: + new_hits = [] + for hit in peptide_id.getHits(): + if hit.getSequence().toString() in not_supported_peptides: + continue + new_hits.append(hit) + if len(new_hits) == 0: + continue + peptide_id.setHits(new_hits) + new_peptide_ids.append(peptide_id) + logging.info( + f"Removed {len(psm_list_peptides) - len(new_psm_list_peptides)} PSMs. Peptides not supported: {not_supported_peptides}" + ) + return new_peptide_ids + + +@click.command() +@click.option( + "-p", "--psm_file", help="Path to PSM file (PIN, mzIdentML, MaxQuant msms, X!Tandem XML, idXML)", required=True +) +@click.option( + "-s", + "--spectrum_path", + help="Path to MGF/mzML spectrum file or directory with spectrum files (default: derived from identification file)", + required=True, +) +@click.option( + "-o", "--output_path", help="Path and stem for output file names (default: derive from identification file)" +) +@click.option("-l", "--log_level", help="Logging level (default: `info`)", default="info") +@click.option("-n", "--processes", help="Number of parallel processes available to MS²Rescore", type=int, default=16) +@click.option("-f", "--fasta_file", help="Path to FASTA file") +@click.option("-t", "--test_fdr", help="The false-discovery rate threshold at which to evaluate the learned models. (default: 0.05)", default=0.05) +@click.option( + "-fg", + "--feature_generators", + help="Comma-separated list of feature generators to use (default: `ms2pip,deeplc`). See ms2rescore doc for further information", + default="", +) +@click.option("-pipm", "--ms2pip_model", help="MS²PIP model (default: `Immuno-HCD`)", type=str, default="Immuno-HCD") +@click.option( + "-ms2tol", "--ms2_tolerance", help="Fragment mass tolerance [Da](default: `0.02`)", type=float, default=0.02 +) +@click.option( + "-cs", + "--calibration_set_size", + help="Percentage of number of calibration set for DeepLC (default: `0.15`)", + default=0.15, +) +@click.option("-re", "--rescoring_engine", help="Either mokapot or percolator (default: `mokapot`)", default="mokapot") +@click.option( + "-rng", "--rng", help="Seed for mokapot's random number generator (default: `4711`)", type=int, default=4711 +) +@click.option("-d", "--id_decoy_pattern", help="Regex decoy pattern (default: `DECOY_`)", default="^DECOY_") +@click.option( + "-lsb", + "--lower_score_is_better", + help="Interpretation of primary search engine score (default: True)", + default=True, +) +def main(**kwargs): + config = parse_cli_arguments_to_config(**kwargs) + logging.info("MS²Rescore config:") + logging.info(config) + rescore_idxml(kwargs["psm_file"], kwargs["output_path"], config) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/pyquantms/psm/__init__py.py b/pyquantms/psm/__init__py.py new file mode 100644 index 0000000..e69de29 diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py new file mode 100644 index 0000000..7342ecc --- /dev/null +++ b/pyquantms/psm/psm_conversion.py @@ -0,0 +1,121 @@ +import click +import numpy as np +import pyopenms as oms +import pandas as pd +import re +import os +from pathlib import Path + +_parquet_field = [ + "sequence", "protein_accessions", "protein_start_positions", "protein_end_positions", + "modifications", "retention_time", "charge", "calc_mass_to_charge", "reference_file_name", + "scan_number", "peptidoform", "posterior_error_probability", "global_qvalue", "is_decoy", + "consensus_support", "mz_array", "intensity_array", "num_peaks", "search_engines", "id_scores", "hit_rank" +] + + +def mods_position(peptide): + pattern = re.compile(r"\((.*?)\)") + original_mods = pattern.findall(peptide) + peptide = re.sub(r"\(.*?\)", ".", peptide) + position = [i.start() for i in re.finditer(r"\.", peptide)] + for j in range(1, len(position)): + position[j] -= j + + for k in range(0, len(original_mods)): + original_mods[k] = str(position[k]) + "-" + original_mods[k] + + original_mods = [str(i) for i in original_mods] if len(original_mods) > 0 else np.nan + + return original_mods + + +@click.command("convert_psm") +@click.argument("idxml", type=click.Path(exists=True)) +@click.argument("spectra_file", type=click.Path(exists=True)) +@click.argument("export_decoy_psm", is_flag=True) +@click.pass_context +def convert_psm(ctx, idxml: str, spectra_file: str, export_decoy_psm: bool = False): + + """ + Convert idXML to csv file with PSMs information. + :param ctx: click context + :param idxml: Input idXML file + :param spectra_file: Spectra file + :param export_decoy_psm: Export decoy PSM + :return: None + """ + + prot_ids = [] + pep_ids = [] + parquet_data = [] + consensus_support = np.nan + mz_array = [] + intensity_array = [] + num_peaks = np.nan + id_scores = [] + search_engines = [] + + oms.IdXMLFile().load(idxml, prot_ids, pep_ids) + if "ConsensusID" in prot_ids[0].getSearchEngine(): + if prot_ids[0].getSearchParameters().metaValueExists("SE:MS-GF+"): + search_engines = ["MS-GF+"] + if prot_ids[0].getSearchParameters().metaValueExists("SE:Comet"): + search_engines.append("Comet") + if prot_ids[0].getSearchParameters().metaValueExists("SE:Sage"): + search_engines.append("Sage") + else: + search_engines = [prot_ids[0].getSearchEngine()] + + reference_file_name = os.path.splitext(prot_ids[0].getMetaValue("spectra_data")[0].decode("UTF-8"))[0] + spectra_df = pd.read_csv(spectra_file) if spectra_file else None + + for peptide_id in pep_ids: + retention_time = peptide_id.getRT() + calc_mass_to_charge = peptide_id.getMZ() + scan_number = int(re.findall(r"(spectrum|scan)=(\d+)", peptide_id.getMetaValue("spectrum_reference"))[0][1]) + + if isinstance(spectra_df, pd.DataFrame): + spectra = spectra_df[spectra_df["scan"] == scan_number] + mz_array = spectra["mz"].values + intensity_array = spectra["intensity"].values + num_peaks = len(mz_array) + + for hit in peptide_id.getHits(): + # if remove decoy when mapped to target+decoy? + is_decoy = 0 if hit.getMetaValue("target_decoy") == "target" else 1 + if export_decoy_psm and is_decoy: + continue + global_qvalue = np.nan + if len(search_engines) > 1: + if "q-value" in peptide_id.getScoreType(): + global_qvalue = hit.getScore() + consensus_support = hit.getMetaValue("consensus_support") + elif search_engines == "Comet": + id_scores = ["Comet:Expectation value: " + str(hit.getScore())] + elif search_engines == "MS-GF+": + id_scores = ["MS-GF:SpecEValue: " + str(hit.getScore())] + elif search_engines == "Sage": + id_scores = ["Sage:hyperscore: " + str(hit.getScore())] + + if hit.metaValueExists("MS:1001491"): + global_qvalue = hit.getMetaValue("MS:1001491") + + charge = hit.getCharge() + peptidoform = hit.getSequence().toString() + modifications = mods_position(peptidoform) + sequence = hit.getSequence().toUnmodifiedString() + protein_accessions = [ev.getProteinAccession() for ev in hit.getPeptideEvidences()] + posterior_error_probability = hit.getMetaValue("Posterior Error Probability_score") + protein_start_positions = [ev.getStart() for ev in hit.getPeptideEvidences()] + protein_end_positions = [ev.getEnd() for ev in hit.getPeptideEvidences()] + hit_rank = hit.getRank() + + parquet_data.append([sequence, protein_accessions, protein_start_positions, protein_end_positions, + modifications, retention_time, charge, calc_mass_to_charge, reference_file_name, + scan_number, peptidoform, posterior_error_probability, global_qvalue, is_decoy, + consensus_support, mz_array, intensity_array, num_peaks, search_engines, id_scores, + hit_rank]) + + pd.DataFrame(parquet_data, columns=_parquet_field).to_csv(f"{Path(idxml).stem}_psm.csv",mode="w", index=False, header=True) + diff --git a/pyquantms/sdrf/__init__.py b/pyquantms/sdrf/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py new file mode 100644 index 0000000..dd125b4 --- /dev/null +++ b/pyquantms/sdrf/check_samplesheet.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python + +# nf-core: Update the script to check the sdrf +# This script is based on the example at: https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/samplesheet/samplesheet_test_illumina_amplicon.csv + +import argparse +import errno +import os +import sys + +import pandas as pd +from sdrf_pipelines.sdrf.sdrf import SdrfDataFrame +from sdrf_pipelines.sdrf.sdrf_schema import DEFAULT_TEMPLATE, MASS_SPECTROMETRY + + +def parse_args(args=None): + Description = "Reformat nf-core/quantms sdrf file and check its contents." + Epilog = "Example usage: python validate_sdrf.py " + + parser = argparse.ArgumentParser(description=Description, epilog=Epilog) + parser.add_argument("SDRF", help="SDRF/Expdesign file to be validated") + parser.add_argument("ISSDRF", help="SDRF file or Expdesign file") + parser.add_argument("--CHECK_MS", help="check mass spectrometry fields in SDRF.", action="store_true") + + return parser.parse_args(args) + + +def make_dir(path): + if len(path) > 0: + try: + os.makedirs(path) + except OSError as exception: + if exception.errno != errno.EEXIST: + raise exception + + +def print_error(error, context="Line", context_str=""): + error_str = "ERROR: Please check samplesheet -> {}".format(error) + if context != "" and context_str != "": + error_str = "ERROR: Please check samplesheet -> {}\n{}: '{}'".format( + error, context.strip(), context_str.strip() + ) + print(error_str) + sys.exit(1) + + +def check_sdrf(check_ms, sdrf): + df = SdrfDataFrame.parse(sdrf) + errors = df.validate(DEFAULT_TEMPLATE) + if check_ms: + errors = errors + df.validate(MASS_SPECTROMETRY) + for error in errors: + print(error) + if not errors: + print("Everying seems to be fine. Well done.") + else: + print("There were validation errors!") + sys.exit(bool(errors)) + + +def check_expdesign(expdesign): + data = pd.read_csv(expdesign, sep="\t", header=0, dtype=str) + data = data.dropna() + schema_file = ["Fraction_Group", "Fraction", "Spectra_Filepath", "Label", "Sample"] + schema_sample = ["Sample", "MSstats_Condition", "MSstats_BioReplicate"] + + # check table format: two table + with open(expdesign, "r") as f: + lines = f.readlines() + try: + empty_row = lines.index("\n") + except ValueError: + print("the one-table format parser is broken in OpenMS2.5, please use one-table or sdrf") + sys.exit(1) + if lines.index("\n") >= len(lines): + print("the one-table format parser is broken in OpenMS2.5, please use one-table or sdrf") + sys.exit(1) + + s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1 :]][1:] + s_header = lines[empty_row + 1].replace("\n", "").split("\t") + s_DataFrame = pd.DataFrame(s_table, columns=s_header) + + # check missed mandatory column + missed_columns = set(schema_file) - set(data.columns) + if len(missed_columns) != 0: + print("{0} column missed".format(" ".join(missed_columns))) + sys.exit(1) + + missed_columns = set(schema_sample) - set(s_DataFrame.columns) + if len(missed_columns) != 0: + print("{0} column missed".format(" ".join(missed_columns))) + sys.exit(1) + + if len(set(data.Label)) != 1 and "MSstats_Mixture" not in s_DataFrame.columns: + print("MSstats_Mixture column missed in ISO experiments") + sys.exit(1) + + # check logical problem: may be improved + check_expdesign_logic(data, s_DataFrame) + + +def check_expdesign_logic(fTable, sTable): + if int(max(fTable.Fraction_Group)) > len(set(fTable.Fraction_Group)): + print("Fraction_Group discontinuous!") + sys.exit(1) + fTable_D = fTable.drop_duplicates(["Fraction_Group", "Fraction", "Label", "Sample"]) + if fTable_D.shape[0] < fTable.shape[0]: + print("Existing duplicate entries in Fraction_Group, Fraction, Label and Sample") + sys.exit(1) + if len(set(sTable.Sample)) < sTable.shape[0]: + print("Existing duplicate Sample in sample table!") + sys.exit(1) + + +def main(args=None): + # TODO validate expdesign file + args = parse_args(args) + + if args.ISSDRF == "true": + check_sdrf(args.CHECK_MS, args.SDRF) + else: + check_expdesign(args.SDRF) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/pyquantms/sdrf/extract_sample.py b/pyquantms/sdrf/extract_sample.py new file mode 100644 index 0000000..c4d1e16 --- /dev/null +++ b/pyquantms/sdrf/extract_sample.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python + +import argparse +import sys +from pathlib import Path +import pandas as pd + + +def parse_args(args=None): + Description = "Extract sample information from an experiment design file" + Epilog = "Example usage: python extract_sample.py " + + parser = argparse.ArgumentParser(description=Description, epilog=Epilog) + parser.add_argument("EXP", help="Expdesign file to be extracted") + return parser.parse_args(args) + + +def extract_sample(expdesign): + data = pd.read_csv(expdesign, sep="\t", header=0, dtype=str) + fTable = data.dropna() + + # two table format + with open(expdesign, "r") as f: + lines = f.readlines() + empty_row = lines.index("\n") + s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1:]][1:] + s_header = lines[empty_row + 1].replace("\n", "").split("\t") + s_DataFrame = pd.DataFrame(s_table, columns=s_header) + + sample_dt = pd.DataFrame() + if "MSstats_Mixture" not in s_DataFrame.columns: + fTable = fTable[["Spectra_Filepath", "Sample"]] + fTable.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) + else: + fTable.drop_duplicates(subset=["Spectra_Filepath"], inplace=True) + for _, row in fTable.iterrows(): + mixture_id = s_DataFrame[s_DataFrame["Sample"] == row["Sample"]]["MSstats_Mixture"] + sample_dt = sample_dt.append({"Spectra_Filepath": row["Spectra_Filepath"], "Sample": mixture_id}, + ignore_index=True) + sample_dt.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) + + +def main(args=None): + args = parse_args(args) + extract_sample(args.EXP) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/requirements.txt b/requirements.txt index 023502a..e421fa3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,6 @@ click sdrf_pipelines pyopenms -ms2rescore \ No newline at end of file +ms2rescore +psm-utils +pydantic \ No newline at end of file diff --git a/tests/test_future.py b/tests/test_future.py index b4b21dd..5d3220d 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -19,4 +19,11 @@ def test_add_sage_feature(): runner = CliRunner() result = runner.invoke(cli,['add_sage_feature','--help']) + assert result.exit_code == 0 + +# test for the mzml_statistics command in cli +def test_mzml_statistics(): + runner = CliRunner() + result = runner.invoke(cli,['mzml_statistics','--help']) + assert result.exit_code == 0 \ No newline at end of file From 9b770a6824faae3a071d0ec8c1696ab81b046722 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Fri, 24 May 2024 17:53:15 +0100 Subject: [PATCH 14/53] mzml statistics --- pyproject.toml | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 71229c2..bad8e3d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,7 +16,7 @@ sdrf_pipelines = ">=0.0.26" pyopenms = ">=2.6.0" ms2rescore = ">=3.0.2" psm-utils = ">=0.8.0" -pydantic = ">=1.10" +#pydantic = ">=1.10" [tool.poetry.dev-dependencies] pytest = "*" diff --git a/setup.py b/setup.py index 2d08098..34f64ab 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ "Development Status :: 5 - Production/Stable", ] -INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms", "ms2rescore=>3.0.2", "psm-utils=>0.8.0" "pydantic=>1.10"] +INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms", "ms2rescore", "psm-utils", "pydantic"] PYTHON_REQUIRES = ">=3.7,<4" with open("README.md", "r") as fh: From 903b1e54996d29d6ac7755d49cf1caf7bdbe2ab1 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 05:56:26 +0100 Subject: [PATCH 15/53] mzml statistics --- pyquantms/mzml/{__init__py.py => __init__.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename pyquantms/mzml/{__init__py.py => __init__.py} (100%) diff --git a/pyquantms/mzml/__init__py.py b/pyquantms/mzml/__init__.py similarity index 100% rename from pyquantms/mzml/__init__py.py rename to pyquantms/mzml/__init__.py From 092f5dfe65f5cbfef5f10b03fdb2b34c61c9ad0b Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 06:00:31 +0100 Subject: [PATCH 16/53] diann convert --- pyquantms/diann/diann_convert.py | 14 ++------------ pyquantms/pyquantmsc.py | 2 ++ tests/test_future.py | 8 +++++++- 3 files changed, 11 insertions(+), 13 deletions(-) diff --git a/pyquantms/diann/diann_convert.py b/pyquantms/diann/diann_convert.py index fb2bdd2..cfd7d02 100644 --- a/pyquantms/diann/diann_convert.py +++ b/pyquantms/diann/diann_convert.py @@ -1,4 +1,3 @@ -#!/usr/bin/env python """ This script converts the output from DIA-NN into three standard formats: MSstats, Triqler and mzTab. License: Apache 2.0 @@ -30,12 +29,7 @@ logger = logging.getLogger(__name__) -@click.group(context_settings=CONTEXT_SETTINGS) -def cli(): - pass - - -@click.command("convert") +@click.command("diann_convert") @click.option("--folder", "-f") @click.option("--exp_design", "-d") @click.option("--diann_version", "-v") @@ -44,7 +38,7 @@ def cli(): @click.option("--missed_cleavages", "-m") @click.option("--qvalue_threshold", "-q", type=float) @click.pass_context -def convert(ctx, folder, exp_design, dia_params, diann_version, charge, missed_cleavages, qvalue_threshold): +def diann_convert(ctx, folder, exp_design, dia_params, diann_version, charge, missed_cleavages, qvalue_threshold): """ Convert DIA-NN output to MSstats, Triqler or mzTab. The output formats are used for quality control and downstream analysis. @@ -1371,7 +1365,3 @@ def calculate_protein_coverages(report: pd.DataFrame, out_mztab_PRH: pd.DataFram return out -cli.add_command(convert) - -if __name__ == "__main__": - cli() diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index 600c229..7172249 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -1,6 +1,7 @@ import click from pyquantms.diann.create_diann_cfg import create_diann_cfg +from pyquantms.diann.diann_convert import diann_convert from pyquantms.features.add_sage_feature import add_sage_feature from pyquantms.mzml.mzml_statistics import mzml_statistics @@ -15,6 +16,7 @@ def cli(): cli.add_command(create_diann_cfg) cli.add_command(add_sage_feature) cli.add_command(mzml_statistics) +cli.add_command(diann_convert) if __name__ == "__main__": cli() diff --git a/tests/test_future.py b/tests/test_future.py index 5d3220d..b40216a 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -1,4 +1,3 @@ -import pytest from click.testing import CliRunner from pyquantms.pyquantmsc import cli @@ -26,4 +25,11 @@ def test_mzml_statistics(): runner = CliRunner() result = runner.invoke(cli,['mzml_statistics','--help']) + assert result.exit_code == 0 + +# test for the diann_convert command in cli +def test_diann_convert(): + runner = CliRunner() + result = runner.invoke(cli,['diann_convert','--help']) + assert result.exit_code == 0 \ No newline at end of file From 79141ab22c19864d91415cdf1088bd800cb28d9d Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 06:12:01 +0100 Subject: [PATCH 17/53] extra sample script --- pyquantms/pyquantmsc.py | 2 ++ pyquantms/sdrf/extract_sample.py | 41 +++++++++++--------------------- tests/test_future.py | 7 ++++++ 3 files changed, 23 insertions(+), 27 deletions(-) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index 7172249..f1162a5 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -4,6 +4,7 @@ from pyquantms.diann.diann_convert import diann_convert from pyquantms.features.add_sage_feature import add_sage_feature from pyquantms.mzml.mzml_statistics import mzml_statistics +from pyquantms.sdrf.extract_sample import extract_sample_from_expdesign CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) @@ -17,6 +18,7 @@ def cli(): cli.add_command(add_sage_feature) cli.add_command(mzml_statistics) cli.add_command(diann_convert) +cli.add_command(extract_sample_from_expdesign) if __name__ == "__main__": cli() diff --git a/pyquantms/sdrf/extract_sample.py b/pyquantms/sdrf/extract_sample.py index c4d1e16..64b8785 100644 --- a/pyquantms/sdrf/extract_sample.py +++ b/pyquantms/sdrf/extract_sample.py @@ -1,25 +1,21 @@ -#!/usr/bin/env python - -import argparse -import sys from pathlib import Path import pandas as pd - - -def parse_args(args=None): - Description = "Extract sample information from an experiment design file" - Epilog = "Example usage: python extract_sample.py " - - parser = argparse.ArgumentParser(description=Description, epilog=Epilog) - parser.add_argument("EXP", help="Expdesign file to be extracted") - return parser.parse_args(args) - - -def extract_sample(expdesign): +import click + +@click.command("extract_sample") +@click.option("--expdesign", help = "Experiment design file", type=click.Path(exists=True)) +@click.pass_context +def extract_sample_from_expdesign(cxt, expdesign: str) -> None: + """ + Extract sample information from an experiment design file + :param cxt: Context object + :param expdesign: Experiment design file + :return: None + """ data = pd.read_csv(expdesign, sep="\t", header=0, dtype=str) fTable = data.dropna() - # two table format + # two table formats with open(expdesign, "r") as f: lines = f.readlines() empty_row = lines.index("\n") @@ -37,13 +33,4 @@ def extract_sample(expdesign): mixture_id = s_DataFrame[s_DataFrame["Sample"] == row["Sample"]]["MSstats_Mixture"] sample_dt = sample_dt.append({"Spectra_Filepath": row["Spectra_Filepath"], "Sample": mixture_id}, ignore_index=True) - sample_dt.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) - - -def main(args=None): - args = parse_args(args) - extract_sample(args.EXP) - - -if __name__ == "__main__": - sys.exit(main()) + sample_dt.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) \ No newline at end of file diff --git a/tests/test_future.py b/tests/test_future.py index b40216a..685b6d7 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -32,4 +32,11 @@ def test_diann_convert(): runner = CliRunner() result = runner.invoke(cli,['diann_convert','--help']) + assert result.exit_code == 0 + +# test for the extract_sample_from_expdesign command in cli +def test_extract_sample_from_expdesign(): + runner = CliRunner() + result = runner.invoke(cli,['extract_sample','--help']) + assert result.exit_code == 0 \ No newline at end of file From f7ebf0b7c6458e83e621ace3c77c032a3b69fef6 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 07:32:12 +0100 Subject: [PATCH 18/53] ms2rescore added --- pyproject.toml | 2 +- pyquantms/ms2rescore/ms2rescore.py | 194 +++++++++++++++++++++++++ pyquantms/ms2rescore/ms2rescore_cli.py | 178 ----------------------- pyquantms/pyquantmsc.py | 2 + requirements.txt | 2 +- setup.py | 2 +- tests/test_future.py | 7 + 7 files changed, 206 insertions(+), 181 deletions(-) create mode 100644 pyquantms/ms2rescore/ms2rescore.py delete mode 100644 pyquantms/ms2rescore/ms2rescore_cli.py diff --git a/pyproject.toml b/pyproject.toml index bad8e3d..daa8fe3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ python = "^3.7" sdrf_pipelines = ">=0.0.26" pyopenms = ">=2.6.0" ms2rescore = ">=3.0.2" -psm-utils = ">=0.8.0" +psm-utils = "==0.8.0" #pydantic = ">=1.10" [tool.poetry.dev-dependencies] diff --git a/pyquantms/ms2rescore/ms2rescore.py b/pyquantms/ms2rescore/ms2rescore.py new file mode 100644 index 0000000..c36dec8 --- /dev/null +++ b/pyquantms/ms2rescore/ms2rescore.py @@ -0,0 +1,194 @@ +#!/usr/bin/env python +# Written by Jonas Scheid under the MIT license +# Contributions by Yasset Perez-Riverol and Dai Chengxin +# This script is part of the pyquantms package + +import click +import importlib.resources +import json +import logging +from typing import List +from ms2rescore import rescore, package_data +from psm_utils.io.idxml import IdXMLReader, IdXMLWriter +from psm_utils import PSMList +import pyopenms as oms + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") + + +def parse_cli_arguments_to_config(config_file: str = None, feature_generators: str = None, ms2pip_model: str = None, + ms2_tolerance: float = None, calibration_set_size: float = None, + rescoring_engine: str = None, rng: int = None, test_fdr: float = None, + processes: int = None, spectrum_path: str = None, fasta_file: str = None, + id_decoy_pattern: str = None, lower_score_is_better: bool = None) -> dict: + + if config_file is None: + config = json.load(importlib.resources.open_text(package_data, "config_default.json")) + else: + with open(config_file) as f: + config = json.load(f) + if feature_generators is not None: + feature_generators_list = feature_generators.split(",") + config["ms2rescore"]["feature_generators"] = {} + if "basic" in feature_generators_list: + config["ms2rescore"]["feature_generators"]["basic"] = {} + if "ms2pip" in feature_generators_list: + config["ms2rescore"]["feature_generators"]["ms2pip"] = { + "model": ms2pip_model, + "ms2_tolerance": ms2_tolerance, + } + if "deeplc" in feature_generators_list: + config["ms2rescore"]["feature_generators"]["deeplc"] = { + "deeplc_retrain": False, + "calibration_set_size": calibration_set_size, + } + if "maxquant" in feature_generators_list: + config["ms2rescore"]["feature_generators"]["maxquant"] = {} + if "ionmob" in feature_generators: + config["ms2rescore"]["feature_generators"]["ionmob"] = {} + + if rescoring_engine is not None: + # Reset rescoring engine dict we want to allow only computing features + config["ms2rescore"]["rescoring_engine"] = {} + if rescoring_engine == "mokapot": + config["ms2rescore"]["rescoring_engine"]["mokapot"] = { + "write_weights": True, + "write_txt": False, + "write_flashlfq": False, + "rng": rng, + "test_fdr": test_fdr, + "max_workers": processes, + } + if rescoring_engine == "percolator": + logging.info("Percolator rescoring engine has been specified. Use the idXML containing rescoring features and run Percolator in a separate step.") + + if ms2pip_model is not None: + config["ms2rescore"]["ms2pip_model"] = ms2pip_model + if ms2_tolerance is not None: + config["ms2rescore"]["ms2_tolerance"] = ms2_tolerance + if calibration_set_size is not None: + config["ms2rescore"]["calibration_set_size"] = calibration_set_size + if rng is not None: + config["ms2rescore"]["rng"] = rng + if spectrum_path is not None: + config["ms2rescore"]["spectrum_path"] = spectrum_path + if fasta_file is not None: + config["ms2rescore"]["fasta_file"] = fasta_file + if id_decoy_pattern is not None: + config["ms2rescore"]["id_decoy_pattern"] = id_decoy_pattern + if lower_score_is_better is not None: + config["ms2rescore"]["lower_score_is_better"] = lower_score_is_better + + return config + + +def rescore_idxml(input_file, output_file, config) -> None: + """Rescore PSMs in an idXML file and keep other information unchanged.""" + # Read PSMs + reader = IdXMLReader(input_file) + psm_list = reader.read_file() + + # Rescore + rescore(config, psm_list) + + # Filter out PeptideHits within PeptideIdentification(s) that could not be processed by all feature generators + peptide_ids_filtered = filter_out_artifact_psms(psm_list, reader.peptide_ids) + + # Write + writer = IdXMLWriter(output_file, reader.protein_ids, peptide_ids_filtered) + writer.write_file(psm_list) + + +def filter_out_artifact_psms( + psm_list: PSMList, peptide_ids: List[oms.PeptideIdentification] +) -> List[oms.PeptideIdentification]: + """Filter out PeptideHits that could not be processed by all feature generators""" + num_mandatory_features = max([len(psm.rescoring_features) for psm in psm_list]) + new_psm_list = PSMList(psm_list=[psm for psm in psm_list if len(psm.rescoring_features) == num_mandatory_features]) + + # get differing peptidoforms of both psm lists + psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in psm_list]) + new_psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in new_psm_list]) + not_supported_peptides = psm_list_peptides - new_psm_list_peptides + + # no need to filter if all peptides are supported + if len(not_supported_peptides) == 0: + return peptide_ids + # Create new peptide ids and filter out not supported peptides + new_peptide_ids = [] + for peptide_id in peptide_ids: + new_hits = [] + for hit in peptide_id.getHits(): + if hit.getSequence().toString() in not_supported_peptides: + continue + new_hits.append(hit) + if len(new_hits) == 0: + continue + peptide_id.setHits(new_hits) + new_peptide_ids.append(peptide_id) + logging.info( + f"Removed {len(psm_list_peptides) - len(new_psm_list_peptides)} PSMs. Peptides not supported: {not_supported_peptides}" + ) + return new_peptide_ids + + +@click.command("ms2rescore", short_help= "Rescore PSMs in an idXML file and keep other information unchanged.") +@click.option("-p", "--psm_file", help="Path to PSM file (idXML)", required=True, type=click.Path(exists=True)) +@click.option("-s", "--spectrum_path", help="Path to MGF/mzML spectrum file or directory with spectrum files (default: derived from identification file)", required=True, type=click.Path(exists=True)) +@click.option("-o", "--output_path", help="Path and stem for output file names (default: derive from identification file)") +@click.option("-l", "--log_level", help="Logging level (default: `info`)", default="info") +@click.option("-n", "--processes", help="Number of parallel processes available to MS²Rescore", type=int, default=16) +@click.option("-f", "--fasta_file", help="Path to FASTA file") +@click.option("-t", "--test_fdr", help="The false-discovery rate threshold at which to evaluate the learned models. (default: 0.05)", default=0.05) +@click.option( "-fg","--feature_generators", help="Comma-separated list of feature generators to use (default: `ms2pip,deeplc`). See ms2rescore doc for further information", default="",) +@click.option("-pipm", "--ms2pip_model", help="MS²PIP model (default: `Immuno-HCD`)", type=str, default="Immuno-HCD") +@click.option("-ms2tol", "--ms2_tolerance", help="Fragment mass tolerance [Da](default: `0.02`)", type=float, default=0.02) +@click.option("-cs", "--calibration_set_size", help="Percentage of number of calibration set for DeepLC (default: `0.15`)", default=0.15,) +@click.option("-re", "--rescoring_engine", help="Either mokapot or percolator (default: `mokapot`)", default="mokapot", type=click.Choice(["mokapot", "percolator"])) +@click.option("-rng", "--rng", help="Seed for mokapot's random number generator (default: `4711`)", type=int, default=4711) +@click.option("-d", "--id_decoy_pattern", help="Regex decoy pattern (default: `DECOY_`)", default="^DECOY_") +@click.option("-lsb","--lower_score_is_better", help="Interpretation of primary search engine score (default: True)", default=True,) +@click.option("--config_file", help="Path to MS²Rescore config file (default: `config_default.json`)", default=None) +@click.pass_context +def ms2rescore(ctx, psm_file: str, spectrum_path, output_path: str, log_level, processes, fasta_file, test_fdr, feature_generators, + ms2pip_model, ms2_tolerance, calibration_set_size, rescoring_engine, rng, id_decoy_pattern, + lower_score_is_better, config_file: str): + """ + Rescore PSMs in an idXML file and keep other information unchanged. + :param ctx: Click context object + :param psm_file: PSM file (idXML) + :param spectrum_path: Spectrum file or dictionary with spectrum files (MGF/mzML) + :param output_path: Output path for the new featured idXML file + :param log_level: log_level for the logger + :param processes: Number of parallel processes available to MS²Rescore + :param fasta_file: Fasta file for the database search + :param test_fdr: test FDR for the rescoring engine + :param feature_generators: feature generators to use + :param ms2pip_model: ms2pip model to use + :param ms2_tolerance: ms2 tolerance + :param calibration_set_size: calibration set size + :param rescoring_engine: rescoring engine to use (mokapot or percolator) + :param rng: random number generator seed + :param id_decoy_pattern: id decoy pattern + :param lower_score_is_better: lower score is better + :param config_file: config file + :return: + """ + logging.getLogger().setLevel(log_level.upper()) + config = parse_cli_arguments_to_config(config_file=config_file, feature_generators=feature_generators, ms2pip_model=ms2pip_model, + processes=processes, ms2_tolerance=ms2_tolerance, + calibration_set_size=calibration_set_size, + rescoring_engine=rescoring_engine, rng=rng, + test_fdr=test_fdr, spectrum_path=spectrum_path, fasta_file=fasta_file, + id_decoy_pattern=id_decoy_pattern,lower_score_is_better=lower_score_is_better) + logging.info("MS²Rescore config:") + logging.info(config) + rescore_idxml(psm_file, output_path, config) + + + + +def main(**kwargs): + config = parse_cli_arguments_to_config(**kwargs) + rescore_idxml(kwargs["psm_file"], kwargs["output_path"], config) + diff --git a/pyquantms/ms2rescore/ms2rescore_cli.py b/pyquantms/ms2rescore/ms2rescore_cli.py deleted file mode 100644 index 4c5cd7f..0000000 --- a/pyquantms/ms2rescore/ms2rescore_cli.py +++ /dev/null @@ -1,178 +0,0 @@ -#!