def main(options):
out = options.outfile
chromat_in = options.infile
traml_in = options.traml_in
trafo_in = options.trafo_in
pp = pyopenms.MRMTransitionGroupPicker()
metabolomics = False
# this is an important weight for RT-deviation -- the larger the value, the less importance will be given to exact RT matches
# for proteomics data it tends to be a good idea to set it to the length of
# the RT space (e.g. for 100 second RT space, set it to 100)
rt_normalization_factor = 100.0
pp_params = pp.getDefaults();
pp_params.setValue("PeakPickerMRM:remove_overlapping_peaks", options.remove_overlapping_peaks, '')
pp_params.setValue("PeakPickerMRM:method", options.method, '')
if (metabolomics):
# Need to change those for metabolomics and very short peaks!
pp_params.setValue("PeakPickerMRM:signal_to_noise", 0.01, '')
pp_params.setValue("PeakPickerMRM:peak_width", 0.1, '')
pp_params.setValue("PeakPickerMRM:gauss_width", 0.1, '')
pp_params.setValue("resample_boundary", 0.05, '')
pp_params.setValue("compute_peak_quality", "true", '')
pp.setParameters(pp_params)
scorer = pyopenms.MRMFeatureFinderScoring()
scoring_params = scorer.getDefaults();
# Only report the top 5 features
scoring_params.setValue("stop_report_after_feature", 5, '')
scoring_params.setValue("rt_normalization_factor", rt_normalization_factor, '')
scorer.setParameters(scoring_params);
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(chromat_in, chromatograms)
targeted = pyopenms.TargetedExperiment();
tramlfile = pyopenms.TraMLFile();
tramlfile.load(traml_in, targeted);
trafoxml = pyopenms.TransformationXMLFile()
trafo = pyopenms.TransformationDescription()
if trafo_in is not None:
model_params = pyopenms.Param()
model_params.setValue("symmetric_regression", "false", "", [])
model_type = "linear"
trafoxml.load(trafo_in, trafo, True)
trafo.fitModel(model_type, model_params);
light_targeted = pyopenms.LightTargetedExperiment();
pyopenms.OpenSwathDataAccessHelper().convertTargetedExp(targeted, light_targeted)
output = algorithm(chromatograms, light_targeted, pp, scorer, trafo)
pyopenms.FeatureXMLFile().store(out, output);
def setUp(self):
lt = pyopenms.LightTransition()
lt.charge = 2
lt.transition_name = b"X"
lt.peptide_ref = b"Y"
lt.library_intensity = 12.0
lt.product_mz = 22.0
self.lt = lt
lm = pyopenms.LightModification()
lm.location = 13
lm.unimod_id = b"ID"
self.lm = lm
lpep = pyopenms.LightPeptide()
lpep.rt = 12.0
lpep.charge = 2
lpep.sequence = b"SEQ"
lpep.protein_ref = b"REF"
lpep.modifications = [lm]
self.lpep = lpep
lprot = pyopenms.LightProtein()
lprot.id = b"1234"
lprot.sequence = b"ABC"
self.lprot = lprot
lte = pyopenms.LightTargetedExperiment()
lte.peptides = [self.lpep]
lte.proteins = [self.lprot]
lte.transitions = [self.lt]
self.lte = lte