def parse_pseudomonas(session):
# get all the kegg compounds if they were not obtained yet
if not kegg_compounds:
get_kegg_compounds()
# add PAO1 and PA14 KEGG sources
# Note: is_experimental is set to 2 since no way to deteremine if detection method was experimental or not
source_PAO1 = InteractionSource(data_source='KEGG(PAO1)', is_experimental=2)
source_PA14 = InteractionSource(data_source='KEGG(PA14)', is_experimental=2)
session.add(source_PAO1), session.add(source_PA14), session.commit()
# parse PAO1 and PA14 Kegg interactions
parse_kegg('pae', 'PAO1', 'KEGG(PAO1)', session)
parse_kegg('pau', 'PA14', 'KEGG(PA14)', session)
def parse(session):
with open('Data/PAO1/Zhang.csv') as csvfile:
reader = csv.DictReader(csvfile)
source = InteractionSource(data_source='Zhang', is_experimental=0)
session.add(source), session.commit()
for row in reader:
if float(row['Confidence']) < 0.9: continue
interactor_A = session.query(Interactor).get(row['Protein1'])
if interactor_A is None: continue
interactor_B = session.query(Interactor).get(row['Protein2'])
if interactor_B is None: continue
homogenous = (interactor_A == interactor_B)
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_A),
Interaction.interactors.contains(interactor_B),
Interaction.homogenous == homogenous).first()
if interaction is None:
interaction = Interaction(
strain='PAO1',
homogenous=homogenous,
type='p-p',
interactors=[interactor_A, interactor_B])
interaction.sources.append(source)
session.add(interaction), session.commit()
elif source not in interaction.sources:
interaction.sources.append(source)
reference = session.query(InteractionReference).filter_by(
detection_method='computational prediction',
pmid='22848443',
interaction_type='predicted',
confidence=row['Confidence'],
comment=row['Comment']).first()
if reference is None:
reference = InteractionReference(
detection_method='computational prediction',
author_ln='Zhang',
pub_date='2012',
pmid='22848443',
interaction_type='predicted',
confidence=row['Confidence'],
comment=row['Comment'])
interaction.references.append(reference)
reference.sources.append(source)
else:
if reference not in interaction.references:
interaction.references.append(reference)
if source not in reference.sources:
reference.sources.append(source)
session.commit()
print('zhang', session.query(Interaction).count())
def parse(session):
# parse ecocyc paths from file from EcoCyc and pyut into ecocyc_paths
get_ecocyc_paths()
# parse compounds from ecocyc interactor files, put into ecocyc_compounds
get_ecocyc_compounds(session)
update_metabolite_info_ecocyc(session)
# create and add new source for EcoCyc (no references for any interactions so is_experimental = 2)
source = InteractionSource(data_source='EcoCyc', is_experimental=2)
session.add(source), session.commit()
# parse PAO1 and PA14 separately
parse_ecocyc('PAO1', session)
parse_ecocyc('PA14', session)
def parse_ecoli(session):
# get all kegg compounds if they were not obtained yet
if not kegg_compounds:
get_kegg_compounds()
# update metabolite info for existing metabolites which may be missing ids
update_metabolite_info_kegg(session)
# add Ecoli KEGG source
# Note: is_experimental is set to 2 since no way to determine if detection method was experimental or not
source = InteractionSource(data_source='KEGG(Ecoli)', is_experimental=2)
session.add(source), session.commit()
# parse kegg interactions from Ecoli, doing PAO1 and PA14 ortholog mapping separately
parse_kegg('eco', 'PAO1', 'KEGG(Ecoli)', session)
parse_kegg('eco', 'PA14', 'KEGG(Ecoli)', session)
def parse(session):
with open('Data/PAO1/xlinkdb.txt') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
reference = InteractionReference(
detection_method='chemical cross-linking mass spectrometry',
interaction_type='physical association',
author_ln='Navari',
pub_date='2015',
pmid='25800553',
source_db='xlinkdb')
source = InteractionSource(data_source='XLinkDB', is_experimental=1)
source.references.append(reference)
session.add(source), session.add(reference), session.commit()
for row in reader:
interactor_A = session.query(Interactor).get(row['proA'])
if interactor_A is None:
interactor_A = session.query(Protein).filter_by(
uniprotkb=row['proA']).first()
if interactor_A is None: continue
interactor_B = session.query(Interactor).get(row['proB'])
if interactor_B is None:
interactor_B = session.query(Protein).filter_by(