/usr/bin/env python -# Written by Jonas Scheid under the MIT license - - -import sys -import click -import importlib.resources -import json -import logging -from typing import List - -import pandas as pd - -from ms2rescore import rescore, package_data -from psm_utils.io.idxml import IdXMLReader, IdXMLWriter -from psm_utils import PSMList -import pyopenms as oms - -logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") - - -def parse_cli_arguments_to_config(**kwargs): - """Update default MS²Rescore config with CLI arguments""" - config = json.load(importlib.resources.open_text(package_data, "config_default.json")) - - for key, value in kwargs.items(): - # Skip these arguments since they need to set in a nested dict of feature_generators - if key in ["ms2pip_model", "ms2_tolerance", "rng", "calibration_set_size"]: - continue - - elif key == "feature_generators": - feature_generators = value.split(",") - # Reset feature generator dict since there might be default generators we don't want - config["ms2rescore"]["feature_generators"] = {} - if "basic" in feature_generators: - config["ms2rescore"]["feature_generators"]["basic"] = {} - if "ms2pip" in feature_generators: - config["ms2rescore"]["feature_generators"]["ms2pip"] = { - "model": kwargs["ms2pip_model"], - "ms2_tolerance": kwargs["ms2_tolerance"], - } - if "deeplc" in feature_generators: - config["ms2rescore"]["feature_generators"]["deeplc"] = { - "deeplc_retrain": False, - "calibration_set_size": kwargs["calibration_set_size"], - } - if "maxquant" in feature_generators: - config["ms2rescore"]["feature_generators"]["maxquant"] = {} - if "ionmob" in feature_generators: - config["ms2rescore"]["feature_generators"]["ionmob"] = {} - - elif key == "rescoring_engine": - # Reset rescoring engine dict we want to allow only computing features - config["ms2rescore"]["rescoring_engine"] = {} - if value == "mokapot": - config["ms2rescore"]["rescoring_engine"]["mokapot"] = { - "write_weights": True, - "write_txt": False, - "write_flashlfq": False, - "rng": kwargs["rng"], - "test_fdr": kwargs["test_fdr"], - "max_workers": kwargs["processes"], - } - if value == "percolator": - logging.info( - "Percolator rescoring engine has been specified. Use the idXML containing rescoring features and run Percolator in a separate step." - ) - continue - - else: - config["ms2rescore"][key] = value - - return config - - -def rescore_idxml(input_file, output_file, config) -> None: - """Rescore PSMs in an idXML file and keep other information unchanged.""" - # Read PSMs - reader = IdXMLReader(input_file) - psm_list = reader.read_file() - - # Rescore - rescore(config, psm_list) - - # Filter out PeptideHits within PeptideIdentification(s) that could not be processed by all feature generators - peptide_ids_filtered = filter_out_artifact_psms(psm_list, reader.peptide_ids) - - # Write - writer = IdXMLWriter(output_file, reader.protein_ids, peptide_ids_filtered) - writer.write_file(psm_list) - - -def filter_out_artifact_psms( - psm_list: PSMList, peptide_ids: List[oms.PeptideIdentification] -) -> List[oms.PeptideIdentification]: - """Filter out PeptideHits that could not be processed by all feature generators""" - num_mandatory_features = max([len(psm.rescoring_features) for psm in psm_list]) - new_psm_list = PSMList(psm_list=[psm for psm in psm_list if len(psm.rescoring_features) == num_mandatory_features]) - - # get differing peptidoforms of both psm lists - psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in psm_list]) - new_psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in new_psm_list]) - not_supported_peptides = psm_list_peptides - new_psm_list_peptides - - # no need to filter if all peptides are supported - if len(not_supported_peptides) == 0: - return peptide_ids - # Create new peptide ids and filter out not supported peptides - new_peptide_ids = [] - for peptide_id in peptide_ids: - new_hits = [] - for hit in peptide_id.getHits(): - if hit.getSequence().toString() in not_supported_peptides: - continue - new_hits.append(hit) - if len(new_hits) == 0: - continue - peptide_id.setHits(new_hits) - new_peptide_ids.append(peptide_id) - logging.info( - f"Removed {len(psm_list_peptides) - len(new_psm_list_peptides)} PSMs. Peptides not supported: {not_supported_peptides}" - ) - return new_peptide_ids - - -@click.command() -@click.option( - "-p", "--psm_file", help="Path to PSM file (PIN, mzIdentML, MaxQuant msms, X!Tandem XML, idXML)", required=True -) -@click.option( - "-s", - "--spectrum_path", - help="Path to MGF/mzML spectrum file or directory with spectrum files (default: derived from identification file)", - required=True, -) -@click.option( - "-o", "--output_path", help="Path and stem for output file names (default: derive from identification file)" -) -@click.option("-l", "--log_level", help="Logging level (default: `info`)", default="info") -@click.option("-n", "--processes", help="Number of parallel processes available to MS²Rescore", type=int, default=16) -@click.option("-f", "--fasta_file", help="Path to FASTA file") -@click.option("-t", "--test_fdr", help="The false-discovery rate threshold at which to evaluate the learned models. (default: 0.05)", default=0.05) -@click.option( - "-fg", - "--feature_generators", - help="Comma-separated list of feature generators to use (default: `ms2pip,deeplc`). See ms2rescore doc for further information", - default="", -) -@click.option("-pipm", "--ms2pip_model", help="MS²PIP model (default: `Immuno-HCD`)", type=str, default="Immuno-HCD") -@click.option( - "-ms2tol", "--ms2_tolerance", help="Fragment mass tolerance [Da](default: `0.02`)", type=float, default=0.02 -) -@click.option( - "-cs", - "--calibration_set_size", - help="Percentage of number of calibration set for DeepLC (default: `0.15`)", - default=0.15, -) -@click.option("-re", "--rescoring_engine", help="Either mokapot or percolator (default: `mokapot`)", default="mokapot") -@click.option( - "-rng", "--rng", help="Seed for mokapot's random number generator (default: `4711`)", type=int, default=4711 -) -@click.option("-d", "--id_decoy_pattern", help="Regex decoy pattern (default: `DECOY_`)", default="^DECOY_") -@click.option( - "-lsb", - "--lower_score_is_better", - help="Interpretation of primary search engine score (default: True)", - default=True, -) -def main(**kwargs): - config = parse_cli_arguments_to_config(**kwargs) - logging.info("MS²Rescore config:") - logging.info(config) - rescore_idxml(kwargs["psm_file"], kwargs["output_path"], config) - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index f1162a5..b67d019 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -3,6 +3,7 @@ from pyquantms.diann.create_diann_cfg import create_diann_cfg from pyquantms.diann.diann_convert import diann_convert from pyquantms.features.add_sage_feature import add_sage_feature +from pyquantms.ms2rescore.ms2rescore import ms2rescore from pyquantms.mzml.mzml_statistics import mzml_statistics from pyquantms.sdrf.extract_sample import extract_sample_from_expdesign @@ -19,6 +20,7 @@ def cli(): cli.add_command(mzml_statistics) cli.add_command(diann_convert) cli.add_command(extract_sample_from_expdesign) +cli.add_command(ms2rescore) if __name__ == "__main__": cli() diff --git a/requirements.txt b/requirements.txt index e421fa3..0a7b261 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,5 +2,5 @@ click sdrf_pipelines pyopenms ms2rescore -psm-utils +psm-utils==0.8.0 pydantic \ No newline at end of file diff --git a/setup.py b/setup.py index 34f64ab..f696fb2 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ "Development Status :: 5 - Production/Stable", ] -INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms", "ms2rescore", "psm-utils", "pydantic"] +INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms", "ms2rescore", "psm-utils==0.8.0", "pydantic"] PYTHON_REQUIRES = ">=3.7,<4" with open("README.md", "r") as fh: diff --git a/tests/test_future.py b/tests/test_future.py index 685b6d7..f5fcf03 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -39,4 +39,11 @@ def test_extract_sample_from_expdesign(): runner = CliRunner() result = runner.invoke(cli,['extract_sample','--help']) + assert result.exit_code == 0 + +# test for the ms2rescore command in cli +def test_ms2rescore(): + runner = CliRunner() + result = runner.invoke(cli,['ms2rescore','--help']) + assert result.exit_code == 0 \ No newline at end of file From 65d34b10c1f5192156e9811cd7a8c76869b6d0bd Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 07:36:21 +0100 Subject: [PATCH 19/53] arguments -> options --- pyquantms/mzml/mzml_statistics.py | 2 +- pyquantms/psm/psm_conversion.py | 6 +++--- pyquantms/pyquantmsc.py | 2 ++ tests/test_future.py | 7 +++++++ 4 files changed, 13 insertions(+), 4 deletions(-) diff --git a/pyquantms/mzml/mzml_statistics.py b/pyquantms/mzml/mzml_statistics.py index 67ba86d..7d9266c 100644 --- a/pyquantms/mzml/mzml_statistics.py +++ b/pyquantms/mzml/mzml_statistics.py @@ -10,7 +10,7 @@ @click.command("mzml_statistics") -@click.argument("ms_path", type=click.Path(exists=True)) +@click.option("--ms_path", type=click.Path(exists=True)) @click.option("--id_only", is_flag=True, help="Generate a csv with the spectrum id and the peaks") @click.pass_context def mzml_statistics(ctx, ms_path: str, id_only: bool = False) -> None: diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py index 7342ecc..e3ffb9b 100644 --- a/pyquantms/psm/psm_conversion.py +++ b/pyquantms/psm/psm_conversion.py @@ -31,9 +31,9 @@ def mods_position(peptide): @click.command("convert_psm") -@click.argument("idxml", type=click.Path(exists=True)) -@click.argument("spectra_file", type=click.Path(exists=True)) -@click.argument("export_decoy_psm", is_flag=True) +@click.option("--idxml", type=click.Path(exists=True)) +@click.option("--spectra_file", type=click.Path(exists=True)) +@click.option("--export_decoy_psm", is_flag=True) @click.pass_context def convert_psm(ctx, idxml: str, spectra_file: str, export_decoy_psm: bool = False): diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index b67d019..203ae6b 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -5,6 +5,7 @@ from pyquantms.features.add_sage_feature import add_sage_feature from pyquantms.ms2rescore.ms2rescore import ms2rescore from pyquantms.mzml.mzml_statistics import mzml_statistics +from pyquantms.psm.psm_conversion import convert_psm from pyquantms.sdrf.extract_sample import extract_sample_from_expdesign CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) @@ -21,6 +22,7 @@ def cli(): cli.add_command(diann_convert) cli.add_command(extract_sample_from_expdesign) cli.add_command(ms2rescore) +cli.add_command(convert_psm) if __name__ == "__main__": cli() diff --git a/tests/test_future.py b/tests/test_future.py index f5fcf03..9800ca6 100644 --- a/tests/test_future.py +++ b/tests/test_future.py @@ -46,4 +46,11 @@ def test_ms2rescore(): runner = CliRunner() result = runner.invoke(cli,['ms2rescore','--help']) + assert result.exit_code == 0 + +# test for the convert_psm command in cli +def test_convert_psm(): + runner = CliRunner() + result = runner.invoke(cli,['convert_psm','--help']) + assert result.exit_code == 0 \ No newline at end of file From 1103e7530782a5e6dd3518d62f28c6680f0f059b Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 07:38:40 +0100 Subject: [PATCH 20/53] arguments -> options --- pyquantms/ms2rescore/{__init__py.py => __init__.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename pyquantms/ms2rescore/{__init__py.py => __init__.py} (100%) diff --git a/pyquantms/ms2rescore/__init__py.py b/pyquantms/ms2rescore/__init__.py similarity index 100% rename from pyquantms/ms2rescore/__init__py.py rename to pyquantms/ms2rescore/__init__.py From 3781e8d18c48650b06794c4d356e76bc82199b17 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 07:41:46 +0100 Subject: [PATCH 21/53] arguments -> options --- pyquantms/psm/{__init__py.py => __init__.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename pyquantms/psm/{__init__py.py => __init__.py} (100%) diff --git a/pyquantms/psm/__init__py.py b/pyquantms/psm/__init__.py similarity index 100% rename from pyquantms/psm/__init__py.py rename to pyquantms/psm/__init__.py From d2305334400a6c3b4d61a5ae4e3a456b08ba7fd1 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 07:42:17 +0100 Subject: [PATCH 22/53] arguments -> options --- tests/{test_future.py => test_commands.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/{test_future.py => test_commands.py} (100%) diff --git a/tests/test_future.py b/tests/test_commands.py similarity index 100% rename from tests/test_future.py rename to tests/test_commands.py From 6df9789de5f64c6107b2cd7f505f0d27ec62467a Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 09:10:24 +0100 Subject: [PATCH 23/53] black run --- pyquantms/diann/create_diann_cfg.py | 39 ++- pyquantms/diann/diann_convert.py | 463 +++++++++++++++++++------ pyquantms/features/add_sage_feature.py | 16 +- pyquantms/ms2rescore/ms2rescore.py | 204 ++++++++--- pyquantms/mzml/mzml_statistics.py | 89 +++-- pyquantms/psm/psm_conversion.py | 89 ++++- pyquantms/sdrf/check_samplesheet.py | 46 ++- pyquantms/sdrf/extract_sample.py | 33 +- setup.py | 16 +- tests/test_commands.py | 24 +- 10 files changed, 774 insertions(+), 245 deletions(-) diff --git a/pyquantms/diann/create_diann_cfg.py b/pyquantms/diann/create_diann_cfg.py index 5c8d540..707e5f7 100644 --- a/pyquantms/diann/create_diann_cfg.py +++ b/pyquantms/diann/create_diann_cfg.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python - """ This script converts SDRF parameters to DIA-NN parameters License: Apache 2.0 @@ -46,11 +44,18 @@ def convert_mod(unimod_database, fix_mod: str, var_mod: str) -> Tuple[List, List tag = 0 for modification in unimod_database.modifications: if modification.get_name() == mod.split(" ")[0]: - diann_mod = modification.get_name() + "," + str(modification._delta_mono_mass) + diann_mod = ( + modification.get_name() + + "," + + str(modification._delta_mono_mass) + ) tag = 1 break if tag == 0: - print("Warning: Currently only supported unimod modifications for DIA pipeline. Skipped: " + mod) + print( + "Warning: Currently only supported unimod modifications for DIA pipeline. Skipped: " + + mod + ) continue site = re.findall(pattern, " ".join(mod.split(" ")[1:]))[0] if site == "Protein N-term": @@ -58,7 +63,12 @@ def convert_mod(unimod_database, fix_mod: str, var_mod: str) -> Tuple[List, List elif site == "N-term": site = "n" - if "TMT" in diann_mod or "Label" in diann_mod or "iTRAQ" in diann_mod or "mTRAQ" in diann_mod: + if ( + "TMT" in diann_mod + or "Label" in diann_mod + or "iTRAQ" in diann_mod + or "mTRAQ" in diann_mod + ): fix_ptm.append(diann_mod + "," + site + "," + "label") else: fix_ptm.append(diann_mod + "," + site) @@ -68,11 +78,18 @@ def convert_mod(unimod_database, fix_mod: str, var_mod: str) -> Tuple[List, List tag = 0 for modification in unimod_database.modifications: if modification.get_name() == mod.split(" ")[0]: - diann_mod = modification.get_name() + "," + str(modification._delta_mono_mass) + diann_mod = ( + modification.get_name() + + "," + + str(modification._delta_mono_mass) + ) tag = 1 break if tag == 0: - print("Warning: Currently only supported unimod modifications for DIA pipeline. Skipped: " + mod) + print( + "Warning: Currently only supported unimod modifications for DIA pipeline. Skipped: " + + mod + ) continue site = re.findall(pattern, " ".join(mod.split(" ")[1:]))[0] if site == "Protein N-term": @@ -80,7 +97,12 @@ def convert_mod(unimod_database, fix_mod: str, var_mod: str) -> Tuple[List, List elif site == "N-term": site = "n" - if "TMT" in diann_mod or "Label" in diann_mod or "iTRAQ" in diann_mod or "mTRAQ" in diann_mod: + if ( + "TMT" in diann_mod + or "Label" in diann_mod + or "iTRAQ" in diann_mod + or "mTRAQ" in diann_mod + ): var_ptm.append(diann_mod + "," + site + "," + "label") else: var_ptm.append(diann_mod + "," + site) @@ -100,4 +122,3 @@ def convert_mod(unimod_database, fix_mod: str, var_mod: str) -> Tuple[List, List def enzyme_cut(enzyme: str) -> str: return _ENZYME_SPECIFICITY.get(enzyme) or "--cut" - diff --git a/pyquantms/diann/diann_convert.py b/pyquantms/diann/diann_convert.py index cfd7d02..4d3717f 100644 --- a/pyquantms/diann/diann_convert.py +++ b/pyquantms/diann/diann_convert.py @@ -5,6 +5,7 @@ Revisions: 2023-Aug-05: J. Sebastian Paez """ + import logging import os import re @@ -25,7 +26,9 @@ CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) REVISION = "0.1.1" -logging.basicConfig(format="%(asctime)s [%(funcName)s] - %(message)s", level=logging.DEBUG) +logging.basicConfig( + format="%(asctime)s [%(funcName)s] - %(message)s", level=logging.DEBUG +) logger = logging.getLogger(__name__) @@ -38,7 +41,16 @@ @click.option("--missed_cleavages", "-m") @click.option("--qvalue_threshold", "-q", type=float) @click.pass_context -def diann_convert(ctx, folder, exp_design, dia_params, diann_version, charge, missed_cleavages, qvalue_threshold): +def diann_convert( + ctx, + folder, + exp_design, + dia_params, + diann_version, + charge, + missed_cleavages, + qvalue_threshold, +): """ Convert DIA-NN output to MSstats, Triqler or mzTab. The output formats are used for quality control and downstream analysis. @@ -62,7 +74,7 @@ def diann_convert(ctx, folder, exp_design, dia_params, diann_version, charge, mi logger.debug("Reading input files...") diann_directory = DiannDirectory(folder, diann_version_file=diann_version) report = diann_directory.main_report_df(qvalue_threshold=qvalue_threshold) - s_DataFrame, f_table = get_exp_design_dfs(exp_design) + s_data_frame, f_table = get_exp_design_dfs(exp_design) # Convert to MSstats msstats_columns_keep = [ @@ -76,7 +88,14 @@ def diann_convert(ctx, folder, exp_design, dia_params, diann_version, charge, mi logger.debug("Converting to MSstats format...") out_msstats = report[msstats_columns_keep] - out_msstats.columns = ["ProteinName", "PeptideSequence", "PrecursorCharge", "Intensity", "Reference", "Run"] + out_msstats.columns = [ + "ProteinName", + "PeptideSequence", + "PrecursorCharge", + "Intensity", + "Reference", + "Run", + ] out_msstats = out_msstats[out_msstats["Intensity"] != 0] # Q: What is this line doing? @@ -86,7 +105,9 @@ def diann_convert(ctx, folder, exp_design, dia_params, diann_version, charge, mi out_msstats["FragmentIon"] = "NA" out_msstats["ProductCharge"] = "0" out_msstats["IsotopeLabelType"] = "L" - unique_reference_map = {k: os.path.basename(k) for k in out_msstats["Reference"].unique()} + unique_reference_map = { + k: os.path.basename(k) for k in out_msstats["Reference"].unique() + } out_msstats["Reference"] = out_msstats["Reference"].map(unique_reference_map) del unique_reference_map @@ -99,17 +120,23 @@ def diann_convert(ctx, folder, exp_design, dia_params, diann_version, charge, mi logger.debug(f"\n\nf_table ({f_table.shape})>>>") logger.debug(f_table.head(5)) - logger.debug(f"\n\ns_DataFrame ({s_DataFrame.shape})>>>") - logger.debug(s_DataFrame.head(5)) + logger.debug(f"\n\ns_DataFrame ({s_data_frame.shape})>>>") + logger.debug(s_data_frame.head(5)) logger.debug("Adding Fraction, BioReplicate, Condition columns") # Changing implementation from apply to merge went from several minutes to # ~50ms out_msstats = out_msstats.merge( ( - s_DataFrame[["Sample", "MSstats_Condition", "MSstats_BioReplicate"]] + s_data_frame[["Sample", "MSstats_Condition", "MSstats_BioReplicate"]] .merge(f_table[["Fraction", "Sample", "run"]], on="Sample") - .rename(columns={"run": "Run", "MSstats_BioReplicate": "BioReplicate", "MSstats_Condition": "Condition"}) + .rename( + columns={ + "run": "Run", + "MSstats_BioReplicate": "BioReplicate", + "MSstats_Condition": "Condition", + } + ) .drop(columns=["Sample"]) ), on="Run", @@ -120,10 +147,24 @@ def diann_convert(ctx, folder, exp_design, dia_params, diann_version, charge, mi logger.info(f"MSstats input file is saved as {exp_out_prefix}_msstats_in.csv") # Convert to Triqler - triqler_cols = ["ProteinName", "PeptideSequence", "PrecursorCharge", "Intensity", "Run", "Condition"] + triqler_cols = [ + "ProteinName", + "PeptideSequence", + "PrecursorCharge", + "Intensity", + "Run", + "Condition", + ] out_triqler = out_msstats[triqler_cols] del out_msstats - out_triqler.columns = ["proteins", "peptide", "charge", "intensity", "run", "condition"] + out_triqler.columns = [ + "proteins", + "peptide", + "charge", + "intensity", + "run", + "condition", + ] out_triqler = out_triqler[out_triqler["intensity"] != 0] out_triqler.loc[:, "searchScore"] = report["Q.Value"] @@ -180,13 +221,16 @@ def get_exp_design_dfs(exp_design_file): f_table = [i.replace("\n", "").split("\t") for i in data[1:empty_row]] f_header = data[0].replace("\n", "").split("\t") f_table = pd.DataFrame(f_table, columns=f_header) - f_table.loc[:, "run"] = f_table.apply(lambda x: _true_stem(x["Spectra_Filepath"]), axis=1) + f_table.loc[:, "run"] = f_table.apply( + lambda x: _true_stem(x["Spectra_Filepath"]), axis=1 + ) s_table = [i.replace("\n", "").split("\t") for i in data[empty_row + 1 :]][1:] s_header = data[empty_row + 1].replace("\n", "").split("\t") - s_DataFrame = pd.DataFrame(s_table, columns=s_header) + s_data_frame = pd.DataFrame(s_table, columns=s_header) + + return s_data_frame, f_table - return s_DataFrame, f_table def compute_mass_modified_peptide(peptide_seq: str) -> float: """ @@ -217,14 +261,41 @@ def compute_mass_modified_peptide(peptide_seq: str) -> float: # Check aminoacid letter if aa in aa_mass and not_mod: aa = aa_mass[aa] - elif aa not in ['G','A','V','L','I','F','M','P','W','S','C','T','Y','N','Q','D','E','K','R','H'] and not_mod and aa != ")": + elif ( + aa + not in [ + "G", + "A", + "V", + "L", + "I", + "F", + "M", + "P", + "W", + "S", + "C", + "T", + "Y", + "N", + "Q", + "D", + "E", + "K", + "R", + "H", + ] + and not_mod + and aa != ")" + ): logger.info(f"Unknown amino acid with mass not known:{aa}") peptide_parts.append(aa) - new_peptide_seq = ''.join(peptide_parts) + new_peptide_seq = "".join(peptide_parts) mass = AASequence.fromString(new_peptide_seq).getMonoWeight() logger.debug(new_peptide_seq + ":" + str(mass)) return mass + class DiannDirectory: def __init__(self, base_path, diann_version_file): self.base_path = Path(base_path) @@ -281,7 +352,9 @@ def diann_version(self) -> str: diann_version_id = line.rstrip("\n").split(": ")[1] if diann_version_id is None: - raise ValueError(f"Could not find DIA-NN version in file {self.diann_version_file}") + raise ValueError( + f"Could not find DIA-NN version in file {self.diann_version_file}" + ) return diann_version_id @@ -291,7 +364,13 @@ def validate_diann_version(self) -> None: raise ValueError(f"Unsupported DIANN version {self.diann_version}") def convert_to_mztab( - self, report, f_table, charge: int, missed_cleavages: int, dia_params: List[Any], out: os.PathLike + self, + report, + f_table, + charge: int, + missed_cleavages: int, + dia_params: List[Any], + out: os.PathLike, ) -> None: logger.info("Converting to mzTab") self.validate_diann_version() @@ -312,18 +391,31 @@ def convert_to_mztab( logger.info("Mapping run information to report") index_ref = f_table.copy() - index_ref.rename(columns={"Fraction_Group": "ms_run", "Sample": "study_variable", "run": "Run"}, inplace=True) + index_ref.rename( + columns={ + "Fraction_Group": "ms_run", + "Sample": "study_variable", + "run": "Run", + }, + inplace=True, + ) index_ref["ms_run"] = index_ref["ms_run"].astype("int") index_ref["study_variable"] = index_ref["study_variable"].astype("int") - report = report.merge(index_ref[["ms_run", "Run", "study_variable"]], on="Run", validate="many_to_one") + report = report.merge( + index_ref[["ms_run", "Run", "study_variable"]], + on="Run", + validate="many_to_one", + ) - MTD, database = mztab_MTD(index_ref, dia_params, str(self.fasta), charge, missed_cleavages) + mtd, database = mztab_MTD( + index_ref, dia_params, str(self.fasta), charge, missed_cleavages + ) pg = pd.read_csv( self.pg_matrix, sep="\t", header=0, ) - PRH = mztab_PRH(report, pg, index_ref, database, fasta_df) + prh = mztab_PRH(report, pg, index_ref, database, fasta_df) del pg pr = pd.read_csv( self.pr_matrix, @@ -331,17 +423,17 @@ def convert_to_mztab( header=0, ) precursor_list = list(report["Precursor.Id"].unique()) - PEH = mztab_PEH(report, pr, precursor_list, index_ref, database) + peh = mztab_PEH(report, pr, precursor_list, index_ref, database) del pr PSH = mztab_PSH(report, str(self.base_path), database) del report - MTD.loc["", :] = "" - PRH.loc[len(PRH) + 1, :] = "" - PEH.loc[len(PEH) + 1, :] = "" + mtd.loc["", :] = "" + prh.loc[len(prh) + 1, :] = "" + peh.loc[len(peh) + 1, :] = "" with open(out, "w", newline="") as f: - MTD.to_csv(f, mode="w", sep="\t", index=False, header=False) - PRH.to_csv(f, mode="w", sep="\t", index=False, header=True) - PEH.to_csv(f, mode="w", sep="\t", index=False, header=True) + mtd.to_csv(f, mode="w", sep="\t", index=False, header=False) + prh.to_csv(f, mode="w", sep="\t", index=False, header=True) + peh.to_csv(f, mode="w", sep="\t", index=False, header=True) PSH.to_csv(f, mode="w", sep="\t", index=False, header=True) logger.info(f"mzTab file generated successfully! at {out}_out.mzTab") @@ -371,22 +463,29 @@ def main_report_df(self, qvalue_threshold: float) -> pd.DataFrame: report = pd.read_csv(self.report, sep="\t", header=0, usecols=remain_cols) # filter based on qvalue parameter for downstream analysiss - logger.debug(f"Filtering report based on qvalue threshold: {qvalue_threshold}, {len(report)} rows") + logger.debug( + f"Filtering report based on qvalue threshold: {qvalue_threshold}, {len(report)} rows" + ) report = report[report["Q.Value"] < qvalue_threshold] logger.debug(f"Report filtered, {len(report)} rows remaining") logger.debug("Calculating Precursor.Mz") # Making the map is 10x faster, and includes the mass of # the modification. with respect to the previous implementation. - uniq_masses = {k: compute_mass_modified_peptide(k) for k in report["Modified.Sequence"].unique()} + uniq_masses = { + k: compute_mass_modified_peptide(k) + for k in report["Modified.Sequence"].unique() + } mass_vector = report["Modified.Sequence"].map(uniq_masses) - report["Calculate.Precursor.Mz"] = (mass_vector + (PROTON_MASS_U * report["Precursor.Charge"])) / report[ - "Precursor.Charge" - ] + report["Calculate.Precursor.Mz"] = ( + mass_vector + (PROTON_MASS_U * report["Precursor.Charge"]) + ) / report["Precursor.Charge"] logger.debug("Indexing Precursors") # Making the map is 1500x faster - precursor_index_map = {k: i for i, k in enumerate(report["Precursor.Id"].unique())} + precursor_index_map = { + k: i for i, k in enumerate(report["Precursor.Id"].unique()) + } report["precursor.Index"] = report["Precursor.Id"].map(precursor_index_map) logger.debug(f"Shape of main report {report.shape}") @@ -417,7 +516,9 @@ def MTD_mod_info(fix_mod, var_mod): mod_name = mod_obj.getId() mod_accession = mod_obj.getUniModAccession() site = mod_obj.getOrigin() - fix_ptm.append(("[UNIMOD, " + mod_accession.upper() + ", " + mod_name + ", ]", site)) + fix_ptm.append( + ("[UNIMOD, " + mod_accession.upper() + ", " + mod_name + ", ]", site) + ) else: fix_flag = 0 fix_ptm.append("[MS, MS:1002453, No fixed modifications searched, ]") @@ -429,7 +530,9 @@ def MTD_mod_info(fix_mod, var_mod): mod_name = mod_obj.getId() mod_accession = mod_obj.getUniModAccession() site = mod_obj.getOrigin() - var_ptm.append(("[UNIMOD, " + mod_accession.upper() + ", " + mod_name + ", ]", site)) + var_ptm.append( + ("[UNIMOD, " + mod_accession.upper() + ", " + mod_name + ", ]", site) + ) else: var_flag = 0 var_ptm.append("[MS, MS:1002454, No variable modifications searched, ]") @@ -470,56 +573,91 @@ def mztab_MTD(index_ref, dia_params, fasta, charge, missed_cleavages): out_mztab_MTD.loc[1, "mzTab-type"] = "Quantification" out_mztab_MTD.loc[1, "title"] = "ConsensusMap export from OpenMS" out_mztab_MTD.loc[1, "description"] = "OpenMS export from consensusXML" - out_mztab_MTD.loc[1, "protein_search_engine_score[1]"] = "[, , DIA-NN Global.PG.Q.Value, ]" + out_mztab_MTD.loc[1, "protein_search_engine_score[1]"] = ( + "[, , DIA-NN Global.PG.Q.Value, ]" + ) out_mztab_MTD.loc[1, "peptide_search_engine_score[1]"] = ( "[, , DIA-NN Q.Value (minimum of the respective precursor q-values), ]" ) - out_mztab_MTD.loc[1, "psm_search_engine_score[1]"] = "[MS, MS:MS:1001869, protein-level q-value, ]" + out_mztab_MTD.loc[1, "psm_search_engine_score[1]"] = ( + "[MS, MS:MS:1001869, protein-level q-value, ]" + ) out_mztab_MTD.loc[1, "software[1]"] = "[MS, MS:1003253, DIA-NN, Release (v1.8.1)]" out_mztab_MTD.loc[1, "software[1]-setting[1]"] = fasta out_mztab_MTD.loc[1, "software[1]-setting[2]"] = "db_version:null" - out_mztab_MTD.loc[1, "software[1]-setting[3]"] = "fragment_mass_tolerance:" + FragmentMassTolerance - out_mztab_MTD.loc[1, "software[1]-setting[4]"] = "fragment_mass_tolerance_unit:" + FragmentMassToleranceUnit - out_mztab_MTD.loc[1, "software[1]-setting[5]"] = "precursor_mass_tolerance:" + PrecursorMassTolerance - out_mztab_MTD.loc[1, "software[1]-setting[6]"] = "precursor_mass_tolerance_unit:" + PrecursorMassToleranceUnit + out_mztab_MTD.loc[1, "software[1]-setting[3]"] = ( + "fragment_mass_tolerance:" + FragmentMassTolerance + ) + out_mztab_MTD.loc[1, "software[1]-setting[4]"] = ( + "fragment_mass_tolerance_unit:" + FragmentMassToleranceUnit + ) + out_mztab_MTD.loc[1, "software[1]-setting[5]"] = ( + "precursor_mass_tolerance:" + PrecursorMassTolerance + ) + out_mztab_MTD.loc[1, "software[1]-setting[6]"] = ( + "precursor_mass_tolerance_unit:" + PrecursorMassToleranceUnit + ) out_mztab_MTD.loc[1, "software[1]-setting[7]"] = "enzyme:" + Enzyme out_mztab_MTD.loc[1, "software[1]-setting[8]"] = "enzyme_term_specificity:full" out_mztab_MTD.loc[1, "software[1]-setting[9]"] = "charges:" + str(charge) - out_mztab_MTD.loc[1, "software[1]-setting[10]"] = "missed_cleavages:" + str(missed_cleavages) - out_mztab_MTD.loc[1, "software[1]-setting[11]"] = "fixed_modifications:" + FixedModifications - out_mztab_MTD.loc[1, "software[1]-setting[12]"] = "variable_modifications:" + VariableModifications + out_mztab_MTD.loc[1, "software[1]-setting[10]"] = "missed_cleavages:" + str( + missed_cleavages + ) + out_mztab_MTD.loc[1, "software[1]-setting[11]"] = ( + "fixed_modifications:" + FixedModifications + ) + out_mztab_MTD.loc[1, "software[1]-setting[12]"] = ( + "variable_modifications:" + VariableModifications + ) - (fixed_mods, variable_mods, fix_flag, var_flag) = MTD_mod_info(FixedModifications, VariableModifications) + (fixed_mods, variable_mods, fix_flag, var_flag) = MTD_mod_info( + FixedModifications, VariableModifications + ) if fix_flag == 1: for i in range(1, len(fixed_mods) + 1): out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]"] = fixed_mods[i - 1][0] - out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]-site"] = fixed_mods[i - 1][1] + out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]-site"] = fixed_mods[i - 1][ + 1 + ] out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]-position"] = "Anywhere" else: out_mztab_MTD.loc[1, "fixed_mod[1]"] = fixed_mods[0] if var_flag == 1: for i in range(1, len(variable_mods) + 1): - out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]"] = variable_mods[i - 1][0] - out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]-site"] = variable_mods[i - 1][1] + out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]"] = variable_mods[i - 1][ + 0 + ] + out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]-site"] = variable_mods[ + i - 1 + ][1] out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]-position"] = "Anywhere" else: out_mztab_MTD.loc[1, "variable_mod[1]"] = variable_mods[0] - out_mztab_MTD.loc[1, "quantification_method"] = "[MS, MS:1001834, LC-MS label-free quantitation analysis, ]" + out_mztab_MTD.loc[1, "quantification_method"] = ( + "[MS, MS:1001834, LC-MS label-free quantitation analysis, ]" + ) out_mztab_MTD.loc[1, "protein-quantification_unit"] = "[, , Abundance, ]" out_mztab_MTD.loc[1, "peptide-quantification_unit"] = "[, , Abundance, ]" for i in range(1, max(index_ref["ms_run"]) + 1): - out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-format"] = "[MS, MS:1000584, mzML file, ]" + out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-format"] = ( + "[MS, MS:1000584, mzML file, ]" + ) out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-location"] = ( - "file://" + index_ref[index_ref["ms_run"] == i]["Spectra_Filepath"].values[0] + "file://" + + index_ref[index_ref["ms_run"] == i]["Spectra_Filepath"].values[0] ) out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-id_format"] = ( "[MS, MS:1000777, spectrum identifier nativeID format, ]" ) - out_mztab_MTD.loc[1, "assay[" + str(i) + "]-quantification_reagent"] = "[MS, MS:1002038, unlabeled sample, ]" - out_mztab_MTD.loc[1, "assay[" + str(i) + "]-ms_run_ref"] = "ms_run[" + str(i) + "]" + out_mztab_MTD.loc[1, "assay[" + str(i) + "]-quantification_reagent"] = ( + "[MS, MS:1002038, unlabeled sample, ]" + ) + out_mztab_MTD.loc[1, "assay[" + str(i) + "]-ms_run_ref"] = ( + "ms_run[" + str(i) + "]" + ) with warnings.catch_warnings(): warnings.simplefilter("ignore") @@ -528,8 +666,12 @@ def mztab_MTD(index_ref, dia_params, fasta, charge, missed_cleavages): study_variable = [] for j in list(index_ref[index_ref["study_variable"] == i]["ms_run"].values): study_variable.append("assay[" + str(j) + "]") - out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-assay_refs"] = ",".join(study_variable) - out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-description"] = "no description given" + out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-assay_refs"] = ( + ",".join(study_variable) + ) + out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-description"] = ( + "no description given" + ) # The former loop makes a very sharded frame, this # makes the frame more compact in memory. @@ -578,20 +720,28 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): col = {} for i in file: col[i] = ( - "protein_abundance_assay[" + str(index_ref[index_ref["Run"] == _true_stem(i)]["ms_run"].values[0]) + "]" + "protein_abundance_assay[" + + str(index_ref[index_ref["Run"] == _true_stem(i)]["ms_run"].values[0]) + + "]" ) pg.rename(columns=col, inplace=True) logger.debug("Classifying results type ...") pg["opt_global_result_type"] = "single_protein" - pg.loc[pg["Protein.Ids"].str.contains(";"), "opt_global_result_type"] = "indistinguishable_protein_group" + pg.loc[pg["Protein.Ids"].str.contains(";"), "opt_global_result_type"] = ( + "indistinguishable_protein_group" + ) out_mztab_PRH = pg del pg out_mztab_PRH = out_mztab_PRH.drop(["Protein.Names"], axis=1) out_mztab_PRH.rename( - columns={"Protein.Group": "accession", "First.Protein.Description": "description"}, inplace=True + columns={ + "Protein.Group": "accession", + "First.Protein.Description": "description", + }, + inplace=True, ) out_mztab_PRH.loc[:, "database"] = database @@ -608,20 +758,34 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): out_mztab_PRH.loc[:, i] = "null" logger.debug("Extracting accession values (keeping first)...") - out_mztab_PRH.loc[:, "accession"] = out_mztab_PRH.apply(lambda x: x["accession"].split(";")[0], axis=1) + out_mztab_PRH.loc[:, "accession"] = out_mztab_PRH.apply( + lambda x: x["accession"].split(";")[0], axis=1 + ) - protein_details_df = out_mztab_PRH[out_mztab_PRH["opt_global_result_type"] == "indistinguishable_protein_group"] - prh_series = protein_details_df["Protein.Ids"].str.split(";", expand=True).stack().reset_index(level=1, drop=True) + protein_details_df = out_mztab_PRH[ + out_mztab_PRH["opt_global_result_type"] == "indistinguishable_protein_group" + ] + prh_series = ( + protein_details_df["Protein.Ids"] + .str.split(";", expand=True) + .stack() + .reset_index(level=1, drop=True) + ) prh_series.name = "accession" protein_details_df = ( - protein_details_df.drop("accession", axis=1).join(prh_series).reset_index().drop(columns="index") + protein_details_df.drop("accession", axis=1) + .join(prh_series) + .reset_index() + .drop(columns="index") ) if len(protein_details_df) > 0: logger.info(f"Found {len(protein_details_df)} indistinguishable protein groups") # The Following line fails if there are no indistinguishable protein groups protein_details_df.loc[:, "col"] = "protein_details" # protein_details_df = protein_details_df[-protein_details_df["accession"].str.contains("-")] - out_mztab_PRH = pd.concat([out_mztab_PRH, protein_details_df]).reset_index(drop=True) + out_mztab_PRH = pd.concat([out_mztab_PRH, protein_details_df]).reset_index( + drop=True + ) else: logger.info("No indistinguishable protein groups found") @@ -638,14 +802,22 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): # out_mztab_PRH.loc[out_mztab_PRH["opt_global_result_type"] == "single_protein", "ambiguity_members"] = "null" # or out_mztab_PRH.loc[out_mztab_PRH["Protein.Ids"] == out_mztab_PRH["accession"], "ambiguity_members"] = "null" out_mztab_PRH.loc[:, "ambiguity_members"] = out_mztab_PRH.apply( - lambda x: x["Protein.Ids"] if x["opt_global_result_type"] == "indistinguishable_protein_group" else "null", + lambda x: ( + x["Protein.Ids"] + if x["opt_global_result_type"] == "indistinguishable_protein_group" + else "null" + ), axis=1, ) logger.debug("Matching PRH to best search engine score...") score_looker = ModScoreLooker(report) - out_mztab_PRH[["modifiedSequence", "best_search_engine_score[1]"]] = out_mztab_PRH.apply( - lambda x: score_looker.get_score(x["Protein.Ids"]), axis=1, result_type="expand" + out_mztab_PRH[["modifiedSequence", "best_search_engine_score[1]"]] = ( + out_mztab_PRH.apply( + lambda x: score_looker.get_score(x["Protein.Ids"]), + axis=1, + result_type="expand", + ) ) logger.debug("Matching PRH to modifications...") @@ -665,7 +837,10 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): .pivot(columns=["study_variable"], index="Protein.Ids") .reset_index() ) - protein_agg_report.columns = ["::".join([str(s) for s in col]).strip() for col in protein_agg_report.columns.values] + protein_agg_report.columns = [ + "::".join([str(s) for s in col]).strip() + for col in protein_agg_report.columns.values + ] subname_mapper = { "Protein.Ids::::": "Protein.Ids", "PG.MaxLFQ::mean": "protein_abundance_study_variable", @@ -679,7 +854,11 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): # Oddly enough the last implementation mapped the the accession (Q9NZJ9) in the mztab # to the Protein.Ids (A0A024RBG1;Q9NZJ9;Q9NZJ9-2), leading to A LOT of missing values. out_mztab_PRH = out_mztab_PRH.merge( - protein_agg_report, on="Protein.Ids", how="left", validate="many_to_one", copy=True + protein_agg_report, + on="Protein.Ids", + how="left", + validate="many_to_one", + copy=True, ) del name_mapper del subname_mapper @@ -688,7 +867,9 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): out_mztab_PRH.loc[:, "PRH"] = "PRT" index = out_mztab_PRH.loc[:, "PRH"] - out_mztab_PRH.drop(["PRH", "Genes", "modifiedSequence", "Protein.Ids"], axis=1, inplace=True) + out_mztab_PRH.drop( + ["PRH", "Genes", "modifiedSequence", "Protein.Ids"], axis=1, inplace=True + ) out_mztab_PRH.insert(0, "PRH", index) out_mztab_PRH.fillna("null", inplace=True) out_mztab_PRH.loc[:, "database"] = database @@ -700,7 +881,11 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): def mztab_PEH( - report: pd.DataFrame, pr: pd.DataFrame, precursor_list: List[str], index_ref: pd.DataFrame, database: os.PathLike + report: pd.DataFrame, + pr: pd.DataFrame, + precursor_list: List[str], + index_ref: pd.DataFrame, + database: os.PathLike, ) -> pd.DataFrame: """ Construct PEH sub-table. @@ -728,7 +913,9 @@ def mztab_PEH( out_mztab_PEH = pd.DataFrame() out_mztab_PEH = pr.iloc[:, 0:10] out_mztab_PEH.drop( - ["Protein.Group", "Protein.Names", "First.Protein.Description", "Proteotypic"], axis=1, inplace=True + ["Protein.Group", "Protein.Names", "First.Protein.Description", "Proteotypic"], + axis=1, + inplace=True, ) out_mztab_PEH.rename( columns={ @@ -742,20 +929,35 @@ def mztab_PEH( logger.debug("Finding modifications...") out_mztab_PEH.loc[:, "modifications"] = out_mztab_PEH.apply( - lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), axis=1, result_type="expand" + lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), + axis=1, + result_type="expand", ) logger.debug("Extracting sequence...") - out_mztab_PEH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = out_mztab_PEH.apply( - lambda x: AASequence.fromString(x["opt_global_cv_MS:1000889_peptidoform_sequence"]).toString(), axis=1 + out_mztab_PEH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = ( + out_mztab_PEH.apply( + lambda x: AASequence.fromString( + x["opt_global_cv_MS:1000889_peptidoform_sequence"] + ).toString(), + axis=1, + ) ) logger.debug("Checking accession uniqueness...") out_mztab_PEH.loc[:, "unique"] = out_mztab_PEH.apply( - lambda x: "0" if ";" in str(x["accession"]) else "1", axis=1, result_type="expand" + lambda x: "0" if ";" in str(x["accession"]) else "1", + axis=1, + result_type="expand", ) - null_col = ["database_version", "search_engine", "retention_time_window", "mass_to_charge", "opt_global_feature_id"] + null_col = [ + "database_version", + "search_engine", + "retention_time_window", + "mass_to_charge", + "opt_global_feature_id", + ] for i in null_col: out_mztab_PEH.loc[:, i] = "null" out_mztab_PEH.