CoarseIsotopePatternGenerator#
- class pyopenms.CoarseIsotopePatternGenerator#
Bases:
object
Cython implementation of _CoarseIsotopePatternGenerator
Original C++ documentation is available here
- __init__()#
Overload:
- __init__(self) None
Overload:
- __init__(self, max_isotope: int) None
Overload:
- __init__(self, max_isotope: int, round_masses: bool) None
Methods
Overload:
approximateFromPeptideWeight
(self, mass, ...)Roughly approximate peptide IsotopeDistribution from monoisotopic weight using Poisson distribution.
approximateIntensities
(self, mass, num_peaks)Roughly approximate peptidic isotope pattern intensities from monoisotopic weight using Poisson distribution.
calcFragmentIsotopeDist
(self, ...)Calculate isotopic distribution for a fragment molecule
estimateForFragmentFromDNAWeight
(self, ...)Estimate DNA fragment IsotopeDistribution from the precursor's average weight, fragment's average weight, and a set of isolated precursor isotopes.
estimateForFragmentFromPeptideWeight
(self, ...)Estimate peptide fragment IsotopeDistribution from the precursor's average weight, fragment's average weight, and a set of isolated precursor isotopes
Estimate peptide fragment IsotopeDistribution from the precursor's average weight, number of sulfurs in the precursor, fragment's average weight, number of sulfurs in the fragment, and a set of isolated precursor isotopes.
estimateForFragmentFromRNAWeight
(self, ...)Estimate RNA fragment IsotopeDistribution from the precursor's average weight, fragment's average weight, and a set of isolated precursor isotopes
estimateForFragmentFromWeightAndComp
(self, ...)Estimate fragment IsotopeDistribution from the precursor's average weight, fragment's average weight, a set of isolated precursor isotopes, and average composition
estimateFromDNAWeight
(self, average_weight)Estimate Nucleotide Isotopedistribution from weight
estimateFromPeptideWeight
(self, average_weight)Estimate Peptide Isotopedistribution from weight and number of isotopes that should be reported
estimateFromPeptideWeightAndS
(self, ...)Estimate peptide IsotopeDistribution from average weight and exact number of sulfurs
estimateFromRNAWeight
(self, average_weight)Estimate Nucleotide Isotopedistribution from weight
estimateFromWeightAndComp
(self, ...)estimateFromWeightAndCompAndS
(self, ...)Estimate IsotopeDistribution from weight, exact number of sulfurs, and average remaining composition
getMaxIsotope
(self)Returns the currently set maximum isotope
getRoundMasses
(self)Returns the current value of the flag to round masses to integer values (true) or return accurate masses (false)
run
(self, in_0)setMaxIsotope
(self, max_isotope)Sets the maximal isotope with 'max_isotope'
setRoundMasses
(self, round_masses_)Sets the round_masses_ flag to round masses to integer values (true) or return accurate masses (false)
- approximateFromPeptideWeight(self, mass: float, num_peaks: int, charge: int) IsotopeDistribution #
Roughly approximate peptide IsotopeDistribution from monoisotopic weight using Poisson distribution. m/z values approximated by adding one neutron mass (divided by charge) for every peak, starting at the given monoisotopic weight. Foundation from: Bellew et al, https://dx.doi.org/10.1093/bioinformatics/btl276 This method is around 50 times faster than estimateFromPeptideWeight, but only an approximation. The following are the intensities of the first 6 peaks generated for a monoisotopic mass of 1000: estimateFromPeptideWeight: 0.571133000;0.306181000;0.095811100;0.022036900;0.004092170;0.000644568 approximateFromPeptideWeight: 0.573753000;0.318752000;0.088542200;0.016396700;0.002277320;0.000253036 KL divergences of the first 20 intensities of estimateFromPeptideWeight and this approximation range from 4.97E-5 for a monoisotopic mass of 20 to 0.0144 for a mass of 2500. For comparison, when comparing an observed pattern with a theoretical ground truth, the observed pattern is said to be an isotopic pattern if the KL between the two is below 0.05 for 2 peaks and below 0.6 for >=6 peaks by Guo Ci Teo et al.