uniprotkb=row['proB']).first()
if interactor_B is None: continue
homogenous = (interactor_A == interactor_B)
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_A),
Interaction.interactors.contains(interactor_B),
Interaction.homogenous == homogenous).first()
if interaction is None:
interaction = Interaction(
strain='PAO1',
homogenous=homogenous,
type='p-p',
interactors=[interactor_A, interactor_B])
interaction.references.append(reference)
interaction.sources.append(source)
session.add(interaction), session.commit()
else:
if reference not in interaction.references:
interaction.references.append(reference)
if source not in interaction.sources:
interaction.sources.append(source)
session.commit()
print('xlinkdb', session.query(Interaction).count())
def parse(session):
with open('Data/PAO1/GeoffWinsor.csv') as csvfile:
reader = csv.DictReader(csvfile)
source = InteractionSource(data_source='Geoff', is_experimental=1)
session.add(source), session.commit()
for row in reader:
interactor_A = session.query(Interactor).get(row['locus_tag'])
if interactor_A is None: continue
row = next(reader)
interactor_B = session.query(Interactor).get(row['locus_tag'])
if interactor_B is None: continue
homogenous = (interactor_A == interactor_B)
interaction = session.query(Interaction).filter(Interaction.interactors.contains(interactor_A),
Interaction.interactors.contains(interactor_B),
Interaction.homogenous == homogenous).first()
if interaction is None:
interaction = Interaction(strain='PAO1', homogenous=homogenous , type='p-p',
interactors = [interactor_A, interactor_B])
interaction.sources.append(source)
session.add(interaction), session.commit()
elif source not in interaction.sources:
interaction.sources.append(source)
reference = session.query(InteractionReference).filter_by(detection_method=row['experimental_type'],
pmid=row['pmid']).first()
if reference is None:
reference = InteractionReference(detection_method=row['experimental_type'], pmid=row['pmid'])
interaction.references.append(reference)
reference.sources.append(source)
else:
if interaction not in reference.interactions:
reference.interactions.append(interaction)
if source not in reference.sources:
reference.sources.append(source)
session.commit()
print('geoff', session.query(Interaction).count())
def parse(session):
with open('Data/Ecoli/RegulonDB.csv') as csvfile:
reader = csv.DictReader(csvfile)
# since all the interactions from here will use the same source, create and add it at the beginning
# Note: since no references are available, is_experimental is set to 2
source = InteractionSource(data_source='RegulonDB(Ecoli)',
is_experimental=2)
session.add(source), session.commit()
for row in reader:
interactors = []
orthologs_A = session.query(OrthologEcoli).filter_by(
ortholog_name=(row['TF name'][0].lower() +
row['TF name'][1:])).all()
# if no orthologs for first interactor were found, skip to next interaction
if orthologs_A is None: continue
orthologs_B = session.query(OrthologEcoli).filter_by(
ortholog_name=row['Regulated gene']).all()
# if no orthologs for second interactor were found, skip to next interaction
if orthologs_B is None: continue
# iterate through each ortholog in ortholog A and B to create interactor pairs from their
# respective pseudomonas proteins
for ortholog_A in orthologs_A:
for ortholog_B in orthologs_B:
# only add the pseudomonas interactors if their strains match
if ortholog_A.strain_protein == ortholog_B.strain_protein:
# make sure to add ortholog id for creating the interaction reference later
interactors.append(
[[ortholog_A.protein, ortholog_A.ortholog_id],
[ortholog_B.protein, ortholog_B.ortholog_id]])
# iterate through each interactor pair, create a new interaction if it doesnt exist yet
for interactor_pair in interactors:
homogenous = (interactor_pair[0][0] == interactor_pair[1][0])
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_pair[0][0]),
Interaction.interactors.contains(interactor_pair[1][0]),
Interaction.homogenous == homogenous).first()
if interaction is None:
# if interaction is None, make ortholog_derived = Ecoli and add source to interaction sources
interaction = Interaction(
strain=interactor_pair[0][0].strain,
interactors=[
interactor_pair[0][0], interactor_pair[1][0]
],
type='p-p',
ortholog_derived='Ecoli')
interaction.sources.append(source)
session.add(interaction), session.commit()
elif source not in interaction.sources:
interaction.sources.append(source)
# in case the interaction already existed, make sure interactor_a and interactor_b variables for
# new interaction reference match up with the first and second interactors of the existing
# interaction (so it's easy to see which Pseudomonas interactor matches up with which Ecoli
# ortholog)
interactor_a, interactor_b = None, None
if interaction.interactors[0] == interactor_pair[0][0]:
interactor_a = interactor_pair[0][1]
interactor_b = interactor_pair[1][1]
else:
interactor_b = interactor_pair[0][1]
interactor_a = interactor_pair[1][1]
type = 'TF/sigma-binding site (' + row[
'Regulatory effect'] + 'regulation)'
comment = interactor_pair[0][1] + ' regulates(' + row[
'Regulatory effect'] + ') ' + interactor_pair[1][1]
# create a reference for each evidence type listed for interaction
for evidence in row['Evidence'][1:-1].split(', '):
# check if interaction reference already exists in db
reference = session.query(InteractionReference).filter_by(
detection_method=evidence,
interaction_type=type,
source_db='regulondb',
confidence=row['Evidence type'],
comment=comment,
interactor_a=interactor_a,
interactor_b=interactor_b).first()
if reference is None:
# if reference is None, add reference to interaction references list and add source
# to reference sources list
reference = InteractionReference(
detection_method=evidence,
interaction_type=type,
comment=comment,
source_db='regulondb',
confidence=row['Evidence type'],
interactor_a=interactor_a,
interactor_b=interactor_b)
interaction.references.append(reference)
reference.sources.append(source)
# if reference exists, check that its interactions contains interaction, and sources contains
# source, and add if they are not present
else:
if interaction not in reference.interactions:
interaction.references.append(reference)
if source not in reference.sources:
reference.sources.append(source)
session.commit()
print('regulondb', session.query(Interaction).count())
def parse_ecoli_uniprot(session):
with open('Ecoli/PSICQUIC/UniProt.txt') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
interactors = []
orthologs_B = []
id_B = row['ID(s) interactor B'].split(':')
if id_B[0] == 'uniprotkb':
orthologs_B = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == id_B[1]).all()
if len(orthologs_B) == 0: continue
orthologs_A = []
metabolite = None
id_A = row['#ID(s) interactor A'].split(':')
if id_A[0] == 'uniprotkb':
orthologs_A = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == id_A[1]).all()
elif id_A[0] == 'chebi':
metabolite = session.query(Metabolite).filter(
Metabolite.chebi == id_A[1]).first()
if metabolite is None:
metabolite = Metabolite(id=id_A[1], chebi=id_A[1])
session.add(metabolite), session.commit()
for ortholog_A in orthologs_A:
for ortholog_B in orthologs_B:
if (ortholog_A is not None) and (ortholog_B is not None):
if ortholog_A.strain_protein == ortholog_B.strain_protein:
interactors.append(
[[ortholog_A.protein, ortholog_A.ortholog_id],
[ortholog_B.protein, ortholog_B.ortholog_id]])
if metabolite is not None:
for ortholog_B in orthologs_B:
interactors.append(
[[metabolite, metabolite.id],
[ortholog_B.protein, ortholog_B.ortholog_id]])
for interactor_pair in interactors:
homogenous = (interactor_pair[0][0] == interactor_pair[1][0])
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_pair[0][0]),
Interaction.interactors.contains(interactor_pair[1][0]),
Interaction.homogenous == homogenous).first()
if interaction is not None:
if interaction.ortholog_derived is None:
interaction.ortholog_derived = 'cfe'
elif 'fe' not in interaction.ortholog_derived:
interaction.ortholog_derived += ', cfe'
session.commit()
else:
interaction = Interaction(
strain=interactor_pair[0][0].strain,
interactors=[
interactor_pair[0][0], interactor_pair[1][0]
],
type=(interactor_pair[0][0].type + '-' +
interactor_pair[1][0]),
ortholog_derived='fe')
if 'MI:' in row['Interaction detection method(s)']:
if is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
session.add(interaction), session.commit()
interactor_a, interactor_b = None, None
if interaction.interactors[0] == interactor_pair[0][0]:
interactor_a = interactor_pair[0][1]
interactor_b = interactor_pair[1][1]
else:
interactor_b = interactor_pair[0][1]
interactor_a = interactor_pair[1][1]
reference = InteractionReference(
interaction_id=interaction.id,
psimi_detection=row['Interaction detection method(s)'].