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" @@ -777,7 +979,9 @@ def mztab_PEH( .pivot(columns=["ms_run"], index="precursor.Index") .reset_index() ) - tmp.columns = pd.Index(["::".join([str(s) for s in col]).strip() for col in tmp.columns.values]) + tmp.columns = pd.Index( + ["::".join([str(s) for s in col]).strip() for col in tmp.columns.values] + ) subname_mapper = { "precursor.Index::::": "precursor.Index", "Q.Value::min": "search_engine_score[1]_ms_run", @@ -785,7 +989,9 @@ def mztab_PEH( name_mapper = name_mapper_builder(subname_mapper) tmp.rename(columns=name_mapper, inplace=True) out_mztab_PEH = out_mztab_PEH.merge( - tmp.rename(columns={"precursor.Index": "pr_id"}), on="pr_id", validate="one_to_one" + tmp.rename(columns={"precursor.Index": "pr_id"}), + on="pr_id", + validate="one_to_one", ) del tmp del subname_mapper @@ -793,7 +999,9 @@ def mztab_PEH( logger.debug("Getting peptide abundances per study variable") pep_study_report = per_peptide_study_report(report) - out_mztab_PEH = out_mztab_PEH.merge(pep_study_report, on="pr_id", how="left", validate="one_to_one", copy=True) + out_mztab_PEH = out_mztab_PEH.merge( + pep_study_report, on="pr_id", how="left", validate="one_to_one", copy=True + ) del pep_study_report logger.debug("Getting peptide properties...") @@ -887,7 +1095,11 @@ def __find_info(directory, n): target = pd.read_csv(file, sep="\t") group.sort_values(by="RT.Start", inplace=True) target = target[["Retention_Time", "SpectrumID", "Exp_Mass_To_Charge"]] - target.columns = ["RT.Start", "opt_global_spectrum_reference", "exp_mass_to_charge"] + target.columns = [ + "RT.Start", + "opt_global_spectrum_reference", + "exp_mass_to_charge", + ] # Standardize spectrum identifier format for bruker data if type(target.loc[0, "opt_global_spectrum_reference"]) != str: target.loc[:, "opt_global_spectrum_reference"] = "scan=" + target.loc[ @@ -896,7 +1108,12 @@ def __find_info(directory, n): # TODO seconds returned from precursor.getRT() target.loc[:, "RT.Start"] = target.apply(lambda x: x["RT.Start"] / 60, axis=1) - out_mztab_PSH = pd.concat([out_mztab_PSH, pd.merge_asof(group, target, on="RT.Start", direction="nearest")]) + out_mztab_PSH = pd.concat( + [ + out_mztab_PSH, + pd.merge_asof(group, target, on="RT.Start", direction="nearest"), + ] + ) del report ## Score at PSM level: Q.Value @@ -936,7 +1153,9 @@ def __find_info(directory, n): out_mztab_PSH.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" out_mztab_PSH.loc[:, "PSM_ID"] = out_mztab_PSH.index out_mztab_PSH.loc[:, "unique"] = out_mztab_PSH.apply( - lambda x: "0" if ";" in str(x["accession"]) else "1", axis=1, result_type="expand" + lambda x: "0" if ";" in str(x["accession"]) else "1", + axis=1, + result_type="expand", ) out_mztab_PSH.loc[:, "database"] = database @@ -955,19 +1174,28 @@ def __find_info(directory, n): logger.info("Finding Modifications ...") out_mztab_PSH.loc[:, "modifications"] = out_mztab_PSH.apply( - lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), axis=1, result_type="expand" + lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), + axis=1, + result_type="expand", ) out_mztab_PSH.loc[:, "spectra_ref"] = out_mztab_PSH.apply( - lambda x: "ms_run[{}]:".format(x["ms_run"]) + x["opt_global_spectrum_reference"], axis=1, result_type="expand" - ) - - out_mztab_PSH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = out_mztab_PSH.apply( - lambda x: AASequence.fromString(x["opt_global_cv_MS:1000889_peptidoform_sequence"]).toString(), + lambda x: "ms_run[{}]:".format(x["ms_run"]) + + x["opt_global_spectrum_reference"], axis=1, result_type="expand", ) + out_mztab_PSH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = ( + out_mztab_PSH.apply( + lambda x: AASequence.fromString( + x["opt_global_cv_MS:1000889_peptidoform_sequence"] + ).toString(), + axis=1, + result_type="expand", + ) + ) + out_mztab_PSH.loc[:, "PSH"] = "PSM" index = out_mztab_PSH.loc[:, "PSH"] out_mztab_PSH.drop(["PSH", "ms_run"], axis=1, inplace=True) @@ -1036,7 +1264,15 @@ def match_in_report(report, target, max_, flag, level): PEH_params = [] for i in range(1, max_ + 1): match = result[result["study_variable"] == i] - PEH_params.extend([match["Precursor.Normalised"].mean(), "null", "null", "null", match["RT.Start"].mean()]) + PEH_params.extend( + [ + match["Precursor.Normalised"].mean(), + "null", + "null", + "null", + match["RT.Start"].mean(), + ] + ) return tuple(PEH_params) @@ -1045,7 +1281,11 @@ def match_in_report(report, target, max_, flag, level): q_value = [] for i in range(1, max_ + 1): match = result[result["ms_run"] == i] - q_value.append(match["Q.Value"].values[0] if match["Q.Value"].values.size > 0 else np.nan) + q_value.append( + match["Q.Value"].values[0] + if match["Q.Value"].values.size > 0 + else np.nan + ) return tuple(q_value) @@ -1089,7 +1329,8 @@ def make_lookup_dict(self, report) -> Dict[str, Tuple[str, float]]: # 103588 NPVGYPLAWQFLR Q9NZ08;Q9NZ08-2 0.000252 out = { - row["Protein.Ids"]: (row["Modified.Sequence"], row["Global.PG.Q.Value"]) for _, row in grouped_df.iterrows() + row["Protein.Ids"]: (row["Modified.Sequence"], row["Global.PG.Q.Value"]) + for _, row in grouped_df.iterrows() } return out @@ -1146,7 +1387,9 @@ def find_modification(peptide): for k in range(0, len(original_mods)): original_mods[k] = str(position[k]) + "-" + original_mods[k].upper() - original_mods = ",".join(str(i) for i in original_mods) if len(original_mods) > 0 else "null" + original_mods = ( + ",".join(str(i) for i in original_mods) if len(original_mods) > 0 else "null" + ) return original_mods @@ -1229,13 +1472,22 @@ def per_peptide_study_report(report: pd.DataFrame) -> pd.DataFrame: """ pep_study_grouped = ( report.groupby(["study_variable", "precursor.Index"]) - .agg({"Precursor.Normalised": ["mean", "std", "sem"], "RT.Start": ["mean"], "Calculate.Precursor.Mz": ["mean"]}) + .agg( + { + "Precursor.Normalised": ["mean", "std", "sem"], + "RT.Start": ["mean"], + "Calculate.Precursor.Mz": ["mean"], + } + ) .reset_index() .pivot(columns=["study_variable"], index="precursor.Index") .reset_index() ) pep_study_grouped.columns = pd.Index( - ["::".join([str(s) for s in col]).strip() for col in pep_study_grouped.columns.values] + [ + "::".join([str(s) for s in col]).strip() + for col in pep_study_grouped.columns.values + ] ) # Columns here would be like: # [ @@ -1301,7 +1553,10 @@ def calculate_coverage(ref_sequence: str, sequences: Set[str]): merged_lengths: list = [] for start, length in sorted(zip(starts, lengths)): if merged_starts and merged_starts[-1] + merged_lengths[-1] >= start: - merged_lengths[-1] = max(merged_starts[-1] + merged_lengths[-1], start + length) - merged_starts[-1] + merged_lengths[-1] = ( + max(merged_starts[-1] + merged_lengths[-1], start + length) + - merged_starts[-1] + ) else: merged_starts.append(start) merged_lengths.append(length) @@ -1311,7 +1566,9 @@ def calculate_coverage(ref_sequence: str, sequences: Set[str]): return coverage -def calculate_protein_coverages(report: pd.DataFrame, out_mztab_PRH: pd.DataFrame, fasta_df: pd.DataFrame) -> List[str]: +def calculate_protein_coverages( + report: pd.DataFrame, out_mztab_PRH: pd.DataFrame, fasta_df: pd.DataFrame +) -> List[str]: """Calculates protein coverages for the PRH table. The protein coverage is calculated as the fraction of the protein sequence @@ -1340,7 +1597,9 @@ def calculate_protein_coverages(report: pd.DataFrame, out_mztab_PRH: pd.DataFram # I am pretty sure this is the slowest part of the code matches = fasta_df[fasta_df["id"].str.contains(acc)]["id"] if len(matches) == 0: - logger.warning(f"Could not find fasta id for accession {acc} in the fasta file.") + logger.warning( + f"Could not find fasta id for accession {acc} in the fasta file." + ) acc_to_fasta_ids[acc] = None elif len(matches) == 1: acc_to_fasta_ids[acc] = matches.iloc[0] @@ -1357,11 +1616,11 @@ def calculate_protein_coverages(report: pd.DataFrame, out_mztab_PRH: pd.DataFram if f_id is None: out_cov = "null" else: - cov = calculate_coverage(fasta_id_to_seqs[f_id], ids_to_seqs[acc_to_ids[acc]]) + cov = calculate_coverage( + fasta_id_to_seqs[f_id], ids_to_seqs[acc_to_ids[acc]] + ) out_cov = format(cov, ".03f") out[i] = out_cov return out - - diff --git a/pyquantms/features/add_sage_feature.py b/pyquantms/features/add_sage_feature.py index 5d8adf9..e3247f4 100644 --- a/pyquantms/features/add_sage_feature.py +++ b/pyquantms/features/add_sage_feature.py @@ -18,9 +18,9 @@ def add_sage_feature(ctx, idx_file: str, output_file: str, feat_file: str): :return: None """ extra_feat = [] - feat = pd.read_csv(feat_file, sep='\t') + feat = pd.read_csv(feat_file, sep="\t") for _, row in feat.iterrows(): - if row["feature_generator"] == 'psm_file': + if row["feature_generator"] == "psm_file": continue else: extra_feat.append(row["feature_name"]) @@ -28,14 +28,10 @@ def add_sage_feature(ctx, idx_file: str, output_file: str, feat_file: str): protein_ids = [] peptide_ids = [] oms.IdXMLFile().load(idx_file, protein_ids, peptide_ids) - SearchParameters = protein_ids[0].getSearchParameters() - features = SearchParameters.getMetaValue("extra_features") + search_parameters = protein_ids[0].getSearchParameters() + features = search_parameters.getMetaValue("extra_features") extra_features = features + "," + ",".join(extra_feat) - SearchParameters.setMetaValue("extra_features", extra_features) - protein_ids[0].setSearchParameters(SearchParameters) + search_parameters.setMetaValue("extra_features", extra_features) + protein_ids[0].setSearchParameters(search_parameters) oms.IdXMLFile().store(output_file, protein_ids, peptide_ids) print("Done") - - - - diff --git a/pyquantms/ms2rescore/ms2rescore.py b/pyquantms/ms2rescore/ms2rescore.py index c36dec8..15b7939 100644 --- a/pyquantms/ms2rescore/ms2rescore.py +++ b/pyquantms/ms2rescore/ms2rescore.py @@ -1,4 +1,3 @@ -#!/usr/bin/env python # Written by Jonas Scheid under the MIT license # Contributions by Yasset Perez-Riverol and Dai Chengxin # This script is part of the pyquantms package @@ -16,14 +15,25 @@ logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") -def parse_cli_arguments_to_config(config_file: str = None, feature_generators: str = None, ms2pip_model: str = None, - ms2_tolerance: float = None, calibration_set_size: float = None, - rescoring_engine: str = None, rng: int = None, test_fdr: float = None, - processes: int = None, spectrum_path: str = None, fasta_file: str = None, - id_decoy_pattern: str = None, lower_score_is_better: bool = None) -> dict: - +def parse_cli_arguments_to_config( + config_file: str = None, + feature_generators: str = None, + ms2pip_model: str = None, + ms2_tolerance: float = None, + calibration_set_size: float = None, + rescoring_engine: str = None, + rng: int = None, + test_fdr: float = None, + processes: int = None, + spectrum_path: str = None, + fasta_file: str = None, + id_decoy_pattern: str = None, + lower_score_is_better: bool = None, +) -> dict: if config_file is None: - config = json.load(importlib.resources.open_text(package_data, "config_default.json")) + config = json.load( + importlib.resources.open_text(package_data, "config_default.json") + ) else: with open(config_file) as f: config = json.load(f) @@ -60,7 +70,9 @@ def parse_cli_arguments_to_config(config_file: str = None, feature_generators: s "max_workers": processes, } if rescoring_engine == "percolator": - logging.info("Percolator rescoring engine has been specified. Use the idXML containing rescoring features and run Percolator in a separate step.") + logging.info( + "Percolator rescoring engine has been specified. Use the idXML containing rescoring features and run Percolator in a separate step." + ) if ms2pip_model is not None: config["ms2rescore"]["ms2pip_model"] = ms2pip_model @@ -104,11 +116,21 @@ def filter_out_artifact_psms( ) -> List[oms.PeptideIdentification]: """Filter out PeptideHits that could not be processed by all feature generators""" num_mandatory_features = max([len(psm.rescoring_features) for psm in psm_list]) - new_psm_list = PSMList(psm_list=[psm for psm in psm_list if len(psm.rescoring_features) == num_mandatory_features]) + new_psm_list = PSMList( + psm_list=[ + psm + for psm in psm_list + if len(psm.rescoring_features) == num_mandatory_features + ] + ) # get differing peptidoforms of both psm lists - psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in psm_list]) - new_psm_list_peptides = set([next(iter(psm.provenance_data.items()))[1] for psm in new_psm_list]) + psm_list_peptides = set( + [next(iter(psm.provenance_data.items()))[1] for psm in psm_list] + ) + new_psm_list_peptides = set( + [next(iter(psm.provenance_data.items()))[1] for psm in new_psm_list] + ) not_supported_peptides = psm_list_peptides - new_psm_list_peptides # no need to filter if all peptides are supported @@ -132,27 +154,123 @@ def filter_out_artifact_psms( return new_peptide_ids -@click.command("ms2rescore", short_help= "Rescore PSMs in an idXML file and keep other information unchanged.") -@click.option("-p", "--psm_file", help="Path to PSM file (idXML)", required=True, type=click.Path(exists=True)) -@click.option("-s", "--spectrum_path", help="Path to MGF/mzML spectrum file or directory with spectrum files (default: derived from identification file)", required=True, type=click.Path(exists=True)) -@click.option("-o", "--output_path", help="Path and stem for output file names (default: derive from identification file)") -@click.option("-l", "--log_level", help="Logging level (default: `info`)", default="info") -@click.option("-n", "--processes", help="Number of parallel processes available to MS²Rescore", type=int, default=16) +@click.command( + "ms2rescore", + short_help="Rescore PSMs in an idXML file and keep other information unchanged.", +) +@click.option( + "-p", + "--psm_file", + help="Path to PSM file (idXML)", + required=True, + type=click.Path(exists=True), +) +@click.option( + "-s", + "--spectrum_path", + help="Path to MGF/mzML spectrum file or directory with spectrum files (default: derived from identification file)", + required=True, + type=click.Path(exists=True), +) +@click.option( + "-o", + "--output_path", + help="Path and stem for output file names (default: derive from identification file)", +) +@click.option( + "-l", "--log_level", help="Logging level (default: `info`)", default="info" +) +@click.option( + "-n", + "--processes", + help="Number of parallel processes available to MS²Rescore", + type=int, + default=16, +) @click.option("-f", "--fasta_file", help="Path to FASTA file") -@click.option("-t", "--test_fdr", help="The false-discovery rate threshold at which to evaluate the learned models. (default: 0.05)", default=0.05) -@click.option( "-fg","--feature_generators", help="Comma-separated list of feature generators to use (default: `ms2pip,deeplc`). See ms2rescore doc for further information", default="",) -@click.option("-pipm", "--ms2pip_model", help="MS²PIP model (default: `Immuno-HCD`)", type=str, default="Immuno-HCD") -@click.option("-ms2tol", "--ms2_tolerance", help="Fragment mass tolerance [Da](default: `0.02`)", type=float, default=0.02) -@click.option("-cs", "--calibration_set_size", help="Percentage of number of calibration set for DeepLC (default: `0.15`)", default=0.15,) -@click.option("-re", "--rescoring_engine", help="Either mokapot or percolator (default: `mokapot`)", default="mokapot", type=click.Choice(["mokapot", "percolator"])) -@click.option("-rng", "--rng", help="Seed for mokapot's random number generator (default: `4711`)", type=int, default=4711) -@click.option("-d", "--id_decoy_pattern", help="Regex decoy pattern (default: `DECOY_`)", default="^DECOY_") -@click.option("-lsb","--lower_score_is_better", help="Interpretation of primary search engine score (default: True)", default=True,) -@click.option("--config_file", help="Path to MS²Rescore config file (default: `config_default.json`)", default=None) +@click.option( + "-t", + "--test_fdr", + help="The false-discovery rate threshold at which to evaluate the learned models. (default: 0.05)", + default=0.05, +) +@click.option( + "-fg", + "--feature_generators", + help="Comma-separated list of feature generators to use (default: `ms2pip,deeplc`). See ms2rescore doc for further information", + default="", +) +@click.option( + "-pipm", + "--ms2pip_model", + help="MS²PIP model (default: `Immuno-HCD`)", + type=str, + default="Immuno-HCD", +) +@click.option( + "-ms2tol", + "--ms2_tolerance", + help="Fragment mass tolerance [Da](default: `0.02`)", + type=float, + default=0.02, +) +@click.option( + "-cs", + "--calibration_set_size", + help="Percentage of number of calibration set for DeepLC (default: `0.15`)", + default=0.15, +) +@click.option( + "-re", + "--rescoring_engine", + help="Either mokapot or percolator (default: `mokapot`)", + default="mokapot", + type=click.Choice(["mokapot", "percolator"]), +) +@click.option( + "-rng", + "--rng", + help="Seed for mokapot's random number generator (default: `4711`)", + type=int, + default=4711, +) +@click.option( + "-d", + "--id_decoy_pattern", + help="Regex decoy pattern (default: `DECOY_`)", + default="^DECOY_", +) +@click.option( + "-lsb", + "--lower_score_is_better", + help="Interpretation of primary search engine score (default: True)", + default=True, +) +@click.option( + "--config_file", + help="Path to MS²Rescore config file (default: `config_default.json`)", + default=None, +) @click.pass_context -def ms2rescore(ctx, psm_file: str, spectrum_path, output_path: str, log_level, processes, fasta_file, test_fdr, feature_generators, - ms2pip_model, ms2_tolerance, calibration_set_size, rescoring_engine, rng, id_decoy_pattern, - lower_score_is_better, config_file: str): +def ms2rescore( + ctx, + psm_file: str, + spectrum_path, + output_path: str, + log_level, + processes, + fasta_file, + test_fdr, + feature_generators, + ms2pip_model, + ms2_tolerance, + calibration_set_size, + rescoring_engine, + rng, + id_decoy_pattern, + lower_score_is_better, + config_file: str, +): """ Rescore PSMs in an idXML file and keep other information unchanged. :param ctx: Click context object @@ -175,20 +293,26 @@ def ms2rescore(ctx, psm_file: str, spectrum_path, output_path: str, log_level, p :return: """ logging.getLogger().setLevel(log_level.upper()) - config = parse_cli_arguments_to_config(config_file=config_file, feature_generators=feature_generators, ms2pip_model=ms2pip_model, - processes=processes, ms2_tolerance=ms2_tolerance, - calibration_set_size=calibration_set_size, - rescoring_engine=rescoring_engine, rng=rng, - test_fdr=test_fdr, spectrum_path=spectrum_path, fasta_file=fasta_file, - id_decoy_pattern=id_decoy_pattern,lower_score_is_better=lower_score_is_better) + config = parse_cli_arguments_to_config( + config_file=config_file, + feature_generators=feature_generators, + ms2pip_model=ms2pip_model, + processes=processes, + ms2_tolerance=ms2_tolerance, + calibration_set_size=calibration_set_size, + rescoring_engine=rescoring_engine, + rng=rng, + test_fdr=test_fdr, + spectrum_path=spectrum_path, + fasta_file=fasta_file, + id_decoy_pattern=id_decoy_pattern, + lower_score_is_better=lower_score_is_better, + ) logging.info("MS²Rescore config:") logging.info(config) rescore_idxml(psm_file, output_path, config) - - def main(**kwargs): config = parse_cli_arguments_to_config(**kwargs) rescore_idxml(kwargs["psm_file"], kwargs["output_path"], config) - diff --git a/pyquantms/mzml/mzml_statistics.py b/pyquantms/mzml/mzml_statistics.py index 7d9266c..4ac6683 100644 --- a/pyquantms/mzml/mzml_statistics.py +++ b/pyquantms/mzml/mzml_statistics.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python - from pathlib import Path import sqlite3 import re @@ -11,7 +9,9 @@ @click.command("mzml_statistics") @click.option("--ms_path", type=click.Path(exists=True)) -@click.option("--id_only", is_flag=True, help="Generate a csv with the spectrum id and the peaks") +@click.option( + "--id_only", is_flag=True, help="Generate a csv with the spectrum id and the peaks" +) @click.pass_context def mzml_statistics(ctx, ms_path: str, id_only: bool = False) -> None: """ @@ -21,7 +21,6 @@ def mzml_statistics(ctx, ms_path: str, id_only: bool = False) -> None: :param id_only: Generate a csv with the spectrum id and the peaks """ - import sys file_columns = [ "SpectrumID", "MSLevel", @@ -42,7 +41,7 @@ def parse_mzml(file_name: str, file_columns: list, id_only: bool = False): MzMLFile().load(file_name, exp) for spectrum in exp: id_ = spectrum.getNativeID() - MSLevel = spectrum.getMSLevel() + ms_level = spectrum.getMSLevel() rt = spectrum.getRT() if spectrum.getRT() else None peaks_tuple = spectrum.get_peaks() @@ -58,23 +57,66 @@ def parse_mzml(file_name: str, file_columns: list, id_only: bool = False): else: tic = spectrum.getMetaValue("total ion current") - if MSLevel == 1: - info_list = [id_, MSLevel, None, peak_per_ms, bpc, tic, rt, None, acquisition_datetime] - elif MSLevel == 2: + if ms_level == 1: + info_list = [ + id_, + ms_level, + None, + peak_per_ms, + bpc, + tic, + rt, + None, + acquisition_datetime, + ] + elif ms_level == 2: charge_state = spectrum.getPrecursors()[0].getCharge() - emz = spectrum.getPrecursors()[0].getMZ() if spectrum.getPrecursors()[0].getMZ() else None - info_list = [id_, MSLevel, charge_state, peak_per_ms, bpc, tic, rt, emz, acquisition_datetime] + emz = ( + spectrum.getPrecursors()[0].getMZ() + if spectrum.getPrecursors()[0].getMZ() + else None + ) + info_list = [ + id_, + ms_level, + charge_state, + peak_per_ms, + bpc, + tic, + rt, + emz, + acquisition_datetime, + ] mz_array = peaks_tuple[0] intensity_array = peaks_tuple[1] else: - info_list = [id_, MSLevel, None, None, None, None, rt, None, acquisition_datetime] - - if id_only and MSLevel == 2: - psm_part_info.append([re.findall(r"[scan|spectrum]=(\d+)", id_)[0], MSLevel, mz_array, intensity_array]) + info_list = [ + id_, + ms_level, + None, + None, + None, + None, + rt, + None, + acquisition_datetime, + ] + + if id_only and ms_level == 2: + psm_part_info.append( + [ + re.findall(r"[scan|spectrum]=(\d+)", id_)[0], + ms_level, + mz_array, + intensity_array, + ] + ) info.append(info_list) if id_only and len(psm_part_info) > 0: - pd.DataFrame(psm_part_info, columns=["scan", "ms_level", "mz", "intensity"]).to_csv( + pd.DataFrame( + psm_part_info, columns=["scan", "ms_level", "mz", "intensity"] + ).to_csv( f"{Path(ms_path).stem}_spectrum_df.csv", mode="w", index=False, @@ -91,11 +133,16 @@ def parse_bruker_d(file_name: str, file_columns: list): conn = sqlite3.connect(sql_filepath) c = conn.cursor() - datetime_cmd = "SELECT Value FROM GlobalMetadata WHERE key='AcquisitionDateTime'" - AcquisitionDateTime = c.execute(datetime_cmd).fetchall()[0][0] + datetime_cmd = ( + "SELECT Value FROM GlobalMetadata WHERE key='AcquisitionDateTime'" + ) + acquisition_date_time = c.execute(datetime_cmd).fetchall()[0][0] - df = pd.read_sql_query("SELECT Id, MsMsType, NumPeaks, MaxIntensity, SummedIntensities, Time FROM frames", conn) - df["AcquisitionDateTime"] = AcquisitionDateTime + df = pd.read_sql_query( + "SELECT Id, MsMsType, NumPeaks, MaxIntensity, SummedIntensities, Time FROM frames", + conn, + ) + df["AcquisitionDateTime"] = acquisition_date_time # {8:'DDA-PASEF', 9:'DIA-PASEF'} if 8 in df["MsMsType"].values: @@ -113,7 +160,9 @@ def parse_bruker_d(file_name: str, file_columns: list): precursor_df = pd.read_sql_query("SELECT * from Precursors", conn) except sqlite3.OperationalError as e: if "no such table: Precursors" in str(e): - print(f"No precursers recorded in {file_name}, This is normal for DIA data.") + print( + f"No precursers recorded in {file_name}, This is normal for DIA data." + ) precursor_df = pd.DataFrame() else: raise diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py index e3ffb9b..0b769a5 100644 --- a/pyquantms/psm/psm_conversion.py +++ b/pyquantms/psm/psm_conversion.py @@ -7,10 +7,27 @@ from pathlib import Path _parquet_field = [ - "sequence", "protein_accessions", "protein_start_positions", "protein_end_positions", - "modifications", "retention_time", "charge", "calc_mass_to_charge", "reference_file_name", - "scan_number", "peptidoform", "posterior_error_probability", "global_qvalue", "is_decoy", - "consensus_support", "mz_array", "intensity_array", "num_peaks", "search_engines", "id_scores", "hit_rank" + "sequence", + "protein_accessions", + "protein_start_positions", + "protein_end_positions", + "modifications", + "retention_time", + "charge", + "calc_mass_to_charge", + "reference_file_name", + "scan_number", + "peptidoform", + "posterior_error_probability", + "global_qvalue", + "is_decoy", + "consensus_support", + "mz_array", + "intensity_array", + "num_peaks", + "search_engines", + "id_scores", + "hit_rank", ] @@ -25,7 +42,9 @@ def mods_position(peptide): for k in range(0, len(original_mods)): original_mods[k] = str(position[k]) + "-" + original_mods[k] - original_mods = [str(i) for i in original_mods] if len(original_mods) > 0 else np.nan + original_mods = ( + [str(i) for i in original_mods] if len(original_mods) > 0 else np.nan + ) return original_mods @@ -36,7 +55,6 @@ def mods_position(peptide): @click.option("--export_decoy_psm", is_flag=True) @click.pass_context def convert_psm(ctx, idxml: str, spectra_file: str, export_decoy_psm: bool = False): - """ Convert idXML to csv file with PSMs information. :param ctx: click context @@ -67,13 +85,19 @@ def convert_psm(ctx, idxml: str, spectra_file: str, export_decoy_psm: bool = Fal else: search_engines = [prot_ids[0].getSearchEngine()] - reference_file_name = os.path.splitext(prot_ids[0].getMetaValue("spectra_data")[0].decode("UTF-8"))[0] + reference_file_name = os.path.splitext( + prot_ids[0].getMetaValue("spectra_data")[0].decode("UTF-8") + )[0] spectra_df = pd.read_csv(spectra_file) if spectra_file else None for peptide_id in pep_ids: retention_time = peptide_id.getRT() calc_mass_to_charge = peptide_id.getMZ() - scan_number = int(re.findall(r"(spectrum|scan)=(\d+)", peptide_id.getMetaValue("spectrum_reference"))[0][1]) + scan_number = int( + re.findall( + r"(spectrum|scan)=(\d+)", peptide_id.getMetaValue("spectrum_reference") + )[0][1] + ) if isinstance(spectra_df, pd.DataFrame): spectra = spectra_df[spectra_df["scan"] == scan_number] @@ -105,17 +129,44 @@ def convert_psm(ctx, idxml: str, spectra_file: str, export_decoy_psm: bool = Fal peptidoform = hit.getSequence().toString() modifications = mods_position(peptidoform) sequence = hit.getSequence().toUnmodifiedString() - protein_accessions = [ev.getProteinAccession() for ev in hit.getPeptideEvidences()] - posterior_error_probability = hit.getMetaValue("Posterior Error Probability_score") - protein_start_positions = [ev.getStart() for ev in hit.getPeptideEvidences()] + protein_accessions = [ + ev.getProteinAccession() for ev in hit.getPeptideEvidences() + ] + posterior_error_probability = hit.getMetaValue( + "Posterior Error Probability_score" + ) + protein_start_positions = [ + ev.getStart() for ev in hit.getPeptideEvidences() + ] protein_end_positions = [ev.getEnd() for ev in hit.getPeptideEvidences()] hit_rank = hit.getRank() - parquet_data.append([sequence, protein_accessions, protein_start_positions, protein_end_positions, - modifications, retention_time, charge, calc_mass_to_charge, reference_file_name, - scan_number, peptidoform, posterior_error_probability, global_qvalue, is_decoy, - consensus_support, mz_array, intensity_array, num_peaks, search_engines, id_scores, - hit_rank]) - - pd.DataFrame(parquet_data, columns=_parquet_field).to_csv(f"{Path(idxml).stem}_psm.csv",mode="w", index=False, header=True) - + parquet_data.append( + [ + sequence, + protein_accessions, + protein_start_positions, + protein_end_positions, + modifications, + retention_time, + charge, + calc_mass_to_charge, + reference_file_name, + scan_number, + peptidoform, + posterior_error_probability, + global_qvalue, + is_decoy, + consensus_support, + mz_array, + intensity_array, + num_peaks, + search_engines, + id_scores, + hit_rank, + ] + ) + + pd.DataFrame(parquet_data, columns=_parquet_field).to_csv( + f"{Path(idxml).stem}_psm.csv", mode="w", index=False, header=True + ) diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index dd125b4..ceb7516 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -1,5 +1,3 @@ -#!/usr/bin/env python - # nf-core: Update the script to check the sdrf # This script is based on the example at: https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/samplesheet/samplesheet_test_illumina_amplicon.csv @@ -14,13 +12,17 @@ def parse_args(args=None): - Description = "Reformat nf-core/quantms sdrf file and check its contents." - Epilog = "Example usage: python validate_sdrf.py " + description = "Reformat nf-core/quantms sdrf file and check its contents." + epilog = "Example usage: python validate_sdrf.py " - parser = argparse.ArgumentParser(description=Description, epilog=Epilog) + parser = argparse.ArgumentParser(description=description, epilog=epilog) parser.add_argument("SDRF", help="SDRF/Expdesign file to be validated") parser.add_argument("ISSDRF", help="SDRF file or Expdesign file") - parser.add_argument("--CHECK_MS", help="check mass spectrometry fields in SDRF.", action="store_true") + parser.add_argument( + "--CHECK_MS", + help="check mass spectrometry fields in SDRF.", + action="store_true", + ) return parser.parse_args(args) @@ -70,15 +72,19 @@ def check_expdesign(expdesign): try: empty_row = lines.index("\n") except ValueError: - print("the one-table format parser is broken in OpenMS2.5, please use one-table or sdrf") + print( + "the one-table format parser is broken in OpenMS2.5, please use one-table or sdrf" + ) sys.exit(1) if lines.index("\n") >= len(lines): - print("the one-table format parser is broken in OpenMS2.5, please use one-table or sdrf") + print( + "the one-table format parser is broken in OpenMS2.5, please use one-table or sdrf" + ) sys.exit(1) s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1 :]][1:] s_header = lines[empty_row + 1].replace("\n", "").split("\t") - s_DataFrame = pd.DataFrame(s_table, columns=s_header) + s_data_frame = pd.DataFrame(s_table, columns=s_header) # check missed mandatory column missed_columns = set(schema_file) - set(data.columns) @@ -86,28 +92,32 @@ def check_expdesign(expdesign): print("{0} column missed".format(" ".join(missed_columns))) sys.exit(1) - missed_columns = set(schema_sample) - set(s_DataFrame.columns) + missed_columns = set(schema_sample) - set(s_data_frame.columns) if len(missed_columns) != 0: print("{0} column missed".format(" ".join(missed_columns))) sys.exit(1) - if len(set(data.Label)) != 1 and "MSstats_Mixture" not in s_DataFrame.columns: + if len(set(data.Label)) != 1 and "MSstats_Mixture" not in s_data_frame.columns: print("MSstats_Mixture column missed in ISO experiments") sys.exit(1) # check logical problem: may be improved - check_expdesign_logic(data, s_DataFrame) + check_expdesign_logic(data, s_data_frame) -def check_expdesign_logic(fTable, sTable): - if int(max(fTable.Fraction_Group)) > len(set(fTable.Fraction_Group)): +def check_expdesign_logic(f_table, s_table): + if int(max(f_table.Fraction_Group)) > len(set(f_table.Fraction_Group)): print("Fraction_Group discontinuous!") sys.exit(1) - fTable_D = fTable.drop_duplicates(["Fraction_Group", "Fraction", "Label", "Sample"]) - if fTable_D.shape[0] < fTable.shape[0]: - print("Existing duplicate entries in Fraction_Group, Fraction, Label and Sample") + f_table_d = f_table.drop_duplicates( + ["Fraction_Group", "Fraction", "Label", "Sample"] + ) + if f_table_d.shape[0] < f_table.shape[0]: + print( + "Existing duplicate entries in Fraction_Group, Fraction, Label and Sample" + ) sys.exit(1) - if len(set(sTable.Sample)) < sTable.shape[0]: + if len(set(s_table.Sample)) < s_table.shape[0]: print("Existing duplicate Sample in sample table!") sys.exit(1) diff --git a/pyquantms/sdrf/extract_sample.py b/pyquantms/sdrf/extract_sample.py index 64b8785..af6460b 100644 --- a/pyquantms/sdrf/extract_sample.py +++ b/pyquantms/sdrf/extract_sample.py @@ -2,8 +2,11 @@ import pandas as pd import click + @click.command("extract_sample") -@click.option("--expdesign", help = "Experiment design file", type=click.Path(exists=True)) +@click.option( + "--expdesign", help="Experiment design file", type=click.Path(exists=True) +) @click.pass_context def extract_sample_from_expdesign(cxt, expdesign: str) -> None: """ @@ -13,24 +16,28 @@ def extract_sample_from_expdesign(cxt, expdesign: str) -> None: :return: None """ data = pd.read_csv(expdesign, sep="\t", header=0, dtype=str) - fTable = data.dropna() + f_table = data.dropna() # two table formats with open(expdesign, "r") as f: lines = f.readlines() empty_row = lines.index("\n") - s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1:]][1:] + s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1 :]][1:] s_header = lines[empty_row + 1].replace("\n", "").split("\t") - s_DataFrame = pd.DataFrame(s_table, columns=s_header) + s_data_frame = pd.DataFrame(s_table, columns=s_header) sample_dt = pd.DataFrame() - if "MSstats_Mixture" not in s_DataFrame.columns: - fTable = fTable[["Spectra_Filepath", "Sample"]] - fTable.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) + if "MSstats_Mixture" not in s_data_frame.columns: + f_table = f_table[["Spectra_Filepath", "Sample"]] + f_table.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) else: - fTable.drop_duplicates(subset=["Spectra_Filepath"], inplace=True) - for _, row in fTable.iterrows(): - mixture_id = s_DataFrame[s_DataFrame["Sample"] == row["Sample"]]["MSstats_Mixture"] - sample_dt = sample_dt.append({"Spectra_Filepath": row["Spectra_Filepath"], "Sample": mixture_id}, - ignore_index=True) - sample_dt.