FeatureFinderIdentificationAlgorithm

class pyopenms.FeatureFinderIdentificationAlgorithm

Bases: DefaultParamHandler

Algorithm class for FeatureFinderIdentification Optional seeds from e.g. untargeted FeatureFinders can be added with seeds. Results will be written to features . Caution: peptide IDs will be shrunk to best hit, FFid metavalues added and potential seed IDs added.

Usage:

from pyopenms import *
from urllib.request import urlretrieve
urlretrieve("https://raw.githubusercontent.com/OpenMS/OpenMS/develop/src/tests/topp/FeatureFinderIdentification_1_input.mzML", "FeatureFinderIdentification_1_input.mzML")
urlretrieve("https://raw.githubusercontent.com/OpenMS/OpenMS/develop/src/tests/topp/FeatureFinderIdentification_1_input.idXML", "FeatureFinderIdentification_1_input.idXML")

ffid_algo = FeatureFinderIdentificationAlgorithm()
# load ms data from mzML
mzml = MzMLFile()
mzml_options = mzml.getOptions()
mzml_options.addMSLevel(1) # only MS1
mzml.setOptions(mzml_options)

exp = MSExperiment()
mzml.load("FeatureFinderIdentification_1_input.mzML", exp)
ffid_algo.setMSData(exp)
# annotate mzML file
features = FeatureMap()
features.setPrimaryMSRunPath([b"FeatureFinderIdentification_1_input.idXML"], ffid_algo.getMSData())

peptides = []
proteins = []
IdXMLFile().load("FeatureFinderIdentification_1_input.idXML", proteins, peptides)

ffid_algo.run(peptides, proteins, features)

# Terminal output:
# Summary statistics (counting distinct peptides including PTMs):
# 22 peptides identified
# 16 peptides with features
# 6 peptides without features

__init__() → None

Methods

__init__()
getChromatograms()	Returns chromatogram data as MSExperiment
getDefaults()	Returns the default parameters
getLibrary()	Returns constructed assay library
getMSData()	Returns ms data as MSExperiment
getName()	Returns the name
getParameters()	Returns the parameters
getSubsections()
run(peptides, proteins, features[, seeds, ...])	Run feature detection
runOnCandidates(features)	Run feature detection on identified features (e.g. loaded from an IdXML file).
setMSData(ms_data)	Sets ms data
setName(name)	Sets the name
setParameters(param)	Sets the parameters

getChromatograms() → pyopenms.MSExperiment

Returns chromatogram data as MSExperiment

getDefaults() → pyopenms.Param

Returns the default parameters

getLibrary() → pyopenms.TargetedExperiment

Returns constructed assay library

getMSData() → pyopenms.MSExperiment

Returns ms data as MSExperiment

getName() → str

Returns the name

getParameters() → pyopenms.Param

Returns the parameters

getSubsections() → list[str]

run(peptides: pyopenms.PeptideIdentificationList, proteins: collections.abc.Sequence[pyopenms.ProteinIdentification], features: pyopenms.FeatureMap, seeds: pyopenms.FeatureMap = FeatureMap(num_features=0), spectra_file: str = '') → None

Run feature detection

Parameters:

• peptides – Vector of identified peptides

• proteins – Vector of identified proteins

• features – Feature detection results will be added here

• seeds – Optional seeds for feature detection from e.g. untargeted FeatureFinders

• spectra_file – Path will be stored in features in case the MSExperiment has no proper primaryMSRunPath

runOnCandidates(features: pyopenms.FeatureMap) → None

Run feature detection on identified features (e.g. loaded from an IdXML file)

setMSData(ms_data: pyopenms.MSExperiment) → None

setMSData(ms_data: pyopenms.MSExperiment) → None

Sets ms data

setName(name: str) → None

Sets the name

setParameters(param: pyopenms.Param) → None

Sets the parameters