split('MI:')[1][:4],
detection_method=row['Interaction detection method(s)'].
split('(')[1][:-1],
author_ln=row['Publication 1st author(s)'].split(' ')[0],
pub_date=row['Publication 1st author(s)'].split(
'(')[1][:-1],
pmid=row['Publication Identifier(s)'].split(
'pubmed:')[1].split('|')[0],
psimi_type=row['Interaction type(s)'].split('MI:')[1][:4],
interaction_type=row['Interaction type(s)'].split(
'(')[1][:-1],
psimi_db=row['Source database(s)'].split('MI:')[1][:4],
source_db=row['Source database(s)'].split('(')[1][:-1],
confidence_score=row['Confidence value(s)'],
interactor_a_id=interactor_a,
interactor_b_id=interactor_b)
session.add(reference)
source = session.query(InteractionSource).filter(
InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'UniProt').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id,
data_source='UniProt')
session.add(source)
session.commit()
print(session.query(Interaction).count())
def parse(session):
# create and add sources for the interactions (do this before since they all use the same source)
# Note: is_experimental is set to 2 because we cannot confirm that detection method was experimental or not
source_PAO1 = InteractionSource(data_source='Galan-Vasquez(PAO1)',
is_experimental=2)
source_PA14 = InteractionSource(data_source='Galan-Vasquez(PA14)',
is_experimental=2)
session.add(source_PAO1), session.add(source_PA14), session.commit()
with open('Data/PAO1_PA14/regulatory_network.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
# a row describing an interaction may have >1 strain
strains = row['Strain'].split(',')
for strain in strains:
# only care about PAO1 and PA14 strain interactions
if (strain != 'PAO1') and (strain != 'PA14'): continue
# search for interactor A by name
interactor_A = session.query(Protein).filter_by(
name=row['Regulator'], strain=strain).first()
# if no interactor was found by name, id listed may be a gene locus, so search by this id
if interactor_A is None:
interactor_A = session.query(Interactor).get(
row['Regulator'])
# if no interactor A was found for this interaction, skip to next
if interactor_A is None: continue
# same as A above
interactor_B = session.query(Protein).filter_by(
name=row['Target'], strain=strain).first()
if interactor_B is None:
interactor_B = session.query(Interactor).get(row['Target'])
if interactor_B is None: continue
homogenous = (interactor_A == interactor_B)
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_A),
Interaction.interactors.contains(interactor_B),
Interaction.homogenous == homogenous).first()
# if interaction between these 2 interactors does not yet exist, create and add it
if interaction is None:
interaction = Interaction(
strain=strain,
type='p-p',
homogenous=homogenous,
interactors=[interactor_A, interactor_B])
session.add(interaction), session.commit()
# specify the source to be used for the interaction and reference based on strain of interaction
source = None
if strain == 'PAO1':
source = source_PAO1
else:
source = source_PA14
# add the source to the interaction source list if it isn't there already
if source not in interaction.sources:
interaction.sources.append(source)
# get source db and detections if they are present in the file
source_db, detections = None, [None]
if row['source_db'] != '':
source_db = row['source_db']
if row['evidence'] != '':
del detections[0]
for type in row['evidence'].split(', '):
detections.append(type)
# create a new reference for each detection found, add the reference to the interaction's
# reference list, and add the source to the reference's sources
for detection in detections:
reference = InteractionReference(
detection_method=detection,
pmid=row['pmid'],
interaction_type='TF/sigma-binding site (' +
row['mode'] + 'regulation)',
source_db=source_db,
comment=interactor_A.id + ' regulates(' + row['mode'] +
') ' + interactor_B.id)
interaction.references.append(reference)
reference.sources.append(source)
session.commit()
print('regnet', session.query(Interaction).count())
def parse_mpidb(session):
with open('PAO1/PSICQUIC/MPIDB.txt') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
interactors = []
