def setUp(self):
self.prots = data_sets.Proteins(proteins=(data_sets.Protein(
accession='P03995',
gene='Gfap',
description='Glial fibrillary acidic protein',
full_sequence=(
'MERRRITSARRSYASETVVRGLGPSRQLGTMPRFSLSRMTPPLPARVDFSLAG'
'ALNAGFKETRASERAEMMELNDRFASYIEKVRFLEQQNKALAAELNQLRAKEP'
'TKLADVYQAELRELRLRLDQLTANSARLEVERDNFAQDLGTLRQKLQDETNLR'
'LEAENNLAAYRQEADEATLARVDLERKVESLEEEIQFLRKIYEEEVRELREQL'
'AQQQVHVEMDVAKPDLTAALREIRTQYEAVATSNMQETEEWYRSKFADLTDAA'
'SRNAELLRQAKHEANDYRRQLQALTCDLESLRGTNESLERQMREQEERHARES'
'ASYQEALARLEEEGQSLKEEMARHLQEYQDLLNVKLALDIEIATYRKLLEGEE'
'NRITIPVQTFSNLQIRETSLDTKSVSEGHLKRNIVVKTVEMRDGEVIKDSKQE'
'HKDVVM'),
), ), )
self.seq = data_sets.extract_sequence(self.prots, 'QEADEATLAR')
self.mods = data_sets.Modifications(mods=[
data_sets.Modification(
rel_pos=0,
mod_type='TMT6plex',
nterm=True,
sequence=self.seq,
),
], )
self.seq.modifications = self.mods
self.channels = OrderedDict([
('low1', '126'),
('low2', '127'),
('low3', '128'),
('med', '129'),
('high', '130'),
('norm', '131'),
])
self.groups = OrderedDict([
('base', ['low1', 'low2', 'low3']),
('stim', ['med', 'high']),
])
insert = {
'Proteins': self.prots,
'Sequence': self.seq,
'Modifications': self.mods,
'126': 1e4,
'127': 1e4,
'128': np.nan,
'129': 4e4,
'130': 4e4,
'131': 1e4,
}
self.data = data_sets.DataSet(
channels=self.channels,
groups=self.groups,
)
self.data.add_peptide(insert)
def setUp(self):
self.sequence = data_sets.Sequence(
pep_seq="GEPNVsyICSR",
protein_matches=(data_sets.ProteinMatch(
protein=data_sets.Protein(
accession="Q9WV60",
gene="Gsk3b",
description="Glycogen synthase kinase-3 beta",
full_sequence=(
"MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQGPD"
"RPQEVSYTDTKVIGNGSFGVVYQAKLCDSGELVAIKKVLQDKRFKNREL"
"QIMRKLDHCNIVRLRYFFYSSGEKKDEVYLNLVLDYVPETVYRVARHYS"
"RAKQTLPVIYVKLYMYQLFRSLAYIHSFGICHRDIKPQNLLLDPDTAVL"
"KLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGC"
"VLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFP"
"QIKAHPWTKVFRPRTPPEAIALCSRLLEYTPTARLTPLEACAHSFFDEL"
"RDPNVKLPNGRDTPALFNFTTQELSSNPPLATILIPPHARIQAAASPPA"
"NATAASDTNAGDRGQTNNAASASASNST"),
),
rel_pos=209,
exact=True,
), ),
)
self.sequence.modifications = data_sets.Modifications(
(
# S215-p
data_sets.Modification(
rel_pos=5,
mod_type="Phospho",
sequence=self.sequence,
),
# Y216-p
data_sets.Modification(
rel_pos=6,
mod_type="Phospho",
sequence=self.sequence,
),
), )
self.sequences = list(motif.generate_n_mers(self.sequence))
self.foreground = self.sequences
self.background = self.sequences
def _reassign_mods(mods, psp_val, probability_cutoff=75):
reassigned = False
ambiguous = False
# phophoRS example format: 'T(4): 99.6; S(6): 0.4; S(10): 0.0'
# Error messages include: 'Too many isoforms'
if psp_val is None:
psp_val = ''
psp_val = [
RE_PSP.match(i.strip())
for i in psp_val.split(';')
]
psp_val = [
i.groups()
for i in psp_val
if i
]
psp_val = [
(i[0], int(i[1]), float(i[2]))
for i in psp_val
]
o_mods = [i for i in mods if not _is_pmod(i)]
p_mods = [i for i in mods if _is_pmod(i)]
psp_val_f = [i for i in psp_val if i[2] > probability_cutoff]
if len(p_mods) != len(psp_val_f):
LOGGER.debug(
'Not enough info to assign phophosite: {}'.format(psp_val)
)
ambiguous = True
elif set(i.rel_pos + 1 for i in p_mods) != set(i[1] for i in psp_val_f):
p_mods = [
data_sets.Modification(
rel_pos=i[1] - 1,
mod_type='Phospho',
nterm=False,
cterm=False,
sequence=p_mods[0].sequence,
)
for i in psp_val_f
]