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) \ No newline at end of file + f_table.drop_duplicates(subset=["Spectra_Filepath"], inplace=True) + for _, row in f_table.iterrows(): + mixture_id = s_data_frame[s_data_frame["Sample"] == row["Sample"]][ + "MSstats_Mixture" + ] + sample_dt = sample_dt.append( + {"Spectra_Filepath": row["Spectra_Filepath"], "Sample": mixture_id}, + ignore_index=True, + ) + sample_dt.to_csv(f"{Path(expdesign).stem}_sample.csv", sep="\t", index=False) diff --git a/setup.py b/setup.py index f696fb2..baf6389 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ NAME = "pyquantms" LICENSE = "MIT License" DESCRIPTION = "Python package with scripts and helpers for the QuantMS workflow" -AUTHOR = ("Yasset Perez-Riverol, Dai Chengxin") +AUTHOR = "Yasset Perez-Riverol, Dai Chengxin" AUTHOR_EMAIL = "ypriverol@gmail.com" URL = "https://www.github.com/bigbio/pyquantms" PROJECT_URLS = { @@ -28,7 +28,14 @@ "Development Status :: 5 - Production/Stable", ] -INSTALL_REQUIRES = ["click", "sdrf_pipelines", "pyopenms", "ms2rescore", "psm-utils==0.8.0", "pydantic"] +INSTALL_REQUIRES = [ + "click", + "sdrf_pipelines", + "pyopenms", + "ms2rescore", + "psm-utils==0.8.0", + "pydantic", +] PYTHON_REQUIRES = ">=3.7,<4" with open("README.md", "r") as fh: @@ -50,9 +57,8 @@ packages=find_packages(), include_package_data=True, entry_points={ - "console_scripts": [ - ], + "console_scripts": [], }, install_requires=INSTALL_REQUIRES, python_requires=PYTHON_REQUIRES, -) \ No newline at end of file +) diff --git a/tests/test_commands.py b/tests/test_commands.py index 9800ca6..c8cc01a 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -1,56 +1,62 @@ from click.testing import CliRunner - from pyquantms.pyquantmsc import cli def test_future_test(): assert 1 == 1 + # test for the create_diann_cfg command in cli def test_create_diann_cfg(): runner = CliRunner() - result = runner.invoke(cli,['create_diann_cfg','--help']) + result = runner.invoke(cli, ["create_diann_cfg", "--help"]) assert result.exit_code == 0 + # test for the add_sage_feature command in cli def test_add_sage_feature(): runner = CliRunner() - result = runner.invoke(cli,['add_sage_feature','--help']) + result = runner.invoke(cli, ["add_sage_feature", "--help"]) assert result.exit_code == 0 + # test for the mzml_statistics command in cli def test_mzml_statistics(): runner = CliRunner() - result = runner.invoke(cli,['mzml_statistics','--help']) + result = runner.invoke(cli, ["mzml_statistics", "--help"]) assert result.exit_code == 0 + # test for the diann_convert command in cli def test_diann_convert(): runner = CliRunner() - result = runner.invoke(cli,['diann_convert','--help']) + result = runner.invoke(cli, ["diann_convert", "--help"]) assert result.exit_code == 0 + # test for the extract_sample_from_expdesign command in cli def test_extract_sample_from_expdesign(): runner = CliRunner() - result = runner.invoke(cli,['extract_sample','--help']) + result = runner.invoke(cli, ["extract_sample", "--help"]) assert result.exit_code == 0 + # test for the ms2rescore command in cli def test_ms2rescore(): runner = CliRunner() - result = runner.invoke(cli,['ms2rescore','--help']) + result = runner.invoke(cli, ["ms2rescore", "--help"]) assert result.exit_code == 0 + # test for the convert_psm command in cli def test_convert_psm(): runner = CliRunner() - result = runner.invoke(cli,['convert_psm','--help']) + result = runner.invoke(cli, ["convert_psm", "--help"]) - assert result.exit_code == 0 \ No newline at end of file + assert result.exit_code == 0 From f3c2d59c5469922afa72b751277509a02b8875ee Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 09:23:42 +0100 Subject: [PATCH 24/53] check_samplesheet.py added --- pyquantms/pyquantmsc.py | 9 ++++- pyquantms/sdrf/check_samplesheet.py | 52 ++++++++++++++--------------- tests/test_commands.py | 7 ++++ 3 files changed, 40 insertions(+), 28 deletions(-) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index 203ae6b..c7fa51e 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -6,6 +6,7 @@ from pyquantms.ms2rescore.ms2rescore import ms2rescore from pyquantms.mzml.mzml_statistics import mzml_statistics from pyquantms.psm.psm_conversion import convert_psm +from pyquantms.sdrf.check_samplesheet import check_samplesheet from pyquantms.sdrf.extract_sample import extract_sample_from_expdesign CONTEXT_SETTINGS = dict(help_option_names=["-h", "--help"]) @@ -17,11 +18,17 @@ def cli(): cli.add_command(create_diann_cfg) +cli.add_command(diann_convert) + cli.add_command(add_sage_feature) + cli.add_command(mzml_statistics) -cli.add_command(diann_convert) + cli.add_command(extract_sample_from_expdesign) +cli.add_command(check_samplesheet) + cli.add_command(ms2rescore) + cli.add_command(convert_psm) if __name__ == "__main__": diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index ceb7516..c5524d4 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -6,27 +6,12 @@ import os import sys +import click import pandas as pd from sdrf_pipelines.sdrf.sdrf import SdrfDataFrame from sdrf_pipelines.sdrf.sdrf_schema import DEFAULT_TEMPLATE, MASS_SPECTROMETRY -def parse_args(args=None): - description = "Reformat nf-core/quantms sdrf file and check its contents." - epilog = "Example usage: python validate_sdrf.py " - - parser = argparse.ArgumentParser(description=description, epilog=epilog) - parser.add_argument("SDRF", help="SDRF/Expdesign file to be validated") - parser.add_argument("ISSDRF", help="SDRF file or Expdesign file") - parser.add_argument( - "--CHECK_MS", - help="check mass spectrometry fields in SDRF.", - action="store_true", - ) - - return parser.parse_args(args) - - def make_dir(path): if len(path) > 0: try: @@ -122,15 +107,28 @@ def check_expdesign_logic(f_table, s_table): sys.exit(1) -def main(args=None): - # TODO validate expdesign file - args = parse_args(args) - - if args.ISSDRF == "true": - check_sdrf(args.CHECK_MS, args.SDRF) +@click.command("check_samplesheet", short_help="Check samplesheet") +@click.option("--is_sdrf", "-s", help="In Sdrf format", is_flag=True, default=False) +@click.option( + "--check_ms", + "-m", + required=False, + is_flag=True, + help="Check mass spectrometry fields in sample metadata.", + default=False, +) +@click.option( + "--input", + type=click.Path(exists=True), + required=True, + help="Input SDRF or Expdesign file", +) +@click.pass_context +def check_samplesheet(ctx, is_sdrf: bool, check_ms: bool, input: str) -> None: + """ + Check the samplesheet for errors. + """ + if is_sdrf: + check_sdrf(check_ms, input) else: - check_expdesign(args.SDRF) - - -if __name__ == "__main__": - sys.exit(main()) + check_expdesign(input) diff --git a/tests/test_commands.py b/tests/test_commands.py index c8cc01a..ea3e617 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -60,3 +60,10 @@ def test_convert_psm(): result = runner.invoke(cli, ["convert_psm", "--help"]) assert result.exit_code == 0 + +# test for the check_samplesheet command in cli +def test_check_samplesheet(): + runner = CliRunner() + result = runner.invoke(cli, ["check_samplesheet", "--help"]) + + assert result.exit_code == 0 \ No newline at end of file From 1f8abbc7c5be08c43558f672b92d878440fd2b51 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 09:35:33 +0100 Subject: [PATCH 25/53] refinements --- pyproject.toml | 4 +- pyquantms/diann/diann_convert.py | 452 ++++++++++++------------- pyquantms/features/add_sage_feature.py | 1 + pyquantms/sdrf/check_samplesheet.py | 3 +- pyquantms/sdrf/extract_sample.py | 2 +- requirements.txt | 5 +- setup.py | 3 +- 7 files changed, 236 insertions(+), 234 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index daa8fe3..26274e7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,11 +12,11 @@ build-backend = "setuptools.build_meta" click = ">=7.0" pydantic = ">=1.10,<2" python = "^3.7" -sdrf_pipelines = ">=0.0.26" +sdrf-pipelines = ">=0.0.26" pyopenms = ">=2.6.0" ms2rescore = ">=3.0.2" psm-utils = "==0.8.0" -#pydantic = ">=1.10" +pandas = ">=1.0.0" [tool.poetry.dev-dependencies] pytest = "*" diff --git a/pyquantms/diann/diann_convert.py b/pyquantms/diann/diann_convert.py index 4d3717f..b912987 100644 --- a/pyquantms/diann/diann_convert.py +++ b/pyquantms/diann/diann_convert.py @@ -42,23 +42,24 @@ @click.option("--qvalue_threshold", "-q", type=float) @click.pass_context def diann_convert( - ctx, - folder, - exp_design, - dia_params, - diann_version, - charge, - missed_cleavages, - qvalue_threshold, + ctx, + folder, + exp_design, + dia_params, + diann_version, + charge, + missed_cleavages, + qvalue_threshold, ): """ Convert DIA-NN output to MSstats, Triqler or mzTab. The output formats are used for quality control and downstream analysis. + :param ctx: Click context :param folder: DiannConvert specifies the folder where the required file resides. The folder contains the DiaNN main report, protein matrix, precursor matrix, experimental design file, protein sequence FASTA file, version file of DiaNN and ms_info TSVs - :type folder: str + ?:param exp_design: Experimental design file :param dia_params: A list contains DIA parameters :type dia_params: list :param diann_version: Path to a version file of DIA-NN @@ -225,7 +226,7 @@ def get_exp_design_dfs(exp_design_file): lambda x: _true_stem(x["Spectra_Filepath"]), axis=1 ) - s_table = [i.replace("\n", "").split("\t") for i in data[empty_row + 1 :]][1:] + s_table = [i.replace("\n", "").split("\t") for i in data[empty_row + 1:]][1:] s_header = data[empty_row + 1].replace("\n", "").split("\t") s_data_frame = pd.DataFrame(s_table, columns=s_header) @@ -262,31 +263,31 @@ def compute_mass_modified_peptide(peptide_seq: str) -> float: if aa in aa_mass and not_mod: aa = aa_mass[aa] elif ( - aa - not in [ - "G", - "A", - "V", - "L", - "I", - "F", - "M", - "P", - "W", - "S", - "C", - "T", - "Y", - "N", - "Q", - "D", - "E", - "K", - "R", - "H", - ] - and not_mod - and aa != ")" + aa + not in [ + "G", + "A", + "V", + "L", + "I", + "F", + "M", + "P", + "W", + "S", + "C", + "T", + "Y", + "N", + "Q", + "D", + "E", + "K", + "R", + "H", + ] + and not_mod + and aa != ")" ): logger.info(f"Unknown amino acid with mass not known:{aa}") peptide_parts.append(aa) @@ -364,13 +365,13 @@ def validate_diann_version(self) -> None: raise ValueError(f"Unsupported DIANN version {self.diann_version}") def convert_to_mztab( - self, - report, - f_table, - charge: int, - missed_cleavages: int, - dia_params: List[Any], - out: os.PathLike, + self, + report, + f_table, + charge: int, + missed_cleavages: int, + dia_params: List[Any], + out: os.PathLike, ) -> None: logger.info("Converting to mzTab") self.validate_diann_version() @@ -407,7 +408,7 @@ def convert_to_mztab( validate="many_to_one", ) - mtd, database = mztab_MTD( + mtd, database = mztab_mtd( index_ref, dia_params, str(self.fasta), charge, missed_cleavages ) pg = pd.read_csv( @@ -415,7 +416,7 @@ def convert_to_mztab( sep="\t", header=0, ) - prh = mztab_PRH(report, pg, index_ref, database, fasta_df) + prh = mztab_prh(report, pg, index_ref, database, fasta_df) del pg pr = pd.read_csv( self.pr_matrix, @@ -423,9 +424,9 @@ def convert_to_mztab( header=0, ) precursor_list = list(report["Precursor.Id"].unique()) - peh = mztab_PEH(report, pr, precursor_list, index_ref, database) + peh = mztab_peh(report, pr, precursor_list, index_ref, database) del pr - PSH = mztab_PSH(report, str(self.base_path), database) + psh = mztab_psh(report, str(self.base_path), database) del report mtd.loc["", :] = "" prh.loc[len(prh) + 1, :] = "" @@ -434,7 +435,7 @@ def convert_to_mztab( mtd.to_csv(f, mode="w", sep="\t", index=False, header=False) prh.to_csv(f, mode="w", sep="\t", index=False, header=True) peh.to_csv(f, mode="w", sep="\t", index=False, header=True) - PSH.to_csv(f, mode="w", sep="\t", index=False, header=True) + psh.to_csv(f, mode="w", sep="\t", index=False, header=True) logger.info(f"mzTab file generated successfully! at {out}_out.mzTab") @@ -478,8 +479,8 @@ def main_report_df(self, qvalue_threshold: float) -> pd.DataFrame: } mass_vector = report["Modified.Sequence"].map(uniq_masses) report["Calculate.Precursor.Mz"] = ( - mass_vector + (PROTON_MASS_U * report["Precursor.Charge"]) - ) / report["Precursor.Charge"] + mass_vector + (PROTON_MASS_U * report["Precursor.Charge"]) + ) / report["Precursor.Charge"] logger.debug("Indexing Precursors") # Making the map is 1500x faster @@ -494,7 +495,7 @@ def main_report_df(self, qvalue_threshold: float) -> pd.DataFrame: return report -def MTD_mod_info(fix_mod, var_mod): +def mtd_mod_info(fix_mod, var_mod): """ Convert fixed and variable modifications to the format required by the MTD sub-table. @@ -540,7 +541,7 @@ def MTD_mod_info(fix_mod, var_mod): return fix_ptm, var_ptm, fix_flag, var_flag -def mztab_MTD(index_ref, dia_params, fasta, charge, missed_cleavages): +def mztab_mtd(index_ref, dia_params, fasta, charge, missed_cleavages): """ Construct MTD sub-table. @@ -560,103 +561,103 @@ def mztab_MTD(index_ref, dia_params, fasta, charge, missed_cleavages): logger.info("Constructing MTD sub-table...") dia_params_list = dia_params.split(";") dia_params_list = ["null" if i == "" else i for i in dia_params_list] - FragmentMassTolerance = dia_params_list[0] - FragmentMassToleranceUnit = dia_params_list[1] - PrecursorMassTolerance = dia_params_list[2] - PrecursorMassToleranceUnit = dia_params_list[3] - Enzyme = dia_params_list[4] - FixedModifications = dia_params_list[5] - VariableModifications = dia_params_list[6] - out_mztab_MTD = pd.DataFrame() - out_mztab_MTD.loc[1, "mzTab-version"] = "1.0.0" - out_mztab_MTD.loc[1, "mzTab-mode"] = "Summary" - out_mztab_MTD.loc[1, "mzTab-type"] = "Quantification" - out_mztab_MTD.loc[1, "title"] = "ConsensusMap export from OpenMS" - out_mztab_MTD.loc[1, "description"] = "OpenMS export from consensusXML" - out_mztab_MTD.loc[1, "protein_search_engine_score[1]"] = ( + fragment_mass_tolerance = dia_params_list[0] + fragment_mass_tolerance_unit = dia_params_list[1] + precursor_mass_tolerance = dia_params_list[2] + precursor_mass_tolerance_unit = dia_params_list[3] + enzyme = dia_params_list[4] + fixed_modifications = dia_params_list[5] + variable_modifications = dia_params_list[6] + out_mztab_mtd = pd.DataFrame() + out_mztab_mtd.loc[1, "mzTab-version"] = "1.0.0" + out_mztab_mtd.loc[1, "mzTab-mode"] = "Summary" + out_mztab_mtd.loc[1, "mzTab-type"] = "Quantification" + out_mztab_mtd.loc[1, "title"] = "ConsensusMap export from OpenMS" + out_mztab_mtd.loc[1, "description"] = "OpenMS export from consensusXML" + out_mztab_mtd.loc[1, "protein_search_engine_score[1]"] = ( "[, , DIA-NN Global.PG.Q.Value, ]" ) - out_mztab_MTD.loc[1, "peptide_search_engine_score[1]"] = ( + out_mztab_mtd.loc[1, "peptide_search_engine_score[1]"] = ( "[, , DIA-NN Q.Value (minimum of the respective precursor q-values), ]" ) - out_mztab_MTD.loc[1, "psm_search_engine_score[1]"] = ( + out_mztab_mtd.loc[1, "psm_search_engine_score[1]"] = ( "[MS, MS:MS:1001869, protein-level q-value, ]" ) - out_mztab_MTD.loc[1, "software[1]"] = "[MS, MS:1003253, DIA-NN, Release (v1.8.1)]" - out_mztab_MTD.loc[1, "software[1]-setting[1]"] = fasta - out_mztab_MTD.loc[1, "software[1]-setting[2]"] = "db_version:null" - out_mztab_MTD.loc[1, "software[1]-setting[3]"] = ( - "fragment_mass_tolerance:" + FragmentMassTolerance + out_mztab_mtd.loc[1, "software[1]"] = "[MS, MS:1003253, DIA-NN, Release (v1.8.1)]" + out_mztab_mtd.loc[1, "software[1]-setting[1]"] = fasta + out_mztab_mtd.loc[1, "software[1]-setting[2]"] = "db_version:null" + out_mztab_mtd.loc[1, "software[1]-setting[3]"] = ( + "fragment_mass_tolerance:" + fragment_mass_tolerance ) - out_mztab_MTD.loc[1, "software[1]-setting[4]"] = ( - "fragment_mass_tolerance_unit:" + FragmentMassToleranceUnit + out_mztab_mtd.loc[1, "software[1]-setting[4]"] = ( + "fragment_mass_tolerance_unit:" + fragment_mass_tolerance_unit ) - out_mztab_MTD.loc[1, "software[1]-setting[5]"] = ( - "precursor_mass_tolerance:" + PrecursorMassTolerance + out_mztab_mtd.loc[1, "software[1]-setting[5]"] = ( + "precursor_mass_tolerance:" + precursor_mass_tolerance ) - out_mztab_MTD.loc[1, "software[1]-setting[6]"] = ( - "precursor_mass_tolerance_unit:" + PrecursorMassToleranceUnit + out_mztab_mtd.loc[1, "software[1]-setting[6]"] = ( + "precursor_mass_tolerance_unit:" + precursor_mass_tolerance_unit ) - out_mztab_MTD.loc[1, "software[1]-setting[7]"] = "enzyme:" + Enzyme - out_mztab_MTD.loc[1, "software[1]-setting[8]"] = "enzyme_term_specificity:full" - out_mztab_MTD.loc[1, "software[1]-setting[9]"] = "charges:" + str(charge) - out_mztab_MTD.loc[1, "software[1]-setting[10]"] = "missed_cleavages:" + str( + out_mztab_mtd.loc[1, "software[1]-setting[7]"] = "enzyme:" + enzyme + out_mztab_mtd.loc[1, "software[1]-setting[8]"] = "enzyme_term_specificity:full" + out_mztab_mtd.loc[1, "software[1]-setting[9]"] = "charges:" + str(charge) + out_mztab_mtd.loc[1, "software[1]-setting[10]"] = "missed_cleavages:" + str( missed_cleavages ) - out_mztab_MTD.loc[1, "software[1]-setting[11]"] = ( - "fixed_modifications:" + FixedModifications + out_mztab_mtd.loc[1, "software[1]-setting[11]"] = ( + "fixed_modifications:" + fixed_modifications ) - out_mztab_MTD.loc[1, "software[1]-setting[12]"] = ( - "variable_modifications:" + VariableModifications + out_mztab_mtd.loc[1, "software[1]-setting[12]"] = ( + "variable_modifications:" + variable_modifications ) - (fixed_mods, variable_mods, fix_flag, var_flag) = MTD_mod_info( - FixedModifications, VariableModifications + (fixed_mods, variable_mods, fix_flag, var_flag) = mtd_mod_info( + fixed_modifications, variable_modifications ) if fix_flag == 1: for i in range(1, len(fixed_mods) + 1): - out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]"] = fixed_mods[i - 1][0] - out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]-site"] = fixed_mods[i - 1][ + out_mztab_mtd.loc[1, "fixed_mod[" + str(i) + "]"] = fixed_mods[i - 1][0] + out_mztab_mtd.loc[1, "fixed_mod[" + str(i) + "]-site"] = fixed_mods[i - 1][ 1 ] - out_mztab_MTD.loc[1, "fixed_mod[" + str(i) + "]-position"] = "Anywhere" + out_mztab_mtd.loc[1, "fixed_mod[" + str(i) + "]-position"] = "Anywhere" else: - out_mztab_MTD.loc[1, "fixed_mod[1]"] = fixed_mods[0] + out_mztab_mtd.loc[1, "fixed_mod[1]"] = fixed_mods[0] if var_flag == 1: for i in range(1, len(variable_mods) + 1): - out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]"] = variable_mods[i - 1][ + out_mztab_mtd.loc[1, "variable_mod[" + str(i) + "]"] = variable_mods[i - 1][ 0 ] - out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]-site"] = variable_mods[ + out_mztab_mtd.loc[1, "variable_mod[" + str(i) + "]-site"] = variable_mods[ i - 1 - ][1] - out_mztab_MTD.loc[1, "variable_mod[" + str(i) + "]-position"] = "Anywhere" + ][1] + out_mztab_mtd.loc[1, "variable_mod[" + str(i) + "]-position"] = "Anywhere" else: - out_mztab_MTD.loc[1, "variable_mod[1]"] = variable_mods[0] + out_mztab_mtd.loc[1, "variable_mod[1]"] = variable_mods[0] - out_mztab_MTD.loc[1, "quantification_method"] = ( + out_mztab_mtd.loc[1, "quantification_method"] = ( "[MS, MS:1001834, LC-MS label-free quantitation analysis, ]" ) - out_mztab_MTD.loc[1, "protein-quantification_unit"] = "[, , Abundance, ]" - out_mztab_MTD.loc[1, "peptide-quantification_unit"] = "[, , Abundance, ]" + out_mztab_mtd.loc[1, "protein-quantification_unit"] = "[, , Abundance, ]" + out_mztab_mtd.loc[1, "peptide-quantification_unit"] = "[, , Abundance, ]" for i in range(1, max(index_ref["ms_run"]) + 1): - out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-format"] = ( + out_mztab_mtd.loc[1, "ms_run[" + str(i) + "]-format"] = ( "[MS, MS:1000584, mzML file, ]" ) - out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-location"] = ( - "file://" - + index_ref[index_ref["ms_run"] == i]["Spectra_Filepath"].values[0] + out_mztab_mtd.loc[1, "ms_run[" + str(i) + "]-location"] = ( + "file://" + + index_ref[index_ref["ms_run"] == i]["Spectra_Filepath"].values[0] ) - out_mztab_MTD.loc[1, "ms_run[" + str(i) + "]-id_format"] = ( + out_mztab_mtd.loc[1, "ms_run[" + str(i) + "]-id_format"] = ( "[MS, MS:1000777, spectrum identifier nativeID format, ]" ) - out_mztab_MTD.loc[1, "assay[" + str(i) + "]-quantification_reagent"] = ( + out_mztab_mtd.loc[1, "assay[" + str(i) + "]-quantification_reagent"] = ( "[MS, MS:1002038, unlabeled sample, ]" ) - out_mztab_MTD.loc[1, "assay[" + str(i) + "]-ms_run_ref"] = ( - "ms_run[" + str(i) + "]" + out_mztab_mtd.loc[1, "assay[" + str(i) + "]-ms_run_ref"] = ( + "ms_run[" + str(i) + "]" ) with warnings.catch_warnings(): @@ -666,33 +667,33 @@ def mztab_MTD(index_ref, dia_params, fasta, charge, missed_cleavages): study_variable = [] for j in list(index_ref[index_ref["study_variable"] == i]["ms_run"].values): study_variable.append("assay[" + str(j) + "]") - out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-assay_refs"] = ( + out_mztab_mtd.loc[1, "study_variable[" + str(i) + "]-assay_refs"] = ( ",".join(study_variable) ) - out_mztab_MTD.loc[1, "study_variable[" + str(i) + "]-description"] = ( + out_mztab_mtd.loc[1, "study_variable[" + str(i) + "]-description"] = ( "no description given" ) # The former loop makes a very sharded frame, this # makes the frame more compact in memory. - out_mztab_MTD = out_mztab_MTD.copy() - out_mztab_MTD.loc[2, :] = "MTD" + out_mztab_mtd = out_mztab_mtd.copy() + out_mztab_mtd.loc[2, :] = "MTD" # Transpose out_mztab_MTD - col = list(out_mztab_MTD.columns) - row = list(out_mztab_MTD.index) - out_mztab_MTD_T = pd.DataFrame(out_mztab_MTD.values.T, index=col, columns=row) - out_mztab_MTD_T.columns = ["inf", "index"] - out_mztab_MTD_T.insert(0, "title", out_mztab_MTD_T.index) - index = out_mztab_MTD_T.loc[:, "index"] - out_mztab_MTD_T.drop(labels=["index"], axis=1, inplace=True) - out_mztab_MTD_T.insert(0, "index", index) + col = list(out_mztab_mtd.columns) + row = list(out_mztab_mtd.index) + out_mztab_mtd_t = pd.DataFrame(out_mztab_mtd.values.T, index=col, columns=row) + out_mztab_mtd_t.columns = ["inf", "index"] + out_mztab_mtd_t.insert(0, "title", out_mztab_mtd_t.index) + index = out_mztab_mtd_t.loc[:, "index"] + out_mztab_mtd_t.drop(labels=["index"], axis=1, inplace=True) + out_mztab_mtd_t.insert(0, "index", index) database = os.path.basename(fasta.split(".")[-2]) - return out_mztab_MTD_T, database + return out_mztab_mtd_t, database -def mztab_PRH(report, pg, index_ref, database, fasta_df): +def mztab_prh(report, pg, index_ref, database, fasta_df): """ Construct PRH sub-table. @@ -720,9 +721,9 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): col = {} for i in file: col[i] = ( - "protein_abundance_assay[" - + str(index_ref[index_ref["Run"] == _true_stem(i)]["ms_run"].values[0]) - + "]" + "protein_abundance_assay[" + + str(index_ref[index_ref["Run"] == _true_stem(i)]["ms_run"].values[0]) + + "]" ) pg.rename(columns=col, inplace=True) @@ -733,17 +734,17 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): "indistinguishable_protein_group" ) - out_mztab_PRH = pg + out_mztab_prh = pg del pg - out_mztab_PRH = out_mztab_PRH.drop(["Protein.Names"], axis=1) - out_mztab_PRH.rename( + out_mztab_prh = out_mztab_prh.drop(["Protein.Names"], axis=1) + out_mztab_prh.rename( columns={ "Protein.Group": "accession", "First.Protein.Description": "description", }, inplace=True, ) - out_mztab_PRH.loc[:, "database"] = database + out_mztab_prh.loc[:, "database"] = database null_col = [ "taxid", @@ -755,16 +756,16 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): "opt_global_cv_PRIDE:0000303_decoy_hit", ] for i in null_col: - out_mztab_PRH.loc[:, i] = "null" + out_mztab_prh.loc[:, i] = "null" logger.debug("Extracting accession values (keeping first)...") - out_mztab_PRH.loc[:, "accession"] = out_mztab_PRH.apply( + out_mztab_prh.loc[:, "accession"] = out_mztab_prh.apply( lambda x: x["accession"].split(";")[0], axis=1 ) - protein_details_df = out_mztab_PRH[ - out_mztab_PRH["opt_global_result_type"] == "indistinguishable_protein_group" - ] + protein_details_df = out_mztab_prh[ + out_mztab_prh["opt_global_result_type"] == "indistinguishable_protein_group" + ] prh_series = ( protein_details_df["Protein.Ids"] .str.split(";", expand=True) @@ -783,7 +784,7 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): # The Following line fails if there are no indistinguishable protein groups protein_details_df.loc[:, "col"] = "protein_details" # protein_details_df = protein_details_df[-protein_details_df["accession"].str.contains("-")] - out_mztab_PRH = pd.concat([out_mztab_PRH, protein_details_df]).reset_index( + out_mztab_prh = pd.concat([out_mztab_prh, protein_details_df]).reset_index( drop=True ) else: @@ -792,8 +793,8 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): logger.debug("Calculating protein coverage (bottleneck)...") # This is a bottleneck # reimplementation runs in 67s vs 137s (old) in my data - out_mztab_PRH.loc[:, "protein_coverage"] = calculate_protein_coverages( - report=report, out_mztab_PRH=out_mztab_PRH, fasta_df=fasta_df + out_mztab_prh.loc[:, "protein_coverage"] = calculate_protein_coverages( + report=report, out_mztab_prh=out_mztab_prh, fasta_df=fasta_df ) logger.debug("Getting ambiguity members...") @@ -801,7 +802,7 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): # out_mztab_PRH["ambiguity_members"] = out_mztab_PRH["Protein.Ids"] # out_mztab_PRH.loc[out_mztab_PRH["opt_global_result_type"] == "single_protein", "ambiguity_members"] = "null" # or out_mztab_PRH.loc[out_mztab_PRH["Protein.Ids"] == out_mztab_PRH["accession"], "ambiguity_members"] = "null" - out_mztab_PRH.loc[:, "ambiguity_members"] = out_mztab_PRH.apply( + out_mztab_prh.loc[:, "ambiguity_members"] = out_mztab_prh.apply( lambda x: ( x["Protein.Ids"] if x["opt_global_result_type"] == "indistinguishable_protein_group" @@ -812,8 +813,8 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): logger.debug("Matching PRH to best search engine score...") score_looker = ModScoreLooker(report) - out_mztab_PRH[["modifiedSequence", "best_search_engine_score[1]"]] = ( - out_mztab_PRH.apply( + out_mztab_prh[["modifiedSequence", "best_search_engine_score[1]"]] = ( + out_mztab_prh.apply( lambda x: score_looker.get_score(x["Protein.Ids"]), axis=1, result_type="expand", @@ -821,12 +822,12 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): ) logger.debug("Matching PRH to modifications...") - out_mztab_PRH.loc[:, "modifications"] = out_mztab_PRH.apply( + out_mztab_prh.loc[:, "modifications"] = out_mztab_prh.apply( lambda x: find_modification(x["modifiedSequence"]), axis=1, result_type="expand" ) logger.debug("Matching PRH to protein quantification...") - ## quantity at protein level: PG.MaxLFQ + # quantity at protein level: PG.MaxLFQ # This used to be a bottleneck in performance # This implementation drops the run time from 57s to 25ms protein_agg_report = ( @@ -853,7 +854,7 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): # the report table has 'Protein.Group' and 'Protein.Ids': 'Q9NZJ9', 'A0A024RBG1;Q9NZJ9;Q9NZJ9-2' # Oddly enough the last implementation mapped the the accession (Q9NZJ9) in the mztab # to the Protein.Ids (A0A024RBG1;Q9NZJ9;Q9NZJ9-2), leading to A LOT of missing values. - out_mztab_PRH = out_mztab_PRH.merge( + out_mztab_prh = out_mztab_prh.merge( protein_agg_report, on="Protein.Ids", how="left", @@ -865,27 +866,27 @@ def mztab_PRH(report, pg, index_ref, database, fasta_df): del protein_agg_report # end of (former) bottleneck - out_mztab_PRH.loc[:, "PRH"] = "PRT" - index = out_mztab_PRH.loc[:, "PRH"] - out_mztab_PRH.drop( + out_mztab_prh.loc[:, "PRH"] = "PRT" + index = out_mztab_prh.loc[:, "PRH"] + out_mztab_prh.drop( ["PRH", "Genes", "modifiedSequence", "Protein.Ids"], axis=1, inplace=True ) - out_mztab_PRH.insert(0, "PRH", index) - out_mztab_PRH.fillna("null", inplace=True) - out_mztab_PRH.loc[:, "database"] = database - new_cols = [col for col in out_mztab_PRH.columns if not col.startswith("opt_")] + [ - col for col in out_mztab_PRH.columns if col.startswith("opt_") + out_mztab_prh.insert(0, "PRH", index) + out_mztab_prh.fillna("null", inplace=True) + out_mztab_prh.loc[:, "database"] = database + new_cols = [col for col in out_mztab_prh.columns if not col.startswith("opt_")] + [ + col for col in out_mztab_prh.columns if col.startswith("opt_") ] - out_mztab_PRH = out_mztab_PRH[new_cols] - return out_mztab_PRH + out_mztab_prh = out_mztab_prh[new_cols] + return out_mztab_prh -def mztab_PEH( - report: pd.DataFrame, - pr: pd.DataFrame, - precursor_list: List[str], - index_ref: pd.DataFrame, - database: os.PathLike, +def mztab_peh( + report: pd.DataFrame, + pr: pd.DataFrame, + precursor_list: List[str], + index_ref: pd.DataFrame, + database: os.PathLike, ) -> pd.DataFrame: """ Construct PEH sub-table. @@ -910,14 +911,14 @@ def mztab_PEH( f" len(precursor_list): {len(precursor_list)}," f" index_ref.shape: {index_ref.shape}" ) - out_mztab_PEH = pd.DataFrame() - out_mztab_PEH = pr.iloc[:, 0:10] - out_mztab_PEH.drop( + out_mztab_peh = pd.DataFrame() + out_mztab_peh = pr.iloc[:, 0:10] + out_mztab_peh.drop( ["Protein.Group", "Protein.Names", "First.Protein.Description", "Proteotypic"], axis=1, inplace=True, ) - out_mztab_PEH.rename( + out_mztab_peh.rename( columns={ "Stripped.Sequence": "sequence", "Protein.Ids": "accession", @@ -928,15 +929,15 @@ def mztab_PEH( ) logger.debug("Finding modifications...") - out_mztab_PEH.loc[:, "modifications"] = out_mztab_PEH.apply( + out_mztab_peh.loc[:, "modifications"] = out_mztab_peh.apply( lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), axis=1, result_type="expand", ) logger.debug("Extracting sequence...") - out_mztab_PEH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = ( - out_mztab_PEH.apply( + out_mztab_peh.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = ( + out_mztab_peh.apply( lambda x: AASequence.fromString( x["opt_global_cv_MS:1000889_peptidoform_sequence"] ).toString(), @@ -945,7 +946,7 @@ def mztab_PEH( ) logger.debug("Checking accession uniqueness...") - out_mztab_PEH.loc[:, "unique"] = out_mztab_PEH.apply( + out_mztab_peh.loc[:, "unique"] = out_mztab_peh.apply( lambda x: "0" if ";" in str(x["accession"]) else "1", axis=1, result_type="expand", @@ -959,15 +960,15 @@ def mztab_PEH( "opt_global_feature_id", ] for i in null_col: - out_mztab_PEH.loc[:, i] = "null" - out_mztab_PEH.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" + out_mztab_peh.loc[:, i] = "null" + out_mztab_peh.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" logger.debug("Matching precursor IDs...") # Pre-calculating the indices and using a lookup table drops run time from # ~6.5s to 11ms precursor_indices = {k: i for i, k in enumerate(precursor_list)} - pr_ids = out_mztab_PEH["Precursor.Id"].map(precursor_indices) - out_mztab_PEH["pr_id"] = pr_ids + pr_ids = out_mztab_peh["Precursor.Id"].map(precursor_indices) + out_mztab_peh["pr_id"] = pr_ids del precursor_indices logger.debug("Getting scores per run") @@ -988,7 +989,7 @@ def mztab_PEH( } name_mapper = name_mapper_builder(subname_mapper) tmp.rename(columns=name_mapper, inplace=True) - out_mztab_PEH = out_mztab_PEH.merge( + out_mztab_peh = out_mztab_peh.merge( tmp.rename(columns={"precursor.Index": "pr_id"}), on="pr_id", validate="one_to_one", @@ -999,7 +1000,7 @@ def mztab_PEH( logger.debug("Getting peptide abundances per study variable") pep_study_report = per_peptide_study_report(report) - out_mztab_PEH = out_mztab_PEH.merge( + out_mztab_peh = out_mztab_peh.merge( pep_study_report, on="pr_id", how="left", validate="one_to_one", copy=True ) del pep_study_report @@ -1036,25 +1037,25 @@ def mztab_PEH( } ) ) - del out_mztab_PEH["mass_to_charge"] - out_mztab_PEH = out_mztab_PEH.merge(aggtable, on="pr_id", validate="one_to_one") + del out_mztab_peh["mass_to_charge"] + out_mztab_peh = out_mztab_peh.merge(aggtable, on="pr_id", validate="one_to_one") logger.debug("Re-ordering columns...") - out_mztab_PEH.loc[:, "PEH"] = "PEP" - out_mztab_PEH.loc[:, "database"] = str(database) - index = out_mztab_PEH.loc[:, "PEH"] - out_mztab_PEH.drop(["PEH", "Precursor.Id", "Genes", "pr_id"], axis=1, inplace=True) - out_mztab_PEH.insert(0, "PEH", index) - out_mztab_PEH.fillna("null", inplace=True) - new_cols = [col for col in out_mztab_PEH.columns if not col.startswith("opt_")] + [ - col for col in out_mztab_PEH.columns if col.startswith("opt_") + out_mztab_peh.loc[:, "PEH"] = "PEP" + out_mztab_peh.loc[:, "database"] = str(database) + index = out_mztab_peh.loc[:, "PEH"] + out_mztab_peh.drop(["PEH", "Precursor.Id", "Genes", "pr_id"], axis=1, inplace=True) + out_mztab_peh.insert(0, "PEH", index) + out_mztab_peh.fillna("null", inplace=True) + new_cols = [col for col in out_mztab_peh.columns if not col.startswith("opt_")] + [ + col for col in out_mztab_peh.columns if col.startswith("opt_") ] - out_mztab_PEH = out_mztab_PEH[new_cols] + out_mztab_peh = out_mztab_peh[new_cols] - return out_mztab_PEH + return out_mztab_peh -def mztab_PSH(report, folder, database): +def mztab_psh(report, folder, database): """ Construct PSH sub-table. @@ -1083,7 +1084,7 @@ def __find_info(directory, n): return files[0] - out_mztab_PSH = pd.DataFrame() + out_mztab_psh = pd.DataFrame() for n, group in report.groupby(["Run"]): if isinstance(n, tuple) and len(n) == 1: # This is here only to support versions of pandas where the groupby @@ -1103,21 +1104,21 @@ def __find_info(directory, n): # Standardize spectrum identifier format for bruker data if type(target.loc[0, "opt_global_spectrum_reference"]) != str: target.loc[:, "opt_global_spectrum_reference"] = "scan=" + target.loc[ - :, "opt_global_spectrum_reference" - ].astype(str) + :, "opt_global_spectrum_reference" + ].astype(str) # TODO seconds returned from precursor.getRT() target.loc[:, "RT.Start"] = target.apply(lambda x: x["RT.Start"] / 60, axis=1) - out_mztab_PSH = pd.concat( + out_mztab_psh = pd.concat( [ - out_mztab_PSH, + out_mztab_psh, pd.merge_asof(group, target, on="RT.Start", direction="nearest"), ] ) del report - ## Score at PSM level: Q.Value - out_mztab_PSH = out_mztab_PSH[ + # Score at PSM level: Q.Value + out_mztab_psh = out_mztab_psh[ [ "Stripped.Sequence", "Protein.Ids", @@ -1134,7 +1135,7 @@ def __find_info(directory, n): "ms_run", ] ] - out_mztab_PSH.columns = [ + out_mztab_psh.columns = [ "sequence", "accession", "search_engine_score[1]", @@ -1150,14 +1151,14 @@ def __find_info(directory, n): "ms_run", ] - out_mztab_PSH.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" - out_mztab_PSH.loc[:, "PSM_ID"] = out_mztab_PSH.index - out_mztab_PSH.loc[:, "unique"] = out_mztab_PSH.apply( + out_mztab_psh.loc[:, "opt_global_cv_MS:1002217_decoy_peptide"] = "0" + out_mztab_psh.loc[:, "PSM_ID"] = out_mztab_psh.index + out_mztab_psh.loc[:, "unique"] = out_mztab_psh.apply( lambda x: "0" if ";" in str(x["accession"]) else "1", axis=1, result_type="expand", ) - out_mztab_PSH.loc[:, "database"] = database + out_mztab_psh.loc[:, "database"] = database null_col = [ "database_version", @@ -1170,24 +1171,24 @@ def __find_info(directory, n): "opt_global_map_index", ] for i in null_col: - out_mztab_PSH.loc[:, i] = "null" + out_mztab_psh.loc[:, i] = "null" logger.info("Finding Modifications ...") - out_mztab_PSH.loc[:, "modifications"] = out_mztab_PSH.apply( + out_mztab_psh.loc[:, "modifications"] = out_mztab_psh.apply( lambda x: find_modification(x["opt_global_cv_MS:1000889_peptidoform_sequence"]), axis=1, result_type="expand", ) - out_mztab_PSH.loc[:, "spectra_ref"] = out_mztab_PSH.apply( + out_mztab_psh.loc[:, "spectra_ref"] = out_mztab_psh.apply( lambda x: "ms_run[{}]:".format(x["ms_run"]) - + x["opt_global_spectrum_reference"], + + x["opt_global_spectrum_reference"], axis=1, result_type="expand", ) - out_mztab_PSH.