if (row['Taxid interactor A'].split('|')[0] != 'taxid:208964(pseae)') |\
(row['Taxid interactor B'].split('|')[0] != 'taxid:208964(pseae)'):
continue
A_id = row['#ID(s) interactor A'].split(':')[1]
B_id = row['ID(s) interactor B'].split(':')[1]
if session.query(Interactor).filter(
Interactor.id == A_id).first() is not None:
interactors.append(
session.query(Interactor).filter(
Interactor.id == A_id).one())
elif session.query(Protein).filter(
Protein.uniprotkb == A_id).first() is not None:
interactors.append(
session.query(Protein).filter(
Protein.uniprotkb == A_id).one())
if session.query(Interactor).filter(
Interactor.id == B_id).first() is not None:
interactors.append(
session.query(Interactor).filter(
Interactor.id == B_id).one())
elif session.query(Protein).filter(
Protein.uniprotkb == B_id).first() is not None:
interactors.append(
session.query(Protein).filter(
Protein.uniprotkb == B_id).one())
if len(interactors) != 2: continue
homogenous = (interactors[0] == interactors[1])
interaction = session.query(Interaction).filter(
(Interaction.interactors.contains(interactors[0])),
(Interaction.interactors.contains(interactors[1])),
(Interaction.homogenous == homogenous)).first()
if interaction is None:
type = interactors[0].type + '-' + interactors[1].type
interaction = Interaction(strain='PAO1',
type=type,
homogenous=homogenous,
interactors=interactors)
if is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
else:
interaction.is_experimental = 0
session.add(interaction), session.commit()
else:
if is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
reference = InteractionReference(
interaction_id=interaction.id,
detection_method=row['Interaction detection method(s)'].split(
'(')[1][:-1],
author_ln=row['Publication 1st author(s)'].split(' ')[0],
pub_date=row['Publication 1st author(s)'].split('(')[1][:-1],
pmid=row['Publication Identifier(s)'].split('pubmed:')[1][:8],
confidence=row['Confidence value(s)'],
interaction_type=row['Interaction type(s)'].split('(')[1][:-1],
source_db=row['Source database(s)'])
session.add(reference)
for xref in row['Interaction identifier(s)'].split('|'):
xref_field = xref.split(':')
xref = session.query(InteractionXref).filter(
InteractionXref.accession == xref_field[1],
InteractionXref.interaction_id == interaction.id).first()
if xref is None:
xref = InteractionXref(interaction_id=interaction.id,
accession=xref_field[1],
data_source=xref_field[0])
session.add(xref)
source = session.query(InteractionSource).filter(
InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'MPIDB').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id,
data_source='MPIDB')
session.add(source)
session.commit()
print(session.query(Interaction).count())
def parse_psimi(session, file, source):
with open(file) as csvfile:
reader = csv.DictReader(csvfile, fieldnames=cols, delimiter='\t')
# iterate through each interaction
for row in reader:
uniprot_A, refseq_A, orthologs_A, uniprot_B, refseq_B, orthologs_B = None, None, None, None, None, None
# if one of the interactors is metabolite, save it's ids in pubchem and chebi
pubchem, chebi = None, None
# if one of the interactors is a metabolite, metabolite will be that metabolite and orthologs
# will be set to the interaction's protein ortholog(s)
metabolite_info, metabolite, orthologs = None, None, None
# check if interactor A has uniprot or refseq id
if 'uniprotkb' in row['interactor_A']:
uniprot_A = row['interactor_A'].split('uniprotkb:')[1].split(
'|')[0]
if 'refseq' in row['interactor_A']:
refseq_A = row['interactor_A'].split('refseq:')[1].split(
'|')[0]
# if uniprot id was found, look for orthologs matching that id
if uniprot_A is not None:
orthologs_A = session.query(OrthologEcoli).filter_by(
ortholog_uniprot=uniprot_A).all()
# if no orthologs were found but a refseq id was found, try to find ortholog based on refseq
if (orthologs_A is None) and (refseq_A is not None):
orthologs_A = session.query(OrthologEcoli).filter_by(
ortholog_refseq=refseq_A).all()
# if no orthologs were found for interactor A, but a uniprot or refseq does exist,
# that means the ecoli interactor A is a protein without orthologs, so continue to next interaction
if (orthologs_A is None) & ((uniprot_A is not None) |
