"""
I2MassChroQ parameter file parser.
"""
import pathlib
import pandas as pd
from proteobench.io.params import ProteoBenchParameters
def _extract_xtandem_params(params: pd.Series) -> ProteoBenchParameters:
"""
Parse i2MassChroQ parameters when with X!Tandem is used.
Parameters
----------
params : pd.Series
The parameters extracted from the i2MassChroQ parameter file.
Returns
-------
ProteoBenchParameters
The extracted parameters encapsulated in a `ProteoBenchParameters` object.
"""
_tol_frag = "{} {}".format(
params.loc["spectrum, fragment monoisotopic mass error"],
params.loc["spectrum, fragment monoisotopic mass error units"].replace("Daltons", "Da"),
)
# Construct tolerance strings for parent mass error
_tol_prec_lower = "{} {}".format(
params.loc["spectrum, parent monoisotopic mass error minus"],
params.loc["spectrum, parent monoisotopic mass error units"].replace("Daltons", "Da"),
)
_tol_prec_upper = "{} {}".format(
params.loc["spectrum, parent monoisotopic mass error plus"],
params.loc["spectrum, parent monoisotopic mass error units"].replace("Daltons", "Da"),
)
# Max missed cleavage sites, either from scoring or refinement
max_cleavage = params.loc["scoring, maximum missed cleavage sites"]
if params.loc["refine"] == "yes":
max_cleavage = int(params.loc["refine, maximum missed cleavage sites"])
_enzyme = str(params.loc["protein, cleavage site"])
fixed_mods_list = list(params.loc[params.index.str.contains("residue, modification mass")].dropna())
var_mods_list = list(params.loc[params.index.str.contains("residue, potential modification mass")].dropna())
# Add "hidden" modifications when using X!Tandem:
if params.loc["protein, quick acetyl"] == "yes":
var_mods_list.append("Acetyl(N-term)")
if params.loc["protein, quick pyrolidone"] == "yes":
var_mods_list.append("Pyrolidone(N-term)")
# Create and return a ProteoBenchParameters object with the extracted values
params = ProteoBenchParameters(
software_name="i2MassChroQ",
software_version=params.loc["i2MassChroQ_VERSION"],
search_engine=params.loc["AnalysisSoftware_name"],
search_engine_version=str(params.loc["AnalysisSoftware_version"] or ""),
ident_fdr_psm=float(params.loc["psm_fdr"]),
ident_fdr_peptide=float(params.loc["peptide_fdr"]),
ident_fdr_protein=float(params.loc["protein_fdr"]),
# set match between runs to True if it is enabled
enable_match_between_runs=True if params.loc["mcq_mbr"] == "T" else False,
precursor_mass_tolerance="[-" + _tol_prec_lower + ", " + _tol_prec_upper + "]",
fragment_mass_tolerance="[-" + _tol_frag + ", " + _tol_frag + "]",
enzyme=_enzyme,
allowed_miscleavages=max_cleavage,
min_peptide_length=None, # xtandem: "spectrum, minimum fragment mz"
max_peptide_length=None,
fixed_mods=";".join(fixed_mods_list),
variable_mods=";".join(var_mods_list),
max_mods=None,
min_precursor_charge=None,
max_precursor_charge=int(params.loc["spectrum, maximum parent charge"]),
)
params.fill_none()
return params
def _extract_sage_params(params: pd.Series) -> ProteoBenchParameters:
"""
Parse i2MassChroQ parameters when Sage is used.
Parameters
----------
params : pd.Series
The parameters extracted from the i2MassChroQ parameter file.
Returns
-------
ProteoBenchParameters
The extracted parameters encapsulated in a `ProteoBenchParameters` object.
"""
# Construct tolerance strings for fragment and parent mass errors
fragment_mass_tolerance = params.loc["sage_fragment_tol"] # e.g '-0.02 0.02 da'
# Construct tolerance strings for parent mass error
precursor_mass_tolerance = params.loc["sage_precursor_tol"] # e.g. "-10 10 ppm"
# Max missed cleavage sites, either from scoring or refinement
max_cleavage = int(params.loc["sage_database_enzyme_missed_cleavages"]) # e.g. "2"
_enzyme = "{},{},{}".format(
params.loc["sage_database_enzyme_cleave_at"],
params.loc["sage_database_enzyme_restrict"],
params.loc["sage_database_enzyme_c_terminal"],
) # e.g. "KR" and "sage_database_enzyme_restrict" "P" and 'sage_database_enzyme_c_terminal' "true"
# Replace the enzyme pattern with the enzyme name used in ProteoBench
# if _enzyme == "[RK]|{P}":
# _enzyme = "Trypsin"
# elif _enzyme == "[RK]":
# _enzyme = "Trypsin/P"
fixed_mods_list = params.loc["sage_database_static_mods"] # C:57.021465
var_mods_list = params.loc["sage_database_variable_mods"] # "M:15.994915 ^E:-18.010565 ^Q:-17.026548"
min_precursor_charge, max_precursor_charge = params.loc["sage_precursor_charge"].split()
# Create and return a ProteoBenchParameters object with the extracted values
params = ProteoBenchParameters(
software_name="i2MassChroQ",
software_version=params.loc["i2MassChroQ_VERSION"],
search_engine=params.loc["AnalysisSoftware_name"],
search_engine_version=str(params.loc["AnalysisSoftware_version"] or ""),
ident_fdr_psm=float(params.loc["psm_fdr"]),
ident_fdr_peptide=float(params.loc["peptide_fdr"]),
ident_fdr_protein=float(params.loc["protein_fdr"]),
# set match between runs to True if it is enabled
enable_match_between_runs=True if params.loc["mcq_mbr"] == "T" else False,
precursor_mass_tolerance=precursor_mass_tolerance,
fragment_mass_tolerance=fragment_mass_tolerance,
enzyme=_enzyme,
allowed_miscleavages=max_cleavage,
min_peptide_length=int(params.loc["sage_database_enzyme_min_len"]), # 5
max_peptide_length=int(params.loc["sage_database_enzyme_max_len"]), # 50
fixed_mods=fixed_mods_list,
variable_mods=var_mods_list,
max_mods=int(params.loc["sage_database_max_variable_mods"]), # 2
min_precursor_charge=int(min_precursor_charge),
max_precursor_charge=int(max_precursor_charge),
)
params.fill_none()
return params
if __name__ == "__main__":
# Reads i2MassChroQ parameter files, extracts parameters, and writes them to CSV files.
# List of parameter file paths
base_dir = pathlib.Path("../../../test/params/")
for fname in [
"i2mproteobench_2pep_fdr01psm_fdr01prot_xtandem.tsv",
"i2mq_result_parameters.tsv",
"i2mproteobench_params_sage.tsv",
]:
file = base_dir / fname
# Read the parameter file to extract parameters
params = pd.read_csv(file, sep="\t", header=None, index_col=0).squeeze()
params = extract_params(file.with_suffix(".tsv"))
# Convert the parameters to a dictionary and then to a pandas Series
data_dict = params.__dict__
series = pd.Series(data_dict)
print(series)
# Write the Series to a CSV file
series.to_csv(file.parent / (file.stem + "_sel.csv"))