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = ( - out_mztab_PSH.apply( + out_mztab_psh.loc[:, "opt_global_cv_MS:1000889_peptidoform_sequence"] = ( + out_mztab_psh.apply( lambda x: AASequence.fromString( x["opt_global_cv_MS:1000889_peptidoform_sequence"] ).toString(), @@ -1196,18 +1197,18 @@ def __find_info(directory, n): ) ) - out_mztab_PSH.loc[:, "PSH"] = "PSM" - index = out_mztab_PSH.loc[:, "PSH"] - out_mztab_PSH.drop(["PSH", "ms_run"], axis=1, inplace=True) - out_mztab_PSH.insert(0, "PSH", index) - out_mztab_PSH.fillna("null", inplace=True) - new_cols = [col for col in out_mztab_PSH.columns if not col.startswith("opt_")] + [ - col for col in out_mztab_PSH.columns if col.startswith("opt_") + out_mztab_psh.loc[:, "PSH"] = "PSM" + index = out_mztab_psh.loc[:, "PSH"] + out_mztab_psh.drop(["PSH", "ms_run"], axis=1, inplace=True) + out_mztab_psh.insert(0, "PSH", index) + out_mztab_psh.fillna("null", inplace=True) + new_cols = [col for col in out_mztab_psh.columns if not col.startswith("opt_")] + [ + col for col in out_mztab_psh.columns if col.startswith("opt_") ] - out_mztab_PSH = out_mztab_PSH[new_cols] + out_mztab_psh = out_mztab_psh[new_cols] # out_mztab_PSH.to_csv("./out_psms.mztab", sep=",", index=False) - return out_mztab_PSH + return out_mztab_psh def add_info(target, index_ref): @@ -1261,10 +1262,10 @@ def match_in_report(report, target, max_, flag, level): """ # noqa if flag == 1 and level == "pep": result = report[report["precursor.Index"] == target] - PEH_params = [] + peh_params = [] for i in range(1, max_ + 1): match = result[result["study_variable"] == i] - PEH_params.extend( + peh_params.extend( [ match["Precursor.Normalised"].mean(), "null", @@ -1274,7 +1275,7 @@ def match_in_report(report, target, max_, flag, level): ] ) - return tuple(PEH_params) + return tuple(peh_params) if flag == 0 and level == "pep": result = report[report["precursor.Index"] == target] @@ -1291,12 +1292,12 @@ def match_in_report(report, target, max_, flag, level): if flag == 1 and level == "protein": result = report[report["Protein.Ids"] == target] - PRH_params = [] + prh_params = [] for i in range(1, max_ + 1): match = result[result["study_variable"] == i] - PRH_params.extend([match["PG.MaxLFQ"].mean(), "null", "null"]) + prh_params.extend([match["PG.MaxLFQ"].mean(), "null", "null"]) - return tuple(PRH_params) + return tuple(prh_params) class ModScoreLooker: @@ -1346,7 +1347,6 @@ def get_score(self, protein_id: str) -> Tuple[Union[str, float], float]: score of the best one for that accession) :param protein_id: The value of "accession" column in report - :type target: str :return: A tuple that contains (best modified sequence, best score) if the accession is not found, (np.nan, np.nan) is returned. :rtype: tuple @@ -1554,8 +1554,8 @@ def calculate_coverage(ref_sequence: str, sequences: Set[str]): for start, length in sorted(zip(starts, lengths)): if merged_starts and merged_starts[-1] + merged_lengths[-1] >= start: merged_lengths[-1] = ( - max(merged_starts[-1] + merged_lengths[-1], start + length) - - merged_starts[-1] + max(merged_starts[-1] + merged_lengths[-1], start + length) + - merged_starts[-1] ) else: merged_starts.append(start) @@ -1567,7 +1567,7 @@ def calculate_coverage(ref_sequence: str, sequences: Set[str]): def calculate_protein_coverages( - report: pd.DataFrame, out_mztab_PRH: pd.DataFrame, fasta_df: pd.DataFrame + report: pd.DataFrame, out_mztab_prh: pd.DataFrame, fasta_df: pd.DataFrame ) -> List[str]: """Calculates protein coverages for the PRH table. @@ -1586,7 +1586,7 @@ def calculate_protein_coverages( # 1 A0A096LP49;A0A096LP49-2 {SPWAMTERKHSSLER} # 2 A0AVT1;A0AVT1-2 {EDFTLLDFINAVK, KPDHVPISSEDER, QDVIITALDNVEAR,... ids_to_seqs = dict(zip(nested_df["Protein.Ids"], nested_df["Stripped.Sequence"])) - acc_to_ids = dict(zip(out_mztab_PRH["accession"], out_mztab_PRH["Protein.Ids"])) + acc_to_ids = dict(zip(out_mztab_prh["accession"], out_mztab_prh["Protein.Ids"])) fasta_id_to_seqs = dict(zip(fasta_df["id"], fasta_df["seq"])) acc_to_fasta_ids: dict = {} @@ -1609,9 +1609,9 @@ def calculate_protein_coverages( # it entails more un-matched characters. acc_to_fasta_ids[acc] = min(matches, key=len) - out: List[str] = [""] * len(out_mztab_PRH["accession"]) + out: List[str] = [""] * len(out_mztab_prh["accession"]) - for i, acc in enumerate(out_mztab_PRH["accession"]): + for i, acc in enumerate(out_mztab_prh["accession"]): f_id = acc_to_fasta_ids[acc] if f_id is None: out_cov = "null" diff --git a/pyquantms/features/add_sage_feature.py b/pyquantms/features/add_sage_feature.py index e3247f4..ca0c405 100644 --- a/pyquantms/features/add_sage_feature.py +++ b/pyquantms/features/add_sage_feature.py @@ -12,6 +12,7 @@ def add_sage_feature(ctx, idx_file: str, output_file: str, feat_file: str): """ Add extra features in features idXML. Adding extra feature in Sage isn't known input for PSMFeatureExtractor + :param ctx: click context :param idx_file: Original idXML file :param output_file: Outpuf file with the extra feature :param feat_file: Feature file from Sage diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index c5524d4..440e7ae 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -1,7 +1,6 @@ # nf-core: Update the script to check the sdrf # This script is based on the example at: https://raw.githubusercontent.com/nf-core/test-datasets/viralrecon/samplesheet/samplesheet_test_illumina_amplicon.csv -import argparse import errno import os import sys @@ -67,7 +66,7 @@ def check_expdesign(expdesign): ) sys.exit(1) - s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1 :]][1:] + s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1:]][1:] s_header = lines[empty_row + 1].replace("\n", "").split("\t") s_data_frame = pd.DataFrame(s_table, columns=s_header) diff --git a/pyquantms/sdrf/extract_sample.py b/pyquantms/sdrf/extract_sample.py index af6460b..f0ad648 100644 --- a/pyquantms/sdrf/extract_sample.py +++ b/pyquantms/sdrf/extract_sample.py @@ -22,7 +22,7 @@ def extract_sample_from_expdesign(cxt, expdesign: str) -> None: with open(expdesign, "r") as f: lines = f.readlines() empty_row = lines.index("\n") - s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1 :]][1:] + s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1:]][1:] s_header = lines[empty_row + 1].replace("\n", "").split("\t") s_data_frame = pd.DataFrame(s_table, columns=s_header) diff --git a/requirements.txt b/requirements.txt index 0a7b261..ffe7355 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,7 @@ click -sdrf_pipelines +sdrf-pipelines pyopenms ms2rescore psm-utils==0.8.0 -pydantic \ No newline at end of file +pydantic +pandas \ No newline at end of file diff --git a/setup.py b/setup.py index baf6389..63f58c8 100644 --- a/setup.py +++ b/setup.py @@ -30,11 +30,12 @@ INSTALL_REQUIRES = [ "click", - "sdrf_pipelines", + "sdrf-pipelines", "pyopenms", "ms2rescore", "psm-utils==0.8.0", "pydantic", + "pandas", ] PYTHON_REQUIRES = ">=3.7,<4" From 74001d524f0d6e4ba149010836b70080c9a8a4a7 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 09:35:42 +0100 Subject: [PATCH 26/53] refinements --- pyquantms/diann/diann_convert.py | 136 ++++++++++++++-------------- pyquantms/sdrf/check_samplesheet.py | 2 +- pyquantms/sdrf/extract_sample.py | 2 +- tests/test_commands.py | 3 +- 4 files changed, 72 insertions(+), 71 deletions(-) diff --git a/pyquantms/diann/diann_convert.py b/pyquantms/diann/diann_convert.py index b912987..fb7c9de 100644 --- a/pyquantms/diann/diann_convert.py +++ b/pyquantms/diann/diann_convert.py @@ -42,14 +42,14 @@ @click.option("--qvalue_threshold", "-q", type=float) @click.pass_context def diann_convert( - ctx, - folder, - exp_design, - dia_params, - diann_version, - charge, - missed_cleavages, - qvalue_threshold, + ctx, + folder, + exp_design, + dia_params, + diann_version, + charge, + missed_cleavages, + qvalue_threshold, ): """ Convert DIA-NN output to MSstats, Triqler or mzTab. @@ -226,7 +226,7 @@ def get_exp_design_dfs(exp_design_file): lambda x: _true_stem(x["Spectra_Filepath"]), axis=1 ) - s_table = [i.replace("\n", "").split("\t") for i in data[empty_row + 1:]][1:] + s_table = [i.replace("\n", "").split("\t") for i in data[empty_row + 1 :]][1:] s_header = data[empty_row + 1].replace("\n", "").split("\t") s_data_frame = pd.DataFrame(s_table, columns=s_header) @@ -263,31 +263,31 @@ def compute_mass_modified_peptide(peptide_seq: str) -> float: if aa in aa_mass and not_mod: aa = aa_mass[aa] elif ( - aa - not in [ - "G", - "A", - "V", - "L", - "I", - "F", - "M", - "P", - "W", - "S", - "C", - "T", - "Y", - "N", - "Q", - "D", - "E", - "K", - "R", - "H", - ] - and not_mod - and aa != ")" + aa + not in [ + "G", + "A", + "V", + "L", + "I", + "F", + "M", + "P", + "W", + "S", + "C", + "T", + "Y", + "N", + "Q", + "D", + "E", + "K", + "R", + "H", + ] + and not_mod + and aa != ")" ): logger.info(f"Unknown amino acid with mass not known:{aa}") peptide_parts.append(aa) @@ -365,13 +365,13 @@ def validate_diann_version(self) -> None: raise ValueError(f"Unsupported DIANN version {self.diann_version}") def convert_to_mztab( - self, - report, - f_table, - charge: int, - missed_cleavages: int, - dia_params: List[Any], - out: os.PathLike, + self, + report, + f_table, + charge: int, + missed_cleavages: int, + dia_params: List[Any], + out: os.PathLike, ) -> None: logger.info("Converting to mzTab") self.validate_diann_version() @@ -479,8 +479,8 @@ def main_report_df(self, qvalue_threshold: float) -> pd.DataFrame: } mass_vector = report["Modified.Sequence"].map(uniq_masses) report["Calculate.Precursor.Mz"] = ( - mass_vector + (PROTON_MASS_U * report["Precursor.Charge"]) - ) / report["Precursor.Charge"] + mass_vector + (PROTON_MASS_U * report["Precursor.Charge"]) + ) / report["Precursor.Charge"] logger.debug("Indexing Precursors") # Making the map is 1500x faster @@ -587,16 +587,16 @@ def mztab_mtd(index_ref, dia_params, fasta, charge, missed_cleavages): out_mztab_mtd.loc[1, "software[1]-setting[1]"] = fasta out_mztab_mtd.loc[1, "software[1]-setting[2]"] = "db_version:null" out_mztab_mtd.loc[1, "software[1]-setting[3]"] = ( - "fragment_mass_tolerance:" + fragment_mass_tolerance + "fragment_mass_tolerance:" + fragment_mass_tolerance ) out_mztab_mtd.loc[1, "software[1]-setting[4]"] = ( - "fragment_mass_tolerance_unit:" + fragment_mass_tolerance_unit + "fragment_mass_tolerance_unit:" + fragment_mass_tolerance_unit ) out_mztab_mtd.loc[1, "software[1]-setting[5]"] = ( - "precursor_mass_tolerance:" + precursor_mass_tolerance + "precursor_mass_tolerance:" + precursor_mass_tolerance ) out_mztab_mtd.loc[1, "software[1]-setting[6]"] = ( - "precursor_mass_tolerance_unit:" + precursor_mass_tolerance_unit + "precursor_mass_tolerance_unit:" + precursor_mass_tolerance_unit ) out_mztab_mtd.loc[1, "software[1]-setting[7]"] = "enzyme:" + enzyme out_mztab_mtd.loc[1, "software[1]-setting[8]"] = "enzyme_term_specificity:full" @@ -605,10 +605,10 @@ def mztab_mtd(index_ref, dia_params, fasta, charge, missed_cleavages): missed_cleavages ) out_mztab_mtd.loc[1, "software[1]-setting[11]"] = ( - "fixed_modifications:" + fixed_modifications + "fixed_modifications:" + fixed_modifications ) out_mztab_mtd.loc[1, "software[1]-setting[12]"] = ( - "variable_modifications:" + variable_modifications + "variable_modifications:" + variable_modifications ) (fixed_mods, variable_mods, fix_flag, var_flag) = mtd_mod_info( @@ -631,7 +631,7 @@ def mztab_mtd(index_ref, dia_params, fasta, charge, missed_cleavages): ] out_mztab_mtd.loc[1, "variable_mod[" + str(i) + "]-site"] = variable_mods[ i - 1 - ][1] + ][1] out_mztab_mtd.loc[1, "variable_mod[" + str(i) + "]-position"] = "Anywhere" else: out_mztab_mtd.loc[1, "variable_mod[1]"] = variable_mods[0] @@ -647,8 +647,8 @@ def mztab_mtd(index_ref, dia_params, fasta, charge, missed_cleavages): "[MS, MS:1000584, mzML file, ]" ) out_mztab_mtd.loc[1, "ms_run[" + str(i) + "]-location"] = ( - "file://" - + index_ref[index_ref["ms_run"] == i]["Spectra_Filepath"].values[0] + "file://" + + index_ref[index_ref["ms_run"] == i]["Spectra_Filepath"].values[0] ) out_mztab_mtd.loc[1, "ms_run[" + str(i) + "]-id_format"] = ( "[MS, MS:1000777, spectrum identifier nativeID format, ]" @@ -657,7 +657,7 @@ def mztab_mtd(index_ref, dia_params, fasta, charge, missed_cleavages): "[MS, MS:1002038, unlabeled sample, ]" ) out_mztab_mtd.loc[1, "assay[" + str(i) + "]-ms_run_ref"] = ( - "ms_run[" + str(i) + "]" + "ms_run[" + str(i) + "]" ) with warnings.catch_warnings(): @@ -721,9 +721,9 @@ def mztab_prh(report, pg, index_ref, database, fasta_df): col = {} for i in file: col[i] = ( - "protein_abundance_assay[" - + str(index_ref[index_ref["Run"] == _true_stem(i)]["ms_run"].values[0]) - + "]" + "protein_abundance_assay[" + + str(index_ref[index_ref["Run"] == _true_stem(i)]["ms_run"].values[0]) + + "]" ) pg.rename(columns=col, inplace=True) @@ -765,7 +765,7 @@ def mztab_prh(report, pg, index_ref, database, fasta_df): protein_details_df = out_mztab_prh[ out_mztab_prh["opt_global_result_type"] == "indistinguishable_protein_group" - ] + ] prh_series = ( protein_details_df["Protein.Ids"] .str.split(";", expand=True) @@ -882,11 +882,11 @@ def mztab_prh(report, pg, index_ref, database, fasta_df): def mztab_peh( - report: pd.DataFrame, - pr: pd.DataFrame, - precursor_list: List[str], - index_ref: pd.DataFrame, - database: os.PathLike, + report: pd.DataFrame, + pr: pd.DataFrame, + precursor_list: List[str], + index_ref: pd.DataFrame, + database: os.PathLike, ) -> pd.DataFrame: """ Construct PEH sub-table. @@ -1104,8 +1104,8 @@ def __find_info(directory, n): # Standardize spectrum identifier format for bruker data if type(target.loc[0, "opt_global_spectrum_reference"]) != str: target.loc[:, "opt_global_spectrum_reference"] = "scan=" + target.loc[ - :, "opt_global_spectrum_reference" - ].astype(str) + :, "opt_global_spectrum_reference" + ].astype(str) # TODO seconds returned from precursor.getRT() target.loc[:, "RT.Start"] = target.apply(lambda x: x["RT.Start"] / 60, axis=1) @@ -1182,7 +1182,7 @@ def __find_info(directory, n): out_mztab_psh.loc[:, "spectra_ref"] = out_mztab_psh.apply( lambda x: "ms_run[{}]:".format(x["ms_run"]) - + x["opt_global_spectrum_reference"], + + x["opt_global_spectrum_reference"], axis=1, result_type="expand", ) @@ -1554,8 +1554,8 @@ def calculate_coverage(ref_sequence: str, sequences: Set[str]): for start, length in sorted(zip(starts, lengths)): if merged_starts and merged_starts[-1] + merged_lengths[-1] >= start: merged_lengths[-1] = ( - max(merged_starts[-1] + merged_lengths[-1], start + length) - - merged_starts[-1] + max(merged_starts[-1] + merged_lengths[-1], start + length) + - merged_starts[-1] ) else: merged_starts.append(start) @@ -1567,7 +1567,7 @@ def calculate_coverage(ref_sequence: str, sequences: Set[str]): def calculate_protein_coverages( - report: pd.DataFrame, out_mztab_prh: pd.DataFrame, fasta_df: pd.DataFrame + report: pd.DataFrame, out_mztab_prh: pd.DataFrame, fasta_df: pd.DataFrame ) -> List[str]: """Calculates protein coverages for the PRH table. diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index 440e7ae..3949c5a 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -66,7 +66,7 @@ def check_expdesign(expdesign): ) sys.exit(1) - s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1:]][1:] + s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1 :]][1:] s_header = lines[empty_row + 1].replace("\n", "").split("\t") s_data_frame = pd.DataFrame(s_table, columns=s_header) diff --git a/pyquantms/sdrf/extract_sample.py b/pyquantms/sdrf/extract_sample.py index f0ad648..af6460b 100644 --- a/pyquantms/sdrf/extract_sample.py +++ b/pyquantms/sdrf/extract_sample.py @@ -22,7 +22,7 @@ def extract_sample_from_expdesign(cxt, expdesign: str) -> None: with open(expdesign, "r") as f: lines = f.readlines() empty_row = lines.index("\n") - s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1:]][1:] + s_table = [i.replace("\n", "").split("\t") for i in lines[empty_row + 1 :]][1:] s_header = lines[empty_row + 1].replace("\n", "").split("\t") s_data_frame = pd.DataFrame(s_table, columns=s_header) diff --git a/tests/test_commands.py b/tests/test_commands.py index ea3e617..10ab80c 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -61,9 +61,10 @@ def test_convert_psm(): assert result.exit_code == 0 + # test for the check_samplesheet command in cli def test_check_samplesheet(): runner = CliRunner() result = runner.invoke(cli, ["check_samplesheet", "--help"]) - assert result.exit_code == 0 \ No newline at end of file + assert result.exit_code == 0 From f5965b780d0af07323f2983aebb62415fc8eaed3 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 17:49:46 +0100 Subject: [PATCH 27/53] validation of sdrf command check_samplesheet.py --- tests/test_commands.py | 7 +++++++ tests/test_data/PXD000001.sdrf.tsv | 7 +++++++ 2 files changed, 14 insertions(+) create mode 100644 tests/test_data/PXD000001.sdrf.tsv diff --git a/tests/test_commands.py b/tests/test_commands.py index 10ab80c..a0f6360 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -68,3 +68,10 @@ def test_check_samplesheet(): result = runner.invoke(cli, ["check_samplesheet", "--help"]) assert result.exit_code == 0 + +# test the validation of an SDRF file +def test_check_samplesheet_sdrf(): + runner = CliRunner() + result = runner.invoke(cli, ["check_samplesheet", "--is_sdrf", "--check_ms", "--input", "tests/test_data/PXD000001.sdrf.tsv"]) + + assert result.exit_code == 0 diff --git a/tests/test_data/PXD000001.sdrf.tsv b/tests/test_data/PXD000001.sdrf.tsv new file mode 100644 index 0000000..d8e98f4 --- /dev/null +++ b/tests/test_data/PXD000001.sdrf.tsv @@ -0,0 +1,7 @@ +Source Name Characteristics[organism] Characteristics[organism part] Characteristics[age] Characteristics[ancestry category] Characteristics[developmental stage] Characteristics[cell line] Characteristics[cell type] Characteristics[sex] Characteristics[mass] Characteristics[spiked compound] Characteristics[spiked compound 2] Characteristics[spiked compound 3] Characteristics[spiked compound 4] Characteristics[disease] Characteristics[biological replicate] Material Type assay name technology type comment[data file] comment[file uri] comment[technical replicate] comment[fraction identifier] comment[label] comment[instrument] comment[modification parameters] comment[modification parameters] comment[modification parameters] comment[modification parameters] comment[cleavage agent details] comment[dissociation method] comment[precursor mass tolerance] comment[fragment mass tolerance] Factor Value[spiked compound] Factor Value[spiked compound] Factor Value[spiked compound] Factor Value[spiked compound] +Sample 1 Dickeya chrysanthemi whole plant not available not available not available not applicable not available not applicable 1 SP=Yeast;CT=protein;AC=P00924;QY=10 SP=BOVIN;CT=protein;AC=P02769;QY=1 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 not available 1 cell run 1 proteomic profiling by mass spectrometry TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw https://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw 1 1 TMT126 NT=LTQ Orbitrap Velos;AC=MS:1001742 NT=Oxidation;MT=Variable;TA=M;AC=Unimod:35 NT=Methylthio;TA=C;MT=fixed;AC=UNIMOD:39 NT=TMT6plex;TA=K;MT=Fixed;AC=UNIMOD:737 NT=TMT6plex;PP=Any N-term;MT=Fixed;AC=UNIMOD:737 AC=MS:1001313;NT=Trypsin NT=HCD;AC=PRIDE:0000590 not available not available SP=Yeast;CT=protein;AC=P00924;QY=10 SP=BOVIN;CT=protein;AC=P02769;QY=1 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 +Sample 2 Dickeya chrysanthemi whole plant not available not available not available not applicable not available not applicable 1 SP=Yeast;CT=protein;AC=P00924;QY=5 SP=BOVIN;CT=protein;AC=P02769;QY=2.5 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 not available 1 cell run 1 proteomic profiling by mass spectrometry TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw https://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw 1 1 TMT127 NT=LTQ Orbitrap Velos;AC=MS:1001742 NT=Oxidation;MT=Variable;TA=M;AC=Unimod:35 NT=Methylthio;TA=C;MT=fixed;AC=UNIMOD:39 NT=TMT6plex;TA=K;MT=Fixed;AC=UNIMOD:737 NT=TMT6plex;PP=Any N-term;MT=Fixed;AC=UNIMOD:737 AC=MS:1001313;NT=Trypsin NT=HCD;AC=PRIDE:0000590 not available not available SP=Yeast;CT=protein;AC=P00924;QY=5 SP=BOVIN;CT=protein;AC=P02769;QY=2.5 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 +Sample 3 Dickeya chrysanthemi whole plant not available not available not available not applicable not available not applicable 1 SP=Yeast;CT=protein;AC=P00924;QY=2.5 SP=BOVIN;CT=protein;AC=P02769;QY=5 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 not available 1 cell run 1 proteomic profiling by mass spectrometry TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw https://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw 1 1 TMT128 NT=LTQ Orbitrap Velos;AC=MS:1001742 NT=Oxidation;MT=Variable;TA=M;AC=Unimod:35 NT=Methylthio;TA=C;MT=fixed;AC=UNIMOD:39 NT=TMT6plex;TA=K;MT=Fixed;AC=UNIMOD:737 NT=TMT6plex;PP=Any N-term;MT=Fixed;AC=UNIMOD:737 AC=MS:1001313;NT=Trypsin NT=HCD;AC=PRIDE:0000590 not available not available SP=Yeast;CT=protein;AC=P00924;QY=2.5 SP=BOVIN;CT=protein;AC=P02769;QY=5 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 +Sample 4 Dickeya chrysanthemi whole plant not available not available not available not applicable not available not applicable 1 SP=Yeast;CT=protein;AC=P00924;QY=1 SP=BOVIN;CT=protein;AC=P02769;QY=10 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 not available 1 cell run 1 proteomic profiling by mass spectrometry TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw https://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw 1 1 TMT129 NT=LTQ Orbitrap Velos;AC=MS:1001742 NT=Oxidation;MT=Variable;TA=M;AC=Unimod:35 NT=Methylthio;TA=C;MT=fixed;AC=UNIMOD:39 NT=TMT6plex;TA=K;MT=Fixed;AC=UNIMOD:737 NT=TMT6plex;PP=Any N-term;MT=Fixed;AC=UNIMOD:737 AC=MS:1001313;NT=Trypsin NT=HCD;AC=PRIDE:0000590 not available not available SP=Yeast;CT=protein;AC=P00924;QY=1 SP=BOVIN;CT=protein;AC=P02769;QY=10 SP=RABIT;CT=protein;AC=P00489;QY=2 SP=BOVIN;CT=protein;AC=P62894;QY=1 +Sample 5 Dickeya chrysanthemi whole plant not available not available not available not applicable not available not applicable 1 SP=Yeast;CT=protein;AC=P00924;QY=2.5 SP=BOVIN;CT=protein;AC=P02769;QY=5 SP=RABIT;CT=protein;AC=P00489;QY=1 SP=BOVIN;CT=protein;AC=P62894;QY=1 not available 1 cell run 1 proteomic profiling by mass spectrometry TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw https://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw 1 1 TMT130 NT=LTQ Orbitrap Velos;AC=MS:1001742 NT=Oxidation;MT=Variable;TA=M;AC=Unimod:35 NT=Methylthio;TA=C;MT=fixed;AC=UNIMOD:39 NT=TMT6plex;TA=K;MT=Fixed;AC=UNIMOD:737 NT=TMT6plex;PP=Any N-term;MT=Fixed;AC=UNIMOD:737 AC=MS:1001313;NT=Trypsin NT=HCD;AC=PRIDE:0000590 not available not available SP=Yeast;CT=protein;AC=P00924;QY=2.5 SP=BOVIN;CT=protein;AC=P02769;QY=5 SP=RABIT;CT=protein;AC=P00489;QY=1 SP=BOVIN;CT=protein;AC=P62894;QY=1 +Sample 6 Dickeya chrysanthemi whole plant not available not available not available not applicable not available not applicable 1 SP=Yeast;CT=protein;AC=P00924;QY=10 SP=BOVIN;CT=protein;AC=P02769;QY=1 SP=RABIT;CT=protein;AC=P00489;QY=1 SP=BOVIN;CT=protein;AC=P62894;QY=2 not available 1 cell run 1 proteomic profiling by mass spectrometry TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw https://ftp.pride.ebi.ac.uk/pride/data/archive/2012/03/PXD000001/TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01.raw 1 1 TMT131 NT=LTQ Orbitrap Velos;AC=MS:1001742 NT=Oxidation;MT=Variable;TA=M;AC=Unimod:35 NT=Methylthio;TA=C;MT=fixed;AC=UNIMOD:39 NT=TMT6plex;TA=K;MT=Fixed;AC=UNIMOD:737 NT=TMT6plex;PP=Any N-term;MT=Fixed;AC=UNIMOD:737 AC=MS:1001313;NT=Trypsin NT=HCD;AC=PRIDE:0000590 not available not available SP=Yeast;CT=protein;AC=P00924;QY=10 SP=BOVIN;CT=protein;AC=P02769;QY=1 SP=RABIT;CT=protein;AC=P00489;QY=1 SP=BOVIN;CT=protein;AC=P62894;QY=2 From 330b670f5ceb261d5dd466146a1c5475edc797d4 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sat, 25 May 2024 17:57:15 +0100 Subject: [PATCH 28/53] validation of sdrf command check_samplesheet.py --- BSA_design_urls_sample.csv | 7 +++++++ tests/test_commands.py | 7 +++++++ tests/test_data/BSA_design_urls.tsv | 13 +++++++++++++ 3 files changed, 27 insertions(+) create mode 100644 BSA_design_urls_sample.csv create mode 100644 tests/test_data/BSA_design_urls.tsv diff --git a/BSA_design_urls_sample.csv b/BSA_design_urls_sample.csv new file mode 100644 index 0000000..6cc5282 --- /dev/null +++ b/BSA_design_urls_sample.csv @@ -0,0 +1,7 @@ +Spectra_Filepath Sample +https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA1_F1.mzML 1 +https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA1_F2.mzML 1 +https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA2_F1.mzML 2 +https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA2_F2.mzML 2 +https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA3_F1.mzML 3 +https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA3_F2.mzML 3 diff --git a/tests/test_commands.py b/tests/test_commands.py index a0f6360..a6c2e8b 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -75,3 +75,10 @@ def test_check_samplesheet_sdrf(): result = runner.invoke(cli, ["check_samplesheet", "--is_sdrf", "--check_ms", "--input", "tests/test_data/PXD000001.sdrf.tsv"]) assert result.exit_code == 0 + +# test extract_sample_from_expdesign command in cli +def test_extract_sample_from_expdesign(): + runner = CliRunner() + result = runner.invoke(cli, ["extract_sample", "--expdesign", "tests/test_data/BSA_design_urls.tsv"]) + + assert result.exit_code == 0 \ No newline at end of file diff --git a/tests/test_data/BSA_design_urls.tsv b/tests/test_data/BSA_design_urls.tsv new file mode 100644 index 0000000..603406f --- /dev/null +++ b/tests/test_data/BSA_design_urls.tsv @@ -0,0 +1,13 @@ +Fraction_Group Fraction Spectra_Filepath Label Sample +1 1 https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA1_F1.mzML 1 1 +1 2 https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA1_F2.mzML 1 1 +2 1 https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA2_F1.mzML 1 2 +2 2 https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA2_F2.mzML 1 2 +3 1 https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA3_F1.mzML 1 3 +3 2 https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA3_F2.mzML 1 3 + +Sample MSstats_Condition MSstats_BioReplicate +1 1 1 +2 2 2 +3 3 3 + From 8e197d94bd29d999cfd2fb5f5d043ae71f348d8d Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 07:53:00 +0100 Subject: [PATCH 29/53] added more tests --- pyquantms/pyquantmsc.py | 2 +- .../{ms2rescore => rescoring}/__init__.py | 0 .../{ms2rescore => rescoring}/ms2rescore.py | 39 +- setup.py | 2 +- tests/test_commands.py | 18 +- tests/test_data/BSA1_F1.mzML | 40691 ++++++++++++++++ ...1_msgf_idx_fdr_idpep_switched_filter.idXML | 1892 + 7 files changed, 42621 insertions(+), 23 deletions(-) rename pyquantms/{ms2rescore => rescoring}/__init__.py (100%) rename pyquantms/{ms2rescore => rescoring}/ms2rescore.py (89%) create mode 100644 tests/test_data/BSA1_F1.mzML create mode 100644 tests/test_data/BSA1_F1_msgf_idx_fdr_idpep_switched_filter.idXML diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index c7fa51e..78c4918 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -3,7 +3,7 @@ from pyquantms.diann.create_diann_cfg import create_diann_cfg from pyquantms.diann.diann_convert import diann_convert from pyquantms.features.add_sage_feature import add_sage_feature -from pyquantms.ms2rescore.ms2rescore import ms2rescore +from pyquantms.rescoring.ms2rescore import ms2rescore from pyquantms.mzml.mzml_statistics import mzml_statistics from pyquantms.psm.psm_conversion import convert_psm from pyquantms.sdrf.check_samplesheet import check_samplesheet diff --git a/pyquantms/ms2rescore/__init__.py b/pyquantms/rescoring/__init__.py similarity index 100% rename from pyquantms/ms2rescore/__init__.py rename to pyquantms/rescoring/__init__.py diff --git a/pyquantms/ms2rescore/ms2rescore.py b/pyquantms/rescoring/ms2rescore.py similarity index 89% rename from pyquantms/ms2rescore/ms2rescore.py rename to pyquantms/rescoring/ms2rescore.py index 15b7939..492692c 100644 --- a/pyquantms/ms2rescore/ms2rescore.py +++ b/pyquantms/rescoring/ms2rescore.py @@ -7,7 +7,8 @@ import json import logging from typing import List -from ms2rescore import rescore, package_data + +from ms2rescore import package_data, rescore from psm_utils.io.idxml import IdXMLReader, IdXMLWriter from psm_utils import PSMList import pyopenms as oms @@ -39,29 +40,29 @@ def parse_cli_arguments_to_config( config = json.load(f) if feature_generators is not None: feature_generators_list = feature_generators.split(",") - config["ms2rescore"]["feature_generators"] = {} + config["rescoring"]["feature_generators"] = {} if "basic" in feature_generators_list: - config["ms2rescore"]["feature_generators"]["basic"] = {} + config["rescoring"]["feature_generators"]["basic"] = {} if "ms2pip" in feature_generators_list: - config["ms2rescore"]["feature_generators"]["ms2pip"] = { + config["rescoring"]["feature_generators"]["ms2pip"] = { "model": ms2pip_model, "ms2_tolerance": ms2_tolerance, } if "deeplc" in feature_generators_list: - config["ms2rescore"]["feature_generators"]["deeplc"] = { + config["rescoring"]["feature_generators"]["deeplc"] = { "deeplc_retrain": False, "calibration_set_size": calibration_set_size, } if "maxquant" in feature_generators_list: - config["ms2rescore"]["feature_generators"]["maxquant"] = {} + config["rescoring"]["feature_generators"]["maxquant"] = {} if "ionmob" in feature_generators: - config["ms2rescore"]["feature_generators"]["ionmob"] = {} + config["rescoring"]["feature_generators"]["ionmob"] = {} if rescoring_engine is not None: # Reset rescoring engine dict we want to allow only computing features - config["ms2rescore"]["rescoring_engine"] = {} + config["rescoring"]["rescoring_engine"] = {} if rescoring_engine == "mokapot": - config["ms2rescore"]["rescoring_engine"]["mokapot"] = { + config["rescoring"]["rescoring_engine"]["mokapot"] = { "write_weights": True, "write_txt": False, "write_flashlfq": False, @@ -75,21 +76,21 @@ def parse_cli_arguments_to_config( ) if ms2pip_model is not None: - config["ms2rescore"]["ms2pip_model"] = ms2pip_model + config["rescoring"]["ms2pip_model"] = ms2pip_model if ms2_tolerance is not None: - config["ms2rescore"]["ms2_tolerance"] = ms2_tolerance + config["rescoring"]["ms2_tolerance"] = ms2_tolerance if calibration_set_size is not None: - config["ms2rescore"]["calibration_set_size"] = calibration_set_size + config["rescoring"]["calibration_set_size"] = calibration_set_size if rng is not None: - config["ms2rescore"]["rng"] = rng + config["rescoring"]["rng"] = rng if spectrum_path is not None: - config["ms2rescore"]["spectrum_path"] = spectrum_path + config["rescoring"]["spectrum_path"] = spectrum_path if fasta_file is not None: - config["ms2rescore"]["fasta_file"] = fasta_file + config["rescoring"]["fasta_file"] = fasta_file if id_decoy_pattern is not None: - config["ms2rescore"]["id_decoy_pattern"] = id_decoy_pattern + config["rescoring"]["id_decoy_pattern"] = id_decoy_pattern if lower_score_is_better is not None: - config["ms2rescore"]["lower_score_is_better"] = lower_score_is_better + config["rescoring"]["lower_score_is_better"] = lower_score_is_better return config @@ -155,7 +156,7 @@ def filter_out_artifact_psms( @click.command( - "ms2rescore", + "rescoring", short_help="Rescore PSMs in an idXML file and keep other information unchanged.", ) @click.option( @@ -197,7 +198,7 @@ def filter_out_artifact_psms( @click.option( "-fg", "--feature_generators", - help="Comma-separated list of feature generators to use (default: `ms2pip,deeplc`). See ms2rescore doc for further information", + help="Comma-separated list of feature generators to use (default: `ms2pip,deeplc`). See rescoring doc for further information", default="", ) @click.option( diff --git a/setup.py b/setup.py index 63f58c8..2a83e35 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ "click", "sdrf-pipelines", "pyopenms", - "ms2rescore", + "rescoring", "psm-utils==0.8.0", "pydantic", "pandas", diff --git a/tests/test_commands.py b/tests/test_commands.py index a6c2e8b..091f5ea 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -46,10 +46,10 @@ def test_extract_sample_from_expdesign(): assert result.exit_code == 0 -# test for the ms2rescore command in cli +# test for the rescoring command in cli def test_ms2rescore(): runner = CliRunner() - result = runner.invoke(cli, ["ms2rescore", "--help"]) + result = runner.invoke(cli, ["rescoring", "--help"]) assert result.exit_code == 0 @@ -81,4 +81,18 @@ def test_extract_sample_from_expdesign(): runner = CliRunner() result = runner.invoke(cli, ["extract_sample", "--expdesign", "tests/test_data/BSA_design_urls.tsv"]) + assert result.exit_code == 0 + +# test psm conversion command in cli +def test_convert_psm(): + runner = CliRunner() + result = runner.invoke(cli, ["convert_psm", "--idxml", "tests/test_data/BSA1_F1_msgf_idx_fdr_idpep_switched_filter.idXML", "--spectra_file", "tests/test_data/BSA1_F1_ms_info.tsv"]) + + assert result.exit_code == 0 + +# test mzml statistics command in cli +def test_mzml_statistics(): + runner = CliRunner() + result = runner.invoke(cli, ["mzml_statistics", "--ms_path", "tests/test_data/BSA1_F1.mzML"]) + assert result.exit_code == 0 \ No newline at end of file diff --git a/tests/test_data/BSA1_F1.mzML b/tests/test_data/BSA1_F1.mzML new file mode 100644 index 0000000..bcc14b2 --- /dev/null +++ b/tests/test_data/BSA1_F1.mzML @@ -0,0 +1,40691 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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h0AnJoxRZa6HQG5t/gjHsYdAPinWZca5h0D9acg/wMGHQB+3JnvByYdAooBxyT1OiEB8bzlXJrCIQLjIrHMe1ohAAqyxjiLaiEA= 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g0CI0b5JxFWDQD561E7FWYNAZkAhCuWBg0BWR2p9k/iDQMJ9tXL/aIRA8vj7zP5whEBKc++y9niEQMHgvApq2YRA0S3d6mnhhEB0H/GnYumEQHrdJ8B86YRAGuWVLDbxhEDcbTOJYfGEQM3OLRgz+YRAnb2wO6BhhUAChCbxn2mFQAgMS+aZcYVA7BmkpZd5hUAkCJvHkYGFQK3IElJ0qoZAMoGLSie5hkAQyC+VJ/6GQDoGuF8pAodAQrWN0JAph0Dmq0XhkDGHQNR8JF6JOYdA1HXr9FpBh0DaxMp7h0GHQPrUpMUZXodAouankx5ih0Du0njpJGaHQJfzMEM1ZodA2faptGSuh0CEgPnQxrGHQH60oi3IuYdA49ogjr/Bh0CEK5TVxMmHQALYyod9tYhApN1LPiHWiEA= 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iECBaewfIbCIQFwjHenUsohA 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hUD6ZojFoGmFQLiOMH2ZcYVAjLU8SrVzhUBypT+Il3mFQDZf9IaRgYVAZKoB1yS5hkB8Wr1zJsGGQFcHLJeRKYdASt7pL5Ixh0BcKgoFjDmHQEpqNhtfQYdAztq5woxBh0CgWco2HV6HQI5F2RTIsYdAjqVSMse5h0AeEPspwMGHQPjfCda/yYdA 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hEDWALVioGGFQJkP212gaYVAMC4WxJhxhUDh5Vgml3mFQPCcQ8gnuYZAzLC3MCPBhkAcUbxCkSmHQAIMtuqPMYdAaQquGIk5h0C2mLAVW0GHQAKu0Q+KQYdAhKwXrxpeh0BXkCaMx7GHQOhBTgbJuYdAvN+pTMDBh0BdMH+ixcmHQA== 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h0C2NGAokTGHQIKqYdqLOYdAblXGy1xBh0BeBh0FiUGHQNB4eELHsYdAHI8p7sa5h0Bt/ZE0wsGHQJBRAdHAyYdA 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g0CpzEmWKVaDQG66xvkqWoNAtd97deSBg0Ce30NBSYODQP3WPHCV+INAcEhLDf9ohEB+aV86/nCEQHDlz4D4eIRA/hw1DmrZhEA+jNxkauGEQAFErtVj6YRABsL01jXxhEB+xQ2GYvGEQFjfuxE0+YRAYf/lt1r5hEBo1IrnLQGFQF54nkegYYVAWkaaLaBphUAg43/BmHGFQNwzmlyYeYVAXeVugJGBhUCpe+hWJrmGQASWpq8kwYZAitDMK5Eph0A2KICMjzGHQPHLcemIOYdAwrLCwl1Bh0BYLEctiEGHQAlva7DHsYdAbd2e6Me5h0CG4DrYwMGHQDOr1Dy/yYdA 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hUCIKKGLmXGFQAB1YM+0c4VAXjRScJd5hUDOA/xvkoGFQOgiR0gnuYZAZZUtP5Eph0DE2SKkkDGHQPyRB1eKOYdAFimwSFxBh0Bz+1YNi0GHQFKt+6FnjodAIMgZi8exh0BaaOjpxrmHQDbotUzAwYdA0M9m1AHih0A= 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h0DCfqIPGuqHQA== 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hUAJnDEEty6FQLQ7+vO8MoVAQsTzgrk2hUC8nOmWoGGFQMZ9R9GfaYVAqCs5m5hxhUDewLS+tHOFQDJg5ZWXeYVAGMlaH5GBhUCYiI/fJrmGQBgbmoMmwYZALLjveJEph0BaS8HlkTGHQAgw44CKOYdA/ISti4xBh0DG+KJTW0mHQH7x9cxojodAWAvUvMexh0CUrRUqyLmHQAbBWhS/wYdAyBhI7b3Jh0CKxHVhG+aHQI2Kio8e6odA+PTscBvuh0CegMsJJPKHQA== 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h0C2FTk7GuaHQE5U8iMe6odA 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g0AsVWLqJFKDQHFy8+YnVoNAFoXuprCBg0ARu2dT5YGDQG7Cf1RKg4NA3KglrD/Sg0AlW0mZlPiDQNCrwmf/aIRAI2d6h/5whED4CUrA93iEQGZWElZq2YRA98wkDWrhhEAYTRd4Y+mEQLec9rE18YRAwo7eXGLxhECzvujhNvmEQEj9Jh5b+YRAmyO9qi0BhUBWsx1Bty6FQOMb8Q27MoVA8tf+jNhJhUAAdDJdoGGFQCuCXmSgaYVAXJywrphxhUAAQTEltHOFQAKplWCYeYVAvO73xo+BhUBk7v38JbmGQHFKIuGRKYdAsPjEu5Exh0C2GER1iTmHQCjH3AJgQYdAUNhXHIxBh0DaQZ5ax7GHQErqpjLIuYdAeUNuNcDBh0DStG4KwcmHQCCDVg4a5odA2hX5WBzqh0A= 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h0CeCyx+iEGHQAXLxz/HsYdATdXRmcW5h0C+c3ThvsGHQKrq/hDAyYdABmb/xhnmh0Bw93nfI+6HQA== 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h0BqtUiex7mHQP+WGIG/wYdABuGoh8HJh0A= 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g0AFBPyCciGDQB5iMRpxKYNAeZ8I2moxg0CaL9uDJVKDQJ/4FdK0gYNA+le9T+SBg0CgMlKamPiDQPxrMrr/aIRAPMkkJv5whECDeNvP9XiEQKx3e0Bq2YRAaoG/W2rhhEAYqyBZYumEQOF5gbo18YRAZl6mOWPxhEB/oAGWNfmEQIaKX8Ra+YRA26/b6rcuhUCkSxqiuzKFQDGagLS+NoVANkTxQopehUDwKX+foGGFQE+6jo6gaYVA8HZYjZlxhUBCGvrel3mFQFgVDzkmuYZAbp+ied3ahkBGa+fukCmHQPhw2BSRMYdAALykyIg5h0Ag6An9ikGHQHK2kUHHsYdA0tEUPse5h0BYQy/ywMGHQFT/SHjCyYdAc0uq1xfmh0AeNxMiHOqHQA== 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hEB5b5DfM/mEQL4Cr7pZ+YRAtM8TfqBhhUCNsXoYoWmFQD4AkguZcYVAPGMw9pp5hUAI2Hysu3uFQII05UeUgYVATPgc5ye5hkA6tgL2JcGGQMiAI+rb2oZASK5irJEph0D2fxp5kjGHQBK1gI2LOYdAXJ1H/VtBh0ADt9ogjEGHQGDc4irHsYdAln76U8i5h0DQHmsIwcGHQAwKfGq/yYdAZvkoPRnmh0DYj875HuqHQA== 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g0Aq6uGcajGDQBPioknFVYNAJqTt35T4g0CrP6Tu0F2EQLAe9QMAaYRAOifkYP5whEAKUYh093iEQGa0Rm9q2YRAgNAAOWrhhEAhdAYyY+mEQISH7iU18YRAsKs2q2PxhECcw79QNfmEQCUS2vlZ+YRATveX1fsNhUDCET+C+hGFQKZuWbKgYYVAspWR1qBphUCYuB0tmXGFQFQDC8GYeYVAcxg25JGBhUAH6ERiJbmGQKwPNxWRKYdAwbSkCZExh0BVLzbOijmHQBgEHRdcQYdATlXQHYpBh0AUR/AYhkmHQENiX53IsYdABOnkcci5h0BYc0Y2wMGHQLH10EDCyYdALIdm9Brmh0C6z2vvG+qHQN3ggwEa7odA 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h0CeKnrPv8mHQBIp7JW80YdA2N9dVBrmh0AapKNQHOqHQMCZWlId7odA 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gUDvkfzNVY+BQBS2UDlWl4FAine5nEOcgUCAKT1Z756BQLa2Z8aboYFAYxSM4+ahgUDak0IKbqiBQPQW6jzuqYFAuq06UEqvgUCCfknTa7CBQANCl3190YFAhE5v9H7TgUBRx2riK9SBQNXPtacCCYJAqBzpWdgYgkAmjUOF1SCCQL8sEMvQKIJA2PJt188wgkA/Ryz6CYmCQGClWyBDiYJAkvtnNG2JgkD44bgWRJGCQP7TJt88mYJAlkn3HlaZgkBq035jD6GCQO8HClo8oYJA8LGyJA6pgkCZrQ32M6mCQFpO+2EGsYJANaFmFa75gkBEmT4zrAGDQNxSIJd5EYNAGBxbD3oZg0CSAkBRcyGDQO16559yKYNAS4oXZCgqg0BKNqqeaTGDQHRq+mOAMYNAxFt8Ymg5g0Arl1Sv44GDQJixzBJKg4NAzqkSyyrxg0BAC8vq/miEQDzOicr+cIRADO71rfh4hEDguH6T+YCEQEsmHXY5yoRAHhWS+kDShED8TDJOatmEQDzXpZxp4YRAztKI8GLphEB+ifJ8NfGEQHSHY6xi8YRAcmfAmzT5hEBwPDL5WvmEQEhffpYuAYVAsMbYjfsNhUDRlLmK/hGFQKKK5af/FYVAXHdjgaBhhUCErY/poGmFQCIcekSZcYVAdVOnZrRzhUDyarUQmnmFQBfBRFmRgYVAIAWGwiW5hkAz8a92JsGGQAQtiCaRKYdA4Kk3PJExh0C0slq5ijmHQMjVBlKKQYdAvBv+L4VJh0AkA12bx7GHQAzIo6zGuYdAQyuWmr/Bh0DfZFfCwMmHQAQ4Sq0b5odAmqAMnxnqh0AHJtdeHO6HQLSrslMc8odA 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hECMIQ7q93iEQGJo//zLgIRA3FcyfTnKhED6OlIjQdKEQNhZWf1p2YRAnMSAfmrhhEDuWt61YumEQJmERGF86YRAWJbZbjbxhECt71+HYvGEQFp05841+YRAaMaOrFr5hEBetg1/+g2FQMs80YP/EYVAkf+uiKBhhUCktDnUoGmFQGQTCRiZcYVAwr9SxbRzhUAPjNaymHmFQE5DX8qQgYVAyPiUcSa5hkCGgvlukSmHQDSSe/iQMYdANBrfoIk5h0CpUshgXUGHQJqUYgmOQYdA+uD/Vsexh0AKG/hix7mHQKnzwOfAwYdAcK3kf7zJh0CsHZxbGOaHQIrOuqEe6odAjqEsyRruh0A= 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g0ByTMkgazGDQOP4BQ0fOYNAQozmAglmg0B92DDfuW2DQCxc4k5Jg4NAxCTbfZb4g0D3ja+G/2iEQEHqVW7/cIRARO+acfV4hEBC0bzrzICEQFlXIqP1gIRA1j8jmGyuhEBTrDl3atmEQLBPvplq4YRArpz7bWPphEBgTXdlNvGEQOerc2Jk8YRAtDvlaDX5hEDeXQubWvmEQCbU0ZMuAYVA8wFBpqBhhUDERyFpoGmFQO87/8OZcYVAuQLm0LNzhUAHPyZxl3mFQIIlnia6e4VAvKFiKpWBhUCOzRTPJ7mGQIZL0EqRKYdA/FMp+5Axh0A3vJ9+iTmHQNQbmHxdQYdAhnBE4YxBh0DZa0ngx7GHQBuQG67HuYdAePzrOcHBh0BrMGcJwsmHQBIg29T+3YdA7BKrowLih0BAfhfsAeaHQFcoLhIC6odAFOToc/zth0Aoy24JA/KHQA== 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gkBmO/+TeRGDQBaI28N5GYNA3rku+nIhg0AUzHa9cSmDQAow0+VqMYNA8qTX3Rw5g0DkLyhIlfiDQNEG9Or/aIRAqKghDf5whEAGQEPf9niEQB7CmmT4gIRA5fePUGrZhEA+L6EuauGEQBySJi5i6YRARehFkXzphEAQufuJNvGEQClH99Vg8YRA1E6+fzT5hEB4URDjWfmEQD2f5kugYYVABk7sUaBphUBG9NXvmHGFQBiy2omzc4VAWS4kzpl5hUDu7IXrkYGFQErL6c8nuYZASPonhirBhkBBGrKLkSmHQK67HROTMYdAPOLAq4o5h0AuuYiLikGHQACOYPnHsYdAEgnj5ca5h0CDXDb+wsGHQMaN/AHFyYdAtwJSK//dh0AIrc6KAeKHQAppuIUE5odAEsYw2wbqh0A= 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gUAcoW01PXyBQOzn42nkfoFACK7uFZOBgUBm/k0GVY+BQMpylkBVl4FA0oHUr0KcgUCUEH8f8J6BQCBsr9maoYFAip98u+ahgUCPHwsiQ6SBQE5ono1tqIFAevjiXUqvgUDr2ssyTLeBQF5s9epnuIFA8JcfqH7RgUCQ5JcmKtSBQJBGfJ3V1oFA4jvKSAQJgkCm+yLFARGCQOi0KnfYGIJAXH/VndYggkDwEpbi0SiCQGzLfPehKYJA8D6VdaUtgkD39MSwzzCCQBZ9x/yZMYJAgL7rxqwxgkDOnU5vnjWCQMrWhh9DiYJAtm57TUSRgkBsOUtRPZmCQKjVXthWmYJAiPmPSA6hgkAa2d/rO6GCQJVm07kOqYJAoNmu1jSpgkAgW3qKNLGCQFQXCgmv+YJAEn83VnkRg0Bo+MpOehmDQD68RSNzIYNAngo0xXEpg0C8pCJYajGDQIoCbdfCVYNAuNGQiETWg0Aktb7ik/iDQMSTNaLKGYRArCaFZ/9ohECIAuus/HCEQA9k6e/2eIRAUtfdD2rZhEBHMBdCauGEQEzBkYRi6YRA5PLXajXxhEBWAdN6YvGEQAPYLX40+YRArHsGQ1r5hECie3G3bwmFQIZdWkWgYYVAvffTj6BphUCvU1eOmHGFQBZQJKqYeYVAIQJPlyS5hkA+XLlU+fWGQOhbafmQKYdAEtLUVJExh0Cf2r3qiTmHQEdhhh2KQYdAYsOLjlxJh0Aup1s6x7GHQK1F9ALHuYdAUGsTy77Bh0DMvWwJw8mHQLbjzCj/3YdAHqyxVQHih0AgEHNqA+aHQLtscKr96YdA 