Source code for proteobench.io.params.msangel
"""
MSAngel creates modular pipelines that allows several search engines to identify
peptides, which are then quantified with Proline.
The parameters are provided in a .json file.
MSAngel allows for multiple search engines to be used in the same pipeline. So it
requires a list of search engines and their respective parameters, which are then
concatenated.
Relevant information in file:
-
"""
import json
import os
import pathlib
from typing import Union
import pandas as pd
from proteobench.io.params import ProteoBenchParameters
[docs]
def extract_search_engine(search_params: list) -> dict:
"""
Extract search engine name from the JSON data.
It only works for workflows using a single search engine.
Parameters
----------
search_params : list
The list of search parameters extracted from the JSON file.
Returns
-------
dict
The search engine name.
"""
for each_search_params in search_params["operations"]:
if "searchEnginesWithForms" in each_search_params:
return each_search_params["searchEnginesWithForms"][0][0]
[docs]
def extract_params_mascot_specific(search_params: list, input_params: ProteoBenchParameters) -> ProteoBenchParameters:
"""
Extract search parameters from the JSON data of a workflow running Mascot.
Adds them to the partially completed input_params ProteoBenchParameters object.
Parameters
----------
search_params : list
The list of search parameters extracted from the JSON file.
input_params : ProteoBenchParameters
The partially completed input_params object.
Returns
-------
ProteoBenchParameters
The input_params object with the extracted parameters added.
"""
for each_search_params in search_params["operations"]:
if "searchEnginesWithForms" in each_search_params:
# params.search_engine_version =
input_params.enzyme = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["CLE"]
# params.allowed_miscleavages =
input_params.fixed_mods = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["MODS"]
input_params.variable_mods = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["IT_MODS"]
input_params.allowed_miscleavages = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["PFA"]
second_pass = input_params.allowed_miscleavages = each_search_params["searchEnginesWithForms"][0][1][
"paramMap"
]["ERRORTOLERANT"]
if second_pass == "1":
input_params.second_pass = True
else:
input_params.second_pass = False
# get tolerance:
tol = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["TOL"]
unit = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["TOLU"]
tol = float(tol)
print(tol)
input_params.precursor_mass_tolerance = "[-" + str(tol) + " " + unit + ", +" + str(tol) + " " + unit + "]"
if "validationConfig" in each_search_params:
input_params.ident_fdr_psm = each_search_params["validationConfig"]["psmExpectedFdr"] / 100
# input_params.min_peptide_length = each_search_params["validationConfig"]["psmFilters"] #TODO: I am not sure if this is the max or min length
return input_params
[docs]
def extract_params_xtandem_specific(search_params: list, input_params: ProteoBenchParameters) -> ProteoBenchParameters:
"""
Extract search parameters from the JSON data of a workflow running X!Tandem.
Adds them to the partially completed input_params ProteoBenchParameters object.
Parameters
----------
search_params : list
The list of search parameters extracted from the JSON file.
input_params : ProteoBenchParameters
The partially completed input_params object.
Returns
-------
ProteoBenchParameters
The input_params object with the extracted parameters added.
"""
for each_search_params in search_params["operations"]:
if "searchEnginesWithForms" in each_search_params:
# params.search_engine_version =
input_params.enzyme = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["digestionParameters"][
"enzymes"
][0]["name"]
# params.allowed_miscleavages =
input_params.fixed_mods = ", ".join(
each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["modificationParameters"][
"fixedModifications"
]
)
input_params.variable_mods = ", ".join(
each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["modificationParameters"][
"variableModifications"
]
)
## get value of each_search_params['searchEnginesWithForms'][0][1]["paramMap"]["digestionParameters"]["nMissedCleavages"] where key == input_params.enzyme
n_missed_cleavages_dict = each_search_params["searchEnginesWithForms"][0][1]["paramMap"][
"digestionParameters"
]["nMissedCleavages"]
input_params.allowed_miscleavages = n_missed_cleavages_dict.get(input_params.enzyme, None)
# get tolerance for precursors:
tol = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["precursorTolerance"]
unit = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["precursorAccuracyType"]
tol = float(tol)
input_params.precursor_mass_tolerance = "[-" + str(tol) + " " + unit + ", +" + str(tol) + " " + unit + "]"
# get tolerance for fragments:
tol2 = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["fragmentIonMZTolerance"]
unit2 = each_search_params["searchEnginesWithForms"][0][1]["paramMap"]["fragmentAccuracyType"]
tol2 = float(tol2)
input_params.fragment_mass_tolerance = (
"[-" + str(tol2) + " " + unit2 + ", +" + str(tol2) + " " + unit2 + "]"
)
# Add "hidden" modifications when using X!Tandem:
for key, value in each_search_params["searchEnginesWithForms"][0][1]["paramMap"][
"algorithmParameters"
].items():
if value["type"] == "com.compomics.util.parameters.identification.tool_specific.XtandemParameters":
if value["data"]["proteinQuickAcetyl"] == True:
input_params.variable_mods = input_params.variable_mods + ";Acetyl(N-term)"
if value["data"]["quickPyrolidone"] == True:
input_params.variable_mods = input_params.variable_mods + ";Pyrolidone(N-term)"
if "validationConfig" in each_search_params:
input_params.ident_fdr_psm = each_search_params["validationConfig"]["psmExpectedFdr"] / 100
# input_params.min_peptide_length = each_search_params["validationConfig"]["psmFilters"] #TODO: I am not sure if this is the max or min length
return input_params
[docs]
def extract_params(
fname: Union[str, pathlib.Path],
json_file=os.path.join(os.path.dirname(__file__), "json/Quant/quant_lfq_DDA_ion.json"),
) -> ProteoBenchParameters:
"""
Parse MSAangel quantification tool JSON parameter file and extract relevant parameters.
Parameters
----------
fname : str or pathlib.Path
The path to the MSAngel JSON parameter file.
Returns
-------
ProteoBenchParameters
The extracted parameters as a `ProteoBenchParameters` object.
"""
params = ProteoBenchParameters(filename=json_file)
try:
# If the input is a file-like object (e.g., StringIO), decode it
file_contents = fname.getvalue().decode("utf-8")
data = json.loads(file_contents)
except AttributeError:
# Otherwise, treat it as a file path
with open(fname, "r") as file_contents:
data = json.load(file_contents)
# Extract parameters from the JSON data
params.software_name = "MSAngel"
params.software_version = data["msAngelVersion"]
params.search_engine = extract_search_engine(data)
# Params fixed in MSAngel
params.enable_match_between_runs = True
# parameter parsing depends on the search engine used
if params.search_engine == "Mascot":
extract_params_mascot_specific(data, params)
elif params.search_engine == "X!Tandem":
extract_params_xtandem_specific(data, params)
params.fill_none()
return params
if __name__ == "__main__":
"""
Extract parameters from MSAngel JSON files and save them as CSV.
"""
from pathlib import Path
files = [
Path("../../../test/params/MSAngel_Xtandem-export-param.json"),
Path("../../../test/params/MSAngel_fromRAWtoQUANT-Mascot-export-param.json"),
]
for file in files:
# Extract parameters from the file
params = extract_params(file)
# Convert the extracted parameters to a dictionary and then to a pandas Series
data_dict = params.__dict__
series = pd.Series(data_dict)
# Write the Series to a CSV file
series.to_csv(file.with_suffix(".csv"))