"""
Peaks parameter parsing.
"""
import re
from pathlib import Path
from typing import List, Optional
import pandas as pd
import yaml
from proteobench.io.params import ProteoBenchParameters
[docs]
def clean_text(text: str) -> str:
"""
Clean the input text by removing leading and trailing spaces, colons, commas, or tabs.
Parameters
----------
text : str
The text to be cleaned.
Returns
-------
str
The cleaned text.
"""
text = re.sub(r"^[\s:,\t]+|[\s:,\t]+$", "", text)
return text
[docs]
def get_items_between(lines: list, start: str, end: str, only_last: bool = False) -> list:
"""
Find all lines starting with '-' that appear between 'start' and 'end'.
Return them as a list of strings, without the leading dash.
Parameters
----------
lines : list
The list of lines to search through.
start : str
The start term to search for in the lines.
end : str
The end term to search for in the lines.
only_last : bool
If True, only the items found between the last occurrence of start and end will be returned.
Returns
-------
list
The list of items found between the start and end terms.
"""
capturing = False
items = []
temp_items = []
for line in lines:
stripped = line.strip()
if stripped.startswith(start):
capturing = True
temp_items = []
continue
if capturing and stripped.startswith(end):
capturing = False
if only_last:
items = temp_items[:]
else:
items.extend(temp_items)
temp_items = []
if capturing and stripped.startswith("- "):
# Remove the dash and leading space
item = stripped[2:].strip()
temp_items.append(item)
if only_last and capturing:
items = temp_items
return items
[docs]
def read_peaks_settings(file_path: str) -> ProteoBenchParameters:
"""
Read a PEAKS settings file, extract parameters, and return them as a `ProteoBenchParameters` object.
Parameters
----------
file_path : str
The path to the PEAKS settings file.
Returns
-------
ProteoBenchParameters
The extracted parameters encapsulated in a `ProteoBenchParameters` object.
"""
if hasattr(file_path, "read"):
lines = file_path.read().decode("utf-8").splitlines()
else:
try:
with open(file_path, encoding="utf-8") as f:
lines = f.readlines()
except Exception as e:
raise IOError(f"Failed to open or read the file at {file_path}. Error: {e}")
lines = [line.strip() for line in lines]
params = ProteoBenchParameters()
params.software_name = "PEAKS"
params.software_version = extract_value(lines, "PEAKS Version:")
params.search_engine = "PEAKS"
params.search_engine_version = params.software_version
psm_fdr = extract_value(lines, "Precursor FDR:")
# Its either "Precursor FDR:" (DIA) or "PSM FDR:" (DDA)
psm_fdr = extract_value(lines, "PSM FDR:")
peptide_fdr = extract_value(lines, "Peptide FDR:")
params.ident_fdr_peptide = peptide_fdr
params.ident_fdr_psm = psm_fdr
# peaks uses Proteins -10LgP >= 15.0 instead of FDR
params.ident_fdr_protein = None
params.enable_match_between_runs = True if extract_value(lines, "Match Between Run:") == "Yes" else False
params.precursor_mass_tolerance = extract_mass_tolerance(lines, "Precursor Mass Error Tolerance:")
params.fragment_mass_tolerance = extract_mass_tolerance(lines, "Fragment Mass Error Tolerance:")
params.enzyme = extract_value(lines, "Enzyme:")
params.allowed_miscleavages = int(extract_value(lines, "Max Missed Cleavage:"))
try:
peptide_length_range = extract_value(lines, "Peptide Length between:").split(",")
except AttributeError:
peptide_length_range = extract_value(lines, "Peptide Length Range:").split(" - ")
params.max_peptide_length = int(peptide_length_range[1])
params.min_peptide_length = int(peptide_length_range[0])
fixed = get_items_between(lines, "Fixed Modifications:", "Variable Modifications:", only_last=True)
params.fixed_mods = " ,".join(fixed)
varmods = get_items_between(lines, "Variable Modifications:", "Database:", only_last=True)
params.variable_mods = " ,".join(varmods)
params.max_mods = int(extract_value(lines, "Max Variable PTM per Peptide:"))
try:
precursor_charge_between = extract_value(lines, "Precursor Charge between:").split(",")
except AttributeError:
precursor_charge_between = (
extract_value(lines, "Charge between:").replace("[", "").replace("]", "").split(" - ")
)
params.min_precursor_charge = int(precursor_charge_between[0])
params.max_precursor_charge = int(precursor_charge_between[1])
params.scan_window = None
params.quantification_method = extract_value(
lines, "LFQ Method:"
) # "Quantity MS Level:" or "Protein LFQ Method:" or "Quantity Type:"
params.protein_inference = None
params.predictors_library = None
params.abundance_normalization_ions = extract_value(lines, "Normalization Method:")
return params
if __name__ == "__main__":
"""
Reads PEAKS settings files, extracts parameters, and writes them to CSV files.
"""
fnames = [
"../../../test/params/PEAKS_parameters.txt",
"../../../test/params/PEAKS_parameters_DDA.txt",
"../../../test/params/PEAKS_parameters_DIA.txt",
"../../../test/params/PEAKS_parameters_DDA_new.txt",
]
for file in fnames:
# Extract parameters from the settings file
parameters = read_peaks_settings(file)
# Convert parameters to pandas Series and save to CSV
actual = pd.Series(parameters.__dict__)
actual.to_csv(Path(file).with_suffix(".csv"))
# Optionally, print the parameters to the console
print(parameters)