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f0AOf0AZGmZ/QC1EP4O3cX9A6l5qZeF9f0DoXHeasoF/QH6pO3Wjg39Az4vbp6WFf0B4FbF3Y5F/QEKvML2skX9A0P5SE5WTf0CMtOf/spV/QGSgZP1coX9AZdWgwuelf0CFIjBQU7F/QIrQgIqV3X9AEOpvYdXif0ACKrt/L+h/QDm4QUuG7X9A7vsl0zn1f0Aml0sRz/V/QH+VH3ruAoBAbX6UiLwKgECCmucQ3RqAQJHPLkwnG4BAMDu4SSwjgEDjsyFSJi6AQPzKMMspMoBA3yDiHvUygEBgUy+yHDaAQMZ1bUAvNoBAlALoc+o3gECQXBxNKTyAQLKuBEDYPoBAUEX13oRBgECr8NKI8UiAQIgrXNK9TIBAIlxgU+dQgEBcmL4fFVGAQL7XQ6PpWIBAHvrWgg5ZgEBCcwGmIFmAQLTWe7oOYYBAdGskqfBhgEBG9Q/88mWAQAbC6oC9aoBAkpEBACxvgECU2p/csYqAQM69UlT9ioBAYC1c6h2PgECmeb+VbJGAQArp26UCk4BAIGFicRuUgEDW3kGpyJaAQHIjZswTo4BA6kcdgV2jgECaRSHBhqmAQKTBjuYXq4BAI5SNTxW1gEBowA3GFbmAQJZYFdtDuoBAdjA/vFLBgEBcWU3WEcaAQMOrSOf/x4BAgjZPk1PJgECjen0AFsqAQArtslEs0YBAIjqD80zRgECeMIWXLdmAQF7OSZ1K2YBA0bC2QEThgEB2rH/yVuGAQCoNSA326IBA5Kug5LLqgEBKiruDS++AQKAijH3R+oBAHEy4qT//gEBaV1xt0ACBQBNKA9Q/B4FARic/Xj8PgUCLR3YY6hKBQBayYOYzE4FALnuFN/McgUD3EZjjNB+BQNLGl+OxH4FAmFLJ/wYjgUD/hYY3MieBQLQQ7Z7bLoFAyzPG1ogxgUCuGS4EvzGBQAKMBMgtNIFA+EJ0/q41gUA+aFWn2DaBQHndSReyOYFA0n15XqQ9gUAEMXXVtz2BQKSCshOsQYFAc7wf8rtBgUCuDHqw53aBQGBRkeyUeYFAFMVErkB8gUA54IyE536BQFop9TaZgYFALDEW1wmDgUAQB666VY+BQFxY+zxXl4FAunKoqEOcgUCuE8WN756BQMGYwhWaoYFA7o+Pu+ihgUD73hFFTqSBQIrdroxuqIFAxJpRQJipgUAaP0xAS6+BQDFIBOt90YFAOg5R8nrTgUDrY8eGKtSBQPqm4G3a1oFAhOL76H7ZgUBbVLkoAgmCQPBfY3zYGIJA9/GeWNgggkDr1ltj0yiCQLDBQWCiKYJAls3YRqYtgkAkXKVhozCCQMLZkR3PMIJAW2L73JgxgkAQoo1kqzGCQPw1H3mhNYJAUTo7UpNBgkBQhfwo6UqCQK62JNUzS4JAsMNTU31LgkDpjdDrvFqCQNZxErOcZ4JAhj6EDnKEgkBxflmKQ4mCQCAccWdEkYJAJmaiQxWZgkDuZPW+PZmCQPwa6A4PoYJArN7AjzyhgkBuyJgIDamCQPiKC4szqYJA78qOhAi7gkBPB5sVU7uCQMRb1x2eu4JAulvIy6TDgkAbcof1adOCQD6ayjaz04JAcAK7EK75gkC8BcXceRGDQAAbzmd6GYNAmvYXBgobg0AMwqNfUhuDQLf0WNWeG4NAnsh7EHMhg0B/j///jSGDQKAh69BxKYNABi6BBCorg0Bv0+NkcyuDQDSibm69K4NACZURPWsxg0CWtnacgTGDQH3CclR6M4NAwopxPsQzg0Ce37vYSDuDQOYmCgWUO4NAwJm8txxBg0Dw1pmxPEODQGCzHhWJQ4NAwONvf9NDg0CqF1aQ3EuDQGJZXaMqi4NA6H1lK3KLg0Ayp3eLvYuDQArBQLp5k4NAXqf9xwGbg0DdURdTSZuDQOft9HqTm4NA1m06BN6bg0DOFBO0mqODQLT2C13lo4NAjOo7oR6rg0AkpltFaauDQDbZxge0q4NAKE92z+urg0DIqNP4XbODQGLXlv9us4NAMMAN36izg0Dna6mav7ODQBzThkz0s4NAFLAqZLC7g0DYVSgH+7uDQKYLvn3Jw4NAggiowQLEg0Buyg45k/iDQIF30ptJ+4NAM6Q5a5L7g0Aeq1RFav6DQJT1jOpsAoRAFJRCXlEDhEBt4XZomgOEQOYle3hyBoRAKXhnah8LhEANfD+IaQuEQOeknsyzC4RAcLKb8f0LhEBU+ZrVcBOEQP4kXVy6E4RAFGR/KwUUhECgLLkLPhuEQO3YKJ+JG4RATWNR+9MbhECvNbwqCxyEQBN3EIp8I4RAqEpTMZUjhEAyV2UzySOEQLZ7XG0UJIRALlN6kqQrhEAEoZZ7zyuEQMsvPzAbLIRANBSdPp4zhEBwowoB6jOEQJyvY9kfNIRAcvGsbrlLhEB+Q9RIeU+EQCZwdU3/aIRAAnjF3BxrhEBWVhnIa2uEQKJtkVWya4RAbYi+9P1whEBdvo3/bnOEQNGV6mT3eIRAJIBQnj57hEBiAAFhiXuEQAWiQpXTe4RA+ty7ePuAhEAs0z36kIOEQMkRhnzag4RAcl4XYV+LhEC6u94TqYuEQNhQDhj0i4RAoBml2VKThEAy3fCnnpOEQMfa5Ojpk4RAK0IzczSUhEDgbNunf5uEQPxt/o6km4RAjqcyPcmbhEC29hbi8JuEQJnJqO87nIRAMLl1KHajhEC/u/ZFv6OEQCvww5QKpIRAAU/FRcarhEBpoXxbEKyEQApjl4Pgs4RASqEteWrZhEB28Gp5PtuEQDzb+BCK24RA+eIeu2rhhEBhQyqMkOOEQDSMO5Zj6YRAsCBWahnrhEDgoN3bXuuEQKDWV2Gp64RAJa30ZDXxhEAUF+YAZPGEQIadk0Zm84RATOVq97HzhEC3TfVHOvmEQGhEEvFZ+YRAwFswUXH5hEBwy4DPM/uEQKaH3wCA+4RAogSQRMn7hEDSTtxzcgOFQEJ2JvW+A4VALhQa5wkEhUAiDMfKVAuFQJK2mMF9C4VAqk8TvqALhUDHFecwxguFQJG/1SboC4VA97CanRAMhUCQbWdulBOFQE9BQzLfE4VASmWRaCsUhUBW6sletBaFQKPBf9ibG4VAFK6w6OUbhUCKw/EHMRyFQP4Ppba0I4VAGCO19AEkhUDO/DXbE0uFQEScrjdhS4VAlr7+kThbhUBeSeAMf1uFQJrmHLCgYYVAQX6Q+p9phUBjNlkMVmuFQEvR8FCfa4VAGsOH3HZxhUDKFjWsmXGFQMBZKneUc4VA6JzyLd9zhUA2rc+QmHmFQDghmTV1e4VAsAmyEZ17hUDYGDlpwHuFQDdJnJzne4VAhM9FM46BhUDqiyfOaYOFQB8NEQm1g4VAdwn0jv+DhUADMUcmvYuFQDxu6sHdi4VA5H+yIAeMhUB8PINSiZOFQAr/1ELUk4VAoPZ9wR+UhUBoCde33ZuFQAyLXrsrvoVAJBUyulHLhUDI+wiVaOOFQNILjoq044VAvyaIKZXrhUCY474fQ/OFQH+qFZuK84VA7IPrh9bzhUCs9IVUkfuFQPsljQDf+4VAhsgVOKwDhkBwCSYV9QOGQIaNke+2C4ZAOiHHossThkBGEsuOq2OGQJAa+lCAc4ZArCsJYcpzhkAseMjZJLmGQMKEx+siwYZAEEe1rqHjhkAYnkmQ9/WGQBgfcdaQKYdAI3IaKZIxh0A0CfupiDmHQFwGFT9hQYdAklRSZo1Bh0DkK5wsCn6HQFdHxEDHsYdAGiX/cMe5h0DNTPTgn8GHQHYVMarAwYdAM9B46b7Jh0AK+LBG/92HQKAFHWkB4odAaJDy2QXmh0DDxdkZ/+mHQMiwZp249IhAYUuoIrr8iEA= 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h0Dy7Ril/+mHQJKAirIC7odA 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hEA4f+veW/mEQM4jMIWgYYVAEt+XNKFphUDs4ueYmXGFQHv3KRiYeYVApP7qhZCBhUAO3H9rLr6FQDJD9AwywoVADEOwbSW5hkB6MvHxkCmHQPaSHG+QMYdAKsIu6Yo5h0CtdD97XkGHQLaLLfaJQYdADt54xwp+h0DoUTHKDYKHQP4NK1bHsYdAHjJJE8i5h0BbpbXowMGHQIx2J4PAyYdA+FfpQ//dh0CqWrkkAuKHQBb5QKgB5odAXM58cv3ph0AIBW/9Ae6HQA== 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gkAYag09rvmCQCwvBYJ5EYNAMDtNC3oZg0CInDQ/cyGDQKyYaKNyKYNAAmA7dGoxg0AENUUx5IGDQAScyCpKg4NAXHKi9JT4g0A0PHusiESEQBbH4J3/aIRATK3/cv1whEBcgO1C+XiEQHPGl2tq2YRA3VdsaWrhhEBmFjmcY+mEQAT04AQ28YRAPtVhK2HxhECSrmaANfmEQDzd325b+YRAeNu/3S8BhUBSJqySoGGFQIv6NOugaYVAdFXeXphxhUBYH0yrtXOFQEwcjZqaeYVAGst+85GBhUDGMlsnKLmGQIvcvC+RKYdAmwI9nJAxh0AEStjOiDmHQAKKaXhdQYdAF+G8EolBh0BSQIqCG16HQLhIMNHHsYdAMm8jKcm5h0BSJNWev8GHQBCaGKrAyYdAXF+UN//dh0BUmYlmAeKHQJrnJlAE6odA 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g0DYEZ20QNKDQHuvE/GU+INAFhXw3Wz+g0AcWO9a/2iEQIzL5i//cIRAAbLJAPd4hECeWCw1atmEQPTo/79p4YRA0INKumLphEAItqkINvGEQMi4siNi8YRAyi9r3DX5hEBoqg6ULQGFQByoZWWgYYVAgl6GzKBphUCIeO9XmXGFQD3/tauXeYVAiy0NrpGBhUC9KzddJ7mGQPqD/U+RKYdAHFXs65Axh0BGl3RgijmHQLA1dLZcQYdA1o0qN4pBh0D4Pnp2XkmHQJWlB0LHsYdAMrHG7ca5h0AqkJ08v8GHQE4PZhG/yYdABlFmbP7dh0DwUj1KAuKHQLSa5DkE5odATjbw9rb0iEA= 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g0BWYI8X/2iEQN0Lcx3+cIRA5nhj5/d4hED8HSw0atmEQFrF2Dxq4YRAy7xZa2LphECeYhxDfOmEQD6uunA18YRAXuCJQGLxhEDkAIZdOfmEQEwovWagYYVA1iYG8qBphUB5wDiYmHGFQEKEChe1c4VA7IUTy5Z5hUDqg4tbJ7mGQAxb1G6RKYdAixk86pAxh0AJa434iTmHQCJcovdbQYdAYYI0SolBh0BkcJr0x7GHQB4iG7bHuYdAhqUcB7/Bh0BWNp8ivsmHQKTA13T+3YdAavqrAgHih0A= 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gkBuFNs2D6GCQGWroXo7oYJAVHlIeAypgkDrMIqOM6mCQKdxYoQIsYJAMjYnPKnVgkBeTpNar/mCQDjqQ5B5EYNATSU8PXoZg0CclVo4cyGDQPdSqLNxKYNA6nY0x2oxg0DPqf19WDaDQHf0YlnkgYNAmk79hpP4g0AoDPXZzBmEQBGiVuDhWYRAjUOcs+VdhECsPfqN/2iEQL5rgZf/cIRAZhwha/h4hED06ZwY+ICEQDDpzFtq2YRAtpNaRWrhhED0JJ8FY+mEQGIAlUY28YRAZghZnGLxhECk3e2NM/mEQJnk/gxa+YRA8sdxCC8BhUD2uHtmoGGFQB7n3RWhaYVA7ERf3JlxhUCYwo64s3OFQOBw0j6aeYVA7yyYUpKBhUBjICm6JrmGQBBWBGGRKYdAHX09ppAxh0AWzTRdizmHQGaPofBcQYdA9o3OLotBh0Ad0FFZx7GHQPl6R4DHuYdAoH1lDMHBh0BObaH5wcmHQHAyFUL+3YdAeMLmuwHih0C4M6GkTw6IQDTMi5Pz0YhAR74ryPTViEA= 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gkA/nplPQM6CQDJoeOGn1YJAV1c8Ea/5gkBvTOCseRGDQJ2T1955GYNAnCC8U3Ihg0DaAXvziSGDQDXKyUxxKYNAGFmaMmsxg0AIq22sWzaDQFGQXDu5UYNApyMeepX4g0AOk6svzBmEQLDdcmTRHYRA3pTBHeJZhEAY9YA85V2EQFk8e8zaYYRAGJQSxv9ohEB8ZnNt/nCEQKJRZFz1eIRA36drr8F5hEAdutwsxX2EQLg51VZq2YRAfsmzVWrhhEALt4UlY+mEQOxJ0LE18YRAfJDOCWTxhEA4cgeuNvmEQPYN8M5Z+YRAafInAy8BhUDWBD4voGGFQJyKJwagaYVAAuQPYphxhUCq03fHm3mFQIiQwASQgYVAWNQVgNiChkDShd3dJbmGQMxja/CQKYdAaOVXvZAxh0C6j2DMijmHQGKJ6IOLQYdAWAxXt8exh0CmJ1ogx7mHQKqN/Ry/wYdAis770APih0BaV6Q29dWIQA== 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hkCw5hYWkSmHQI9mwkeRMYdAvWUo8Yo5h0CpIkEHi0GHQLNppTDHsYdA3RGZDci5h0DkUfyfwsGHQDEP2+K9yYdA/mJi7QTih0A= 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g0Aq45REcSmDQBlrxqtrMYNAsNz9VOOBg0BqRd9klfiDQNPhGq/NGYRAoCCsYOJZhEDe82S65l2EQHg0EPb/aIRAJypczv5whEDO/jP693iEQJoiSqe/eYRAIiVAOmrZhED59tgVauGEQCPFVCNj6YRAHwOWiTbxhEA29cTSYvGEQDgnrGs1+YRAWxgq41j5hEAYm7aJoGGFQOp3JEqgaYVAt/EnophxhUDUyfQStHOFQATD8miaeYVAziu7p4+BhUDs2ru3JbmGQLL59PyQKYdAUbOuvpExh0Bw24T5ijmHQIaytSCLQYdAxpWQ+8exh0BzIt59x7mHQOrQoLLBwYdAKixnZ8DJh0Ae7b/v/t2HQFdZZXEH4odA 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YKmGlXnAckCwcX+rb8FyQGbtl/SnwnJAi3bWdjzDckAUeFznxcRyQAK4/z9bxXJA3s82EUTSckBiinXttdJyQJgmUgtK03JAm1H0f3bTckC9jqaQzt9yQL0NPdK64HJAmHbcCEzhckACHhC7UeJyQK+HBfCQ4nJAeqvLsiTjckAAB1exUfFyQE7cD7jJ8XJAsLH+hanzckDYjgjppfRyQP5niUrD/3JAAKSIXEUAc0Dm4yfmYABzQGsEVvX5AHNArG9uZBQBc0CDtSgVUQFzQLLoMmo8AnNA+mE+PdACc0DrwThC+wNzQH7c9QvkEXNA3rm1qwoSc0CXtZlshRJzQKrxNCm9EnNAzPNczGITc0AEirWEBhRzQEkCWLt5H3NAxjx4Bw4gc0BgwcWupCBzQBb8grc5IXNATS6OrMkic0DOAhq4ECNzQPWmFNbPJHNA4hKDDEgxc0DnGH+yWDFzQMrG9m0NM3NA2NVTyKAzc0Cos7dd1zNzQCanoIg2NHNAli9GANw0c0Bmqz1puT9zQEIOulnjQHNAWJzSsHhBc0DOw2x+ukJzQNgxZkl7RHNAfAF55Y9Pc0DKawCMEVJzQLnhSwNBU3NAslGG0qRTc0DqUNHFelRzQPkYrKAKVXNA9Q8fnYxgc0BG76NKOmNzQE7EUsjQY3NAdqxTCFNyc0Ak/Ll4aXJzQF4biJq2dHNAlspPDE11c0AKSxyweYBzQKs1VQCkgHNATlw+Kg2Bc0AUxqViXIJzQKrrbYwPhHNAygYnojaFc0CVClwkh5BzQMwmgvv2kXNAhfGGuU2Sc0Bd0oksy5NzQGwOnNJjlHNAkLwf8PeUc0AqBnE8uKBzQKtSYLfloHNA8gIt/kyhc0C+oC1xW6JzQAQio/jmonNAno1boQWlc0DUqxt3ybBzQCb7dgrYsHNAkB/7K/Oyc0AQjswTt7NzQG1a1QQOtHNAsjFlRaK0c0Akn0gM0L9zQOZ0Xcb4wHNAX+a6Kj7Cc0DLZqxH0MJzQA3zIlImw3NAxFGsq9jRc0BPawKAMtNzQOwAxMP103NAIcNem+LUc0DyTcKCOeFzQFrMSJlm4XNAJHh9z43hc0BUSU8+5eFzQA97mIWm8XNAquutJi7yc0C6ckBYOPRzQLk+iaVOAHRArGdnX3kBdEAkAhpCpgF0QIRLv7XjA3RAQpVk9IkRdEA23uUHTxN0QBKzBl7iE3RAzBPK80wgdECSuKL4tyF0QChTSyJkJHRA+11iaVQwdEDqSxD6uzJ0QE2QfARTQHRA+xtYGM1AdEDlbJn5eUN0QIpqoiP3VHRAag+Ul4xVdEC87BiJuGB0QKTCQDEJYnRA+rHHqRBkdEDb4oltTmR0QNAC33EicHRAB6woi0FxdEBfLvbNpHR0QIuzhi82dXRA4UNLA/mAdEBG3H8GT4F0QJLUcwIXkHRAru0oR2yQdECuO/0nz6B0QLBu6lc5oXRADtNVHLmidEDatWnb0KJ0QBTvi+APo3RA5PkBymOldEBKB9XpCrB0QLBsNSrEsHRACDBdK+TAdEB2d9WyeMF0QJKIBsQOwnRADFBxzRLDdEClR2dlI8R0QGowzvl5xHRA92a1ILnEdEA92qay/M50QPjlif6l0nRAlL/8LD3TdEDTAYW54NN0QLByIzN41HRAAoDWIM7UdEC8C3C7ueB0QOjPpcEj4XRAVHSmGbnhdEA7r51xHeJ0QMIWpe1O5XRAVcehxYzzdECF/WR7d/R0QPqb8+mmAHVABEmKdGQBdUCgc+hzXgJ1QGJnq3yaA3VASo1iUjwSdUDuZaIvJBR1QIQveuWdInVA0HEOt8w1dUDoI5KjtEF1QBpQDnvqT3VAhXHAWoFRdUAkEAEYT1R1QGvtfzN4VXVAjaQmEDhhdUDghvnZdnJ1QGypVpWNdHVAgQ7oaniBdUDSpSTVrYF1QGNh9rKmg3VA/Ii1Q5uEdUAiTe1GjZB1QD77NBC7kXVAnc44dj2TdUCOz1LGPZh1QLjVXHiYnXVAuKoT7tqfdUBCQmSflqB1QHhbEQMhoXVA2DGMdrqhdUDsUPhRZMF1QNjHJzP6wXVA3HDn81PDdUBa0AjyzNN1QD5JdYfm33VAmLkRBBDhdUCQweAyCeJ1QLMp/3Q24nVACtwzVkkCdkBGk95J0AN2QDovmR5MFHZAprscwg0WdkDV2YmAmyN2QMCOwreQJHZAQqSFDiAxdkBwSuoTnTR2QAx9/1keQXZAYozLs3pEdkDNjLLYyVB2QJfRcicTUXZAVJSbPM5UdkAki0uBzGJ2QHoDa43cZHZArITxySVodkBGwmMqkoN2QKCaJlWjoXZAjvNxLcOydkATADpOeLN2QHQ6KcYNtHZAMDNRwNS4dkB8IBw4d8B2QAwWtMO9wHZApPZL68/OdkDApAd99eF2QKiJKuwe8XZAvwEaC5PzdkB1DhMxNQJ3QP7EnNBQDXdAo1YpVQISd0AIpnl/qRJ3QDDLI1IEGHdAAI1WU04dd0DaHkzfZB13QOkn1/cNIndAAr+amXMid0CaMq/DqCJ3QFyXiycNNXdAgUYiG51Bd0BgVgYmsEF3QLb6THcbRXdA0ffKHkxRd0AXFxHYklF3QAcRlOQpVXdACHWV8o9hd0ACJtouO2V3QHYTkFAnb3dAVbh5LNxwd0Cqa0kHeYJ3QCg0/z58g3dALjrJ4giSd0CzdcLkSpR3QMT6tIqatHdA0qTElSjDd0DsFP6sjsN3QFjyUXOM1ndAybCaP+3gd0BjgeRvUPJ3QCLwF7xuInhAidzm24wjeEAOMZZUezJ4QG+rjjIPQnhAUFeomnpFeECgqBzYy1F4QPnRHF6VUnhAcmzLB4dVeEBaWJj47ld4QB9GkM81XXhAs+U3nUxdeEBKMhCb+WB4QGRyy7G3YXhAbKOA3bZieEDtyteewXN4QMYN+RMadHhAGHNSvC10eEDxdFrDi3R4QMQHWRHIdHhAwlmSmdt0eECIcoLiooF4QIpaYN2ZhHhAHkwo2JeReED2PgkLqJR4QM0Cr3mvpHhAatF2x8zAeECe7I0M9cF4QKjqQFS41HhAhohqhAL0eEDecq+gjPV4QHDmQvm//nhAurs9pJoFeUCCL8kNqAh5QO1hJo4AKnlA8siarhJCeUCZgs7j9kJ5QDv7oYHNVHlAbifZh0JzeUB+0JJ79oF5QDJzEVrag3lAESoXkfWmeUAsaetBwbJ5QLjJTD5C1HlARkfKcM/VeUDeNA7fxOJ5QB/HMFhQ5HlAxHsjXzgjekA8UR9u4ip6QGaf59zxK3pALMfLvww1ekDvnhliG0V6QI72UJovUHpA/7+O3YFSekB065tQKFV6QMaMkXSNcnpA8Xl0dwqdekBncrv6RqJ6QAVI2bXvo3pA6CVFzqOnekCuI+0g7K16QBOJNJwNtnpApy+/1ivDekC6IddUGcZ6QClgZ7lBznpASN00wgPbekAiOzCneeR6QK8k48017npAiuoXAisOe0CEb/X8TBV7QIIAXphaJXtAUg6PT1Qwe0D+jXsPKzJ7QE2+iczFM3tAvr28I2RAe0CidNqmQ1B7QDF7vLpogntA0MJN/r+He0BKMhSlI5V7QNxBrfh4xntApz/Ls/7je0CScPMOl/F7QIYs326M9XtAGinQr9oBfEDkfK9cmgV8QLSJyCOUC3xAfRRlP4gRfEBr+mV70hF8QFCBxsrMQXxAbM/Z6itEfECqupsk2VF8QOp68jwPYnxAi4lD3gRzfEAC5w07wbJ8QDGurVUStnxAbh7fA1jifEBkxvWMFfJ8QCfGMuZV8nxA07qXvgECfUCwJmP6SwJ9QIpXsfCkEX1AUGou20YSfUD8JW3yJh19QBcHHkw9In1A6LvCnkpWfUCy7D6r81h9QA6+jj6iW31AzkMgzyrDfUBft5viaeB9QPQGsiTY4n1AvpL9gJD1fUCn/yeFjrB+QKRT9jbAs35ANQ41aE/JfkBY5Zbhuex+QIZNVUAfAH9AIq1Q5rhxf0AF4rRAsYF/QPb5vdOig39AXgIhq2ORf0B+SILiXaF/QFx9kHXopH9A+wqe41Sxf0A4EjtmmN1/QOgw9sLV4n9AvYCJtfEJgEBy2NuX9Q2AQKokFfz3EYBAiDdUbN8pgECAUKasJi6AQCC0DWTpL4BA6hWKVikygEDmUfkH6zeAQFQUfTbYPoBAFIEpbPZIgEB4OgwG6FCAQPaFgPgTUYBA6ZpzU+dYgECwYgw3DlmAQGzaOoMpb4BAY0W2sARxgEDpgxgKJneAQPm6qZ8of4BAivJAW9+JgEDE/TrsHY+AQGglNndtkYBAsz3dphqUgEBiwoGFhqmAQJi47OGqsYBATMexnlLBgECOasmCEMaAQP9kMLgAyIBATlugilPJgEAOb51eLNGAQPT2/epM0YBAQguZpGHRgEDctsHbStmAQBCiSaJD4YBA8u83vVXhgEDNu06b9uiAQN3OX65B6YBA+ZtDE0zvgEBOqgY1PvOAQBijsO4//4BA7W62/UEHgUDYynEtPA+BQGgTqonwGYFAbj4PvDQfgUCLVv1TsB+BQJ4fMmlHJoFAM9DrkTMngUDE4V7c2i6BQOcVaWJ7MYFAgPcyWL0xgUACMiLJJzSBQIJ5MxvUNIFAL+AV0s42gUAj4z9ofTeBQFLPqEX0RYFA6t7FZ/dJgUDbvfPN6U2BQOXySmT9TYFAaI7qRO1RgUBimgK3AVKBQGa3WqfmdoFA9Ie0OZR5gUBCllNOYX+BQIKva8tVj4FAR+mJqVSXgUA6H/yRQ5yBQJIQ5SnvnoFA93BaiMChgUDKYfjE56GBQIaxd17EpYFAMnGHeG6ogUBiAjqdxqmBQFSyZf3sqYFAEO9gh0qvgUCieF3ECLKBQPUNW+4pzIFAw46mlNbOgUAA/BTdftGBQPo0NEV904FAzOv1xy3UgUCO6zUdhvGBQMqg98oy9IFAyABUY972gUCYmM3OifmBQKlPGTVYDoJA/us3AtkYgkBsUP791SCCQGCSKzjGJ4JAqIYjs9EogkC7fzMRoimCQGpXr4+lLYJA3aHcrKQwgkCuU5GGqjGCQKwZzmPnOYJASjGOwus9gkDUq2Px7W6CQEZixzCacYJAQ2KYcUOJgkALxbsTRJGCQKosbK08mYJA2ovkMA+hgkCD2CB3O6GCQArWOdEOqYJAyDuYCzSpgkA1viXMCLGCQBRWh+il1YJAMCsla6/5gkDOLFmceRGDQM7+Bld6GYNAovE/sXIhg0A24533cimDQOIBPHZqMYNAPAr4KOWBg0Aq9GgFSoODQKwFd7nMGYRAPmniJtEdhEDytcX941mEQO7aOLHoXYRAn3Wlef9ohEDIBEIk/nCEQCiABV/5eIRAK1eEYWrZhECPLJWBauGEQAZXfSRj6YRAgJDljX3phECi2mpnNvGEQIoIa/pi8YRAyInzjzP5hEDqOu4hWvmEQDBF9GYvAYVAzFvvpaBhhUA4V2z1oGmFQGQVl92YcYVAlnOiEbVzhUD2klO9m3mFQL5VLM2QgYVALxZ1trtIhkCenT6ZJrmGQJITFvIlwYZAxdkaV5Eph0CW6V9GkjGHQCaHeWuJOYdAjGjryYpBh0BaPqK7x7GHQLqEKzrHuYdAQkMy38HBh0AueWCjvsmHQK2DfqwE4odA5Hkoc1ISiEA= 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g0DEj0QDgjGDQOpVsu4bOYNAyO3LmuOBg0CQEGwLA6GDQLydFYX/aIRAqJ7CT/5whEAWma6I+XiEQJxZjS1q2YRA/w7lQWrhhEApCKBOY+mEQLoxfnw18YRAHJ2DY2HxhECCvRnpNPmEQCcIVBBc+YRAYnJZ0SoBhUDKU+xXoGGFQLwf9AygaYVAJOTbIZlxhUBqel8+tHOFQFQbIWeaeYVAAt97f4+BhUDzAiJQJ7mGQOY1blEhyYZANBUY5ZAph0D5OGa2kTGHQK9wG5yIOYdAOFAl0ltBh0Co47Q0iEGHQPfaTYzHsYdAIX8i1ce5h0DaQjB+wcGHQGHFkCDAyYdAhma+igDeh0CQgK/z8dGIQA== 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g0BC11FP/2iEQG1P2YD9cIRA0jPvTfd4hEA4nJhBatmEQAulxTJq4YRAu1syM2PphEBCKHXkfOmEQJf+O5g18YRA8LGaR2PxhEDzlif4NvmEQISqyR9a+YRAxKGLbKBhhUDSkUxnoGmFQCO8XQeZcYVAXO8qzrNzhUC5fzzWl3mFQHaU5aW8SIZAds5aXCe5hkDOnXRzkSmHQIm65SiRMYdABRqQ1Ig5h0AOl6fLXUGHQHPTktuIQYdA8bd+Lsexh0A1jSeEx7mHQOTUDzjBwYdA91OqIcHJh0DYLswQ/t2HQAB4XGnjYYhAuH3DlPTRiEASRuF59dWIQA== 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g0CYJOTllPiDQJDDIVDNGYRAGErlzv5ohECM27iY/nCEQCLunPT1eIRAymtQNvuAhEBX+hMQGaqEQMAeCEZq2YRAjhd0fWrhhEAWGYuQY+mEQAg2ZlM28YRAFWs7KWLxhEAoHNthNfmEQMgP/PpZ+YRAMfELSS8JhUAKaNi3oGGFQOF0/fqfaYVADt0MfJhxhUDHFOp5mXmFQKfZhIiQgYVAljShjArShUBWaQTRJbmGQIwovhMowYZA0DQ/5ZAph0DaRaAIkjGHQJy0Av2KOYdAyVP0RlxBh0DUYVjAjkGHQEgsPwjIsYdASEH4d8W5h0B5rR+qv8GHQF4qX769yYdAaaKVgeNdiEB+mycy89GIQNBhdPj21YhA 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g0CGCGRz4oGDQMgKjTVKg4NAGOFrIJX4g0DkMOoJ/2iEQLdoMGv+cIRATrJHyfh4hEBs+29tatmEQOPl6ytq4YRAiVqVyGPphEB9UuGNNfGEQMyXiOhf8YRAplQMBTj5hEDBk1bgLQGFQLglVb+gYYVAulbW0aBphUD1fquDmXGFQPKInvOzc4VA7qhA4ph5hUBezck/k4GFQJSJYWwmuYZAkzWB/ZAph0B5fiamkDGHQJsJbliKOYdAnKYq31xBh0CpZBLXjEGHQEaLm4zHsYdAwna/j8a5h0CoOigRwMGHQCgfmPbByYdAJGlw9mP+h0CGjEtn5F2IQNr9//ny0YhAYCOII/XViEA= 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iEDR4juhioWIQIRMmqY4iIhAkdhtDOiKiED+fxJrma2IQA== 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hUC1Z7iTJbmGQITfIByRKYdA0uB+ypExh0CgRkKwijmHQA3a/B2IQYdAagrfmsexh0A4nbxTx7mHQLqSt47AwYdASun0QsDJh0D5qc5t4l2IQHbqQ/HkYYhAI9UmGuRliEAplkJt4GmIQK4btlDnbYhA7QG3/zKAiEDcrB1K34KIQKk42nGLhYhAaOFI1DaIiECCWgFI4IqIQJLmZeicrYhA 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h0AscJkdkjGHQGIR9p+KOYdA3gzS/F1Bh0AVZPVBikGHQMR9oqjbdYdAUDAJvsexh0AKS9vIxrmHQMDS9XzBwYdApTXFQ7zJh0CaWSmh4l2IQJhP31rlYYhAGviGeelliEAnV1LL4GmIQJ7Mzaw0gIhAO+lRLeKCiEAqy0rzjIWIQA== 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gUAGSkmFVpeBQCvQuSXooYFAXkuH0W2ogUAorMd0Sq+BQHuYSTIGsoFAgKgXmwm2gUCHFep9DbqBQE7EmIopzIFAAjNXStfOgUDOy86sfdOBQMOMb20U2YFAY64zsUrqgUBIRTknhvGBQGiOsmlR8oFAdlkoagIJgkASfHGf2BiCQLKnO3nVIIJAFbJLJtEogkCaX/Y9oimCQNomG+ymLYJAxnMFZKoxgkCoADqClTmCQJzFVFbnOYJAwciWwuk9gkB0epcA6j6CQMVb8kdzaoJAygnHSkOJgkAsdcPjQ5GCQG+mk6DVloJAMgyypDyZgkDykfq+DqGCQJT7frs7oYJASNxlsQypgkCNL6bTM6mCQBwUN5gFsYJAHxGMqzexgkBM4nUNBbmCQM2Iydx104JALLwUeXkRg0AIM81PyBWDQNmGAUp6GYNAIgw5GssZg0A3CRR8zB2DQMwb+gxzIYNA907ljXEpg0DkFMv3azGDQGJ8mgOTMYNABa92MUE0g0BuY7BRbEaDQKYWKedvSoNAhTO1c3NOg0B+R765lWaDQBXwhAvigYNAOEQzdJX4g0Cah+Fa/2iEQIwkYSH+cIRAOm4PgPh4hEDQxh9iatmEQB49Rd1q4YRAVmOh72LphED6DutmNvGEQG70Gm5j8YRA0N2vPDT5hEDcR9JuWfmEQNCQTHMqAYVAdN82dV0BhUDdqGpqoGGFQDLLleKgaYVAjPA9LppxhUDAPWWFmnmFQDIV7CqXgYVAw0h6DqKKhUDmSvcrqZKFQKzmekWWmoVAxqwJjq6ahUB9SsYnnaKFQHRVV/QluYZAxdafryPBhkBBY25RDxaHQLbdTzKRKYdASD7UxJIxh0Cq/UnYijmHQJbdVrlcQYdABgbdhItBh0C301/JX0mHQDRhc47gdYdAAfLNVMexh0BQzFZkx7mHQC5xJJ3AwYdA/UHzUL3Rh0B5myVv4l2IQKscLSrmYYhAwuR5HeVliEDpeMBi4WmIQKNzDU00gIhAhgjjX0eCiEB+qOOw0IKIQBgcDnmKhYhArI3ZwimIiEA= 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hUC/XwBdoGGFQGbgaKWgaYVACGEg4plxhUCHmtEEmnmFQGwL/wORgYVA3EaNuiO5hkBOrJ1kkSmHQFzzyqqRMYdAzgs0u4k5h0CosIKTXEGHQMbKsHvHsYdAUjaWrsi5h0CnuZS6v8GHQG73qL/CyYdA6MVdUEAWiEAMn1zkQhqIQH43hojlYYhAB5z4heJpiECvoe8t0YKIQHuGlPZ+hYhA3HYIXCyIiECMm/oJ2IqIQA== 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h0Be+IQByLGHQOxwWvvGuYdAZDpy37/Bh0D8ub9Pv8mHQKAKdL4/FohAscg0GEMaiEBGmYEBQx6IQLCBO8rjXYhA5u5DYtCCiEBiJ32BgoWIQJFxQ0IqiIhAERn44zGOiEDkWqiGOJKIQA== 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g0AZxPCKr+aDQMzEV2j/aIRAZBhoJP5whEB1xjkg9niEQPL+k2xq2YRA7zGgl2rhhED5QsbdYumEQBZqGBB76YRAX36yzzXxhEDa5FIeYvGEQNMl5S81+YRAenK8pVz5hEA/cCDBLQGFQAjEt3WgYYVAlnH4mKBphUA2VdPbmXGFQITr7NO0c4VASOwuFJh5hUAkIGt/4n2FQNgdFw2TgYVA4IbOqia5hkCErAIJJsGGQP4mB3LW6oZAwB+IbZEph0DuirOxkDGHQGzuUOWKOYdAJLuJRl1Bh0B9fXtijkGHQICMiGbHsYdAWT7CCsm5h0CS4wsBwsGHQBosMt+/yYdAEuAc0D8WiEDKjNgpQxqIQAn+3JhDHohA/mjtB0YiiEDa1nL+4l2IQLSgqqLPgohA6HW/S3+FiEDGoYTSLIiIQEbNPKUxkohA 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g0DHL5/PcEqDQNDD0KnjgYNAK7G8MuqZg0Ag86zP7J2DQMhxhJXwoYNAKs0GEDDDg0B2aAkolPiDQMqmGnH/aIRAHcQ9zABxhEC26bKA+niEQNVT0Glq2YRABi4txmrhhEC+2VM2Y+mEQDCrPp018YRAjYAemGPxhEDQYjv2NPmEQAqIvL9d+YRA1IsX+ywBhUBbSFapoGGFQG7uxLWgaYVA6jlsrplxhUDD8p7ns3OFQJWviYuaeYVAGDsfs3BqhkDo4S7sJbmGQJAuHqyRKYdAtvLSj5Exh0BqrsHDijmHQLFeY1hfQYdA7KKLvIlBh0AGLd6Mx7GHQMidFBbHuYdADyASrMDBh0BIejVwvsmHQJjNp5UC/4dAeP0kZEAWiEAPbXmGQRqIQDKFNsJHHohAcEUsNeNdiEBFWcwjldqIQBO2lfhA3YhAQvKHKu7fiEA= 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gkDfgRJbMrGCQKBKwsyjsoJA47tg2675gkAlrkM46A6DQJQvmox5EYNAKC25/8cVg0CjqSalehmDQBTsp7HLGYNAMoWPKtEdg0DK+awlcyGDQK5DuuQNJoNAGjBH63Epg0BV0dTSECqDQBD76d4DLoNAo44enxYug0DuJ/uRazGDQP/Zd1WCMYNAxiPOiZQxg0BgGYFo6jaDQMrN5+FrRoNAVrXn9+WBg0BUfard6ZmDQFz2JeTsnYNAgdmAn/Ghg0BpBl1FkgCEQOyf7QL/aIRA7ETq8ABxhEAuYDMF+HiEQMbgK7DZpYRA9p5oKmrZhEDcaeR3auGEQBJQ2hJj6YRA8SInUX3phECWwNxyNfGEQHaooK1i8YRAfIbnSDP5hEDuL7hfWvmEQA7AppEtAYVAfOIEsDsDhUBmKZ7pQQuFQIv0pmOgYYVADFiESqBphUBWbYGbmHGFQEXhtRGyc4VArHcQdph5hUDEJktNkoGFQGBHSBIouYZAxAM8xSfBhkBQVemJlNKGQCzxJ72X1oZA3uam45vahkCkKOAvoN6GQOgZ5xGRKYdArC9zp5Axh0DUKC7giTmHQBglNXZeQYdAxPRoWItBh0CW6Szxx7GHQDLXTuHFuYdAB12aU7/Bh0AfJePpwMmHQKIKCClAFohAfMS5PEQaiEDOhFt+RB6IQBjw5OjVgohASVcZcn6FiECQYwCLk9qIQCi1DnhD3YhAKPKVG+3fiEA= 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gUDRmobQ5TGBQOMXCD+8OYFAOLZ12fNFgUA00ThM90mBQFAoS9grXYFAhGNQ4SthgUDdR03cYH+BQKTmqwEEioFAwMPTNFaPgUAedv7FVZeBQHic1XFTn4FA6hoWngmhgUCwOYFy56GBQN7XG9oNpYFAgclZdW6ogUB2CB95BamBQHBDsGz1qYFAhPIz4EqvgUBYFpcjBrKBQPxXaFwKtoFARG5Hgn3RgUAEg55HfdOBQPXSVKAT2YFA9oHIYiT0gUDECANO4faBQFBDxFgADoJAUJIzdgQSgkBY8UF92BiCQM7Gu7PWIIJASsGVltEogkCCz1sToSmCQP7wUfilLYJAaXG4Deg5gkBM8uKIQ4mCQGie6D7UjoJAHNeaikORgkD5bWk9fpGCQL5jbLo8mYJAkuSw/FWZgkABKt4rD6GCQOKqgNU6oYJAfOrYPA6pgkCksPGqMqmCQPaFy9wIsYJAa6AhMK75gkAmSDAfPAyDQG5KG3voDoNAnsv+onkRg0BaLhCZyBWDQMg8U2J6GYNA9sqLlMwZg0AmuIuuciGDQAqnoTSLIYNA2jkXuw0mg0CZ4cAlcimDQNxxLhMRKoNAovcfkGgxg0Cv0QL6lDGDQPm7dfVANINAfzAycWxGg0BPjzcl54GDQGDT8+rpmYNA8DCl/eudg0DA+9qrlfiDQHbqPJH/aIRAZiRduP5whEAMWCyb9niEQJn16hLZpYRAgKQpYmrZhEAsk9RxauGEQLxrEOli6YRA2CWNDTbxhECVUzS9ZPGEQCSWF242+YRAWu0wXGD5hEBIywbDMAGFQJ5B8Wk8A4VAbLpOsKBhhUCBRROloGmFQJCwfTKacYVAO/SgN7VzhUDtLl0NmnmFQKLoWDvgfYVACI8V5t+NhUACFwe145GFQBcu2Nzl+YVAHgdE7SguhkAkcy63JrmGQGJvbfCU0oZAZFqor5jWhkDU79VxmtqGQPJu5W+RKYdAfqZuxpAxh0C+INIjijmHQLb1g5KKQYdAhk7oSVhJh0Cmcu9KyLGHQBuaOBPLuYdAQ2pxPb/Bh0BIDoWWwcmHQJQLB33s4YdAwMWXHVMKiEC8qjyXPhaIQHLHm5ZDGohAT8PZu0MiiEBsXD29sYSIQA== 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gEAkdABNStmAQHCZuiFe2YBAfhuFqEXhgEA+++6t5eGAQGDDlsP36IBAWAeOckrvgEDuKaWFPfOAQEzoPW5A/4BATiFsOZECgUAaJiU6lQaBQGQZbFxAB4FAFs0tg/MMgUAUoyhnPA+BQB6qLMzzGYFAYWsJ7eMegUDw8/efNB+BQC4GvFv1IYFAQZM+GPYlgUAiPPXCNSeBQMqxMPvkLIFA0UFrseUtgUA0CtU35C+BQAiXNtq9MYFAUfBu1yc/gUBwRL4z9EWBQApYg3P4SYFA3rKUdPxNgUAU6ER4K12BQEINAo4tYYFA6lKMzYqCgUD+0Kj8A4qBQJrOMP0IjoFAXHTfaVaPgUC0/KraVJeBQDnnPhRUn4FAUTQhXQqhgUAjjL3n56GBQHhmkboOpYFAxLNskm2ogUDcUjKuBKmBQOrqvKhKr4FAS3ATYQaygUCChsrcCLaBQJbrwBdKt4FAjsDITn3RgUCKZijUfdOBQFybC/4T2YFARqJRc9H2gUD4n/YaBAmCQGTBnqEADoJA6Te04wMSgkBM+3PEBhaCQGTI05/YGIJA/qL32dcggkD0HYbJ0CiCQGzp5PmhKYJACDWmAaUtgkDsfte4pTCCQDjqa7zQMIJAbE4qL+c5gkAw0Mg87D2CQLgjWiVqdIJA4bs3tkOJgkBe0f4iRJGCQMzFJwA9mYJAcuZZW1aZgkBs+MqID6GCQEus0QM8oYJAsiIGtA+pgkBnZMuUNKmCQMwQisEIsYJAaG9b+Qa5gkDlW0Bkr/mCQHqxIeC4AoNA8kKd8HkRg0CwjPooxxWDQMkPjSJ6GYNAfkAdsnMhg0Ai/UaNDSaDQM5kkKdxKYNAyX+S0hEqg0BZeKPjGS6DQLxbkQ5sMYNAHkDp744xg0BC8eHs5IGDQId/VcPtmYNAQP5w/5T4g0BXim+r/2iEQJK+aHv+cIRA6KzmrW1yhED28y4e+HiEQP5jc4Zq2YRAJDAfeWrhhEBQI0K0YumEQP44ZN018YRAGNXaw2LxhEACrC8ANPmEQCLEhVta+YRAWj5PNDEBhUC7xkAqMQmFQFbuZg2hYYVAZEAMwaBphUASS03MmXGFQImYlo20c4VAuBO6yph5hUCm5FCrkoGFQDxvE7zfjYVAnH3tYOKRhUAoR4+P4pWFQMlinx7yqoVAfi6NGuf5hUD3Qx3L6f2FQIjvJl3oioZAYHEbAfKShkAyI6AzJrmGQCwQcD6V0oZAfktKn5rWhkC8AwlRm9qGQEq14eaRKYdAKNV3jpExh0CsxE3iizmHQGUQOlZcQYdAeGXeBIlBh0Dn2tAwx7GHQIR1q2PHuYdA0Vc2l8LBh0CbR2Oaw8mHQD6cubFQCohA1x65Q1UOiECgApzjPRaIQNlJhkxDGohA 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gEChwvBuDlmAQEz01iY2WYBAmITs1ddZgEBcN2ocKm+AQEaFAzEEcYBArA4iHQh1gEB8hdvaHo+AQAKCXzmGqYBAtzYW7waqgEAxhXp0UsGAQOLUd3NTyYBANyDgfajMgEDFrYoTV8+AQNRZDawt0YBAKCrQdUzRgEBaJxZcYdGAQEcMb28C0oBAiz2AChzYgEC4ep7NSdmAQOJPcshE4YBAtI3+79HhgED3aS5JMOaAQH4Ckkr36IBAXs7HqUHpgEAyMcK2S++AQGaFZNM+84BANY51yz//gEDgozRaQAeBQHPx7IY+D4FAviheJTQfgUCAK34QMyeBQDlTVwfuLYFAyJ1tL70xgUDky1899EWBQLO1KYv3SYFAKgi7Re9NgUCFfMAyXFiBQDl7jTwpXYFANE4Cdi1hgUA2JOGrX3+BQIw9cNVVj4FA1Jydf1aXgUCI7rjiUp+BQKgOnh0LoYFABJNWyeehgUDlz4ZabaiBQGir+XbtqYFAW+TZBUqvgUAuGdI9abCBQKKb/CIGsoFA4/MygQi2gUAcT0ZRTreBQGRFELnIzoFAxOTlyH3TgUDWZcZEE9mBQJCbFFoX3YFAWDt3R7LygUAw5R+tAwmCQMQwAncADoJAxHBhXwQSgkC1j4UYBRaCQBRabEjYGIJAaI8BAE4agkDrAqam1iCCQG7GM4zRKIJAEjgVXaIpgkAgFT8ioy2CQNYMiQPQMIJAp55d1ec5gkCModvN6j2CQJh1Z20GWoJAWN5lJEOJgkCoxaCIQ5GCQBb3qF88mYJAdPZQ5Q6hgkBOetRWO6GCQEWEgH8MqYJAMgyCNDSpgkBeWcU7abCCQATvFrwHsYJA+HgdEq35gkAF0V5OuAKDQO51lbjkDoNA680ce3kRg0DeEOolehmDQMLOBs9yIYNAYH8rXYshg0CAql23DCaDQJjaKXdyKYNA2m8pgg8qg0AJa9qnajGDQFIUqBFFNINAum2HF5g5g0DqdzsJa0aDQJAUsX7jgYNAlJl5QJiNg0DJB73BlPiDQLZ20l//aIRA6g1y9P1whEAaLrhk9niEQIw9G0Nq2YRAAHSDFGrhhEAknXZrY+mEQBwFx4U28YRALteLumHxhEBM+L43NfmEQFCmVf4wAYVA/GSRWaBhhUDMlHSXoGmFQE5ygDuZcYVAy1FEfbNzhUAK1p2gl3mFQI4jJZCRgYVAKHvUot6NhUA+Iwjt4ZGFQD2LE7LllYVAuHq3xeKZhUDYG+KI8aqFQAAiQlj5soVAHAyiSOj5hUCiFSQF6IqGQJg5Ck0luYZATkCFAJbShkCKcDe/kCmHQKBrL6OQMYdAYDZ0tYs5h0ANREfTW0GHQF4WKAGMQYdAcLhAO8exh0CixOFqyLmHQFo69+DAwYdAlShj7FQOiEDaxZgVPhaIQN+Rv0ZCGohAsOPnh0MeiEA= 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hEAcL4o/sJmEQKzX9F9q2YRA0NOOnmrhhECix/13Y+mEQE7LA4418YRAmSyf6mDxhEBIzVeqNfmEQIIEieguAYVACL7WdKBhhUDOHktLoGmFQOShH3KZcYVAPMR86Jd5hUBkxAIZ342FQE6VdpjikYVASVPf4eaVhUA+pcoZ45mFQEj3bE7m+YVAxZPgCur9hUBSA0Uym5KGQFISA8mdloZAG2XdxqKahkD+cjZiJrmGQKqrgmCV0oZAnMiBM5jWhkDVNUadm9qGQIoo+56b3oZAUs+NZJEph0CgD/K4kTGHQKxGWxuIOYdAFG2kxFxBh0DWUujDjEGHQKyamIFfSYdAfb76xMexh0AkpolVyrmHQM41epi9wYdAHcLgNT4WiED3pf1FQhqIQK/nwYEjlohAzt0aSSWaiEA= 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h0DMAGPeQRaIQGokOmXYSohAm83FGyOWiEDQx1f1JpqIQA== 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QKpjRGBVMUTqOkVFB+p3RInMO0e4SARGm8iYRNyUgUQJIXlGmuoiRfz0OkQDX/ZG+WKLREqjY0SV/7VECf1LRaN3qUQU8WZEcmxrRfBbtURjm41E0aB2RGcqkkQh8t5F5HykRJRw2UQZjepEoP//RqHbWEQ7h0pEL7xURIX+EEbnRm9FUr1BRTdBqUWlVPFE9UaqRLxCR0TYaA5FbnjMRsU0T0RTKlVE29BuRP14cETSzotFq1OZREbzTETlerlEF1SYRDoBWkSGj2xEHZnaRD2cdUXCbz5EG1TsRVHHI0STTnJFJYKLRIA8XUSOOJVETpbfRMTxdUTwM39EjxFTRFO8+USHldpGMabcRJnnsEQ/24dELIetRG1IBUU1FkRHRwoYRV3rPEb7h9ZFliraRbGm+kSJZBFFSnZZRMwNUkW5x8REwTCpRCGIjEX8bitFfkGLRHMEC0WeDH5E9mxiRMf9lkQZUopFy51GRB8jdUTt2OlFAL86RO6a4UR+ioJEQuylRKgMokQwvl9GLeCWRAIUe0QUhjNFyopPRBlrT0RkOB9FOpCvRPACPET4DUVEmmdARI40jESZtatEIv+oRGe7ekUj6VNFKSCXRIqUFEVqdq5EDWuCRQeNOkcd+ZlFxl5zRKgrLkXHFPZFT8FrRAkskUQA7KlFzdtPRDpcPER5DItEQ3mCRJ6apkTYCOBEpE2AROcRkkTWgN1EsLlaRIfqTUSn6XhEwmigRCgWkUR9wNhEiHu2RM7cZ0QdbZdEv+s+RGNSWURVrd9EaE2zRLYZfkQtGzJFciqQRQp9X0SVqWtE+sNkRIbwO0SijFhE/WmIRJp6bEZ2qYFEQ4DzRJceEUWQvfJE8NloRbubPkZSCVpF/D8QRYrWo0SYLnVEd0ihRMy3akSGpf1E1xNzRLZJsUQLXbxEnAwORYjpr0RJ2SRFup1ORmLYxESp0ZJFnLCWRANvJ0a+USJFDNJvRFABUkTkP4xEilRiRJh5VERP1XZEQgxbRP9dQ0QOszBE/QXrRChmmERusEBEjrOLRG+h0EQ2VHxEwM+wRcS2bkSqs9dETPjLRHMBpUSdY+dE7IIHRuJrK0asea9G9+1NRQ/ToUTTz59FFWs5RbUyf0RfOzJFlL6RRtX7z0Q0jwVGWAlQRRtgQURyJJtEAmxKRL57pUQ8SaVEe6s+RhRWS0R85t5E6l0vRY8Ar0VmQ59FOePiRBznSUS+eJ1EUTowRbuvNkRqVaBEWo0MRTaW3UTG/1FG1wR+RiF2ZEZBx9VFsif2RdACuUTG/SxFNL7KRGm6RkSDW5NEj17jR7SF0kROTbhGZR3UROLAtkbDcVdEqQQpRbCsbUYCsO9ETrLuRC87ykTu12lEUtmERJOKJUVWGptEfQNVRNwQAkUi9l5EXz/YRJ2Nd0Qpxl5EJGuZRATW60TNuMBEwIeLRCDpkkSHbVxE64MsRrfYz0UoAv5Fnn6+RCHtDkVqiZxEBYmDROhEFkfZVZJFC5RKRd1PX0SwTndEzf2+ROaL1UQWQR5GVZ9uRXcpQUZfB69ECrOMRW6k70Sw4ZhETpXXRANZBkWetoBElm9dRDN6K0kKrvBEF6btROCWMUhLfYBEH5QaRdMceUYrPI9EEXzcRHYTNUQwX45FKKwnRTJHo0TqZGFFrddnRSZ4a0RkGjxEEOMFRqs/4kTvB4xE1E3jRIrSSUVgFnpEzE1tRENPeERvcpxGQyHsRXrh70TWGJtEfnVRRRl2pUU2volFeKnMRCSRkkThbdtEzPM+SNJRmEcPnUFEWrrrRib9v0TMsoNGviU1RW3KDEXg1qxElXS0RLL7VUSvaypGrSyORDJot0Qeqn9EgDyqRbdtRURS9eVE5rtbRN7960QTyJ5EHCOcRETue0jlKypITwecR579L0YqyxdFgGi/RDgcrERuzRBG3VIURWoZVUTFe/JEAb/dRGgVVUTeOutEr9ZfRVYtSkT9DhBH7fBcRqqdwUQ4WvxEmU5rRvDsjUTxthxGERIVRV5uiEXASSlEGBVhRUrApkV+oW1EQyCuRFNvo0X7SnlEJcJVRKBmfUSDxohEII1zRHXlD0WQ2cBHtYPxREd72EawfwxFA82SRth/OEW75YxEwLteRO7RI0RxApNECwIaRSKRmkR9SEVEeplmRCq9k0QdELtEjctCRGnoe0RHIE1EIh/QRJ7qkETInXlEhR3FRApZikQgpNhEsxc5RNr5m0Qj/ZlEKvs9RARvvkSizVREun25RtkNz0T0sbNEP5UNRmISuETpeOFEK/SfRWten0TOklVED9IjRucgwkTdgZ1Ev0Y3RTkeFkUL+FREeVF1RCUm8kWoQlVFxxVNRHIfS0Z/Vj1GcZ5oRd/M+EQ850hEA6JaRKuKQkTi0b5EV6jrRM8uZkZD9kdF516lRMAaVkQEIH9EfTtaR81lJUda2ZhGv1KhRZHY6UR+pvxEo16hR+Z9GkdNkANGxliyRPwdiEW7l3hFlkyFRJlXw0RM5TtFZYiBRIp9DEVWLI9E0o3TREOJW0SVJlhISVfIR8MXVkRdyhhFcc5cRBbmXEcJ3FREvTWYRO6hLkbQ54RFYUabRKG2hkUYvchET6t3RDkzokRGGpZEJherRLQeD0aCqP5EtmggRYvzOkW1Zm9FezWQRVNdwUSoAepE/f4bRcMYV0RWIDZGsQcJRX5+u0SUt0lENid8RCvMgUTcjpNEocqORPfvPUWMPGZFEYqTRP11E0YjHHdGxprDRbURb0XJst1Ef16ARHQQhESDAH9FTuDQRApe8EQmHIxEghVVRR3d6kRU+cZEwD+7RB5OMUVodV1E4AwRRafVT0QzFqpEoJBiRAZTB0h0/pdHmkdbRrrRZ0QFYEdFkFAuRavNkkTXeSVGmc8zRfzq8UQlQ1VF4glhRXE/jES201tEWHpnROUlEkWdXqtEPaTjRDoKwUfV8p5EOh1TRICsJ0dAnY1EGOtmRKeL10akF7JFAjS5RdW7xEQTlxBFVN2XRPfYzURKqrZHojwwR+slskbJgg9FyX1TRQUk20U0oq5EHlj7RBGzO0T5ApNEVP3FRt+kbEYcL1pFx1/SRZ/cGkVyJBZFWqCKRNl1lkQMs4pE6UXyRiEGgkZftxFGfYCKRXnXOkXPsqxEty2KRBW3v0Y1sGpGsygPRlytTUVQ+6hE7QjARNELmERtU9BEBBS6RMhXkUTLhiZGXNajRX8ZNkW8HINEFtrWRACPXUQTpkFEpDNoRKIi/UVee2JFnWnvRLigjES/oGJEIYENRRBr6URHC7ZE3aSeRA== 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gkB+o4Dt442CQAK66LpDkYJAojDjg+aRgkBu9HGJPJmCQLfGWksOoYJAdv82NTuhgkDoZM04C6mCQBY4YvwzqYJAVkxbcwexgkAmFVdkeRGDQEZiZvZ5GYNAEJtL43Ihg0CW24cCiSGDQEZdFt4MJoNAvWys4XEpg0B0OGiZESqDQER6SNBpMYNAtRtXdOaBg0D4rX9JSYODQAnUQ/qT+INAaiZPk3AyhEA3k4T0czaEQGz3JDbHOYRA2jdcSnk6hEA6g/Z2yj2EQFhm+Ki/QYRAYKaUaMZFhEDCNJcj/2iEQDh3a3z/cIRAlhWULPh4hEDs9B0batmEQL4Go51q4YRAD29DvWLphEBYOAAVNvGEQHTknSJi8YRA8Az57TH5hEDNUYxvG/2EQNCzjLq6LYVAqMePpNNJhUDE481LoGGFQJqE7gGhaYVArqdVG5hxhUCKWj9vsnGFQIzL0v+0c4VAskNd/Jl5hUCbsoU0koGFQGAYsWTl+YVAoxsT1ZmShkD+wnrvnJaGQOqBqGKfmoZA6kL7O6OehkDBT7Gcp6KGQOoAsIcnuYZAZi8sT5TShkBNkCN4tu2GQBiqVfyQKYdA4rQIZpExh0DBKVzWiTmHQBKSehlZQYdAaLzyLIxBh0ARxE5Yx7GHQEEj2uLGuYdAIn6Wy8LBh0A93SlpxcmHQAxyV7jRYYhAeIWQWNVliECG+Sfwzm2IQHxOnWo1iohABTDGBCOWiEDQ5fvvI5qIQCDO7PEqnohA 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hkBJ7ElIn6aGQPGeY0MluYZAQhIWF5PShkBeRQyZkSmHQG1VuDeRMYdARxoXI4s5h0D+xymfWUmHQCkWzjB7UodAZdqoNB1eh0CxsOK7rYKHQLk64AazhodAqF//fcexh0BjG0XMyLmHQOiUOBfAwYdAnko7f8HJh0A21iZk0WGIQMgryr/VZYhAxIMx5DSKiEDANNClOI6IQEjkwW8hlohAoNcMHCWaiEA= 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h0DQjM2beFKHQIaERXvHsYdAZMmuNsm5h0DmtFriwsGHQNoPuLO/yYdAvj5RxyGWiEBnBVFCJpqIQA== 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h0ByDd+Bx7mHQBya+gu/wYdApXFpEyGWiEA= 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gECru/AxMBSAQAAoL+vcFoBAcpf/nYAZgEDh6Y4GAyaAQEz+N3UIKoBAhrLj6vNIgECO0or+1U2AQPRm7vHmUIBA0MMwtBRRgEB9RS3LDlmAQKvHEa1HWoBAp82O70xegEBixkBz32CAQE5D90QMYYBA7hrhnsVmgEBukRAacmmAQANfb1wgbIBAI6ueAsRugEDtK+2jKG+AQDhutRrgdYBA7ALp6CV3gEAYPhqPMYSAQDxy6uLehoBAW5zI5h6PgEC+Bg48H5eAQD7DEpzQpYBAGgZsr4epgED/xyrd0a6AQCbL/SISr4BABJ0Hfgi0gEB7P+GQUsGAQLhScnFTyYBAkAJGm6jMgEDyEIwqVc+AQEZ+d3wr0YBAY51Lp0zRgECyT1FTYdGAQAUTckRK2YBAAGuVokThgEBagDVE9uiAQI6N/oZL74BAjG7s3T/zgEAyNnjwP/+AQMyqe7tAB4FAIKiwcGgJgUC3w0M9FAyBQF4svqD3DIFAA64HdMEOgUAvUVGXPQ+BQNWCTwnoD4FA4oR38GQRgUCDAoe0mBGBQNmRQ7HlHYFAgJSZqDQfgUAi9xYDMyeBQI+/1+S2J4FAbroY2PEtgUBPZK0L6C6BQIxCcCAxL4FAfrWXY+QvgUAcQpKZvDGBQKCHRVMpP4FAys6U+5FBgUDhLXmk80WBQMztf7rkSYFAwPKZuudNgUDeLPAKM1KBQKTRPkfCaYFAUMZwx8B0gUBC1BpUbneBQNgcWpIZeoFAkFA0p2F/gUAHs/riVY+BQLrh1olWl4FALnW8dFKfgUDWdl7Z56GBQBiUxwzXpoFA0lyIOGyogUBOrwtMg6mBQPn9Vp3tqYFATLHj6iqsgUBI457p1K6BQGkXiqNKr4FAOOhuPQmygUAgIqWsSreBQODqxOEEzoFAQi2IbAnSgUCeKCNWBAmCQAZ28qh/DoJA7QkC0dgYgkBVnVlB1yCCQLaT5QLRKIJAHmK6TqIpgkBQOVicpy2CQG6zh/ejMIJAh2D7FOg5gkDSzeb66j2CQNkIxSzpTYJAJFvsHQZOgkCKGSq861GCQNCfuXjoVYJA+BHiyOVZgkBt7P0U+lmCQIgat0zqXYJArkYluAxigkDtitkpC2qCQNK1ZrIbdoJAkWZ2ECF6gkDMynn7D36CQEYv/NUogoJAhC688BmFgkDMAO46Q4mCQBqPS0LkjYJAAFh6vEORgkA2Tr9o55GCQMeNw4s8mYJAq95XSVWZgkBuhLAwD6GCQAD5Ts06oYJA1GJxKw+pgkC8Q14zNKmCQCAPK6UGsYJAY/dVqZ67gkAsdgP9rfmCQOOq3bZ5EYNAJPKzbXoZg0A3QqBgtBmDQBGD0kpzIYNAAnSER44hg0CdGJrSDSaDQAbIPuFxKYNA9kfb1xAqg0Dm2/fivSuDQBq22lKoMINAfhZrhmoxg0DmKR6ugjGDQCB6AKbFM4NAiOz58Z1Ag0AGnjSR3EGDQEI1V/zSQ4NACnBAK99Fg0Bi2O9N5oGDQPL4ls5Ig4NAvif/qMaOg0CmUcoG3puDQP1ZARNaoINAyCsMReSjg0CyKgFn87ODQFZPD439u4NAcGFTiN69g0Co4x6llfiDQBzGitm9AYRAVj7kCMEFhEAncL0XxgmEQFqjKy3+C4RAZGwQ3LYNhEAtL3nDyg2EQKpW3LgEFIRASDgDnsEhhEAGJX37FCSEQBNzGrkaLIRAnH12MXAyhEAkVsrVczaEQOhRggnGOYRAStwc8g1KhEC0mDVEy1WEQIj0nZv/aIRA45JEb/5whED9JsPM9XiEQN42a8nKgIRAkgJR9feAhEAMT/uZ94GEQNz72XBhiIRAP20lizSUhEB0mQJ/cqKEQFon9v8WzYRAVre8JcTPhEDaL64patmEQCh9tvNp4YRAHCmACWPphEAxIgGQNvGEQM7DGhhj8YRA4gpRZL/3hECAfAXPMvmEQDC/R5RZ+YRAlvD7E2n6hECqn+iAoGGFQGoYMJegaYVAENsh05hxhUBi5fLLmHmFQAR96LuQgYVALoBg323mhUCgxFyAPCOGQCTTXDdEK4ZAOpmubHouhkDWVMWKfTKGQFy2gPiZkoZAktvERJ2WhkAylUq8n5qGQHay55ygnoZAFGQidia5hkBYHTqnKMGGQHulQ2iRKYdADPIF8ZExh0AOumFIizmHQET3TipbQYdAjfWGG4xBh0B4YPcEeVKHQOwT7o99VodAtppirKyqh0BclctQrq6HQHAtqVXHsYdAsOmRrMe5h0BYDrFKjL6HQEYiIpi/wYdAuh4xHcPJh0Boem0MIZaIQCLHmQtEwohA 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h0DUuHBWiUGHQIvyJVmqqodAZEueOa+uh0AbMQD7x7GHQMqdmR/IuYdAHCnTPcLBh0DPUpXZIJaIQA== 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hUCU9KfAmXmFQAn3TkNHpoVALEL4ruAZhkAYM6L05R2GQI76b5uhVoZArXFfeopahkDcc97Qjl6GQHyeY9InuYZAZ73RWc7ahkDM328IfRKHQIunjc6AFodAhfZDKpEph0BsXKRnkjGHQG6OrlKKOYdAADtQrV1Bh0AJApGijEGHQMq+uyZnW4dAeBrO7m5jh0AY2vPMcWuHQK73KtnHsYdAIqYd0Me5h0AAl11dv8GHQAiGfmEA3odA 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hEC2QDHzU7KEQEKbDHGxtYRAyQIqdle2hEDhGv9IatmEQLETsRBr4YRAAo+VhWLphEAWp0+RNvGEQPBQ4xZj8YRAxjcgTb73hEBvVNORNPmEQKQXGeNv+oRAYDJWWB39hEAHiZZcMAGFQPRmu8qgYYVABB8CoqBphUD6qbmkmXGFQIhAU4G0c4VAOC/POZx5hUCG3sGMkIGFQBn0AvuiVoZAOQweFotahkBu7F0hkl6GQFBl6g3O2oZAPc2913oSh0BKzTTckCmHQJH2QIeSMYdAGyX9M4o5h0D+7DN6Z1uHQGY6RIVtY4dAyz+eT8exh0B5SrhcybmHQEDNxIC/wYdAxrsVzb3Jh0A= 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hEDuFKdXV7aEQHVloR1ZuoRAFIBRHhjNhEC7p4nBatmEQOIBiIdq4YRADDkgHGPphED6qqIxNvGEQH0DLrlg8YRA0EZAATb5hECuNOjsLgGFQIFjg5OgYYVAHvEC2KBphUC9IPUZmXGFQB+HTK+zc4VAJ+SQP5l5hUAS8WaydUWGQJ7pVtaiVoZAFnGi2qVahkBrjxDUJbmGQMi5Ib+QKYdAug6KFpIxh0BFPag2ijmHQHii9FmLQYdAtWAVY8exh0BNoy4yx7mHQJjuFpLBwYdA8qoMDcHJh0B+vKmQ/92HQMbPAohNPohA 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gUDFkt51IkKBQFJVoDnzRYFA2q7BvuNJgUCIkf926U2BQLDo/19cWIFA6EekUqRdgUCXQKEnw3SBQE+8ofkVfIFAKQ0SdGB/gUD+mRxRZoqBQNZCjVQUjYFA7Ff6gFWPgUDMmXorWJeBQO2VUBfnoYFAgPAKD9emgUAODv1PbqiBQKjbooCDqYFA4LPZw0mvgUBmisBfjraBQJiK1JxMt4FAPb7gZ3W4gUDcqKk9lLqBQEyN0IetyYFA3l/XUATKgUA3SXgossyBQJTKwGvNzYFAprD2FNHRgUBo0vNRfdOBQN6hpv+G5YFA+vWMI4vpgUDugEiKBO2BQNA7Zor58YFAsPz7Lf31gUCturAfAgmCQDhYNvXXGIJA2Jg6fdYggkDNKWq00SiCQO/mYqShKYJAkOQIFKMwgkB6oqo15TmCQH7R8FIjRIJAJt7OMtFGgkB0WkvNIUmCQDCro5ToTYJAeHr+++tRgkBs0Czr5FWCQMxJvSaAaYJACiZUsCtsgkAffukvQ4mCQGLvpaJDkYJARDZHjzyZgkDKZP6yDqGCQBA60h07oYJAHrBpNFiigkA6orv4DamCQJgQP+EyqYJAzv4P2fq5gkCQnSrU/b2CQIbXEpkAwoJARVksDPzFgkBkZ+lkJMqCQK/GJFPMzYJAsF07jM/RgkBax/TRxdWCQERjM07J2YJAU7oPbf/1gkAMI9xHA/qCQMYJr195EYNA9b/NzHkZg0BgTz3mciGDQDDBdvqJIYNAtr174gwmg0AT64MucSmDQOjTAqIPKoNAPgKvskYwg0BU72EkaTGDQHXDeNVsOYNAww/7DNxBg0C6H6qB30WDQG+S15LTSYNA8t9PjNpNg0D7I84Z5YGDQI7pLlJaoINA+DpPEGu+g0B0ogWGv8GDQFaMVoxtwoNAKONOdsLFg0BXkEyeu8mDQEYmyvC8zYNARaNkkUsOhEA7bLFyAhKEQLDlKRAIFoRAGqK0GwIuhEAAZm3MBDKEQFQaAP1wMoRA7CvSPXU2hECgK5E3/2iEQDhiQRr9cIRAyLaH4vd4hEDjjdW6cKKEQOlcBh8fpYRAg8Kt61OyhECSaLW7VraEQGvHj/5ZuoRAaM1qjxnNhEBiMmUFAs6EQNOs6VkE0oRABn7b42nZhEDO7jgaauGEQJ6KuqFi6YRAgoyyBDbxhECkCv5bY/GEQC+a64O994RAqNskszH5hEC2fjN0WvmEQLO606Rt+oRAzmsO3mUehUDG/x4OZyKFQPrJB0SgYYVAvsRI2KBphUBGP7RGmXGFQGezR9S0c4VAkt+ZVJl5hUDkmPWI/YqFQEzLXA1MqoVAVmoZDMiWhkDoPetF2uWGQPynwNWQKYdApn+RCZExh0CsN8jUiTmHQJK9soqwOodAhmIJx7I+h0DxzwckW0GHQAZYihmJQYdAz8ojgLpCh0B0yCaWkH6HQGxbRPsFnodAqDWpH8exh0D46fSgxrmHQD9VdefAwYdAjkYNXsLJh0DWUl3/Tj6IQA== 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B3uNRDEMfEXZ9ZhEYywtR88onEWjclJGMBRWRQUBJ0eX/IBEcEOcRAms7ETENYZEKbU/RScQDEXfLIRFIfxCRX01sUQyjK9EpdWWRDSQgEQTHvdEWqccR3fOjkRLnltGds1WRXrujUT7j6VEn2m9RMrbtEaJX3xETgKhRCjkZkQ8aIVFcg8RRZbvYES9c5FFkgrmRQ72ZUSBZFRFDvPfRFbFCEU/coVHzjXwROAHBEev961E0kUrRVqmgkVcQOdEHFvlRmSI8ERAb8dEGrb6RI6rOkdp58dE9dOcROhOGkb4NwRGXE7LRKj0v0Ur80RFBB6NRPjL3kRjDgpFfl+yRBePmUQtNbZEACSTRQ5WDkVEMh9FxM6WRVZR8kXkTbRE29ynRFCk4UQ8Z1JFpMXNRCwFh0YTU4BEsqCARCGtB0Wg7uxESqqFRCgpjkTEeZhE2GvsSGM7ZEXdGmdIrXluRYoVCUUnm41HXJppRLFB0kSXvShGiCt+REeZgEWGaidH+txpRMxemkXu2rlElpsxRaUuu0Xjz59EhE5kRVhJmkSAHo5E+tlbR1h2sERJQtREyOmQRJtCBkW5UTZF5mp/RH8gHEVVf5xGi4TjRaC/BEW6G71FL46VRHp6eEQbdVxFqijfRG7YqEQsMY1Eu155RPE1GkVGoqxEQSmdRZklrURC9D5GZ9ZaRaN7p0RU8IFE0BfwRNsgqUXInbhEzvC/ROeIpURNWn1E0ERrRcJQqEShgvdEAvm0RPs0k0QBT/xFWOLBRFHlvkXh8u1EoAy5Rrho1kQMLY1E219RSA64CUVKWJpEoJyOR/7foEQLZntEUDMaRvnMlUTQD8xEUtQURRDKFEb7zQ5F2XaORQUji0QZtghFd7HfRC2HBkWb8NFEVutIRTqkw0V0i4BELn+QRH38ykQY2S5FCJ4QRg9yjUZIwatGrPC4RXBLtUSko4NFskfcRLZ5AEWF4JdE/nVVRRgbyETQRjlFaoa4RGk7Kkal5sdG8k7DRKpSEEW8shxFMzmdRTmDK0V5CJhFKnSXRYugkURwEA9F2u2qRFuTmEQ3BEdFQPJ7RJveI0Wo975EK/PLRQ5djkZanmlF3RhtRbFoKEVFRFxF6MKjRHoS5UTF8epHUFsgRdNquUbHUeVEPlXJRiDvl0UX/VJGOHIKRZruN0ZMyx5FFX+hRIezJkVgziVFcI9kREXki0XdbKBEP9qKRTjRiUSkjNREaD6TRBWeSUTmEcBE3u+jRAjAOUX1xHJF2Q/DRfCh+0QTVclEM0gQRuR1R0XV4U9FBTMERuEZS0Y686xF83lARiYJzESuJ/xEgg1PRT55B0Uyb0lFW4zoRJfQMElhHplFAZlbRO2WXkVTMjRIIjXMRPwIh0TXqyNFdm93Rq4rukTRFeJEChvwREVTFUX1eXVEEIdPRYTMb0W7V5xEJlGrRDrr8kalxYhF/OmQRCy6O0VcEaREZB5ERnxm50WZWQxFY6mZREKEaEVbachFKuuLRBAnsESMXoFEB1MVRsyHMkUwn61Ewc+1RMd3mkce3plE8O2fRoUUzUSa2A5FWdeLRHSD0kRzsH1EuvKeRXNt/UTG6IZEne9sRldj70XGz59E5gobRWq9gUSFHpREgAzpRTEt3ET7A9lEhdAER9PNo0R4f4RGF2GBRbmNoURN6WJEpFPbRJsk5kQyAJZEx7kMRgEYkEaGrBJGPYGIRWYrK0UjQBJFekwhRamguUQ8EQ5FhtCgRP1LAUX95J5EMN0LRYabj0fF6DxHGUFJRjmTSEVjZBRFk7DkRBReY0WsYpFFmWxlRujZH0YKqT5FKiWwRDyAg0XGHGVFD2DIRaUtAEVYtJtGJYQURiFmnEWNe69FdEDfREd6iUTSDJFEUL6xRJLKtEUQHGFFtBK2RFoe8ERnHHpEWk2PRA1JzEeB301GXqD7RWpcH0X7vAlFhR/JRprsrkQ1kVVFO5KbRvKX10ZL0XVEguRiRdDcYkW87jlG5Em6RGPBmkTXYYRFDPFzRGwfiESgLoVEHUFARdPO40Ua2HlFnYYZRZpNmkQBGplG/U0PRqGgb0WGNxJFdqaFRLKA6UQ7cKxEG37+SCnDi0RbzX1IOS2qR3DmlESXpxZGs2ejRMvLR0VJ8WlEtZ4bRUpYhUS3OqNEBAOBRcnDKkZkL41Em+2pRNA0IEUnUt5EU48ERZnr3kX7y5BFVnzNRKF8qUTTzfZEacibRCD0WEVb4jJGOP4/RVBQXkUdFwdFepLRRHgOnEXtDvVE9LIERaLIM0bSTthFFpbfRGUSjkRoF5JEBzwARV73V0X/cgpF74KGRFlaxkVi1Y1EcvevRNb2Y0UJ9cxEyA8NRVijh0R6sG1EzcGDSE8DiEQw6vRHRsTtROWgEUUn/rpEHE+FR96dfEXNLFZGy3xCRUbQqUWk4ZVHq9utRAU6E0eG3mJFzESuRUy2nET1w4ZEcWlqRkOQO0X51LBE5J11RGDpr0QpMxNFTjzZRLbcgEbxmIBE/sN4RVZp80Th1M5EuFqkRKIgs0SIMLVERmS3RK4va0UscaFEOuFbRJ7E6kTu8Y5EXsuyRZiQ30TDJfBE4K7uRHDFI0X6Zh9FdychRZSAc0QYjw1F/7N9RErHhEUX05NE+Vz1ROrgnUTfqM9EcRGKRCy9wkR2KQNF+kAkRXMavERAUphFcQNFRZxFm0Q6E0BGTlt8RRrJQkXpKLFEJqmtRHaBuERkFDRFxqObRGxXgkTvgKhEYqC1RLumwUSZxWxEW73FR+7uKEckgMJGEjElRXz400WUgNlFGQ//RAB/IEWLV/BEVv0qR+lNuEbmjaFFG7tBRR6IDkf5vJJGsm+oRY4c60Tit/NE0GeCRvqzlURqI/RFcUe0RB0T6UdMPT9H2OIURwHeJEUwqwFGTdwDRZ0sEUUMGyNGljaWRcP8kkTy1y5FtSFKRTi9bEYDAt1Ezj8nRkRgEUU5p+dE+HkRRSH5vEUlXEZF2Oc0RQxbkURrJKxELznsRHxylkVqwQdF3nWeRL/iZESjZWJEO5HxREmo+0Timd9EwzDqRQtulkStLDJFRi7XRK6xm0T4vgpHG41rRjA8JEZ2oW9FOUSBRY9OvEQyAShFzJuiRDQsTUXQQXZEByodR4caeUYGIBpGPHfXRNiigkUwn6RETJICRs8yAEXlnm1EkvuSRL4FwERrRY5EYQUHRaEvUEXQEeJEGYskRg1ZlUXETRFFUQvvRYOqv0V7+ZVElqfERNyovER+orRFnjSnRSXNw0TDacdE/cbhRC7uD0bQvJJFLQiBRb+toEQXR5pE 