reassigned = True
mods = data_sets.Modifications(
mods=_sort_mods(o_mods + p_mods),
)
for mod in mods.mods:
mod.sequence.modifications = mods
return mods, reassigned, ambiguous
def setUp(self):
self.sequence = data_sets.Sequence(
pep_seq='GEPNVsyICSR',
protein_matches=(data_sets.ProteinMatch(
protein=data_sets.Protein(
accession='Q9WV60',
gene='Gsk3b',
description='Glycogen synthase kinase-3 beta',
full_sequence=(
'MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQGPD'
'RPQEVSYTDTKVIGNGSFGVVYQAKLCDSGELVAIKKVLQDKRFKNREL'
'QIMRKLDHCNIVRLRYFFYSSGEKKDEVYLNLVLDYVPETVYRVARHYS'
'RAKQTLPVIYVKLYMYQLFRSLAYIHSFGICHRDIKPQNLLLDPDTAVL'
'KLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGC'
'VLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFP'
'QIKAHPWTKVFRPRTPPEAIALCSRLLEYTPTARLTPLEACAHSFFDEL'
'RDPNVKLPNGRDTPALFNFTTQELSSNPPLATILIPPHARIQAAASPPA'
'NATAASDTNAGDRGQTNNAASASASNST'),
),
rel_pos=209,
exact=True,
), ),
)
self.sequence.modifications = data_sets.Modifications(
(
# S215-p
data_sets.Modification(
rel_pos=5,
mod_type='Phospho',
sequence=self.sequence,
),
# Y216-p
data_sets.Modification(
rel_pos=6,
mod_type='Phospho',
sequence=self.sequence,
),
), )
def _get_modifications(df, cursor, pd_version):
mod_dict = defaultdict(list)
if pd_version[:2] in [(1, 4)]:
aa_mods = cursor.execute(
'''
SELECT
Peptides.PeptideID,
AminoAcidModifications.Abbreviation,
PeptidesAminoAcidModifications.Position
FROM Peptides
JOIN PeptidesAminoAcidModifications
ON Peptides.PeptideID=PeptidesAminoAcidModifications.PeptideID
JOIN AminoAcidModifications
ON PeptidesAminoAcidModifications.AminoAcidModificationID=
AminoAcidModifications.AminoAcidModificationID
''',
)
for peptide_id, name, pos in aa_mods:
if peptide_id not in df.index:
continue
mod = data_sets.Modification(
rel_pos=pos,
mod_type=name,
nterm=False,
cterm=False,
)
mod_dict[peptide_id].append(mod)
term_mods = cursor.execute(
'''
SELECT
Peptides.PeptideID,
Peptides.Sequence,
AminoAcidModifications.Abbreviation,
AminoAcidModifications.PositionType
FROM Peptides
JOIN PeptidesTerminalModifications
ON Peptides.PeptideID=PeptidesTerminalModifications.PeptideID
JOIN AminoAcidModifications
ON PeptidesTerminalModifications.TerminalModificationID=
AminoAcidModifications.AminoAcidModificationID
''',
)
# PositionType rules taken from:
#
# https://github.com/compomics/thermo-msf-parser/blob/
# 697a2fe94de2e960a9bb962d1f263dc983461999/thermo_msf_parser_API/
# src/main/java/com/compomics/thermo_msf_parser_API/highmeminstance/
# Parser.java#L1022
for peptide_id, pep_seq, name, pos_type in term_mods:
if peptide_id not in df.index:
continue
nterm = pos_type == 1
pos = 0 if nterm else len(pep_seq)
mod = data_sets.Modification(
rel_pos=pos,
mod_type=name,
nterm=nterm,
cterm=not nterm,
)
mod_dict[peptide_id].append(mod)
elif pd_version[:2] in [(2, 2)]:
aa_mods = cursor.execute(
'''
SELECT
TargetPsms.PeptideID,
FoundModifications.Abbreviation,
TargetPsmsFoundModifications.Position
FROM TargetPsms
JOIN TargetPsmsFoundModifications
ON
TargetPsmsFoundModifications.TargetPsmsPeptideID=TargetPsms.PeptideID
JOIN FoundModifications
ON
TargetPsmsFoundModifications.FoundModificationsModificationID=
FoundModifications.ModificationID
WHERE
FoundModifications.PositionType NOT IN (1, 2)
''',
)
for peptide_id, name, pos in aa_mods:
if peptide_id not in df.index:
continue
pos -= 1
mod = data_sets.Modification(
rel_pos=pos,
mod_type=name,
nterm=False,
cterm=False,
