def deconvolute(peaklist):
"""Recalculate peaklist to singly charged.
peaklist (mspy.peaklist) - peak list to deconvolute
"""
# recalculate peaks
buff = []
for peak in copy.deepcopy(peaklist):
# uncharged peak
if not peak.charge:
continue
# charge is correct
elif abs(peak.charge) == 1:
buff.append(peak)
# recalculate peak
else:
# set fwhm
if peak.fwhm:
new_fwhm = abs(peak.fwhm * peak.charge)
peak.setfwhm(new_fwhm)
# set m/z and charge
if peak.charge < 0:
new_mz = masstools.mz(peak.mz, -1, peak.charge)
peak.set_mz(new_mz)
peak.setcharge(-1)
else:
new_mz = masstools.mz(peak.mz, 1, peak.charge)
peak.set_mz(new_mz)
peak.setcharge(1)
# store peak
buff.append(peak)
# remove baseline
if buff:
for peak in buff:
peak.setsn(None)
peak.setai(peak.intensity)
peak.setbase(0.)
# update peaklist
return type(peaklist)(buff)
def deisotope(self):
for x, parent in enumerate(self.peaklist):
if self.isotopes[x] is not None:
continue
for charge in self.charges:
cluster = self._get_cluster(x, parent, charge)
# no isotope found
if len(cluster) == 1:
continue
# get theoretical isotopic pattern
mass = int(masstools.mz(parent.mz, 0, charge))
mass = min(15000, mass) // 200
pattern = proteins.MASS_PATTERN_LOOKUP[mass]
if self._too_few_isotopes(pattern, cluster, charge):
continue
if self.valid_cluster(pattern, cluster, charge):
# valid ID, skip other charges
parent.setisotope(0)
parent.setcharge(charge)
break
def deisotope(self):
for x, parent in enumerate(self.peaklist):
if self.isotopes[x] is not None:
continue
for charge in self.charges:
cluster = self._get_cluster(x, parent, charge)
# no isotope found
if len(cluster) == 1:
continue
# get theoretical isotopic pattern
mass = int(masstools.mz(parent.mz, 0, charge))
mass = min(15000, mass) // 200
pattern = proteins.MASS_PATTERN_LOOKUP[mass]
if self._too_few_isotopes(pattern, cluster, charge):
continue
if self.valid_cluster(pattern, cluster, charge):
# valid ID, skip other charges
parent.setisotope(0)
parent.setcharge(charge)
break
def get_exper_mass(self, index):
'''Calculates the experimental MS1 mass for a given peptide.'''
index = index[0]
mz = self.data['precursor_mz'][index]
charge = self.data['precursor_z'][index]
return masstools.mz(mz, 0, charge)
def get_indexes(self):
'''Extract the match indexes for the given scans'''
mass = masstools.mz(self.search['mass'], self.query.z, 0)
exper = self.query.exper[self.index]
indexes, = np.where(abs(exper - mass) / mass <= self._ppm_thresh)
return indexes
def set_mass(self, monoisotopic):
'''Sets the isotopic mass for XICs and then adds self to flattened'''
mass = monoisotopic + (ISOTOPE_DISTANCE * self.isotope)
self.setattr('isotope_mass', mass)
mz = masstools.mz(mass, self.charge, 0)
self.setattr('isotope_mz', mz)
def set_mass(self, monoisotopic):
'''Sets the isotopic mass for XICs and then adds self to flattened'''
mass = monoisotopic + (ISOTOPE_DISTANCE * self.isotope)
self.setattr('isotope_mass', mass)
mz = masstools.mz(mass, self.charge, 0)
self.setattr('isotope_mz', mz)
def scan(self, attrs):
'''Initializes the scan data and sets base attributes'''
num = int(attrs['start_scan'])
self._scan = matched.Scan(num=num, fraction=self._fraction)
self._scan['z'] = charge = int(attrs.get('assumed_charge', 1))
# calculate m/z
neutral_mass = float(attrs['precursor_neutral_mass'])
self._scan['mz'] = masstools.mz(neutral_mass, charge, 0)
def scan(self, attrs):
'''Initializes the scan data and sets base attributes'''
num = int(attrs['start_scan'])
self._scan = matched.Scan(num=num, fraction=self._fraction)
self._scan['z'] = charge = int(attrs.get('assumed_charge', 1))
# calculate m/z
neutral_mass = float(attrs['precursor_neutral_mass'])
self._scan['mz'] = masstools.mz(neutral_mass, charge, 0)
def scan(self, attrs):
'''Initializes the scan data and sets base attributes'''
title = attrs['spectrum']
match = self.titleformatter(title)
num = int(match.group('num'))
self._scan = matched.Scan(num=num, fraction=self._fraction)
self._scan['z'] = charge = int(attrs.get('assumed_charge', 1))
# calculate m/z
neutral_mass = float(attrs['precursor_neutral_mass'])
self._scan['mz'] = masstools.mz(neutral_mass, charge, 0)
def scan(self, attrs):
'''Initializes the scan data and sets base attributes'''
self._scan = {}
self._scan['num'] = int(attrs['start_scan'])
self._scan['z'] = charge = int(attrs.get('assumed_charge', 1))
self._scan['fraction'] = self._fraction
# the neutral mass is actually MH+ in the specification
mh_plus_mass = float(attrs['precursor_neutral_mass'])
neutral_mass = mh_plus_mass - params.PROTON_MASS
self._scan['m/z'] = masstools.mz(neutral_mass, charge, 0)
def scan(self, attrs):
'''Initializes the scan data and sets base attributes'''
title = attrs['spectrum']
match = self.titleformatter(title)
num = int(match.group('num'))
self._scan = matched.Scan(num=num, fraction=self._fraction)
self._scan['z'] = charge = int(attrs.get('assumed_charge', 1))
# calculate m/z
neutral_mass = float(attrs['precursor_neutral_mass'])
self._scan['mz'] = masstools.mz(neutral_mass, charge, 0)
def calculate_ppm(self, atom_counts, mods, exper, charge):
'''Calculates the theroetical PPM from the exper mass'''
atom_counts = copy.deepcopy(atom_counts)
mods = unpack_mods(mods)
for name, pos in mods.items():
if name in self.fragments:
formula = self.fragments[name]
count = len(pos)
atom_counts.update_formula(formula, count=count)
theor = masstools.mz(atom_counts.mass, charge, 0)
return (theor - exper) / exper * 1e6
def calculate_ppm(self, atom_counts, mods, exper, charge):
'''Calculates the theroetical PPM from the exper mass'''
atom_counts = copy.deepcopy(atom_counts)
mods = unpack_mods(mods)
for name, pos in mods.items():
if name in self.fragments:
formula = self.fragments[name]
count = len(pos)
atom_counts.update_formula(formula, count=count)
theor = masstools.mz(atom_counts.mass, charge, 0)
return (theor - exper) / exper * 1e6
def _b_series(self):
'''
Calculates the b-ion series, which is characterized by the following
formula: [N]+[M]-H, starting from the N-terminus.
