MRMAssay

class pyopenms.MRMAssay

Bases: object

Cython implementation of _MRMAssay

Documentation is available at http://www.openms.de/current_doxygen/html/classOpenMS_1_1MRMAssay.html

– Inherits from [‘ProgressLogger’]

__init__()

  • Cython signature: void MRMAssay()

  • Cython signature: void MRMAssay(MRMAssay &)

Methods

__init__

  • Cython signature: void MRMAssay()

detectingTransitions

Cython signature: void detectingTransitions(TargetedExperiment & exp, int min_transitions, int max_transitions)

endProgress

Cython signature: void endProgress() Ends the progress display

filterMinMaxTransitionsCompound

Cython signature: void filterMinMaxTransitionsCompound(TargetedExperiment & exp, int min_transitions, int max_transitions)

filterUnreferencedDecoysCompound

Cython signature: void filterUnreferencedDecoysCompound(TargetedExperiment & exp)

getLogType

Cython signature: LogType getLogType() Returns the type of progress log being used

nextProgress

Cython signature: void nextProgress() Increment progress by 1 (according to range begin-end)

reannotateTransitions

Cython signature: void reannotateTransitions(TargetedExperiment & exp, double precursor_mz_threshold, double product_mz_threshold, libcpp_vector[String] fragment_types, libcpp_vector[size_t] fragment_charges, bool enable_specific_losses, bool enable_unspecific_losses, int round_decPow)

restrictTransitions

Cython signature: void restrictTransitions(TargetedExperiment & exp, double lower_mz_limit, double upper_mz_limit, libcpp_vector[libcpp_pair[double,double]] swathes)

setLogType

Cython signature: void setLogType(LogType) Sets the progress log that should be used.

setProgress

Cython signature: void setProgress(ptrdiff_t value) Sets the current progress

startProgress

Cython signature: void startProgress(ptrdiff_t begin, ptrdiff_t end, String label)

uisTransitions

Cython signature: void uisTransitions(TargetedExperiment & exp, libcpp_vector[String] fragment_types, libcpp_vector[size_t] fragment_charges, bool enable_specific_losses, bool enable_unspecific_losses, bool enable_ms2_precursors, double mz_threshold, libcpp_vector[libcpp_pair[double,double]] swathes, int round_decPow, size_t max_num_alternative_localizations, int shuffle_seed)
detectingTransitions()

Cython signature: void detectingTransitions(TargetedExperiment & exp, int min_transitions, int max_transitions)

Parameters

  • exp – The input, unfiltered transitions

  • min_transitions – The minimum number of transitions required per assay

  • max_transitions – The maximum number of transitions required per assay

endProgress()

Cython signature: void endProgress() Ends the progress display

filterMinMaxTransitionsCompound()

Cython signature: void filterMinMaxTransitionsCompound(TargetedExperiment & exp, int min_transitions, int max_transitions)

Parameters

  • exp – The transition list which will be filtered

  • min_transitions – The minimum number of transitions required per assay (targets only)

  • max_transitions – The maximum number of transitions allowed per assay

filterUnreferencedDecoysCompound()

Cython signature: void filterUnreferencedDecoysCompound(TargetedExperiment & exp)

Filters decoy transitions, which do not have respective target transition based on the transitionID. —– References between targets and decoys will be constructed based on the transitionsID and the “_decoy_” string. For example: —– target: 84_CompoundName_[M+H]+_88_22 decoy: 84_CompoundName_decoy_[M+H]+_88_22 —– :param exp: The transition list which will be filtered

getLogType()

Cython signature: LogType getLogType() Returns the type of progress log being used

nextProgress()

Cython signature: void nextProgress() Increment progress by 1 (according to range begin-end)

reannotateTransitions()

Cython signature: void reannotateTransitions(TargetedExperiment & exp, double precursor_mz_threshold, double product_mz_threshold, libcpp_vector[String] fragment_types, libcpp_vector[size_t] fragment_charges, bool enable_specific_losses, bool enable_unspecific_losses, int round_decPow)

Parameters

  • exp – The input, unfiltered transitions

  • precursor_mz_threshold – The precursor m/z threshold in Th for annotation

  • product_mz_threshold – The product m/z threshold in Th for annotation

  • fragment_types – The fragment types to consider for annotation

  • fragment_charges – The fragment charges to consider for annotation

  • enable_specific_losses – Whether specific neutral losses should be considered

  • enable_unspecific_losses – Whether unspecific neutral losses (H2O1, H3N1, C1H2N2, C1H2N1O1) should be considered

  • round_decPow – Round product m/z values to decimal power (default: -4)

restrictTransitions()

Cython signature: void restrictTransitions(TargetedExperiment & exp, double lower_mz_limit, double upper_mz_limit, libcpp_vector[libcpp_pair[double,double]] swathes)

Parameters

  • exp – The input, unfiltered transitions

  • lower_mz_limit – The lower product m/z limit in Th

  • upper_mz_limit – The upper product m/z limit in Th

  • swathes – The swath window settings (to exclude fragment ions falling into the precursor isolation window)

setLogType()

Cython signature: void setLogType(LogType) Sets the progress log that should be used. The default type is NONE!

setProgress()

Cython signature: void setProgress(ptrdiff_t value) Sets the current progress

startProgress()

Cython signature: void startProgress(ptrdiff_t begin, ptrdiff_t end, String label)

uisTransitions()

Cython signature: void uisTransitions(TargetedExperiment & exp, libcpp_vector[String] fragment_types, libcpp_vector[size_t] fragment_charges, bool enable_specific_losses, bool enable_unspecific_losses, bool enable_ms2_precursors, double mz_threshold, libcpp_vector[libcpp_pair[double,double]] swathes, int round_decPow, size_t max_num_alternative_localizations, int shuffle_seed)

  • Step 1: For each peptide, compute all theoretical alternative peptidoforms; see transitions generateTargetInSilicoMap_()

  • Step 2: Generate target identification transitions; see generateTargetAssays_()

  • Step 3a: Generate decoy sequences that share peptidoform properties with targets; see generateDecoySequences_()

  • Step 3b: Generate decoy in silico peptide map containing theoretical transition; see generateDecoyInSilicoMap_()

  • Step 4: Generate decoy identification transitions; see generateDecoyAssays_()

The IPF algorithm uses the concept of “identification transitions” that are used to discriminate different peptidoforms, these are generated in this function. In brief, the algorithm takes the existing set of peptides and transitions and then appends these “identification transitions” for targets and decoys. The novel transitions are set to be non-detecting and non-quantifying and are annotated with the set of peptidoforms to which they map. —– :param exp: The input, unfiltered transitions :param fragment_types: The fragment types to consider for annotation :param fragment_charges: The fragment charges to consider for annotation :param enable_specific_losses: Whether specific neutral losses should be considered :param enable_unspecific_losses: Whether unspecific neutral losses (H2O1, H3N1, C1H2N2, C1H2N1O1) should be considered :param enable_ms2_precursors: Whether MS2 precursors should be considered :param mz_threshold: The product m/z threshold in Th for annotation :param swathes: The swath window settings (to exclude fragment ions falling :param round_decPow: Round product m/z values to decimal power (default: -4) :param max_num_alternative_localizations: Maximum number of allowed peptide sequence permutations :param shuffle_seed: Set seed for shuffle (-1: select seed based on time) :param disable_decoy_transitions: Whether to disable generation of decoy UIS transitions