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gkCwnog8+7mCQG5pqt39vYJA/hDnuwLCgkAQagOBBMaCQGDZTUfMzYJAZIBsDc/RgkB8w0gg//WCQPelgJit+YJA+DxoqgL6gkCSyT26Bv6CQIGAHnb9AYNAqjF6VAYOg0A1sn2ieRGDQDRjXO55GYNAzn5TqnIhg0AgZ9lRDSaDQIF6n6dxKYNAHAljbWwxg0B2dsF320GDQE7ZvUPfRYNAJlJfcFmgg0A65ch4wMGDQPKcFiDCxYNAlD6RhbjJg0CKPjfv8umDQOAS2/6T+INA3BcOkkoOhEB2VElqAhKEQFHvzs1OEoRA2YG/vwcWhECmjfzLAi6EQKZXI6MFMoRA/EukF3AyhEA97sDoBjaEQMTvkfFyNoRA0yAErgI6hEBEZXs+ezqEQMiP8a8DPoRAAHzH4v5ohEBO3gU8/3CEQJ5sSdX2eIRArplu9nCihECU0fY0H6WEQN4QuYDIp4RAaoovl1SyhEBJJ4lhsbWEQJWJgQVYtoRAEenTiRbNhED2BpcLyM+EQOjCdz5q2YRAIMdpymrhhED3GM2xYumEQKSi3AA28YRAfMypsGPxhEAanKkiNfmEQKzzvQBv+oRAPimeLTABhUAedETIZh6FQN0r3SpoIoVA2TEceTcyhUDH/aUhOzaFQDSnHoneWYVAXuL2UaBhhUDQ77LXoGmFQGE8HAr7boVArBk4p5lxhUCKBdZVtHOFQOQTr8iZeYVAq7d8SJCBhUCrk311/IqFQC6rIb8Dk4VA/aAB9QqbhUDJh7ZEbpKGQFyIqWlxloZAyFHcUouahkCm3JIfJrmGQHmPD55yyoZAIM+g+HwSh0DgunJ9fxaHQKC9D3N/GodAdm4udYEeh0Bl88ptgCKHQGwzyY2CJodAQYPmWJAph0AAQv6MjzGHQLaafceJOYdAGFQvYlxBh0Aby4dhikGHQMeeI4mPfodAyQ+GeZKCh0CGxkWclYaHQNAgaswCnodAVLUZlMexh0D7v3KdxrmHQOYhwZS+wYdAhE7vDsDJh0A1x2xeKuaHQKbw/7+UAohAmj45+faRiEA= 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5qYr0AzBckA+Xw1sb8FyQKaO62dUwnJAZOEPp6fCckB1tgn9O8NyQDSCzc7GxHJAWOj1elnFckA5FgCZAMhyQOTNHVlazXJAdPncgUPSckBFWETmdtNyQITFyY1l1HJAnNmous7fckAiQ7xatOByQB79lWxO4XJA0ksiYlDickDDJkEjkeJyQDTtfIgi43JAiOzpVMrxckAskYy3EfNyQBKlCUm383JALBUMMKP0ckCUxGpUwv9yQKD5scOUAHNACFyMlvgAc0CvnJ25jQFzQFwRXV07AnNAbb+7A9ACc0DoT/7F+gNzQIHCPp0jBXNAo2Bj3QoSc0Amy/XXvBJzQJ03sPkFFHNASa1G2kwUc0AI4Q1z9BdzQN4/S0lTHXNA19K4CXkfc0B/Xi1ApSBzQKCjbTQ5IXNApjhYE2Yhc0DmP7uJyiJzQGUTYQcQI3NALSYwBs8kc0C06uUESDFzQMa2OuRbMXNAujirTSEyc0CojS+ayTJzQPRWBC8MM3NAJr6V0tk0c0AYMVLEuz9zQFIa48zkQHNAf8RBF3lBc0AUm2bIJUJzQPm5ni+qQnNA1K0l3kJEc0CgMMpcekRzQJ5ke3BDU3NAHhrcM6NTc0CnupYKhlRzQFQurVX9X3NA4b6dWZBgc0CUfb2u+mJzQFpMlU45Y3NAEnCiztBjc0CKy67oK21zQIXTBxRKcXNA/Guiok9yc0Dy1P8gaHJzQNrx3Y+FcnNA5C7VcLh0c0DYpmqbTHVzQCPae1JtdnNA2UhOCc53c0BiFk8j33dzQNtt16krfXNARDVZRHmAc0AfKvKmpYBzQPrI5tsLgXNAZSYbG16Cc0DWm96c/4JzQHwUQ9WGkHNAzqxHTNSRc0Ao1HtcTZJzQKLdv3rOk3NAsnnTQ2CUc0CwrKiU95RzQELjePy4oHNAuo0u+1qic0AmD8dT5qJzQBTC5+YDpXNAljy9GMawc0B8x4S12LBzQP1hysF/sXNAEFnRjVuyc0COQRkVuLNzQACLNOAMtHNAvDnVbvfAc0AWH9gCOsJzQIeQvt/PwnNAiouTrybDc0Cit3qZwsNzQETct/7X0XNApD2SzLrSc0AV10n+3NJzQEQifbL303NARczoDuPUc0AVdLl2o+BzQJyxMbg44XNArsWAbmThc0DFf8E9jOFzQOaTLBTm4XNA3jRuwQ/jc0CyPNshfONzQPpRWcDs5HNAOa7Dhjj0c0AWZwyrxvdzQDz+WXXR93NAG5NFFeUAdEAs2yY1eQF0QM/l5cClAXRAqDfRVYsRdEAcVjtQTRN0QKQ+V/pLIHRALWecDLkhdEBp1EofZyJ0QM4+kqFUMHRA2NlKfuUydECeS1SZFjN0QJzx3E7iN3RAgm8FXTk9dEDCmOfAoEJ0QNKYp0N7Q3RAYvmbHKhDdEBJ5bGEPVJ0QGh1DM20U3RA7qjWaPhUdEAOpD1RjVV0QE0nkiTcV3RA2mzBNjVddEAudw5quGB0QDY9pJAHYnRAsMpPhI5idEBa2MZouGN0QD56ooNNZHRAAnqoHedndECG8vOzQW10QDaKmscicHRAOmeTE0FxdEAwotQqOnN0QHrttbKgdHRA77v15zd1dEAcPPEzRIB0QDH0yBj5gHRA7ikChk6BdED2AetvJoN0QBwteAgWkHRAuIvCaWqQdECkckbjUZN0QJKu7Kfvn3RA2Gq2Q9CgdEA915CyOaF0QCmRx1u5onRABbdBdA+jdEA6Buei+q90QChmHERvsnRAaZI+18q3dEC4Ibs1E710QMjdxvXdv3RAT9UAkeHAdECg2ExOeMF0QOWu/Y3pwXRAOfTvpOrCdEC8TOexesR0QMH103W5xHRAvML6aPzOdED0ez2yEtN0QNXGq3Lh03RAkKo19XfUdECNRYV8zdR0QMKtUW3a13RAT1c4MfvfdEB7IQFZt+B0QEQyqAoj4XRAJoNB5LfhdEDEX+0YT+V0QL4xhBVP6HRAWDH92L/wdEBoZMmijfN0QMdo3JCkAHVADiz6PdAAdUAhYhiVYwF1QBNbmD5dAnVActTUAfECdUDarFZMqgN1QPLB7EFqEnVAOInKKiMUdUA+uIZmnSJ1QLZQynvNNXVACpOZPJBCdUDOrQo30kJ1QLMfwCIoRHVA3Mfq7vFPdUDsk16UFlB1QEKXijh4VXVAFzkS4g1gdUAGD0JEOWF1QOY5V3ZlY3VAVHL9U3ZydUBaRMWsjXR1QMp+NxF5gXVAtDnAKK6BdUAmz2tdpoN1QA42sdWZhHVAyaOzkbuRdUD+CIq+bpJ1QOIyEe2AknVAprKWEwyTdUARKJWYO5N1QDRZ7nmzk3VAVUYlGRObdUAKfAtslaB1QIYzkp23oXVAZlT+c7mkdUCiO7ytZMF1QI9+nvL3wXVAXukp90bNdUAwLcb1odJ1QP91b4HM03VAZBhcSuTfdUDJyQZuDuF1QP1bUwUH4nVAdN4d8DfldUAol181TO11QAxxZAum8nVAyL8zVjrzdUBfBptGAvh1QOYwoDBNAXZAuFktVUgCdkAxi43i0AN2QAyPjupUEnZAcMItVk0UdkA+NPsiDxZ2QM6uuHOIInZAJwEyX5AkdkCE95a+Sit2QMotV5sfMXZAElLGRI4ydkA4OlhDUjN2QHIQLgGdNHZAhK+ZTB1BdkDlmkZ8m0J2QCXGaiLJQnZAjuhFG3pEdkDeCUgI/E52QEaDdP/KUHZAlUTEIhBRdkCX2GngzFR2QKRHFW//WnZANCseMVFjdkAeRklI22R2QOzUEjZVaHZARYLMp1BrdkAFODJTaoJ2QBpc93tqg3ZAMU44spCDdkDbPSgI+oR2QG64dlklk3ZAEN0rprawdkCGPYAawrJ2QB6zCqB4s3ZARCwmAg20dkACOiXw1Lh2QG4mxvLBwHZAltX9iE3BdkAgLNESHMR2QEDdpBLOznZAkQ+ein3QdkBV2WNQ8+F2QF7RFhoL83ZAkj16rJHzdkBWjXALNAJ3QMLgbhdRDXdAjhMKYgESd0BM4v9JkBJ3QCD19v2lEndAtAs0E+cXd0B0wARmkiB3QMzaYF8QIndAMm2qCXcid0Buqj04rC13QN65pjukM3dAVCejCA01d0DQ6yE0Jj53QEjZ9KacQXdAm6CQNPlBd0BjbwUW/EJ3QCjVw8F+Q3dAbJRvfxtFd0BaI4uYSlF3QHgVf6SSUXdAbgBwsghTd0CUrNrDKVV3QFqmctvVWndAFk/eOkhhd0BEf+ddkGF3QOGz1W36YndAXMWymjpld0AROWIDKG93QHZK69c+cXdAzBo5W4pxd0AccwQleYJ3QGzZBdsJkndAGBVr8iGSd0BaTmkOTpR3QAj1+lyrondAmHDa0baid0CCsOBqLcN3QC/O81CKy3dAbLopO+TQd0Bg53Gqy913QDYPW3/r4HdA7yGY+yPjd0BWB94r6fB3QNsDYaw/8ndA2uVfeE/yd0Ae4tl/PPZ3QLJ0fXd8+HdAJ+CTzksDeECxSC4/biJ4QIB81oaJI3hA0S8Bhr8jeEASLR3pzyN4QHL7lup8MnhA7lsFTwpCeEAbW1QBEkR4QD+0tzJ6RXhA0VaHQ8tReEA0ofvklFJ4QDrYTj0fVHhAXJaPTYdVeEDY44L+7ld4QIjHCUH5YHhA7Cxz77ZheEAw63lKuWJ4QEWAYJS/cXhAKNUxB/ZzeED8sLyCJHR4QGq+DMCLdHhAQG2iHT2BeEBDQquHpYF4QNTwvKuZhHhAQIPCrruEeEBs9o+jl5F4QDQsxranlHhADPngbPeyeEDFlYrD1LN4QA+MTkXbu3hAiu0m+s7AeECnzdN89cF4QF7JzaEGxXhAltuWirfUeECcQKhLNuJ4QM2kIJ4C9HhAOGbrCY31eEC0fvakXAN5QApkf6F8A3lA9iPmuQ4EeUBwG//VmwV5QFwOiS+8I3lAqMS3FPYjeUD/mvJlsyh5QGTyoMr8K3lAdJewjAsueUAyXKQ0JTN5QHrc8OzvN3lA1NR2q+Q4eUCmZE5giUJ5QJBSopH4QnlARxG3g/tDeUCiNKkelVN5QHz+eXYIVHlAKsjFlsxUeUB6r0CG22R5QOcCrUZCc3lAZ17mkBF0eUC2Gkzo9YF5QJCC7x1xjXlAdMJaCcqSeUDCgicEKJh5QJL2lCTBm3lAfLt9WDiieUCnC3adyaJ5QAf87NjHo3lAgNeJOM6reUBvnUNQwLJ5QIiN4ITPwnlAarcmp0HUeUDeJVphT+R5QDfC+qn7AnpAY/eY1/kFekC1+V7LpiN6QE4Tbaw6JHpAfl6HwAw1ekCmzpmiGkV6QHFUiljOS3pA4NBv0YJTekDOktPmJ1V6QNS2PU06ZXpA5nIiU4qDekCkbx6gAJR6QCbgwCsNtnpAOVq+kivDekBSS6qlGMZ6QPaoqTMOyHpArP1LPWfNekDui8HCQM56QGptkTrC0npAsPK/iQrYekBU+WB49uN6QCnTw/l65HpAIfLpQzbuekDY3BjRFfF6QGg8SdJe8XpAbKSGN0rzekB1bkbno/h6QIpSOkj//XpAFEeqAVcDe0CTDkBPdAN7QOJk9FN8BHtA1PtdtioOe0AsW/19cRF7QEkciKXDEntAATfUkUwVe0AKbNdAHRh7QH55cFBoHXtAhKnbKXode0B6pfnnWSV7QAfHtwfDM3tAy9So9JlIe0AdILSlC2R7QNO1rd4UbHtACADDc1iSe0B5eWa0IpV7QLwqg8XOnXtANh35KSeje0DQRUFUgqh7QJjaBWvJrXtADH7mPOOte0CgwDZpYbt7QD0wH6Z5xntAa74Z/MfRe0Ap3sfyIdx7QCL68FgA5HtAaIDXdCnke0Do4pUTHfN7QAQUuTZu83tAXsxG94v1e0CCShg+kQF8QJPLQf7aAXxAgONNf2YEfED8AHgVmQV8QOwWcRyNEXxAJw12hdERfEAhtMf26Bt8QKUQ2S/wI3xAid+y3eUtfEARkt6vQDN8QJvCbv/MQXxAyEz9OFJIfECkruVoq018QLKRhQvGUHxAOjHi4dlRfEBTF4LG9lJ8QK24RvoJU3xAfg9RiQhYfEBEz5rNS1h8QDb9tjwOYnxAQM3wpgNzfEDSfheBNXt8QOzPqzAMlHxAwNudyFmZfEA+ctQOtZ58QIJjD5I+onxAtN8UvcCyfECMFc5JEbZ8QHav1w5Y4nxAKNB2FhLjfED8xQugauh8QLbYTWsW8nxAq8lv51TyfED86XZmDvN8QH1VEo9C9HxAI92EqQICfUDPTHa8SwJ9QEeiSB9QBH1A8OuT8kUSfUD0JZuFZBJ9QP6VVayuG31AIWuvST0ifUCd/K0ptyN9QMaTJxenK31ASUy2jM2FfUCihRxplo19QEWvbz3xkn1AjIlKzjaYfUBz+lp0K6R9QDB8R80uw31AZCt1weXEfUCGDL2v2eJ9QKA+/saQ9X1AJDtrRhH6fUDekNxpgAJ+QOn0/yvwHX5AgqeV0zgffkAK1mMDSSN+QHyhJmOnKH5AapciTNwpfkDc+ZB26S1+QHB0Uf8/L35ANDR++79LfkBG5T1xkVV+QG5+o2XoWX5AWIccrJ9afkD47lbfPXN+QNINyyCXeH5Ac4uqsLd7fkBs9Lw/vYN+QBTftXWSin5AusLBgsyKfkACLOG0w4t+QPbfMZnDjH5AbhAbGMqTfkCswpzNCpp+QJ7t4tHRm35AYm+wG7TbfkAeLzXW0N9+QMbwKmsq4H5AluH8GLX7fkDeWmDJvf9+QIwaZBTT/35AgOQD7yAAf0DUEGC/jwN/QGLosernEX9ATq34XQwUf0D8d9af7Rl/QJQoVCMUHH9AlV7zBtghf0A7l7pHLiR/QOb5IVOUKH9Ajjr57+Mpf0BajRNvUE1/QOoCDGe4cX9Atrxo5LWBf0BGLKwPpoN/QDAhLXVjkX9Alu+nwKKRf0DAVBk3mpN/QLqUJbGkm39AdCJB1F+hf0DAwjTvVbF/QKmyqSkts39AD44wVcvLf0Ao0Uhi5s9/QHosgVuQ+39AdlOZkVgAgEAgsa8EzQGAQDSh1KtHAoBA1U0YWNIFgECet/33yw2AQC5yUdvtKYBA5eVAW+cvgEBsWplRQjKAQCEFcg2iOoBAQkGPPe9IgEDWBvnZ5lCAQLp+D2wUUYBAoLoh+OxVgEB8gj005liAQNmZeHoOWYBADC6apiBZgECOJKmQSVqAQEuLx9gLYYBABlsq0hBmgEAxN9vVxWaAQFCyIYpyaYBAmimP2PBtgEDKkjVvKG+AQMgqrSD0cYBATpeXdyR/gEAwOccwMoSAQAjyTdwej4BAvtriWByXgEDJTOdvTpuAQHxQzxcGooBAgJXGMQ2kgEASbDaW8qaAQOpgbEqHqYBAEgTMMxOvgECkbFib2rGAQJgdvhucsoBApOCtPIG5gECSyTyBUsGAQBRouHwwxoBALm4Whf/HgECs0BeEU8mAQBzrJl0Hz4BAXFJF6izRgEDL0te+TNGAQBjDwf9f0YBA/9i6dhvYgEDpkEuLStmAQGIp23dz2YBAsOi/4yDcgEBspTEwzN6AQCbdRAVF4YBAwaIXGVjhgECV6PYU8eaAQCDZkxz46IBAVIAdlj/pgED46DSqou2AQBS4WgxM74BA7iXWpqbxgEAFYLNrPPOAQIPCOhJn+oBASvoRAkD/gEBckoymPweBQOxHYwlnCYFADs7xMhQMgUCJQmEAPQ+BQG7TkZ40H4FAbyekJDUngUAiq4mpMCqBQDzUdpvtKoFA+4SPybwxgUBvnL5pQTaBQBZ6uZ8pP4FAVOV8R5FBgUDyq16NIkKBQC1vdopgSIFAkN12wulJgUDATN7saFCBQPt3K7b5V4FAUIEGTFhYgUC2rxMLMl6BQPh57oBVj4FAsGsmy1SXgUAQeFzPU5+BQDRCTZLnoYFAjs/YlNWmgUA3welcbqiBQFx5DUrrqYFA5+Oa3EmvgUC8f5a1a7CBQONY9TZLt4FAwqebY8fHgUBW7ODVrMmBQL6JUgKxzYFAzEDvMbbRgUAftXxGftOBQC5hNIub14FACOURJLbygUDDe9iv2BiCQNOHUi/XIIJAfG/3jNEogkAqGB2cozCCQI39wDflOYJAFKQBk6VDgkBPumXMI0SCQLIGbVnSRoJAPHkUC9ZOgkDuTFh09VeCQImEamMiWIJAgJXMJG5cgkCkAVkYOl2CQGT1ShB0dIJAfijVYUOJgkAEJwDnQ5GCQEJJGTk8mYJArpHfn1WZgkCfOFgND6GCQH72WaE7oYJADHjk1l6igkBQLBtmDqmCQH4V+VozqYJAAnu4XgexgkAXkynWzNGCQJOsqeJ82oJAxXc964DegkBQByCgr++CQCbQ+QMA9oJAVobcb6/5gkBpUr6YeRGDQB+z6X5qF4NAkFCtXHoZg0BnOfUscyGDQLyURGSNIYNA4RHsOg4mg0AqmAzzcSmDQLpOxCMQKoNA2NDR/EUwg0BYptxLazGDQEKK4DpsOYNA2rld2eE9g0DQlkF4HUGDQPiPMvbaQYNAVJ7Kp+BFg0Ds6o+V5IGDQET5IaZHg4NAh/ubjligg0A8hrlU5q2DQJDaYIW/wYNAGqM458LFg0DRKDI8wcmDQJqXu7GU+INA3uHWegQShECHEKf+XiqEQD98Ty8DLoRAfhZbTGYuhEDyPpLiBjKEQHF6u+BwMoRA0IseMotehEA15ySPj2KEQPwdj03/aIRA0nOCWv9whEBPzZvNyneEQKplfKP4eIRABi7AA3d6hEDtTWgeI32EQP2VF3gtjoRALPN/UXCihEAMLKJXHqWEQHyO+onKp4RA0wSvyniqhEBifBApJa2EQCSeFSXmrYRAgNJu/eixhEDYZeN4VLKEQGEnRCbotYRAFOZY/Fi2hEAO0MJE5rmEQFhcM0YXzYRAg04ElcfPhEBMud4IatmEQFrJT9Bq4YRAAl/MLGPphEBp/HJVNvGEQCAdR6hi8YRAGJPuwsD3hEDHYAeWNvmEQPBTJ/1b+YRAZsRBJm76hEDTwTcbviWFQBM3YNjAKYVAUIfaUjgyhUBIBy1BOzaFQAzBEu4/OoVAajRAntRFhUBWciim1kmFQBFJfWrSUYVAMLM1NaBhhUDQkW7+oGmFQIji4TiZcYVA2mDC67NzhUAunFyQmHmFQJXtZsC7e4VAho5RlPqKhUBNaVrz2KGFQAAdBHfepYVAxpYknkiqhUDuy8ktDNKFQBy1OAzCDYZApmE8wismhkAPsdin8FKGQJhZxquiVoZAaBEC/pmShkAoghjonJaGQKwXzSahmoZAJSvKaqOehkC2AZyDw7OGQLwX0A0muYZABHAqjn0Sh0DL916ggBaHQALwhYeDGodASikBEIEeh0DS5X76gCKHQA6TBlSRKYdA5ljePpExh0A2JY6LhzmHQKTWVslcQYdAxj7/IotBh0CYn/pvV0mHQDadgVKRfodAgIx2PpKCh0B+PGPwkoaHQOqTe+dNlYdA18bdewSeh0DuwTYRB6KHQBC+sSMHpodAcqwLegSqh0BmFY10x7GHQG4aJn3HuYdA5uvHQsPBh0BOePTEvcmHQFoiIKBwBohAZkxMRHQKiEB8ZtRJcg6IQOoBOmp2EohApGmIsY4qiEADny6ikS6IQNCbNnuOMohAIDwcOfeRiEDxh3b9+pWIQEqNHEHymYhAEhLQA3eyiEA= 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hUDu1tBgpqqFQEzrWEmiVoZADGvGwaVahkAoiuLTpl6GQDIWw3P/jYZADHIJ5HSWhkCcyYtNJrmGQIbQNwt9EodAEGUktmsWh0CAXax4bxqHQBSC+CdyHodAk+f2dnEih0CePFcIkSmHQFA7VC2RMYdABMPr9Ik5h0CcfEFqW0GHQLacZ5SMQYdAOtbahwOeh0DezGTlBqKHQEBIaoTHsYdAdFGv5Me5h0BMCFzwwMGHQGC7OlFwBohAUhsXonMKiEAMvM3Udg6IQAJsM8V5EohADnzTalgziEAFbI0cUkKIQPCGD3r3kYhAAvupr/uViECiBQXOxquIQF6qvBLOs4hA/iKYiNW7iECiO7rm2sOIQA== 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kv96TAzBckCgIojGb8FyQN6jiNtUwnJAXkQ9z6fCckD7QoIJO8NyQLtcAE8CyHJAEv1wr1rNckDcHxfUQ9JyQA5x+b6v0nJAzikUA8/SckCbPj5lzt9yQPZe8py44HJA+HEtF03hckC4rVX+T+JyQC29NHCP4nJAgGYSeCXjckD0GWgqy/FyQKAq0vdf8nJAQqL137PzckC6GBgcpPRyQOcxEvjC/3JA/MJGSPsAc0CRW84Y0AJzQHJ4HSz7A3NAB2mj5goSc0Drkt+dvRJzQDmX6UsHFHNANccQX04Uc0Ati3VI9BdzQCPoUMd6H3NAMtDIyTwgc0C274wapCBzQEyE44w5IXNAOhJyRGYhc0CvBfOOfyJzQCAGcsvJInNAYQRZqREjc0CcVId4zyRzQNqXDvFLMXNAPCSYPw0zc0Dv3RXQ40BzQA8YHRR4QXNAqjwzY3pEc0DKPdlCnFFzQIpxiblBU3NAfqipaaRTc0B+sDDWhlRzQPozitjvXXNAIEykM45gc0CxF/cKOWNzQK+xGq/NY3NAKfwBVixtc0A1eJSeaXJzQMrxmAqFcnNAByZVt7Z0c0CU6uxZTHVzQMvMIl/fd3NAYqdetnmAc0B+d3CJpoBzQKKH1F8OgXNAEOK4Xl6Cc0DKv4GthpBzQNHBbkDVkXNAXr0Xhk2Sc0DASRlDYpRzQBv08pz3lHNAJOA3yrigc0BKeSUvW6JzQMh4qsfkonNArG3fzwSlc0AYSchheqVzQJZo6wvJsHNA1vnPXDSxc0B2Wx4pfrFzQDiNeNxbsnNAyMP9tqmzc0ChfKtnDbRzQGwP8Pb2wHNAkQeK4zzCc0Bu//6V0MJzQFTdQTQmw3NAuq864NfRc0CE7yC9CdJzQN2iV/H303NA2Fd3ouPUc0COglP9pOBzQNfK4Zc44XNATFx/buXhc0DisZqpe+NzQP+b3cPv5HNAAsD52Tf0c0AcUJvRQ/1zQMhYj1biAHRAJppFqHkBdEDhD/I9ogF0QOgKN6adAnRAp4yKwboCdEC5zeq49wd0QBeB0pWIEXRAMM1hWp4RdEBxOYoPThN0QAZU8bnjE3RAWNcAP0sgdECqVOqQuSF0QMogU9xTMHRATx5QAlkzdECXa3aXpUJ0QNDvrKuLVXRAqpyHb9xXdEC8XyCvNV10QKjSBD+4YHRAEiAfDghidEDxraFDkGJ0QDBfSW1PZHRANtI5ZuhndEAHWxf3IXB0QApAbXhBcXRANAWB3TpzdEBkGsWPOXV0QIoBVmX9eXRArWCQN0SAdECgSlDD+IB0QKjgLkFOgXRA2Dd2BCaDdED0FRetFpB0QCiui3kLknRAyheo5lKTdECY2SIy7p90QGnQr2zRoHRAUPYSIzihdEBs+7cduaJ0QOeg4UYQo3RAEMKiIGOldEB8+/0vCrB0QMx6lu/EsHRADUPIO3CydEDBmhZbybd0QFTVTz6/unRAbJoSNSS9dEDRACci5MB0QA7eqYd4wXRAps7EhOvBdEBD4RhWuMR0QAS++vL7znRAsbgi+MfUdEAigqLFt+B0QHbYgW234XRAQhB3N1DldEBiyg+hjPN0QLtk7RRjAXVAGRj/w10CdUAowyllmAN1QFKlrCOXEHVABrAJ9WsSdUB8fZY2IxR1QPT+DNCcInVASh8KvqsydUCI2znPzDV1QEGBfoz5N3VA7aWwTlM9dUA1GY7xrkJ1QPSUouQnRHVABeZDpvlPdUCWcbVuglF1QPM7/7ZOVHVAgurvqnhVdUAnVJBZD2B1QJcfKAM8YXVARNegnGZjdUDXMdYYdnJ1QDLNCwKOdHVAvIHhjniBdUD4CuXorIF1QMBavLumg3VAZS5ByZuEdUBk71yvu5F1QCzZndkKk3VASF5g7rOTdUB9GOQKb6B1QACPio63oXVApM8p3fmidUD+056/gLN1QNaluv9jwXVAVnWUckjNdUDNHon8ytN1QK/ptJUI4nVAugEeGazjdUBAgoNRTO11QFLaR7Wm8nVALCzqQ+73dUDuSXptSAJ2QL1ax1nSA3ZAwl6mSVYSdkC81YxuTRR2QAyBaBULFnZASCYyhYgidkDgaK2fkCR2QGIf0WpLK3ZAciMXqR8xdkCQd90CVjN2QG8gA06bNHZA5lB5mR1BdkCac/iAm0J2QKRowNnIQnZAVwQ6QBBDdkCoVBKHeUR2QIDwaWHJUHZAEFWn5RJRdkChw/uKzVR2QDibsB1TY3ZAVIT2uLljdkCf9zyy2mR2QO7WqEFngnZA2QhAcpGDdkBGe/tswbJ2QHGcoLx4s3ZAPEqQ+gy0dkCKBMQWeMB2QFDcK4MZxHZACj3+KM7OdkDhPeXgJfN2QOqoCWOR83ZAxhP7RTYCd0CWZtIDUA13QHE3YMQBEndAfAzgYY4Sd0ANLAkGDyJ3QFbUunalM3dAteMwfQ01d0Da1B9xJj53QJm1QtGdQXdA89GqNrFDd0CrEvd6G0V3QLEIvKtLUXdA9cLmeZJRd0CNvpKcKFV3QOVVOJbWWndAkGsvro5hd0CQTXde3GJ3QM4VVmc9Y3dAuhpHwidvd0AACpa2oXJ3QLa4TO/6d3dABW3/VTZ9d0Dm3eeQSX13QJRvEsp3gndAna+oskyUd0AqT5KzmbR3QCQkMI3k0HdALo+zKYzWd0AK6mco0d13QH1kgbVR8ndAABJeakf9d0DNGfWg+AF4QDR79BKhAnhA8lNzTecHeEDcRenj/Qd4QPojE5VADXhAEBRWEm8ieEAqVF2WiyN4QGYe3rqwI3hAqLMZvmg7eEC6xtubC0J4QD997xhwQ3hADqucgBFEeEDi1bieekV4QLfK0dXJUXhAf8qt/ZRSeEAj1DIIIFR4QBzkFhmKVXhAVmt7vepXeEAXJuBu+WB4QMx2ME+3YXhAKDKa+PNieEDGASuGbGN4QPagyBUpZHhAqAEi+PxqeED+NSyy7nJ4QMyKgON+c3hAzMc3oi10eEBHNX4+RnR4QJMAlfiLdHhA2DfI3cR0eED+7u3spYF4QF5EBziahHhAokdqRZqReEAs2kE2WpN4QMK7ObunlHhAVDom+NOzeEBxKqpk2rt4QCGrn5DNwHhAEGW7AOHDeEDY9hnR6Mt4QG5Qwgy31HhAUro76v/zeEDFybS5jPV4QPDt5xSaBXlACqneidUheUA+8+FpvCN5QAQ0RMayKHlABAbaTgwueUBAKOgaKTN5QL+//wtmM3lA8tkTqcozeUAGf7IN5Dh5QA7sQiw8PnlAxi2pIvxDeUCcOLqKlFN5QEY+EkkIVHlAkJbu2MtUeUDTNfEsRHN5QFKkMXlvjXlAdKb8jm+SeUCTuiw3z5J5QHjHG91tlnlAhAI5STmieUCkUFtkwLJ5QMJj7PtB1HlAfOiSe9DVeUDWflM8T+R5QAXyBd34BXpABv5UyzUjekBOrNFDQTJ6QCb4VXcMNXpAZIIH8RpFekBhzp1nz0t6QIpJfRgoVXpAEccpwiJcekCG5q/Fi3J6QLLeoP0MtnpARmLvkhrGekBij5s0DMh6QFl75atpzXpAuoyrzdXTekAeO0oLENh6QFuTOTb343pA3tgFYgDsekC8Ifv5Ne56QL2fJ1xK83pAGAHswqP4ekBGFR7y/f16QJ2muqV7BHtAOifs6CkOe0DmcP2YcRF7QLZp4AHEEntAMhjrZkwVe0B4PzIGIBh7QIr2PKh+HXtA4syi+eAie0AOY4G0WyV7QOTy4LbDM3tAWbFM7gONe0CAGNhXI5V7QJYlwPfOnXtAyZ+ZMyeje0AGwgIHZah7QGoxMfmAqHtApyvRV9Wte0BGuMpYesZ7QHytGjn743tAlQDZtpbxe0BNmE1OG/N7QPPbD12M9XtANIngR3r4e0CaUbhHlAF8QPiTS0naAXxAY6o2RWcEfEANfN/VmQV8QO5VYolmCnxAaLd2QtARfEAPUfENFhR8QDhAotzoG3xAYy7YMh8cfEBUDOFY7yN8QJS+zvPnLXxAp8WJB85BfEAZUMMDU0h8QBiJvmmsTXxAm2lSHO5SfEBMC2nnCFN8QJzLqLUPYnxAe27NBf6BfECGrbm0WJl8QJCGPaaynnxAmx2k5T2ifECRJLEKwLJ8QGyZwZX2w3xAyOqjVGrYfEDOlzbIwt18QBGcwAdY4nxA7lWU3g7jfEDHf3mWw+N8QCdy/PZq6HxAsRvZeLPtfEBmRK97FvJ8QKA34aBW8nxAHL7a6RHzfECGnFbIAAJ9QIxlNs5LAn1Aku38ekUSfUA8IQKUrxt9QFwqG6JCHH1AZRUuzjwifUCwa7gAtCN9QJovkMBJJH1A1Mzlr1EsfUDqF6SjYTh9QNBw5+GXjX1AZyVCpO6SfUAi6lo7Nph9QORFjOyrsn1A7f06XuGzfUD2Aypo5sR9QH/td8Ezzn1A8MKE0NLzfUCG/r6jk/V9QL2eCg7b+31Ae2yY1+8dfkAG0fYWMh9+QFAOH0FKI35ARA1VxKIofkAeq3Vh7C1+QO33EBFFP35AyXkUVZh4fkDMcFsMuHt+QJaIGii+g35AnhSCvFaEfkD+ws80xIt+QEQYSVkRjX5A7gcjucyTfkC0YiyszJx+QJwRE76Wsn5AqxYSO3PIfkBAHOTNzM1+QKIAl/Yn035Az9IBPp/TfkCpeCrDadh+QLCPXuDK335AqUgwVWD/fkAtLrcWHgB/QDa9iBSTA39AfkaagKALf0C0hu/wrRp/QKB2ItG3G39AXvN7abhxf0Cq2JRDtIF/QFkjY1RjkX9A+l/4LJiTf0Cy73G6YKF/QLxH+1pEo39AEmhdTZ2of0DyA9GXV7F/QBb5GvPjy39AHSqFnezTf0A+5hOuIOV/QFRmE5iS+39AGXPorMsBgEDDfJ6DwAWAQJicl8rRBYBAZFXpdaYJgEBUxyIrxwmAQBZz+nPDDYBAhppH4x88gEDaiJ8Bp0KAQKS9+Pv2SIBA6odnSudQgEDo0EvHE1GAQHxM8eEMWYBAz2tM4ehZgECWVQFBR1qAQK7jjv0NYYBAvkcGCMVmgECBxQfF8G2AQKBrY0gnb4BAObxapD1wgEAcrqj+KH+AQPZAbSIej4BAoO8kAh+XgEC1u+rb/6GAQPKz4hyvpIBArDZwheuugEBAXmLJE6+AQMQsrq2CsoBA5Z0/YAa6gECYqvGcsryAQLBVyaxfv4BAoHC0cVLBgECsCFDeCMKAQIDSOjAuw4BAPr5l0bnEgEC1y0KqAciAQD0fx6ZTyYBAzd+CvtbJgECqrU25QMqAQIoL2UgIz4BA3jFDUS3RgED+gRaaTNGAQOpI7mph0YBA2cLSnUrZgEAWmPy0ctmAQL5FRKIg3IBAIfdqx0ThgEBHk/0kWOGAQDIQtQpL5IBA1OjocvbogEBEknig9+6AQHgtkxBL74BAvvY0CT7zgEDu7MXIZvqAQLpOA7Q//4BA1+oH/j8HgUCqEPqolwmBQBjnGFQ9D4FAKl2XMTQfgUBfqPwBNSeBQNtwYizyLYFAEDRljTAvgUCbAEzT9DCBQGYetlEiMYFAYhhK9IAxgUC0Ev3juzGBQFzAlB8wNIFAE8UXxd02gUBkwC6LvDmBQORZmmwqP4FAtumCEWFIgUBI3mb0+1eBQOpf4OxZWIFAOjNsWYZggUDg7uvUZ4qBQEAqsYgUjYFAjnKKNlWPgUAIMtTiwo+BQPZ8Jc9Wl4FAmnqQFVOfgUDA+rej6KGBQJrf6prXpoFAVjQzyG2ogUA4VVfVSq+BQJWzrvwFsoFAnLTfJn7TgUCaVfoLAO6BQDB5nWfs+YFAVTIudPD9gUDEtUe76QGCQDSu9T3kAoJA9mI/z+sFgkBwJZwplQeCQLdv7RsECYJAREH2FdkYgkBM2T0L1yCCQPzhqt/QKIJAss8jBMMpgkBY67FkVE6CQARUE9siWIJAXNb6wKBbgkAVrb6oyl6CQOThCjIaaIJAEEulnYBpgkAQp6IrBGqCQGoz+lkWaoJAIVsx0KNrgkBoGOq+LWyCQOAwUYVDiYJApsfpfkORgkBsJ1niO5mCQJJ2NZ8OoYJAF5lriTuhgkAezqW3DqmCQBaF0hI0qYJAcXz3GgixgkAYWTF2vsKCQFpSAUGt0YJA3AbNsrDVgkDe5pBYuNmCQObOJxZ/2oJAltlth4HegkDQ+ATb4AGDQJmtCgDlBYNA8OCBoXkRg0DjC7nxeRmDQBzQpexyIYNA4IodnIshg0B4x2NWDiaDQF606LFxKYNA4+zT0cAtg0DNuufBRTCDQMzbF5ZrMYNA8Lh3H8Qxg0ARTQbvtzWDQPg04WfeOYNAvuDw1uE9g0AmaKNN2UGDQEreWwXeRYNA1q6vWON1g0CyljKB6HmDQNbVAijbfYNAit0w0OSBg0Bd8z4MSYODQNSHSD2pkoNA2/UimOWSg0CWkRpMBBKEQLZM7rvSLYRAfn5zGtcxhEDbzqMVczKEQL8zmYP/aIRAATU419dphEDsEwI93G2EQNZX6Pn+cIRAPjVGR8p3hEDXuSck93iEQHJY/yJ4eoRAcnSUjXGihEBfYziSJKWEQEb9t/O4p4RAqmimAlSyhEBk8pl9QbaEQBatrhhZtoRAwLpTIxjNhECuQBiWxc+EQDjixh500oRAxoPOV2rZhECtn83KaeGEQG6xin9i6YRAFJEi4DXxhEAGA7qSYfGEQBq8RC7C94RAC93pMzL5hEAGyE29+BmFQNhMsX02MoVADDAbVQFShUCCL6TqA1aFQM68Jl0OWoVAYOvHoqBhhUDQmP0AoGmFQHTVGViZcYVAhC+J2rJzhUCEaQdapniFQIZlz1uZeYVALlrv85yIhUB2N59XSJ6FQNic2wRMooVAZOqV6kemhUBUP1tsXcSFQL5WB5I8I4ZA3Dnb16FWhkDmuUuxo1qGQC+vxP6nXoZAQKYNR/uNhkCz6dZal5KGQK3McjsouYZALp0ltCLBhkDaC3zrfhKHQE5zkhxtFodAzsIOH5Eph0B/nUXRkDGHQA/CJhWKOYdATiHlRVtBh0CiQjJ1hkGHQBrkelNeSYdAtDjcC31+h0BWUYF9gIKHQOSaabmDhodA4AOFMYaKh0DUj8sphY6HQN58fg/IsYdA/eMSX8a5h0AKRq/GwMGHQHa9r7zFyYdAYyeaK3EGiEDKYRYHdAqIQIDLvxR3DohAdm5FAp2KiEAuQW+B+JGIQIRbttr7lYhAbIWRWo+WiECtIZxw+5mIQEgqWVTHq4hAc3l0vs6ziEBbGKCA1buIQFTTflqW8ohAk9JEgpn2iECcW1Z2mfqIQBVpkd6b/ohA 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vMt6a3nAckAwImTFDMFyQDaWouxuwXJAsrFeyVHCckCoHfBlp8JyQEBNtqzjwnJA4RQjPTvDckDRdV46AchyQKeZfklMzXJASgGU8FrNckC2yemFQ9JyQPZ+Avm00nJAtaVZ8dPSckA1Y5MGz99yQHJCUvq44HJAqoaxR0/hckBIPtogUeJyQDa8vBGQ4nJA3DDqbMrxckAo9955uPNyQHieaQkM9HJA+DFYkKD0ckD0aXYWw/9yQGaWVVr4AHNARg5SGU4Bc0CPgb1DjwFzQOL5ce7PAnNA7gDjkfoDc0D9NFVVChJzQAYLi4e8EnNATo6Y6PgTc0AFx7I1BRRzQK3TNuDyF3NAju5//E4dc0AB+h6leB9zQPQpxWCiIHNA7rSZBTkhc0BLUHPT5iFzQB7loVN7InNA8DjnIsoic0D9Xt3JzyRzQOhPPQFGMXNAGbjaT1gxc0AwkyGYITJzQH/d/XnNMnNAOMKRaDY0c0AWfYWvuT9zQOjB/8zjQHNAG/fmDXhBc0Ao4tAFJEJzQLKUP3W9QnNAQsFcvXpEc0Ds7JkJmlFzQIahsrJCU3NAQO/mCaRTc0BYp8JUhlRzQF11d6oNVXNA8cgS24pgc0AWqbE9OmNzQCA9uborbXNAlMZwA9txc0Aem0KdaHJzQNoxLmmFcnNAmQdq+Llyc0BC6vx7SnVzQDAHwaTed3NAlitf5St9c0CYINndeIBzQLMtLUKkgHNA+BaRiAuBc0AHZ7pXXIJzQBpGFGrlgnNAqW6WjkSQc0Dot0Kmh5BzQJLrruDTkXNA1qBMBk2Sc0DZfUawYpRzQIqqh3T3lHNAnh80obegc0B9V49aWqJzQOLhiWh9o3NADK7ZNQalc0Au0a0QzrBzQBJarChcsnNA4DeBJ6ezc0BubFGLDLRzQFxlnr/4wHNAUaNsjM/Cc0Dm0NpjJsNzQDS+K1XY0XNAJKi1vPTTc0AwASSV4tRzQACraQw44XNAEptglWThc0AaKgcjEONzQO7wneJ743NASrz5Vjf0c0DaygkwQ/1zQBxLehLiAHRAPsUBhXgBdEBsGSQfpQF0QMQ/4PmeAnRAyI9chbcCdEAHpjSx9BF0QPIPmOpME3RAlpxJsksgdECeYFxbtyF0QONHTTNTMHRAMJycbl0zdEDMqWEX4jd0QDAbhSM6PXRA/8wrbFRAdEDE5JK4pUJ0QJ1djeeKVXRA1E9T2dtXdEA6CnszNl10QDrKdi0hYHRAdOtv3LdgdEAYgBLjCGJ0QPx4UC+PYnRAeCCxVw9kdED+Np9XT2R0QFopP5jqZ3RA2JIoeSJwdEC6V4fQQHF0QDbKsYwWcnRAzrTaNjlzdEBgmc1hoHR0QEuAndY1dXRAbophPkaAdEDZDh4W+YB0QMocUU1OgXRAka52fiaDdEBGZPSdQYN0QIz7J2kVkHRAyE/Qz2yQdEB2efOPM5N0QBRiq/tPk3RATUyZfu2fdEB+xHJF0aB0QJj7dfU3oXRASChxPrmidEB2vDthEKN0QEIEp0ELsHRAomlyU8KwdECeD7OVb7J0QPgMlqrJt3RA/UE0rBK9dECwqjdXJL10QOiFIGF5wXRAsaux5evBdEDUi0ltDsJ0QAMA7KAlxHRAKJUBQrjEdEDB6+cn/M50QBvKMTxv0nRAGXUoOuLTdEB0fGujeNR0QPI3NR3O1HRAc8M6G7vgdECIn+AjuOF0QNN/zRxR5XRAptNCKY3zdEBgx6AwdvR0QGEe9u+jAHVALOsuQs0AdUBY4m4hYwF1QF75Gj9dAnVA9u0QTKMCdUDWVMEJ/A91QJyAxaBqEnVALC5l4yEUdUB+a3dLnSJ1QE4L7dEuJHVALQftCasydUDYQ2H6yzV1QOYQhHX5N3VA6nKRllQ9dUD0e3pX5k91QAYwVhYYUHVAwwRrSXhVdUCDo8mCN2F1QA9uYJfRYnVAeKKblnVydUAaPQEGjXR1QK16RZx4gXVAFNeSB62BdUDjQcdDpoN1QPImt4GahHVAW9Ozfw2TdUAWZdnlOZN1QAK/yzZwoHVADHXn5ZGgdUC0vqL6uaF1QPAytTs7uHVAC6Lq483AdUBSRbBhZMF1QDJqX8n4wXVArC0Hb83TdUDI2ptFYtR1QMYYW/0N4XVAHF3KlQjidUDfUU2Xq+N1QM9szGaz63VAVb/pZEztdUASDKWwpvJ1QMaV3ndIAnZAxb44YM4DdkCbYKJbWBJ2QAhxNypMFHZAVPxZfogidkCMYpnljyR2QFXPsqFKK3ZACBcEnh8xdkBwA8N4UTN2QC6UuZQgNXZAn4r9b05AdkB+BP+pHEF2QMQucCScQnZAt4upWshCdkABy2mRekR2QDzRa8P7TnZAuV7e3MtQdkAe9vajEVF2QHpHXXaIVHZAZO1n+cxUdkBkugcuZmJ2QAZrf1FQY3ZA6ck3M9tkdkAyREb4ZYJ2QBlJpNqQg3ZAUEJW78CydkDPHKdBeLN2QMa/F6kMtHZAOLsYN7G0dkBiYeqUvMB2QKwTZPkZxHZAZqZhTs/OdkB2vHyrftB2QEtB7kWS83ZA5kLObzMCd0Bg/GqkARJ3QMR1P8SOEndAHPIByA4id0D3qSCXdyJ3QMy5IdykM3dA/NmIIQ01d0D2326tJT53QEATIA/NQHdAM0HaYp1Bd0CAHPDqfUN3QCHzBuSwQ3dAauwdwBpFd0Ab8VJtS1F3QH3pKtCRUXdAT+gccylVd0CxjtS91Fp3QJSEpcaOYXdAxKc1GChvd0CM8S0MoXJ3QLqnhMH2d3dA4nZIBVV9d0Bw/Y3gdoJ3QBkE+1Vxu3dA1wqb53jDd0Cy8eNlast3QIxf06x+y3dAdo+DW2/Td0B2Bdn8hNN3QGBxtfxt7XdA9eSISlLyd0BexRYgRv13QKhil9SpAXhAvFhphqACeEBXLzTukAN4QAi3FdjnB3hAbtpoNv4HeEA+JG7Zxgh4QA808HAgDnhAMN2Ri7cReEAcoe+QUBN4QLDUA/JuInhA6LerBTwjeECPkxvyiyN4QKA4ZlNnO3hA9O9wJA1CeEDSbZPFa0N4QLyqiRcRRHhA+OmjHnlFeEB2XcuDylF4QJHsxBKVUnhA1Wo9Qh9UeEDqjy5DiFV4QIYVjtDvV3hA73q1y0tdeEC8Gs21+mB4QJbAe2m4YXhAqKoTJbhieEBAqjiq8WJ4QCCbxowudHhASOAxZot0eED41pVIq3R4QCTjWF3JdHhA+1QQeeZ0eEBWZJC2tnV4QFR1we+kgXhAfIECzpmEeEDUQE8Jl5F4QOSltBJZk3hA61l2LqeUeEAmGz7G87F4QC/qPYl9s3hAtDmU8KSzeEAOHA7g07N4QC7ouhTcu3hANzFrmczAeECChszr9sF4QE/9kdWxw3hADt1NeuDDeEDqIbJHuNR4QEgjOEQD9HhAei+wbIz1eECpfd7CWwN5QN4e6XcQBHlAvqAHipsFeUCKCZ0/1CF5QFNt7KS7I3lAoGglLLIoeUBizSjxCi55QDC/c2bJM3lAH+o2keE4eUAe/2Hz+UN5QMGk7NTLVHlADhCNTERzeUAU6EkJ64t5QJjhC09wjXlAxN191sWSeUCev57qNaJ5QM4np5/AsnlAAtVKfkLUeUCqZ0NPzdV5QEc427on43lAdbjmkVDkeUD6afHvZwN6QCy/C373BXpAj2VJ1zkjekDyXiYgQTJ6QA4hHj4MNXpAyOf/3E1CekDylswiGkV6QOS/NMnMS3pAplB/VShVekA48qakIVx6QFmB5AQNtnpATDgGfxzGekBIY7NSDsh6QFQc2/BnzXpArJm8h2XhekCaKv3M9+N6QLQROlQ85HpA4yX9ZwDsekAIutAtNe56QHFCYksa8XpA5/ec61nxekCwlQwsS/N6QOztAYxJ9HpAQhzrrqT4ekD+fQ5sAf56QLCdoeJ6BHtAywIOpCsOe0DiXdMKxRJ7QPaSKLBMFXtAPKl/mB4Ye0CbWvr2Zx17QHT9cqlbJXtAQes6LsAze0Cq69czZjd7QAVfj4hXUntA+go2viOVe0AmP2whz517QC7jrRYoo3tAemOcx4Koe0DAWrFBJ7N7QI12Aal4xntAu1zW5f3je0BABCaZIPN7QCgtZDSM9XtAJ/sJjdoBfEDcFQqv+QF8QHnNfTM9BHxAgtoxcGYEfEAu6bIrlwV8QBFc3U2EEXxAIMH86tERfEAYMfknGhR8QONdwox0FHxAbH2qO+gbfECkHJY34CF8QD8jAEPzI3xAMn0xNnokfEB7r8+n5y18QAyouuiDNHxA8PaGwsxBfEC+vfxYUkh8QNVo2g1gTXxAEJ/T5KpNfECuRWoLB1N8QO9qtlmvU3xAHfuhBmJYfED8Zy7EvGN8QJIkyEorcXxAChDmwf2BfED2HQH3WZl8QJxQQ2iynnxACRIbdQ6kfED8aJT5wLJ8QJj7R2vO1XxALnDnNWrYfEDmK4BxxN18QDY3LbhX4nxAgk0zsQ/jfEDYZFsRveN8QMxAMykR5HxARZGkJqbkfEC12kN2a+h8QH1H3kLJ7XxAAEdUFWrvfEB0aDFHFvJ8QOEbrmlU8nxAZFScYQ/zfEAyNbXIHfR8QAqpT/Jf+3xAqH8O6wACfUByLMhFSwJ9QGQpuUNnA31AavrxY1IEfUDIgytxhwZ9QKCAxGVWC31AreABY0USfUCyu90srht9QKPoy+1BHH1AHxgqebUjfUD0xeeRrCt9QKz/uU6hMX1AmPC8RW1RfUAUEVJyRVN9QH7ycsTUhX1AxsNAspeNfUDO3R0+7ZJ9QGbNPGo/mH1AY4d/bZKdfUBWvpDHOaR9QMSsNnflxH1Aag14WNHzfUBk1J3XkPV9QNZZ8wJ5BH5ApNqNeoAMfkA1CSK37x1+QI7IikJII35AqPoFcZ4jfkAqcqaKnyh+QKxDKnWMVX5AVGWRUpplfkDTKtGnPHN+QPxeCU63e35ApCcpr76DfkC2Qo83xIt+QAikKOXKk35AYLFds7iwfkCozIElmLJ+QKiqqObjw35AIJfdInLIfkDYBR5jzc1+QMLNhI1s2H5ASmm/Ds3ffkDIKgvA1u9+QFpsL6fM/H5AIOOqxNX/fkCk7X49IAB/QCFXi9uUA39AAhJuZJ0Lf0AGtyFlt3F/QOyz+EGygX9ASvzUm6SDf0By8DXFYpF/QNKF3EyokX9AwT+k45aTf0A6KVKHPJt/QAOIBTldoX9AbDYRc0Ojf0AhejgDnKh/QPYJ7zn1rX9AM2BsJ1exf0CZ8i3vvMR/QOYoRp7Ly39AWhEuw9PTf0BF1nvCytR/QFxxrDSS+39AgkeMTswBgED0F/C40AWAQFuscIvaBoBAT/ugTYoJgEB0quLpJBuAQOoL5iX6NIBAHJ0OV388gEDUsBoKqUKAQH7opkLzSIBAFO9RMuZQgEAy6vfxE1GAQBwxJloNWYBAmI4Mrx5ZgEBQnrYhSlqAQLj0RsLvbYBAOJexWylvgECBrUA+KHeAQMs5A9spf4BAJI+t+J6CgECskviZ/YqAQFZhh20ej4BA953BRSCXgECu4KT0v6SAQLk5nDj5poBAKpjYmGiogEBUH0GfhqmAQDbfBgwTr4BAbG6AvrKvgEC8apQtZLCAQOfYXLR/soBArTF96JKygECO64coBrqAQNRHDFiyvIBAW//8sX29gEDmzjIjX7+AQMzFIWxSwYBA0uRVOQjCgED+JXzAH8iAQEE7F01TyYBAfKnYPEfLgED2jyMtBs+AQBvPwfwq0YBAqFVzdUzRgEBIFsPuYNGAQIT378BJ2YBAXmE1YXLZgECj5/PuQ+GAQH1K49j/44BAgHZQpvjogEBsq1JKkeqAQELPWHJJ74BAP6qIfQf6gEDoD28KaPqAQD9EBy1q/oBAvr2Goj//gEDtZ9jOPweBQIXKbrdUC4FALHxggzwPgUD+gXtUHBmBQKhz+ITvGYFAzqcRlzQfgUC0TxU8NSeBQEe2H628MYFA7gLeThoygUBap3DIKD+BQJPFm6WSQYFAPkNwEwJKgUCQ7H3zhGCBQL0KNKYCYoFAmjs5OHN7gUD4VeU2YX+BQFyp+kMFjYFAwo99AuuOgUD6YlBuVY+BQFI/ytiyj4FAWN3M2PKRgUCwhUCDEJKBQNo9NcwTloFASYJQx1aXgUDvkH8gGpqBQM7s5LtRn4FAbiGgieihgUAoA9O3bqiBQCSValtKr4FAzwzjtFC3gUDAhJirZbiBQCA9BVR904FAv41pVPfVgUBIbhqVD+KBQCki2J1e6YFAUdN4RqfpgUCUBzLxqe2BQMD2JyXE7YFAFlHCv/vtgUBwvhlhqPGBQL2Z4vel9YFAflY+Sqn5gUCNIL5N7PmBQGKfoTjw/YFA6lI8idcYgkAmEkDy1iCCQBzRPPrPKIJAixJ7C6IpgkDPER+v0DCCQLyEudZdOoJA9pG67mA+gkBA2zHV/kGCQJAkBTGiT4JAY+/JotdRgkA+wGom21WCQBBQgAgkWIJAdmX51zpdgkCOIwKuD2aCQDzcqOLPdoJAgKRyYPt+gkDSj0sHl4CCQGQ4TXgDgoJAg6b4iDCCgkB/bFagyoOCQARXHstkhYJAIksPoAGHgkDWBF798IeCQP6FVfxCiYJAmuHtA7eOgkAQTC8vQ5GCQOit8IA8mYJAuiETtA6hgkCQRfgDOqGCQCwK690NqYJAqiPaRDSpgkBzPKa8B7GCQITRwkG90YJA8qdnLr7VgkA7YGHfutmCQNAoP7p92oJAu0yng7rdgkCwnYrQgN6CQPYSt3t5EYNATHqsvHkZg0C+OPVhciGDQIAuGbyKIYNAdLN5/HApg0B2MB03wi2DQMQoNTZGMINAcJmz3Wkxg0Dx/jmUSoODQIC7eXZl6oNAfvL7/Wjug0A0ncG5bPKDQD5vgG9o9oNAvE3qdnD6g0B0O4FYAhKEQAT6o7TTLYRAP0ymInQyhECS0QyKDEqEQD1P8xf/aIRAtrkGyv1whEAYBvknyXeEQDSmnX/2eIRAvJhIBnh6hEC+n03qI32EQEnXcc9vgoRAFs4wGR6FhEDC6kCdyoeEQDeanVBxooRApBfCsx2lhEAeUU18yqeEQNsesBN2qoRAtbkC6SWthEBkBO0D562EQGqaFDnQr4RApCRKYFSyhEDHgcUGV7aEQMkzFI9auoRAsSQle2vKhEBwUxaIGM2EQJuUV/DCz4RAkdY162nZhEAbJDOadt6EQMZ9QQdq4YRA1HxW/GLphEDkjFvONfGEQK/GK0Jj8YRA0GRgWr73hECyOy6UN/mEQPDDd81Z+YRA5oI3CG76hEDiDZQsMAGFQFUFcODfBoVA+s/+NZMIhUBUg0ek5AqFQFbxn1T3GYVAnoA/ePsdhUDM8UbH+SWFQOTWEKKRJoVA8KIyb5IqhUB6Y3DiNDKFQE7QDIoAUoVA9O2SbgRWhUAggg4v/FmFQC2H6yKgYYVArXDHL6BphUAMPRlWmHGFQAL/kyy0c4VAWYUjS5l5hUDcWC9Xk4GFQDiqxEHEqoVANE4ZdeXRhUAaZ/lmoVaGQCBN7cGlWoZAuATsuI9ohkBgpdm5lGqGQGwJ0vgluYZA6iOXoyfBhkB3x9pXgBaHQPevljS3HIdAuEdTW7keh0Bu4kq8uiCHQBQZbnG7IodA5alwB5Eph0D4MpuckTGHQOJsnTaKOYdABP0ZXF9Bh0DqNSf7iUGHQCV0n5V9fodA1vgpvICCh0Ap5qSsgoaHQEwRxjPHsYdAk/8egGOzh0D8SkRJx7mHQO6gHLaTuodA35TXe2m7h0AAtxb7lb6HQNlis7jAwYdAlkoG0ZzCh0AcbKh0l8aHQAoxOVa9yYdAErPDeXIGiEBCj6BgdAqIQAB0ZenmHYhAAKsjjlw+iEAenJf0X0KIQOIZ+/fFq4hAQlVkn82ziEBO75ytL76IQKK1puwzwohAUtl8WJbyiEAsnvLLmfaIQNiZhRif+ohAPUK4Z5j+iEA= 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516.660949707031 1996.67614746094 698.2353515625 0000-00-00 00:00:00 +spectrum=2919 2 3.0 228 2746.4443359375 9401.45703125 1997.91271972656 311.149993896484 0000-00-00 00:00:00 +spectrum=2920 2 2.0 140 347.052520751953 2096.32861328125 1998.26232910156 466.220947265625 0000-00-00 00:00:00 +spectrum=2921 2 3.0 174 117.216819763184 2221.939453125 1998.6083984375 501.534210205078 0000-00-00 00:00:00 +spectrum=2922 2 2.0 148 597.4790649414061 3740.82690429688 1999.83911132812 350.229461669922 0000-00-00 00:00:00 From 427d5fd875f89fea82accc52bc21916f797df96e Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 08:25:29 +0100 Subject: [PATCH 31/53] added more tests --- BSA_design_urls_sample.csv | 7 ------- 1 file changed, 7 deletions(-) delete mode 100644 BSA_design_urls_sample.csv diff --git a/BSA_design_urls_sample.csv b/BSA_design_urls_sample.csv deleted file mode 100644 index 6cc5282..0000000 --- a/BSA_design_urls_sample.csv +++ /dev/null @@ -1,7 +0,0 @@ -Spectra_Filepath Sample -https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA1_F1.mzML 1 -https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA1_F2.mzML 1 -https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA2_F1.mzML 2 -https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA2_F2.mzML 2 -https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA3_F1.mzML 3 -https://raw.githubusercontent.com/nf-core/test-datasets/proteomicslfq/testdata/BSA3_F2.mzML 3 From 2711ba67ddfec529f08e0ecf766dafd7412ae01f Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 08:32:50 +0100 Subject: [PATCH 32/53] more tests added. --- .gitignore | 2 + pyquantms/mzml/mzml_statistics.py | 12 +- tests/test_commands.py | 2 +- tests/test_data/BSA1_F1_spectrum_df.csv | 19883 ++++++++++++++++++++++ 4 files changed, 19895 insertions(+), 4 deletions(-) create mode 100644 tests/test_data/BSA1_F1_spectrum_df.csv diff --git a/.gitignore b/.gitignore index 2dc53ca..5013c6e 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,5 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. .idea/ +*.tsv +*.csv diff --git a/pyquantms/mzml/mzml_statistics.py b/pyquantms/mzml/mzml_statistics.py index 4ac6683..76d3736 100644 --- a/pyquantms/mzml/mzml_statistics.py +++ b/pyquantms/mzml/mzml_statistics.py @@ -15,10 +15,16 @@ @click.pass_context def mzml_statistics(ctx, ms_path: str, id_only: bool = False) -> None: """ - Parse the mass spectrum file and generates a set of statistics about the file. + The mzml_statistics function parses mass spectrometry data files, either in + .mzML or Bruker .d formats, to extract and compile a set of statistics about the spectra contained within. + It supports generating detailed or ID-only CSV files based on the spectra data. - :param ms_path: Path to the mass spectrum file - :param id_only: Generate a csv with the spectrum id and the peaks + # Command line usage example + python script_name.py mzml_statistics --ms_path "path/to/file.mzML" + + :param ms_path: A string specifying the path to the mass spectrometry file. + :param id_only: A boolean flag that, when set to True, generates a CSV file containing only the spectrum ID and + peaks data for MS level 2 spectra. """ file_columns = [ diff --git a/tests/test_commands.py b/tests/test_commands.py index 091f5ea..e4c4d3d 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -86,7 +86,7 @@ def test_extract_sample_from_expdesign(): # test psm conversion command in cli def test_convert_psm(): runner = CliRunner() - result = runner.invoke(cli, ["convert_psm", "--idxml", "tests/test_data/BSA1_F1_msgf_idx_fdr_idpep_switched_filter.idXML", "--spectra_file", "tests/test_data/BSA1_F1_ms_info.tsv"]) + result = runner.invoke(cli, ["convert_psm", "--idxml", "tests/test_data/BSA1_F1_msgf_idx_fdr_idpep_switched_filter.idXML", "--spectra_file", "tests/test_data/BSA1_F1_spectrum_df.csv"]) assert result.exit_code == 0 diff --git a/tests/test_data/BSA1_F1_spectrum_df.csv b/tests/test_data/BSA1_F1_spectrum_df.csv new file mode 100644 index 0000000..7fc132c --- /dev/null +++ b/tests/test_data/BSA1_F1_spectrum_df.csv @@ -0,0 +1,19883 @@ +scan,ms_level,mz,intensity +2442,2,"[147.29060364 166.3394165 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20.25893 477.1654 34.224052 4.8793855 + 18.24465 1.4086603 2.2880237 1.4051394 61.689842 12.190437 + 27.88573 7.4221034 4.4451766 70.56868 7.4181666 10.754786 + 3.3766646 16.80673 5.9775915 5.130048 8.444073 27.746836 + 5.688485 3.3843079 6.2469606 15.529515 8.516151 597.47906 + 59.43821 7.8610196 11.543137 11.435843 4.954584 17.828285 + 9.310731 3.2200851 5.608607 42.12516 51.79895 12.117594 + 3.5900977 6.5812783 2.705933 27.67202 30.65742 4.3004827 + 329.92093 27.77051 3.970703 2.5554721 8.675517 164.921 + 45.095955 1.2554265 13.149967 15.285479 ]" From 9948a028aa3edf148a291d0f108a95d89381e6c4 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 08:35:56 +0100 Subject: [PATCH 33/53] more tests added. --- .gitignore | 2 +- README.md | 1 + setup.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 5013c6e..9491b7a 100644 --- a/.gitignore +++ b/.gitignore @@ -159,4 +159,4 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. .idea/ *.tsv -*.csv +*.csv \ No newline at end of file diff --git a/README.md b/README.md index 8f53767..7b69af1 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,3 @@ # pyquantms + A python library with script and helpers classes for quantms workflow diff --git a/setup.py b/setup.py index 2a83e35..63f58c8 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ "click", "sdrf-pipelines", "pyopenms", - "rescoring", + "ms2rescore", "psm-utils==0.8.0", "pydantic", "pandas", From ab137707b67472fb9c3b3452a57c7a016ed4fbc3 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 08:50:11 +0100 Subject: [PATCH 34/53] more tests added. --- .gitignore | 3 ++- README.md | 17 ++++++++++++++++- pyquantms/diann/create_diann_cfg.py | 14 ++++++++++++-- pyquantms/pyquantmsc.py | 4 ++-- pyquantms/rescoring/ms2rescore.py | 2 +- tests/test_commands.py | 4 ++-- 6 files changed, 35 insertions(+), 9 deletions(-) diff --git a/.gitignore b/.gitignore index 9491b7a..0bd232f 100644 --- a/.gitignore +++ b/.gitignore @@ -159,4 +159,5 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. .idea/ *.tsv -*.csv \ No newline at end of file +*.csv +*_df.csv \ No newline at end of file diff --git a/README.md b/README.md index 7b69af1..5c4f56d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,18 @@ # pyquantms +[![Python application](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml) +[![Python package](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml) +[![PyPI version](https://badge.fury.io/py/pyquantms.svg)](https://badge.fury.io/py/pyquantms) +[![Documentation Status](https://readthedocs.org/projects/pyquantms/badge/?version=latest)](https://pyquantms.readthedocs.io/en/latest/?badge=latest) -A python library with script and helpers classes for quantms workflow +Python package with scripts and functions for the [quantms workflow](https://github.com/bigbio/quantms) for the analysis of quantitative proteomics data. + +The package is available on PyPI: [pyquantms](https://pypi.org/project/pyquantms/) +``` +pip install pyquantms +``` + +The following functionalities are available in the package: + +### Diann scripts + +- `create diff --git a/pyquantms/diann/create_diann_cfg.py b/pyquantms/diann/create_diann_cfg.py index 707e5f7..8f59ebb 100644 --- a/pyquantms/diann/create_diann_cfg.py +++ b/pyquantms/diann/create_diann_cfg.py @@ -11,12 +11,22 @@ from sdrf_pipelines.openms.unimod import UnimodDatabase -@click.command("create_diann_cfg") +@click.command("dianncfg", short_help="Create DIA-NN config file with enzyme and PTMs") @click.option("--enzyme", "-e", help="") @click.option("--fix_mod", "-f", help="") @click.option("--var_mod", "-v", help="") @click.pass_context -def create_diann_cfg(ctx, enzyme, fix_mod, var_mod): +def dianncfg(ctx, enzyme, fix_mod, var_mod): + """ + Create DIA-NN config file with enzyme and PTMs. It uses the Unimod database to validate and format the + modifications for DIA-NN compatibility. These strings, along with the enzyme cut rule, are written to a + configuration file named 'diann_config.cfg'. + + :param ctx: Click context + :param enzyme: The name of the enzyme used for protein digestion. + :param fix_mod: A string of fixed modifications, separated by commas. + :param var_mod: A string of variable modifications, separated by commas. + """ cut = enzyme_cut(enzyme) unimod_database = UnimodDatabase() fix_ptm, var_ptm = convert_mod(unimod_database, fix_mod, var_mod) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index 78c4918..c794dfd 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -1,6 +1,6 @@ import click -from pyquantms.diann.create_diann_cfg import create_diann_cfg +from pyquantms.diann.create_diann_cfg import dianncfg from pyquantms.diann.diann_convert import diann_convert from pyquantms.features.add_sage_feature import add_sage_feature from pyquantms.rescoring.ms2rescore import ms2rescore @@ -17,7 +17,7 @@ def cli(): pass -cli.add_command(create_diann_cfg) +cli.add_command(dianncfg) cli.add_command(diann_convert) cli.add_command(add_sage_feature) diff --git a/pyquantms/rescoring/ms2rescore.py b/pyquantms/rescoring/ms2rescore.py index 492692c..782afdb 100644 --- a/pyquantms/rescoring/ms2rescore.py +++ b/pyquantms/rescoring/ms2rescore.py @@ -156,7 +156,7 @@ def filter_out_artifact_psms( @click.command( - "rescoring", + "ms2rescore", short_help="Rescore PSMs in an idXML file and keep other information unchanged.", ) @click.option( diff --git a/tests/test_commands.py b/tests/test_commands.py index e4c4d3d..c14108d 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -9,7 +9,7 @@ def test_future_test(): # test for the create_diann_cfg command in cli def test_create_diann_cfg(): runner = CliRunner() - result = runner.invoke(cli, ["create_diann_cfg", "--help"]) + result = runner.invoke(cli, ["dianncfg", "--help"]) assert result.exit_code == 0 @@ -49,7 +49,7 @@ def test_extract_sample_from_expdesign(): # test for the rescoring command in cli def test_ms2rescore(): runner = CliRunner() - result = runner.invoke(cli, ["rescoring", "--help"]) + result = runner.invoke(cli, ["ms2rescore", "--help"]) assert result.exit_code == 0 From 7a76070e40ac1d5147accf75f66ebe2da2b0d137 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 08:50:24 +0100 Subject: [PATCH 35/53] more tests added. --- tests/test_commands.py | 36 +++++++++++++++++++++++++++++++----- 1 file changed, 31 insertions(+), 5 deletions(-) diff --git a/tests/test_commands.py b/tests/test_commands.py index c14108d..51d2ad5 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -69,30 +69,56 @@ def test_check_samplesheet(): assert result.exit_code == 0 + # test the validation of an SDRF file def test_check_samplesheet_sdrf(): runner = CliRunner() - result = runner.invoke(cli, ["check_samplesheet", "--is_sdrf", "--check_ms", "--input", "tests/test_data/PXD000001.sdrf.tsv"]) + result = runner.invoke( + cli, + [ + "check_samplesheet", + "--is_sdrf", + "--check_ms", + "--input", + "tests/test_data/PXD000001.sdrf.tsv", + ], + ) assert result.exit_code == 0 + # test extract_sample_from_expdesign command in cli def test_extract_sample_from_expdesign(): runner = CliRunner() - result = runner.invoke(cli, ["extract_sample", "--expdesign", "tests/test_data/BSA_design_urls.tsv"]) + result = runner.invoke( + cli, ["extract_sample", "--expdesign", "tests/test_data/BSA_design_urls.tsv"] + ) assert