MRMAssay

class pyopenms.MRMAssay

Bases: object

Cython implementation of _MRMAssay

Original C++ documentation is available here

– Inherits from [‘ProgressLogger’]

__init__()

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: MRMAssay) → None

Methods

__init__	Overload:
detectingTransitions(self, exp, ...)	Select detecting fragment ions
endProgress(self)	Ends the progress display
filterMinMaxTransitionsCompound(self, exp, ...)	Filters target and decoy transitions by intensity, only keeping the top N transitions
filterUnreferencedDecoysCompound(self, exp)	Filters decoy transitions, which do not have respective target transition based on the transitionID.
getLogType(self)	Returns the type of progress log being used
nextProgress(self)	Increment progress by 1 (according to range begin-end)
reannotateTransitions(self, exp, ...)	Annotates and filters transitions in a TargetedExperiment
restrictTransitions(self, exp, ...)	Restrict and filter transitions in a TargetedExperiment
setLogType(self, in_0)	Sets the progress log that should be used.
setProgress(self, value)	Sets the current progress
startProgress(self, begin, end, label)
uisTransitions(self, exp, fragment_types, ...)	Annotate UIS / site-specific transitions

detectingTransitions(self, exp: TargetedExperiment, min_transitions: int, max_transitions: int) → None

Select detecting fragment ions

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(self) → None

Ends the progress display

filterMinMaxTransitionsCompound(self, exp: TargetedExperiment, min_transitions: int, max_transitions: int) → None

Filters target and decoy transitions by intensity, only keeping the top N 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(self, exp: TargetedExperiment) → None

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

Parameters:

exp – The transition list which will be filtered

getLogType(self) → int

Returns the type of progress log being used

nextProgress(self) → None

Increment progress by 1 (according to range begin-end)

reannotateTransitions(self, exp: TargetedExperiment, precursor_mz_threshold: float, product_mz_threshold: float, fragment_types: List[bytes], fragment_charges: List[int], enable_specific_losses: bool, enable_unspecific_losses: bool, round_decPow: int) → None

Annotates and filters transitions in a TargetedExperiment

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(self, exp: TargetedExperiment, lower_mz_limit: float, upper_mz_limit: float, swathes: List[List[float, float]]) → None

Restrict and filter transitions in a TargetedExperiment

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(self, in_0: int) → None

Sets the progress log that should be used. The default type is NONE!

setProgress(self, value: int) → None

Sets the current progress

startProgress(self, begin: int, end: int, label: bytes | str | String) → None

uisTransitions(self, exp: TargetedExperiment, fragment_types: List[bytes], fragment_charges: List[int], enable_specific_losses: bool, enable_unspecific_losses: bool, enable_ms2_precursors: bool, mz_threshold: float, swathes: List[List[float, float]], round_decPow: int, max_num_alternative_localizations: int, shuffle_seed: int) → None

Annotate UIS / site-specific transitions

Performs the following actions:

• 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.

Parameters:

• exp – The input, unfiltered transitions

• 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

• enable_ms2_precursors – Whether MS2 precursors should be considered

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

• swathes – The swath window settings (to exclude fragment ions falling

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

• max_num_alternative_localizations – Maximum number of allowed peptide sequence permutations

• shuffle_seed – Set seed for shuffle (-1: select seed based on time)

• disable_decoy_transitions – Whether to disable generation of decoy UIS transitions