- approximateIntensities(self, mass: float, num_peaks: int) List[float] #
Roughly approximate peptidic isotope pattern intensities from monoisotopic weight using Poisson distribution. Foundation from: Bellew et al, https://dx.doi.org/10.1093/bioinformatics/btl276 This method is around 100 times faster than estimateFromPeptideWeight, but only an approximation, see approximateFromPeptideWeight.
- calcFragmentIsotopeDist(self, fragment_isotope_dist: IsotopeDistribution, comp_fragment_isotope_dist: IsotopeDistribution, precursor_isotopes: Set[int], fragment_mono_mass: float) IsotopeDistribution #
Calculate isotopic distribution for a fragment molecule
- estimateForFragmentFromDNAWeight(self, average_weight_precursor: float, average_weight_fragment: float, precursor_isotopes: Set[int]) IsotopeDistribution #
Estimate DNA fragment IsotopeDistribution from the precursor’s average weight, fragment’s average weight, and a set of isolated precursor isotopes.
- estimateForFragmentFromPeptideWeight(self, average_weight_precursor: float, average_weight_fragment: float, precursor_isotopes: Set[int]) IsotopeDistribution #
Estimate peptide fragment IsotopeDistribution from the precursor’s average weight, fragment’s average weight, and a set of isolated precursor isotopes
- estimateForFragmentFromPeptideWeightAndS(self, average_weight_precursor: float, S_precursor: int, average_weight_fragment: float, S_fragment: int, precursor_isotopes: Set[int]) IsotopeDistribution #
Estimate peptide fragment IsotopeDistribution from the precursor’s average weight, number of sulfurs in the precursor, fragment’s average weight, number of sulfurs in the fragment, and a set of isolated precursor isotopes.
- estimateForFragmentFromRNAWeight(self, average_weight_precursor: float, average_weight_fragment: float, precursor_isotopes: Set[int]) IsotopeDistribution #
Estimate RNA fragment IsotopeDistribution from the precursor’s average weight, fragment’s average weight, and a set of isolated precursor isotopes
- estimateForFragmentFromWeightAndComp(self, average_weight_precursor: float, average_weight_fragment: float, precursor_isotopes: Set[int], C: float, H: float, N: float, O: float, S: float, P: float) IsotopeDistribution #
Estimate fragment IsotopeDistribution from the precursor’s average weight, fragment’s average weight, a set of isolated precursor isotopes, and average composition
- estimateFromDNAWeight(self, average_weight: float) IsotopeDistribution #
Estimate Nucleotide Isotopedistribution from weight
- estimateFromPeptideWeight(self, average_weight: float) IsotopeDistribution #
Estimate Peptide Isotopedistribution from weight and number of isotopes that should be reported
- estimateFromPeptideWeightAndS(self, average_weight: float, S: int) IsotopeDistribution #
Estimate peptide IsotopeDistribution from average weight and exact number of sulfurs
- estimateFromRNAWeight(self, average_weight: float) IsotopeDistribution #
Estimate Nucleotide Isotopedistribution from weight
- estimateFromWeightAndComp(self, average_weight: float, C: float, H: float, N: float, O: float, S: float, P: float) IsotopeDistribution #
- estimateFromWeightAndCompAndS(self, average_weight: float, S: int, C: float, H: float, N: float, O: float, P: float) IsotopeDistribution #
Estimate IsotopeDistribution from weight, exact number of sulfurs, and average remaining composition
- getMaxIsotope(self) int #
Returns the currently set maximum isotope
- getRoundMasses(self) bool #
Returns the current value of the flag to round masses to integer values (true) or return accurate masses (false)
- run(self, in_0: EmpiricalFormula) IsotopeDistribution #
- setMaxIsotope(self, max_isotope: int) None #
Sets the maximal isotope with ‘max_isotope’
- setRoundMasses(self, round_masses_: bool) None #
Sets the round_masses_ flag to round masses to integer values (true) or return accurate masses (false)