result.exit_code == 0 + # test psm conversion command in cli def test_convert_psm(): runner = CliRunner() - result = runner.invoke(cli, ["convert_psm", "--idxml", "tests/test_data/BSA1_F1_msgf_idx_fdr_idpep_switched_filter.idXML", "--spectra_file", "tests/test_data/BSA1_F1_spectrum_df.csv"]) + result = runner.invoke( + cli, + [ + "convert_psm", + "--idxml", + "tests/test_data/BSA1_F1_msgf_idx_fdr_idpep_switched_filter.idXML", + "--spectra_file", + "tests/test_data/BSA1_F1_spectrum_df.csv", + ], + ) assert result.exit_code == 0 + # test mzml statistics command in cli def test_mzml_statistics(): runner = CliRunner() - result = runner.invoke(cli, ["mzml_statistics", "--ms_path", "tests/test_data/BSA1_F1.mzML"]) + result = runner.invoke( + cli, ["mzml_statistics", "--ms_path", "tests/test_data/BSA1_F1.mzML"] + ) - assert result.exit_code == 0 \ No newline at end of file + assert result.exit_code == 0 From 2305145e3fc35edf53ddda6ca3e80ae707eff874 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:08:08 +0100 Subject: [PATCH 36/53] more tests added. --- README.md | 11 ++++++++++- pyquantms/diann/{diann_convert.py => diann2mztab.py} | 4 ++-- pyquantms/diann/{create_diann_cfg.py => dianncfg.py} | 0 pyquantms/pyquantmsc.py | 6 +++--- pyquantms/sdrf/check_samplesheet.py | 2 +- pyquantms/sdrf/extract_sample.py | 2 +- tests/test_commands.py | 10 +++++----- 7 files changed, 22 insertions(+), 13 deletions(-) rename pyquantms/diann/{diann_convert.py => diann2mztab.py} (99%) rename pyquantms/diann/{create_diann_cfg.py => dianncfg.py} (100%) diff --git a/README.md b/README.md index 5c4f56d..4b58b38 100644 --- a/README.md +++ b/README.md @@ -15,4 +15,13 @@ The following functionalities are available in the package: ### Diann scripts -- `create +- `dianncfg` - Create a configuration file for Diann including enzymes, modifications, and other parameters. +- `diann2mztab` - Convert Diann output to mzTab format. In addition, convert DIA-NN output to MSstats, Triqler or mzTab. + The output formats are used for quality control and downstream analysis in quantms. + +### SDRF scripts + +- `openms2sample` - Extra sample information from OpenMS experimental design file. An example of OpenMS experimental design file is available [here](https://github.com/bigbio/pyquantms/blob/dev/tests/test_data/BSA_design_urls.tsv). +- `checksamplesheet` - Check the sample sheet for errors and inconsistencies. The experimental design coult be an OpenMS experimental design file or and SDRF file. + + diff --git a/pyquantms/diann/diann_convert.py b/pyquantms/diann/diann2mztab.py similarity index 99% rename from pyquantms/diann/diann_convert.py rename to pyquantms/diann/diann2mztab.py index fb7c9de..3701d74 100644 --- a/pyquantms/diann/diann_convert.py +++ b/pyquantms/diann/diann2mztab.py @@ -32,7 +32,7 @@ logger = logging.getLogger(__name__) -@click.command("diann_convert") +@click.command("diann2mztab", short_help="Convert DIA-NN output to MSstats, Triqler or mzTab") @click.option("--folder", "-f") @click.option("--exp_design", "-d") @click.option("--diann_version", "-v") @@ -41,7 +41,7 @@ @click.option("--missed_cleavages", "-m") @click.option("--qvalue_threshold", "-q", type=float) @click.pass_context -def diann_convert( +def diann2mztab( ctx, folder, exp_design, diff --git a/pyquantms/diann/create_diann_cfg.py b/pyquantms/diann/dianncfg.py similarity index 100% rename from pyquantms/diann/create_diann_cfg.py rename to pyquantms/diann/dianncfg.py diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index c794dfd..aab00d7 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -1,7 +1,7 @@ import click -from pyquantms.diann.create_diann_cfg import dianncfg -from pyquantms.diann.diann_convert import diann_convert +from pyquantms.diann.dianncfg import dianncfg +from pyquantms.diann.diann2mztab import diann2mztab from pyquantms.features.add_sage_feature import add_sage_feature from pyquantms.rescoring.ms2rescore import ms2rescore from pyquantms.mzml.mzml_statistics import mzml_statistics @@ -18,7 +18,7 @@ def cli(): cli.add_command(dianncfg) -cli.add_command(diann_convert) +cli.add_command(diann2mztab) cli.add_command(add_sage_feature) diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index 3949c5a..0260233 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -106,7 +106,7 @@ def check_expdesign_logic(f_table, s_table): sys.exit(1) -@click.command("check_samplesheet", short_help="Check samplesheet") +@click.command("checksamplesheet", short_help="Check samplesheet") @click.option("--is_sdrf", "-s", help="In Sdrf format", is_flag=True, default=False) @click.option( "--check_ms", diff --git a/pyquantms/sdrf/extract_sample.py b/pyquantms/sdrf/extract_sample.py index af6460b..1b2331d 100644 --- a/pyquantms/sdrf/extract_sample.py +++ b/pyquantms/sdrf/extract_sample.py @@ -3,7 +3,7 @@ import click -@click.command("extract_sample") +@click.command("openms2sample", short_help="Extract sample information from an experiment design file") @click.option( "--expdesign", help="Experiment design file", type=click.Path(exists=True) ) diff --git a/tests/test_commands.py b/tests/test_commands.py index 51d2ad5..16e6e6b 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -33,7 +33,7 @@ def test_mzml_statistics(): # test for the diann_convert command in cli def test_diann_convert(): runner = CliRunner() - result = runner.invoke(cli, ["diann_convert", "--help"]) + result = runner.invoke(cli, ["diann2mztab", "--help"]) assert result.exit_code == 0 @@ -41,7 +41,7 @@ def test_diann_convert(): # test for the extract_sample_from_expdesign command in cli def test_extract_sample_from_expdesign(): runner = CliRunner() - result = runner.invoke(cli, ["extract_sample", "--help"]) + result = runner.invoke(cli, ["openms2sample", "--help"]) assert result.exit_code == 0 @@ -65,7 +65,7 @@ def test_convert_psm(): # test for the check_samplesheet command in cli def test_check_samplesheet(): runner = CliRunner() - result = runner.invoke(cli, ["check_samplesheet", "--help"]) + result = runner.invoke(cli, ["checksamplesheet", "--help"]) assert result.exit_code == 0 @@ -76,7 +76,7 @@ def test_check_samplesheet_sdrf(): result = runner.invoke( cli, [ - "check_samplesheet", + "checksamplesheet", "--is_sdrf", "--check_ms", "--input", @@ -91,7 +91,7 @@ def test_check_samplesheet_sdrf(): def test_extract_sample_from_expdesign(): runner = CliRunner() result = runner.invoke( - cli, ["extract_sample", "--expdesign", "tests/test_data/BSA_design_urls.tsv"] + cli, ["openms2sample", "--expdesign", "tests/test_data/BSA_design_urls.tsv"] ) assert result.exit_code == 0 From 4a803884d5f799d2f1cbdf133c239ac4eeca832f Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:08:19 +0100 Subject: [PATCH 37/53] more tests added. --- pyquantms/diann/diann2mztab.py | 4 +++- pyquantms/sdrf/extract_sample.py | 5 ++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/pyquantms/diann/diann2mztab.py b/pyquantms/diann/diann2mztab.py index 3701d74..1d6faf8 100644 --- a/pyquantms/diann/diann2mztab.py +++ b/pyquantms/diann/diann2mztab.py @@ -32,7 +32,9 @@ logger = logging.getLogger(__name__) -@click.command("diann2mztab", short_help="Convert DIA-NN output to MSstats, Triqler or mzTab") +@click.command( + "diann2mztab", short_help="Convert DIA-NN output to MSstats, Triqler or mzTab" +) @click.option("--folder", "-f") @click.option("--exp_design", "-d") @click.option("--diann_version", "-v") diff --git a/pyquantms/sdrf/extract_sample.py b/pyquantms/sdrf/extract_sample.py index 1b2331d..0d0e7ff 100644 --- a/pyquantms/sdrf/extract_sample.py +++ b/pyquantms/sdrf/extract_sample.py @@ -3,7 +3,10 @@ import click -@click.command("openms2sample", short_help="Extract sample information from an experiment design file") +@click.command( + "openms2sample", + short_help="Extract sample information from an experiment design file", +) @click.option( "--expdesign", help="Experiment design file", type=click.Path(exists=True) ) From 48b75c4748a815862d280e1d53e2e4a8908df74a Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:35:22 +0100 Subject: [PATCH 38/53] more tests added. --- README.md | 16 ++++++++++++++++ .../{add_sage_feature.py => sage_feature.py} | 2 +- pyquantms/mzml/mzml_statistics.py | 2 +- pyquantms/psm/psm_conversion.py | 2 +- pyquantms/pyquantmsc.py | 2 +- tests/test_commands.py | 10 +++++----- 6 files changed, 25 insertions(+), 9 deletions(-) rename pyquantms/features/{add_sage_feature.py => sage_feature.py} (97%) diff --git a/README.md b/README.md index 4b58b38..d54721b 100644 --- a/README.md +++ b/README.md @@ -24,4 +24,20 @@ The following functionalities are available in the package: - `openms2sample` - Extra sample information from OpenMS experimental design file. An example of OpenMS experimental design file is available [here](https://github.com/bigbio/pyquantms/blob/dev/tests/test_data/BSA_design_urls.tsv). - `checksamplesheet` - Check the sample sheet for errors and inconsistencies. The experimental design coult be an OpenMS experimental design file or and SDRF file. +### ms2rescore scripts +- `ms2rescore` - Rescore MS2 spectra using the MS2PIP model. The output is a mzML file with the rescored MS2 spectra. + +### Features to percolator scripts + +- `sage2feature` - The add_sage_feature function enhances an idXML file by appending additional features from a Sage feature table, excluding those generated by 'psm_file'. + +### Other scripts + +- `psmconvert` - The convert_psm function converts peptide spectrum matches (PSMs) from an idXML file to a CSV file, optionally filtering out decoy matches. It extracts and processes data from both the idXML and an associated spectra file, handling multiple search engines and scoring systems. +- `mzmlstats` - The `mzmlstats` processes mass spectrometry data files in either `.mzML` or `Bruker .d` formats to extract and compile statistics about the spectra. It supports generating detailed or ID-only CSV files based on the spectra data. + + +### Contributions and issues + +Contributions and issues are welcome. Please, open an issue or a pull request in the [GitHub repository](https://github.com/bigbio/pyquantms). diff --git a/pyquantms/features/add_sage_feature.py b/pyquantms/features/sage_feature.py similarity index 97% rename from pyquantms/features/add_sage_feature.py rename to pyquantms/features/sage_feature.py index ca0c405..1f352f7 100644 --- a/pyquantms/features/add_sage_feature.py +++ b/pyquantms/features/sage_feature.py @@ -3,7 +3,7 @@ import pandas as pd -@click.command("add_sage_feature") +@click.command("sage2feature") @click.option("--idx_file", "-i", help="Input idXML file") @click.option("--output_file", "-o", help="Output idXML file") @click.option("--feat_file", "-f", help="Input feature table file") diff --git a/pyquantms/mzml/mzml_statistics.py b/pyquantms/mzml/mzml_statistics.py index 76d3736..694ba59 100644 --- a/pyquantms/mzml/mzml_statistics.py +++ b/pyquantms/mzml/mzml_statistics.py @@ -7,7 +7,7 @@ from pyopenms import MSExperiment, MzMLFile -@click.command("mzml_statistics") +@click.command("mzmlstats") @click.option("--ms_path", type=click.Path(exists=True)) @click.option( "--id_only", is_flag=True, help="Generate a csv with the spectrum id and the peaks" diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py index 0b769a5..55811db 100644 --- a/pyquantms/psm/psm_conversion.py +++ b/pyquantms/psm/psm_conversion.py @@ -49,7 +49,7 @@ def mods_position(peptide): return original_mods -@click.command("convert_psm") +@click.command("psmconvert", short_help="Convert idXML to csv file with PSMs information.") @click.option("--idxml", type=click.Path(exists=True)) @click.option("--spectra_file", type=click.Path(exists=True)) @click.option("--export_decoy_psm", is_flag=True) diff --git a/pyquantms/pyquantmsc.py b/pyquantms/pyquantmsc.py index aab00d7..b48ebf0 100644 --- a/pyquantms/pyquantmsc.py +++ b/pyquantms/pyquantmsc.py @@ -2,7 +2,7 @@ from pyquantms.diann.dianncfg import dianncfg from pyquantms.diann.diann2mztab import diann2mztab -from pyquantms.features.add_sage_feature import add_sage_feature +from pyquantms.features.sage_feature import add_sage_feature from pyquantms.rescoring.ms2rescore import ms2rescore from pyquantms.mzml.mzml_statistics import mzml_statistics from pyquantms.psm.psm_conversion import convert_psm diff --git a/tests/test_commands.py b/tests/test_commands.py index 16e6e6b..f960376 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -17,7 +17,7 @@ def test_create_diann_cfg(): # test for the add_sage_feature command in cli def test_add_sage_feature(): runner = CliRunner() - result = runner.invoke(cli, ["add_sage_feature", "--help"]) + result = runner.invoke(cli, ["sage2feature", "--help"]) assert result.exit_code == 0 @@ -25,7 +25,7 @@ def test_add_sage_feature(): # test for the mzml_statistics command in cli def test_mzml_statistics(): runner = CliRunner() - result = runner.invoke(cli, ["mzml_statistics", "--help"]) + result = runner.invoke(cli, ["mzmlstats", "--help"]) assert result.exit_code == 0 @@ -57,7 +57,7 @@ def test_ms2rescore(): # test for the convert_psm command in cli def test_convert_psm(): runner = CliRunner() - result = runner.invoke(cli, ["convert_psm", "--help"]) + result = runner.invoke(cli, ["psmconvert", "--help"]) assert result.exit_code == 0 @@ -103,7 +103,7 @@ def test_convert_psm(): result = runner.invoke( cli, [ - "convert_psm", + "psmconvert", "--idxml", "tests/test_data/BSA1_F1_msgf_idx_fdr_idpep_switched_filter.idXML", "--spectra_file", @@ -118,7 +118,7 @@ def test_convert_psm(): def test_mzml_statistics(): runner = CliRunner() result = runner.invoke( - cli, ["mzml_statistics", "--ms_path", "tests/test_data/BSA1_F1.mzML"] + cli, ["mzmlstats", "--ms_path", "tests/test_data/BSA1_F1.mzML"] ) assert result.exit_code == 0 From 665ffa24c13f808006e21a10a7770e351854d7f9 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:35:30 +0100 Subject: [PATCH 39/53] more tests added. --- pyquantms/psm/psm_conversion.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py index 55811db..4a3c5c4 100644 --- a/pyquantms/psm/psm_conversion.py +++ b/pyquantms/psm/psm_conversion.py @@ -49,7 +49,9 @@ def mods_position(peptide): return original_mods -@click.command("psmconvert", short_help="Convert idXML to csv file with PSMs information.") +@click.command( + "psmconvert", short_help="Convert idXML to csv file with PSMs information." +) @click.option("--idxml", type=click.Path(exists=True)) @click.option("--spectra_file", type=click.Path(exists=True)) @click.option("--export_decoy_psm", is_flag=True) From cf4e9257ecdf306a314ca0ea68a14522f9713d49 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:36:56 +0100 Subject: [PATCH 40/53] more tests added. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d54721b..99d5cdb 100644 --- a/README.md +++ b/README.md @@ -40,4 +40,4 @@ The following functionalities are available in the package: ### Contributions and issues -Contributions and issues are welcome. Please, open an issue or a pull request in the [GitHub repository](https://github.com/bigbio/pyquantms). +Contributions and issues are welcome. Please, open an issue in the [GitHub repository](https://github.com/bigbio/quantms) or PR in the [GitHub repository](https://github.com/bigbio/pyquantms). From 6ebda00705f786ea3289fe0a51d62c2cc36b6a30 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:43:50 +0100 Subject: [PATCH 41/53] more tests added. --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 99d5cdb..8d2b3fb 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![Python application](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml) [![Python package](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml) [![PyPI version](https://badge.fury.io/py/pyquantms.svg)](https://badge.fury.io/py/pyquantms) -[![Documentation Status](https://readthedocs.org/projects/pyquantms/badge/?version=latest)](https://pyquantms.readthedocs.io/en/latest/?badge=latest) + Python package with scripts and functions for the [quantms workflow](https://github.com/bigbio/quantms) for the analysis of quantitative proteomics data. @@ -37,7 +37,6 @@ The following functionalities are available in the package: - `psmconvert` - The convert_psm function converts peptide spectrum matches (PSMs) from an idXML file to a CSV file, optionally filtering out decoy matches. It extracts and processes data from both the idXML and an associated spectra file, handling multiple search engines and scoring systems. - `mzmlstats` - The `mzmlstats` processes mass spectrometry data files in either `.mzML` or `Bruker .d` formats to extract and compile statistics about the spectra. It supports generating detailed or ID-only CSV files based on the spectra data. - ### Contributions and issues Contributions and issues are welcome. Please, open an issue in the [GitHub repository](https://github.com/bigbio/quantms) or PR in the [GitHub repository](https://github.com/bigbio/pyquantms). From af629112d7b5846271f2134b5b69f89ca5d96211 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:49:20 +0100 Subject: [PATCH 42/53] more tests added. --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8d2b3fb..8fdf987 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # pyquantms [![Python application](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml) [![Python package](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml) +[![Codacy Badge](https://app.codacy.com/project/badge/Grade/ea6903630b3a4d15b674a16b8ce594a7)](https://app.codacy.com/gh/bigbio/pyquantms/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) [![PyPI version](https://badge.fury.io/py/pyquantms.svg)](https://badge.fury.io/py/pyquantms) From 5d9afcfd45dc5591274dcc2e60688baa9e079e8e Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:59:43 +0100 Subject: [PATCH 43/53] more tests added. --- README.md | 4 +++- pyquantms/sdrf/check_samplesheet.py | 8 ++++---- tests/test_commands.py | 18 +++++++++--------- 3 files changed, 16 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 8fdf987..3bb610f 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,8 @@ The package is available on PyPI: [pyquantms](https://pypi.org/project/pyquantms pip install pyquantms ``` +## Available Scripts + The following functionalities are available in the package: ### Diann scripts @@ -38,6 +40,6 @@ The following functionalities are available in the package: - `psmconvert` - The convert_psm function converts peptide spectrum matches (PSMs) from an idXML file to a CSV file, optionally filtering out decoy matches. It extracts and processes data from both the idXML and an associated spectra file, handling multiple search engines and scoring systems. - `mzmlstats` - The `mzmlstats` processes mass spectrometry data files in either `.mzML` or `Bruker .d` formats to extract and compile statistics about the spectra. It supports generating detailed or ID-only CSV files based on the spectra data. -### Contributions and issues +## Contributions and issues Contributions and issues are welcome. Please, open an issue in the [GitHub repository](https://github.com/bigbio/quantms) or PR in the [GitHub repository](https://github.com/bigbio/pyquantms). diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index 0260233..6d607ed 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -117,17 +117,17 @@ def check_expdesign_logic(f_table, s_table): default=False, ) @click.option( - "--input", + "-in", "--input_file", type=click.Path(exists=True), required=True, help="Input SDRF or Expdesign file", ) @click.pass_context -def check_samplesheet(ctx, is_sdrf: bool, check_ms: bool, input: str) -> None: +def check_samplesheet(ctx, is_sdrf: bool, check_ms: bool, input_file: str) -> None: """ Check the samplesheet for errors. """ if is_sdrf: - check_sdrf(check_ms, input) + check_sdrf(check_ms, input_file) else: - check_expdesign(input) + check_expdesign(input_file) diff --git a/tests/test_commands.py b/tests/test_commands.py index f960376..f8a0200 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -7,7 +7,7 @@ def test_future_test(): # test for the create_diann_cfg command in cli -def test_create_diann_cfg(): +def test_create_diann_cfg_help(): runner = CliRunner() result = runner.invoke(cli, ["dianncfg", "--help"]) @@ -15,7 +15,7 @@ def test_create_diann_cfg(): # test for the add_sage_feature command in cli -def test_add_sage_feature(): +def test_add_sage_feature_help(): runner = CliRunner() result = runner.invoke(cli, ["sage2feature", "--help"]) @@ -23,7 +23,7 @@ def test_add_sage_feature(): # test for the mzml_statistics command in cli -def test_mzml_statistics(): +def test_mzml_statistics_help(): runner = CliRunner() result = runner.invoke(cli, ["mzmlstats", "--help"]) @@ -31,7 +31,7 @@ def test_mzml_statistics(): # test for the diann_convert command in cli -def test_diann_convert(): +def test_diann_convert_help(): runner = CliRunner() result = runner.invoke(cli, ["diann2mztab", "--help"]) @@ -39,7 +39,7 @@ def test_diann_convert(): # test for the extract_sample_from_expdesign command in cli -def test_extract_sample_from_expdesign(): +def test_extract_sample_from_expdesign_help(): runner = CliRunner() result = runner.invoke(cli, ["openms2sample", "--help"]) @@ -47,7 +47,7 @@ def test_extract_sample_from_expdesign(): # test for the rescoring command in cli -def test_ms2rescore(): +def test_ms2rescore_help(): runner = CliRunner() result = runner.invoke(cli, ["ms2rescore", "--help"]) @@ -55,7 +55,7 @@ def test_ms2rescore(): # test for the convert_psm command in cli -def test_convert_psm(): +def test_convert_psm_help(): runner = CliRunner() result = runner.invoke(cli, ["psmconvert", "--help"]) @@ -63,7 +63,7 @@ def test_convert_psm(): # test for the check_samplesheet command in cli -def test_check_samplesheet(): +def test_check_samplesheet_help(): runner = CliRunner() result = runner.invoke(cli, ["checksamplesheet", "--help"]) @@ -79,7 +79,7 @@ def test_check_samplesheet_sdrf(): "checksamplesheet", "--is_sdrf", "--check_ms", - "--input", + "--input_file", "tests/test_data/PXD000001.sdrf.tsv", ], ) From 285e34a8d51a3c6b8f5342ebf68a8feb340c2dfc Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 10:09:06 +0100 Subject: [PATCH 44/53] more tests added. --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 63f58c8..e538f95 100644 --- a/setup.py +++ b/setup.py @@ -37,7 +37,7 @@ "pydantic", "pandas", ] -PYTHON_REQUIRES = ">=3.7,<4" +PYTHON_REQUIRES = ">=3.8,<4" with open("README.md", "r") as fh: LONG_DESCRIPTION = fh.read() From 552bcac973ab3d8cb9c3f8823745a2effbde00d6 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 10:19:04 +0100 Subject: [PATCH 45/53] more tests added. --- README.md | 2 +- pyquantms/sdrf/check_samplesheet.py | 3 ++- tests/test_commands.py | 5 ----- 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 3bb610f..a305ecb 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![Python package](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/ea6903630b3a4d15b674a16b8ce594a7)](https://app.codacy.com/gh/bigbio/pyquantms/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) [![PyPI version](https://badge.fury.io/py/pyquantms.svg)](https://badge.fury.io/py/pyquantms) - +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) Python package with scripts and functions for the [quantms workflow](https://github.com/bigbio/quantms) for the analysis of quantitative proteomics data. diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index 6d607ed..f1c50e5 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -117,7 +117,8 @@ def check_expdesign_logic(f_table, s_table): default=False, ) @click.option( - "-in", "--input_file", + "-in", + "--input_file", type=click.Path(exists=True), required=True, help="Input SDRF or Expdesign file", diff --git a/tests/test_commands.py b/tests/test_commands.py index f8a0200..7095ce1 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -1,11 +1,6 @@ from click.testing import CliRunner from pyquantms.pyquantmsc import cli - -def test_future_test(): - assert 1 == 1 - - # test for the create_diann_cfg command in cli def test_create_diann_cfg_help(): runner = CliRunner() From def41a0525961969ab24f28f2b73ae6c2ff40f27 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 10:27:50 +0100 Subject: [PATCH 46/53] more tests added. --- .codacy.yml | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 .codacy.yml diff --git a/.codacy.yml b/.codacy.yml new file mode 100644 index 0000000..2fab84e --- /dev/null +++ b/.codacy.yml @@ -0,0 +1,8 @@ +engines: + eslint: + enabled: true + pylint: + enabled: true + +ignore_patterns: + - "tests/*" From 4fcbab453948e0fb696ab57964e5fbbf1245cc94 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 10:39:47 +0100 Subject: [PATCH 47/53] more tests added. --- pyquantms/psm/psm_conversion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py index 4a3c5c4..86e650a 100644 --- a/pyquantms/psm/psm_conversion.py +++ b/pyquantms/psm/psm_conversion.py @@ -39,7 +39,7 @@ def mods_position(peptide): for j in range(1, len(position)): position[j] -= j - for k in range(0, len(original_mods)): + for k, mod in enumerate(original_mods): original_mods[k] = str(position[k]) + "-" + original_mods[k] original_mods = ( From 6ac597b4675a99b8cb0a068cdc6b92d5554ded62 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 11:45:32 +0100 Subject: [PATCH 48/53] more tests added. --- pyquantms/diann/diann2mztab.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyquantms/diann/diann2mztab.py b/pyquantms/diann/diann2mztab.py index 1d6faf8..e2dbfd6 100644 --- a/pyquantms/diann/diann2mztab.py +++ b/pyquantms/diann/diann2mztab.py @@ -1386,7 +1386,7 @@ def find_modification(peptide): for j in range(1, len(position)): position[j] -= j - for k in range(0, len(original_mods)): + for k, mod in enumerate(original_mods): original_mods[k] = str(position[k]) + "-" + original_mods[k].upper() original_mods = ( From 455dcbfa701189a8d3106d9113b77d64201f1192 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 11:53:04 +0100 Subject: [PATCH 49/53] more tests added. --- pyquantms/diann/diann2mztab.py | 2 +- pyquantms/psm/psm_conversion.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyquantms/diann/diann2mztab.py b/pyquantms/diann/diann2mztab.py index e2dbfd6..9935b4c 100644 --- a/pyquantms/diann/diann2mztab.py +++ b/pyquantms/diann/diann2mztab.py @@ -1387,7 +1387,7 @@ def find_modification(peptide): position[j] -= j for k, mod in enumerate(original_mods): - original_mods[k] = str(position[k]) + "-" + original_mods[k].upper() + original_mods[k] = str(position[k]) + "-" + mod.upper() original_mods = ( ",".join(str(i) for i in original_mods) if len(original_mods) > 0 else "null" diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py index 86e650a..02d8eba 100644 --- a/pyquantms/psm/psm_conversion.py +++ b/pyquantms/psm/psm_conversion.py @@ -40,7 +40,7 @@ def mods_position(peptide): position[j] -= j for k, mod in enumerate(original_mods): - original_mods[k] = str(position[k]) + "-" + original_mods[k] + original_mods[k] = str(position[k]) + "-" + mod original_mods = ( [str(i) for i in original_mods] if len(original_mods) > 0 else np.nan From ac3812a0223f0e6e7a6a3577351dcb9044188d4d Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 11:56:54 +0100 Subject: [PATCH 50/53] more tests added. --- pyquantms/diann/diann2mztab.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyquantms/diann/diann2mztab.py b/pyquantms/diann/diann2mztab.py index 9935b4c..14a5fa9 100644 --- a/pyquantms/diann/diann2mztab.py +++ b/pyquantms/diann/diann2mztab.py @@ -1104,7 +1104,7 @@ def __find_info(directory, n): "exp_mass_to_charge", ] # Standardize spectrum identifier format for bruker data - if type(target.loc[0, "opt_global_spectrum_reference"]) != str: + if not isinstance(target.loc[0, "opt_global_spectrum_reference"], str): target.loc[:, "opt_global_spectrum_reference"] = "scan=" + target.loc[ :, "opt_global_spectrum_reference" ].astype(str) From f591b9feb62721d1880b43797b4d70c4dae61cd5 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 12:01:44 +0100 Subject: [PATCH 51/53] more tests added. --- .codacy.yml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/.codacy.yml b/.codacy.yml index 2fab84e..91d20e1 100644 --- a/.codacy.yml +++ b/.codacy.yml @@ -6,3 +6,12 @@ engines: ignore_patterns: - "tests/*" + +eslint: + enabled: true + config_file: .eslintrc.json + exclude_paths: + - 'tests/**' + +exclude_paths: + - 'tests/**' \ No newline at end of file From 355cd6f388c7416c3a3137317a83c089cba26223 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 09:43:50 +0100 Subject: [PATCH 52/53] Multiple fixes for Codacy --- .codacy.yml | 17 +++++++++++++++++ README.md | 8 +++++--- pyquantms/diann/diann2mztab.py | 6 +++--- pyquantms/psm/psm_conversion.py | 4 ++-- pyquantms/sdrf/check_samplesheet.py | 9 +++++---- setup.py | 2 +- tests/test_commands.py | 23 +++++++++-------------- 7 files changed, 42 insertions(+), 27 deletions(-) create mode 100644 .codacy.yml diff --git a/.codacy.yml b/.codacy.yml new file mode 100644 index 0000000..91d20e1 --- /dev/null +++ b/.codacy.yml @@ -0,0 +1,17 @@ +engines: + eslint: + enabled: true + pylint: + enabled: true + +ignore_patterns: + - "tests/*" + +eslint: + enabled: true + config_file: .eslintrc.json + exclude_paths: + - 'tests/**' + +exclude_paths: + - 'tests/**' \ No newline at end of file diff --git a/README.md b/README.md index 99d5cdb..a305ecb 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,9 @@ # pyquantms [![Python application](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-app.yml) [![Python package](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml/badge.svg)](https://github.com/bigbio/pyquantms/actions/workflows/python-package.yml) +[![Codacy Badge](https://app.codacy.com/project/badge/Grade/ea6903630b3a4d15b674a16b8ce594a7)](https://app.codacy.com/gh/bigbio/pyquantms/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) [![PyPI version](https://badge.fury.io/py/pyquantms.svg)](https://badge.fury.io/py/pyquantms) -[![Documentation Status](https://readthedocs.org/projects/pyquantms/badge/?version=latest)](https://pyquantms.readthedocs.io/en/latest/?badge=latest) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) Python package with scripts and functions for the [quantms workflow](https://github.com/bigbio/quantms) for the analysis of quantitative proteomics data. @@ -11,6 +12,8 @@ The package is available on PyPI: [pyquantms](https://pypi.org/project/pyquantms pip install pyquantms ``` +## Available Scripts + The following functionalities are available in the package: ### Diann scripts @@ -37,7 +40,6 @@ The following functionalities are available in the package: - `psmconvert` - The convert_psm function converts peptide spectrum matches (PSMs) from an idXML file to a CSV file, optionally filtering out decoy matches. It extracts and processes data from both the idXML and an associated spectra file, handling multiple search engines and scoring systems. - `mzmlstats` - The `mzmlstats` processes mass spectrometry data files in either `.mzML` or `Bruker .d` formats to extract and compile statistics about the spectra. It supports generating detailed or ID-only CSV files based on the spectra data. - -### Contributions and issues +## Contributions and issues Contributions and issues are welcome. Please, open an issue in the [GitHub repository](https://github.com/bigbio/quantms) or PR in the [GitHub repository](https://github.com/bigbio/pyquantms). diff --git a/pyquantms/diann/diann2mztab.py b/pyquantms/diann/diann2mztab.py index 1d6faf8..14a5fa9 100644 --- a/pyquantms/diann/diann2mztab.py +++ b/pyquantms/diann/diann2mztab.py @@ -1104,7 +1104,7 @@ def __find_info(directory, n): "exp_mass_to_charge", ] # Standardize spectrum identifier format for bruker data - if type(target.loc[0, "opt_global_spectrum_reference"]) != str: + if not isinstance(target.loc[0, "opt_global_spectrum_reference"], str): target.loc[:, "opt_global_spectrum_reference"] = "scan=" + target.loc[ :, "opt_global_spectrum_reference" ].astype(str) @@ -1386,8 +1386,8 @@ def find_modification(peptide): for j in range(1, len(position)): position[j] -= j - for k in range(0, len(original_mods)): - original_mods[k] = str(position[k]) + "-" + original_mods[k].upper() + for k, mod in enumerate(original_mods): + original_mods[k] = str(position[k]) + "-" + mod.upper() original_mods = ( ",".join(str(i) for i in original_mods) if len(original_mods) > 0 else "null" diff --git a/pyquantms/psm/psm_conversion.py b/pyquantms/psm/psm_conversion.py index 4a3c5c4..02d8eba 100644 --- a/pyquantms/psm/psm_conversion.py +++ b/pyquantms/psm/psm_conversion.py @@ -39,8 +39,8 @@ def mods_position(peptide): for j in range(1, len(position)): position[j] -= j - for k in range(0, len(original_mods)): - original_mods[k] = str(position[k]) + "-" + original_mods[k] + for k, mod in enumerate(original_mods): + original_mods[k] = str(position[k]) + "-" + mod original_mods = ( [str(i) for i in original_mods] if len(original_mods) > 0 else np.nan diff --git a/pyquantms/sdrf/check_samplesheet.py b/pyquantms/sdrf/check_samplesheet.py index 0260233..f1c50e5 100644 --- a/pyquantms/sdrf/check_samplesheet.py +++ b/pyquantms/sdrf/check_samplesheet.py @@ -117,17 +117,18 @@ def check_expdesign_logic(f_table, s_table): default=False, ) @click.option( - "--input", + "-in", + "--input_file", type=click.Path(exists=True), required=True, help="Input SDRF or Expdesign file", ) @click.pass_context -def check_samplesheet(ctx, is_sdrf: bool, check_ms: bool, input: str) -> None: +def check_samplesheet(ctx, is_sdrf: bool, check_ms: bool, input_file: str) -> None: """ Check the samplesheet for errors. """ if is_sdrf: - check_sdrf(check_ms, input) + check_sdrf(check_ms, input_file) else: - check_expdesign(input) + check_expdesign(input_file) diff --git a/setup.py b/setup.py index 63f58c8..e538f95 100644 --- a/setup.py +++ b/setup.py @@ -37,7 +37,7 @@ "pydantic", "pandas", ] -PYTHON_REQUIRES = ">=3.7,<4" +PYTHON_REQUIRES = ">=3.8,<4" with open("README.md", "r") as fh: LONG_DESCRIPTION = fh.read() diff --git a/tests/test_commands.py b/tests/test_commands.py index f960376..7095ce1 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -1,13 +1,8 @@ from click.testing import CliRunner from pyquantms.pyquantmsc import cli - -def test_future_test(): - assert 1 == 1 - - # test for the create_diann_cfg command in cli -def test_create_diann_cfg(): +def test_create_diann_cfg_help(): runner = CliRunner() result = runner.invoke(cli, ["dianncfg", "--help"]) @@ -15,7 +10,7 @@ def test_create_diann_cfg(): # test for the add_sage_feature command in cli -def test_add_sage_feature(): +def test_add_sage_feature_help(): runner = CliRunner() result = runner.invoke(cli, ["sage2feature", "--help"]) @@ -23,7 +18,7 @@ def test_add_sage_feature(): # test for the mzml_statistics command in cli -def test_mzml_statistics(): +def test_mzml_statistics_help(): runner = CliRunner() result = runner.invoke(cli, ["mzmlstats", "--help"]) @@ -31,7 +26,7 @@ def test_mzml_statistics(): # test for the diann_convert command in cli -def test_diann_convert(): +def test_diann_convert_help(): runner = CliRunner() result = runner.invoke(cli, ["diann2mztab", "--help"]) @@ -39,7 +34,7 @@ def test_diann_convert(): # test for the extract_sample_from_expdesign command in cli -def test_extract_sample_from_expdesign(): +def test_extract_sample_from_expdesign_help(): runner = CliRunner() result = runner.invoke(cli, ["openms2sample", "--help"]) @@ -47,7 +42,7 @@ def test_extract_sample_from_expdesign(): # test for the rescoring command in cli -def test_ms2rescore(): +def test_ms2rescore_help(): runner = CliRunner() result = runner.invoke(cli, ["ms2rescore", "--help"]) @@ -55,7 +50,7 @@ def test_ms2rescore(): # test for the convert_psm command in cli -def test_convert_psm(): +def test_convert_psm_help(): runner = CliRunner() result = runner.invoke(cli, ["psmconvert", "--help"]) @@ -63,7 +58,7 @@ def test_convert_psm(): # test for the check_samplesheet command in cli -def test_check_samplesheet(): +def test_check_samplesheet_help(): runner = CliRunner() result = runner.invoke(cli, ["checksamplesheet", "--help"]) @@ -79,7 +74,7 @@ def test_check_samplesheet_sdrf(): "checksamplesheet", "--is_sdrf", "--check_ms", - "--input", + "--input_file", "tests/test_data/PXD000001.sdrf.tsv", ], ) From 840b4f328b93a4b1d8a8fe81a1d91a6121987aa8 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Sun, 26 May 2024 12:09:02 +0100 Subject: [PATCH 53/53] more tests added. --- tests/test_commands.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_commands.py b/tests/test_commands.py index 7095ce1..7a93800 100644 --- a/tests/test_commands.py +++ b/tests/test_commands.py @@ -1,6 +1,7 @@ from click.testing import CliRunner from pyquantms.pyquantmsc import cli + # test for the create_diann_cfg command in cli def test_create_diann_cfg_help(): runner = CliRunner()