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 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_proteins(df, cursor, pd_version):
if pd_version[:2] in [(1, 4)]:
prots = cursor.execute(
'''
SELECT
Peptides.PeptideID,
ProteinAnnotations.Description,
Proteins.Sequence
FROM Peptides
JOIN PeptidesProteins
ON Peptides.PeptideID=PeptidesProteins.PeptideID
JOIN ProteinAnnotations
ON ProteinAnnotations.ProteinID=PeptidesProteins.ProteinID
JOIN Proteins
ON Proteins.ProteinID=PeptidesProteins.ProteinID
''',
)
elif pd_version[:2] in [(2, 2)]:
prots = cursor.execute(
'''
SELECT
TargetPsms.PeptideID,
TargetProteins.FastaTitleLines,
TargetProteins.Sequence
FROM TargetPsms
JOIN TargetProteinsTargetPsms
ON TargetPsms.PeptideID=
TargetProteinsTargetPsms.TargetPsmsPeptideID
JOIN TargetProteins
ON TargetProteins.UniqueSequenceID=
TargetProteinsTargetPsms.TargetProteinsUniqueSequenceID
''',
)
else:
raise Exception(
'Unsupported Proteome Discoverer Version: {}'.format(pd_version)
)
accessions = defaultdict(list)
genes = defaultdict(list)
descriptions = defaultdict(list)
sequences = defaultdict(list)
for peptide_id, prot_string, seq in prots:
for fasta_line in prot_string.split('\n'):
try:
accessions[peptide_id].append(
pypuniprot.RE_DISCOVERER_ACCESSION.match(fasta_line).group(2)
)
except:
print(fasta_line)
raise
gene = RE_GENE.match(prot_string)
if gene:
gene = gene.group(1)
else:
gene = RE_GENE_BACKUP.match(prot_string).group(2)
genes[peptide_id].append(
gene
)
descriptions[peptide_id].append(
RE_DESCRIPTION.match(prot_string).group(2)
)
sequences[peptide_id].append(seq)
df['Protein Descriptions'] = df.index.map(
lambda peptide_id:
'; '.join(descriptions[peptide_id])
)
df['Protein Group Accessions'] = df.index.map(
lambda peptide_id:
'; '.join(accessions[peptide_id])
)
df['Proteins'] = df.index.map(
lambda peptide_id:
data_sets.Proteins(
proteins=tuple(
data_sets.Protein(
accession=accession,
gene=gene,
full_sequence=seq,
description=desc,
)
for accession, gene, seq, desc in zip(
accessions[peptide_id],
genes[peptide_id],
sequences[peptide_id],
descriptions[peptide_id],
)
)
)
)
return df
def _get_proteins(df, cursor, pd_version):
if pd_version[:2] in [(1, 4)] or pd_version[:2] in [(2, 1)]:
prots = cursor.execute(
'''
SELECT
Peptides.PeptideID,
ProteinAnnotations.Description,
Proteins.Sequence
FROM Peptides
JOIN PeptidesProteins
ON Peptides.PeptideID=PeptidesProteins.PeptideID
JOIN ProteinAnnotations
ON ProteinAnnotations.ProteinID=PeptidesProteins.ProteinID
JOIN Proteins
ON Proteins.ProteinID=PeptidesProteins.ProteinID
''', )
elif pd_version[:2] in [(2, 2)]:
prots = cursor.execute(
'''
SELECT
TargetPsms.PeptideID,
TargetProteins.FastaTitleLines,
TargetProteins.Sequence
FROM TargetPsms
JOIN TargetProteinsTargetPsms
ON TargetPsms.PeptideID=
TargetProteinsTargetPsms.TargetPsmsPeptideID
JOIN TargetProteins
ON TargetProteins.UniqueSequenceID=
TargetProteinsTargetPsms.TargetProteinsUniqueSequenceID
''', )
else:
raise Exception(
'Unsupported Proteome Discoverer Version: {}'.format(pd_version))
accessions = defaultdict(list)
genes = defaultdict(list)
descriptions = defaultdict(list)
sequences = defaultdict(list)
for peptide_id, prot_string, seq in prots:
for fasta_line in prot_string.split('\n'):
accessions[peptide_id].append(fasta_line.replace(">", ""))
gene = fasta_line.replace(">", "")
genes[peptide_id].append(gene)
descriptions[peptide_id].append("")
sequences[peptide_id].append(seq)
df['Protein Descriptions'] = df.index.map(
lambda peptide_id: '; '.join(descriptions[peptide_id]))
df['Protein Group Accessions'] = df.index.map(
lambda peptide_id: '; '.join(accessions[peptide_id]))
df['Proteins'] = df.index.map(
lambda peptide_id: data_sets.Proteins(proteins=tuple(
data_sets.Protein(
accession=accession,
gene=gene,
full_sequence=seq,
description=desc,
) for accession, gene, seq, desc in zip(
accessions[peptide_id],
genes[peptide_id],
sequences[peptide_id],
descriptions[peptide_id],
))))
return df
def _get_proteins(df, cursor):
prots = cursor.execute(
"""
SELECT
Peptides.PeptideID,
ProteinAnnotations.Description,
Proteins.Sequence
FROM Peptides
JOIN PeptidesProteins
ON Peptides.PeptideID=PeptidesProteins.PeptideID
JOIN ProteinAnnotations
ON ProteinAnnotations.ProteinID=PeptidesProteins.ProteinID
JOIN Proteins
ON Proteins.ProteinID=PeptidesProteins.ProteinID
""",
)
accessions = defaultdict(list)
genes = defaultdict(list)
descriptions = defaultdict(list)
sequences = defaultdict(list)
for peptide_id, prot_string, seq in prots:
accessions[peptide_id].append(
pypuniprot.RE_DISCOVERER_ACCESSION.match(prot_string).group(1)
)
gene = RE_GENE.match(prot_string)
if not gene:
gene = RE_GENE_BACKUP.match(prot_string)
genes[peptide_id].append(
gene.group(1)
)
descriptions[peptide_id].append(
RE_DESCRIPTION.match(prot_string).group(1)
)
sequences[peptide_id].append(seq)
df["Protein Descriptions"] = df.index.map(
lambda peptide_id:
"; ".join(descriptions[peptide_id])
)
df["Protein Group Accessions"] = df.index.map(
lambda peptide_id:
"; ".join(accessions[peptide_id])
)
df["Proteins"] = df.index.map(
lambda peptide_id:
data_sets.Proteins(
proteins=tuple(
data_sets.Protein(
accession=accession,
gene=gene,
full_sequence=seq,
description=desc,
)
for accession, gene, seq, desc in zip(
accessions[peptide_id],
genes[peptide_id],
sequences[peptide_id],
descriptions[peptide_id],
)
)
)
)
return df