For example, the b3 ion for KPIDWGAASPAVQSFR is the mass of
KPI + # proton(s) * charge.
'''
for position in range(2, self.length):
for charge in self._charges:
seq = self.peptide[:position]
mass = (chemical.Molecule(self._aminoacids[i]) for i in seq)
mass = (i.mass for i in mass)
mz = masstools.mz(mass, charge, 0)
self.mzs.append(mz)
self.charges.append(charge)
self.position.append(position)
self._toarray()
def _y_series(self):
'''
Calculates the y-ion series, which is characterized by the following
formula: [C]+[M]+H, starting from the C-terminus.
For example, the y3 ion for KPIDWGAASPAVQSFR is the mass of
SFR + water + # proton(s) * charge.
'''
for position in range(1, self.length):
for charge in self._charges:
seq = self.peptide[-position:]
mass = sum(chemical.Molecule(self._aminoacids[i]) for i in seq)
mass = (i.mass for i in mass)
mass += WATER_MASS
mz = masstools.mz(mass, charge, 0)
self.mzs.append(mz)
self.charges.append(charge)
self.position.append(position)
self._toarray()
def process_hit(self, query, peptide_info, rank):
'''Initializes the hit with all the default peptide parameters'''
hit = {'rank': int(rank)}
variable_modification_string = peptide_info[6]
hit['peptide'] = peptide = peptide_info[4]
hit['modifications'] = self.modifications.process(
peptide, variable_modification_string)
# score for Mascot is defined as -log(p), where p is chance of ion
# match by chance
hit['score'] = score = float(peptide_info[7])
hit['ev'] = 10**(-score / 10)
mass = float(peptide_info[1])
charge = self.queries[query]['z']
mz_theor = masstools.mz(mass, charge, 0)
mz_exper = self.queries[query]['mz']
hit['ppm'] = (mz_theor - mz_exper) * 1e6 / mz_exper
return hit
def process_hit(self, query, peptide_info, rank):
'''Initializes the hit with all the default peptide parameters'''
hit = {'rank': int(rank)}
variable_modification_string = peptide_info[6]
hit['peptide'] = peptide = peptide_info[4]
hit['modifications'] = self.modifications.process(
peptide, variable_modification_string)
# score for Mascot is defined as -log(p), where p is chance of ion
# match by chance
hit['score'] = score = float(peptide_info[7])
hit['ev'] = 10 ** (-score/10)
mass = float(peptide_info[1])
charge = self.queries[query]['z']
mz_theor = masstools.mz(mass, charge, 0)
mz_exper = self.queries[query]['mz']
hit['ppm'] = (mz_theor - mz_exper) * 1e6 / mz_exper
return hit
def get_missing_mass(self, link_ends, fragments):
'''
Grabs the missing basemass and initiates a named tuple for
the mass lists form the link and deadends.
'''
max_links = self.max_link_ends()
dead_ends = self.get_dead_ends(link_ends, max_links)
ends = Ends(link_ends, dead_ends, self.xlnum)
# now need to calculate the base mass and mass permutations
basemass = self.missing_ms1_mass(dead_ends)
assert basemass > self._threshold
mass_perm = list(self.get_fragment_combinations(sum(fragments), False))
mzs = []
for mass in mass_perm:
mz_ = masstools.mz(basemass + sum(mass), self.missing_z(), 0)
mzs.append(mz_)
mzs = np.array(mzs).reshape((-1, 1))
ms1 = self.ms1(ends, basemass, mass_perm, mzs)
return ms1
def getmz(self, label, spreadsheet):
'''Returns the m/z value for the given label'''
charge = spreadsheet['MS2 z'][0]
return masstools.mz(label.mass, charge, 0)
def fromfragment(cls, fragment):
'''Initializes a reporter ion from a Fragment instance'''
mass = chemical.Molecule(fragment.formula).mass
mz = masstools.mz(mass, fragment.charge)
return cls(fragment.name, mz)
def fromfragment(cls, fragment):
'''Initializes a reporter ion from a Fragment instance'''
mass = chemical.Molecule(fragment.formula).mass
mz = masstools.mz(mass, fragment.charge)
return cls(fragment.name, mz)
def getmz(self, currentcharge=0):
return masstools.mz(self.precursor_mz, currentcharge, self.precursor_z)