)
mod_dict[peptide_id].append(mod)
term_mods = cursor.execute(
'''
SELECT
TargetPsms.PeptideID,
TargetPsms.Sequence,
FoundModifications.Abbreviation,
FoundModifications.PositionType
FROM TargetPsms
JOIN TargetPsmsFoundModifications
ON
TargetPsmsFoundModifications.TargetPsmsPeptideID=TargetPsms.PeptideID
JOIN FoundModifications
ON
TargetPsmsFoundModifications.FoundModificationsModificationID=
FoundModifications.ModificationID
WHERE
FoundModifications.PositionType IN (1, 2)
''',
)
# PositionType rules taken from:
#
# https://github.com/compomics/thermo-msf-parser/blob/
# 697a2fe94de2e960a9bb962d1f263dc983461999/thermo_msf_parser_API/
# src/main/java/com/compomics/thermo_msf_parser_API/highmeminstance/
# Parser.java#L1022
for peptide_id, pep_seq, name, pos_type in term_mods:
if peptide_id not in df.index:
continue
nterm = pos_type == 1
pos = 0 if nterm else len(pep_seq)
mod = data_sets.Modification(
rel_pos=pos,
mod_type=name,
nterm=nterm,
cterm=not nterm,
)
mod_dict[peptide_id].append(mod)
else:
raise Exception(
'Unsupported Proteome Discoverer Version: {}'.format(pd_version)
)
mod_dict = {
key: _sort_mods(val)
for key, val in mod_dict.items()
}
def _get_mods(row):
peptide_id = row.name
mods = data_sets.Modifications(
mods=mod_dict.get(peptide_id, tuple()),
)
for mod in mods.mods:
assert mod.sequence is None
mod.sequence = row['Sequence']
row['Sequence'].modifications = mods
return mods
df['Modifications'] = df.apply(_get_mods, axis=1)
return df
def _get_modifications(df, cursor):
aa_mods = cursor.execute(
"""
SELECT
Peptides.PeptideID,
AminoAcidModifications.Abbreviation,
PeptidesAminoAcidModifications.Position
FROM Peptides
JOIN PeptidesAminoAcidModifications
ON Peptides.PeptideID=PeptidesAminoAcidModifications.PeptideID
JOIN AminoAcidModifications
ON PeptidesAminoAcidModifications.AminoAcidModificationID=
AminoAcidModifications.AminoAcidModificationID
""",
)
mod_dict = defaultdict(list)
for peptide_id, name, pos in aa_mods:
if peptide_id not in df.index:
continue
mod = data_sets.Modification(
rel_pos=pos,
mod_type=name,
nterm=False,
cterm=False,
)
mod_dict[peptide_id].append(mod)
term_mods = cursor.execute(
"""
SELECT
Peptides.PeptideID,
Peptides.Sequence,
AminoAcidModifications.Abbreviation,
AminoAcidModifications.PositionType
FROM Peptides
JOIN PeptidesTerminalModifications
ON Peptides.PeptideID=PeptidesTerminalModifications.PeptideID
JOIN AminoAcidModifications
ON PeptidesTerminalModifications.TerminalModificationID=
AminoAcidModifications.AminoAcidModificationID
""",
)
# PositionType rules taken from:
#
# https://github.com/compomics/thermo-msf-parser/blob/
# 697a2fe94de2e960a9bb962d1f263dc983461999/thermo_msf_parser_API/
# src/main/java/com/compomics/thermo_msf_parser_API/highmeminstance/
# Parser.java#L1022
for peptide_id, pep_seq, name, pos_type in term_mods:
if peptide_id not in df.index:
continue
nterm = pos_type == 1
pos = 0 if nterm else len(pep_seq)
mod = data_sets.Modification(
rel_pos=pos,
mod_type=name,
nterm=nterm,
cterm=not nterm,
)
mod_dict[peptide_id].append(mod)
mod_dict = {
key: _sort_mods(val)
for key, val in mod_dict.items()
}
def _get_mods(row):
peptide_id = row.name
mods = data_sets.Modifications(
mods=mod_dict.get(peptide_id, tuple()),
)
for mod in mods.mods:
assert mod.sequence is None
mod.sequence = row["Sequence"]
row["Sequence"].modifications = mods
return mods
df["Modifications"] = df.apply(_get_mods, axis=1)
return df