From 10228a56cb73cfa46211d3fa99f4c9fb4faccf56 Mon Sep 17 00:00:00 2001 From: Mario Picciani Date: Fri, 2 Jun 2023 19:02:58 +0200 Subject: [PATCH 1/3] added support for msamanda search results --- spectrum_io/search_result/msamanda.py | 132 ++++++++++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 spectrum_io/search_result/msamanda.py diff --git a/spectrum_io/search_result/msamanda.py b/spectrum_io/search_result/msamanda.py new file mode 100644 index 0000000..9d02611 --- /dev/null +++ b/spectrum_io/search_result/msamanda.py @@ -0,0 +1,132 @@ +import logging +from pathlib import Path +from typing import Union + +import pandas as pd +from spectrum_fundamentals.constants import PARTICLE_MASSES + +from .search_results import filter_valid_prosit_sequences + +logger = logging.getLogger(__name__) + + +def _update_columns_for_prosit(df: pd.DataFrame): + """ + Update columns of df to work with Prosit. + + :param df: df to modify + :return: modified df as pd.DataFrame + """ + + def _check_unsupported_mods(modstring: str): + if modstring: + for mod in modstring.split(";"): + if not mod.split("(", 1)[1].startswith(("Oxidation", "Carbamidomethyl")): + raise AssertionError(f"Unsupported modification: {mod}") + + def _make_mod_seq(seq: str): + seq = seq.replace("m", "M[UNIMOD:35]") + seq = seq.replace("c", "C[UNIMOD:4]") + return seq + + df["REVERSE"] = df["Protein Accessions"].str.startswith("REV_") + df["MASS"] = df["m/z"] * df["PRECURSOR_CHARGE"] - (df["PRECURSOR_CHARGE"] * PARTICLE_MASSES["PROTON"]) + df["Modifications"].fillna("").apply(_check_unsupported_mods) + df["MODIFIED_SEQUENCE"] = df["SEQUENCE"].apply(_make_mod_seq) + df["SEQUENCE"] = df["SEQUENCE"].str.upper() + df["PEPTIDE_LENGTH"] = df["SEQUENCE"].str.len() + df["RAW_FILE"] = df["RAW_FILE"].str.replace(r"\.\w+$", "", regex=True) + + return df[ + [ + "SCORE", + "MASS", + "PRECURSOR_CHARGE", + "RAW_FILE", + "SCAN_NUMBER", + "REVERSE", + "MODIFIED_SEQUENCE", + "SEQUENCE", + "PEPTIDE_LENGTH", + ] + ] + + +def _remove_decoys_in_targets(full_df): + duplicated_psms = full_df[["SCAN_NUMBER", "RAW_FILE", "MODIFIED_SEQUENCE"]].duplicated(keep=False) + logger.info(f"Removing {sum(duplicated_psms)} duplicated PSMs...") + full_df = full_df[~(full_df["REVERSE"] & duplicated_psms)] + if any(full_df[["SCAN_NUMBER", "RAW_FILE", "MODIFIED_SEQUENCE"]].duplicated(keep=False)): + raise AssertionError("There are duplicate target PSMs. This is a bug of msamanda!") + logger.info(f"{len(full_df)} remaining PSMs") + + return full_df + + +def _filter_unmatched_scans(df): + unmatched = df["Sequence"].isnull() + logger.info(f"Removing {sum(unmatched)} unmatched PSMs") + return df[~df["Sequence"].isnull()] + + +def read_result(path: Union[str, Path], suffix: str = "output.csv") -> pd.DataFrame: + """ + Function to read a msms txt and perform some basic formatting. + + :param path: path to msms.txt to read + :param suffix: Optional suffix to determine which fileresult files should be taken from the supplied path + :raises FileNotFoundError: If the supplied path is not found + :raises AssertionError: If the supplied path does not contain any files matching the provided suffix. + :return: pd.DataFrame with the formatted data + """ + if isinstance(path, str): + path = Path(path) + if path.is_file(): + pathlist = [path] + elif path.is_dir(): + pathlist = list(path.glob(f"*{suffix}")) + if not pathlist: + raise AssertionError(f"The directory does not contain any files that match the pattern *{suffix}") + else: + raise FileNotFoundError(f"{path} does not exist.") + + df_list = [] + for output_file in pathlist: + logger.info(f"Reading {output_file}...") + df = pd.read_csv( + output_file, + sep="\t", + skiprows=1, + usecols=[ + "Scan Number", + "Sequence", + "Modifications", + "Filename", + "Amanda Score", + "m/z", + "Charge", + "Protein Accessions", + ], + ) + df = _filter_unmatched_scans(df) + df.columns = [ + "SCAN_NUMBER", + "SEQUENCE", + "Modifications", + "Protein Accessions", + "SCORE", + "m/z", + "PRECURSOR_CHARGE", + "RAW_FILE", + ] + logger.info(f"Finished reading {output_file}") + + df = _update_columns_for_prosit(df) + df = filter_valid_prosit_sequences(df) + df_list.append(df) + + if len(df_list) == 1: + full_df = df_list[0] + full_df = pd.concat(df_list, axis=0) + full_df = _remove_decoys_in_targets(full_df) + return full_df From 3113f34293dad216e5142436e7f9970c7f309238 Mon Sep 17 00:00:00 2001 From: Mario Picciani Date: Sun, 25 Jun 2023 14:11:21 +0200 Subject: [PATCH 2/3] remvoed filtering function --- spectrum_io/search_result/msamanda.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/spectrum_io/search_result/msamanda.py b/spectrum_io/search_result/msamanda.py index 9d02611..4be13e6 100644 --- a/spectrum_io/search_result/msamanda.py +++ b/spectrum_io/search_result/msamanda.py @@ -63,12 +63,6 @@ def _remove_decoys_in_targets(full_df): return full_df -def _filter_unmatched_scans(df): - unmatched = df["Sequence"].isnull() - logger.info(f"Removing {sum(unmatched)} unmatched PSMs") - return df[~df["Sequence"].isnull()] - - def read_result(path: Union[str, Path], suffix: str = "output.csv") -> pd.DataFrame: """ Function to read a msms txt and perform some basic formatting. @@ -108,7 +102,6 @@ def read_result(path: Union[str, Path], suffix: str = "output.csv") -> pd.DataFr "Protein Accessions", ], ) - df = _filter_unmatched_scans(df) df.columns = [ "SCAN_NUMBER", "SEQUENCE", From b65fcafb826009a6399f92b89d7c2cb688fcde8d Mon Sep 17 00:00:00 2001 From: Mario Picciani Date: Sun, 25 Jun 2023 14:13:43 +0200 Subject: [PATCH 3/3] updated poetry lock to spectrum-fundamentals 0.4.1 --- poetry.lock | 668 ++++++++++++++++++++++++++-------------------------- 1 file changed, 340 insertions(+), 328 deletions(-) diff --git a/poetry.lock b/poetry.lock index a30283a..a49f3ef 100644 --- a/poetry.lock +++ b/poetry.lock @@ -292,78 +292,62 @@ files = [ [[package]] name = "contourpy" -version = "1.0.7" +version = "1.1.0" 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