(refseq_A is not None)):
continue
# same as for interactor A above
if 'uniprotkb' in row['interactor_B']:
uniprot_B = row['interactor_B'].split('uniprotkb:')[1].split(
'|')[0]
if 'refseq' in row['interactor_B']:
refseq_B = row['interactor_B'].split('refseq:')[1].split(
'|')[0]
if uniprot_B is not None:
orthologs_B = session.query(OrthologEcoli).filter_by(
ortholog_uniprot=uniprot_B).all()
if (orthologs_B is None) and (refseq_B is not None):
orthologs_B = session.query(OrthologEcoli).filter_by(
ortholog_refseq=refseq_B).all()
if (orthologs_B is None) & ((uniprot_B is not None) |
(refseq_B is not None)):
continue
# if both orthologs_A and orthologs_B are None, then there are no protein interactors for this
# interaction, so move on to the next interaction
if (orthologs_A is None) and (orthologs_B is None): continue
# if there were no orthologs for interactor A (and no refseq or uniprot was found),
# search the file for pubchem or chebi ids for interactor A (as it may be a metabolite)
if orthologs_A is None:
if 'chebi' in row['interactor_A']:
chebi = row['interactor_A'].split('CHEBI:')[1].split(
'|')[0][:-1]
if 'pubchem' in row['altID_A']:
pubchem = row['altID_A'].split('pubchem:')[1].split('|')[0]
if (chebi is None) & ('chebi' in row['altID_A']):
chebi = row['altID_A'].split('CHEBI:')[1].split(
'|')[0][:-1]
# if no metabolite ids were found in the interaction row, then move on to the next interaction
# because no interactor_A was identified
if (chebi is None) & (pubchem is None): continue
# if a pubchem or chebi id was found, then this interaction will be a p-m interaction, so
# set the protein interactors(orthologs) to orthologs_B
orthologs = orthologs_B
# other case where orthologs_B were not identified so need to check if interactor B has metabolite ids
elif orthologs_B is None:
if 'chebi' in row['interactor_B']:
chebi = row['interactor_B'].split('CHEBI:')[1].split(
'|')[0][:-1]
if 'pubchem' in row['altID_B']:
pubchem = row['altID_B'].split('pubchem:')[1].split('|')[0]
if (chebi is None) & ('chebi' in row['altID_B']):
chebi = row['altID_B'].split('CHEBI:')[1].split(
'|')[0][:-1]
if (chebi is None) & (pubchem is None): continue
orthologs = orthologs_A
# if one of the interactors was identified to be a metabolite, search for the metabolite and set metabolite
# variable to that value. if the metabolite doesnt exist create it
# Note: if this point was reached, it means one of the interactors had protein orthologs,
# so we can safely create a new metabolite knowing it will have a protein interaction partner
if (chebi is not None) | (pubchem is not None):
id = None
# preferentially set id for new metabolites to be chebi
if chebi is not None:
id = chebi
metabolite = session.query(Metabolite).filter_by(
chebi=chebi).first()
# if no metabolite with chebi was found, but pubchem id exists, try to find
# metabolite with that pubchem
if (metabolite is None) & (pubchem is not None):
id = pubchem
metabolite = session.query(Metabolite).filter_by(
pubchem=pubchem).first()
# if no metabolite was found with pubchem or chebi id, create new metabolite
if metabolite is None:
metabolite = Metabolite(id=id,
chebi=chebi,
pubchem=pubchem)
session.add(metabolite)
# if a metabolite was found, update its chebi and pubchem if it has none
else:
if metabolite.pubchem is None:
metabolite.pubchem = pubchem
if metabolite.chebi is None:
metabolite.chebi = chebi
# list of interactor pairs for interaction
interactors = []
# if no metabolite was found for interaction, it is a p-p interaction, so iterate through
# orthologs to create interactor pairs
if metabolite is None:
for ortholog_A in orthologs_A:
for ortholog_B in orthologs_B:
if (ortholog_A is not None) and (ortholog_B
is not None):
# only add the interactor pair if the protein strains match
if ortholog_A.strain_protein == ortholog_B.strain_protein:
interactors.append([[
ortholog_A.protein, ortholog_A.ortholog_id
], [
ortholog_B.protein, ortholog_B.ortholog_id
]])
else:
# if a metabolite was found, add pairs of all orthologs with metabolite to interactor pairs
for ortholog in orthologs:
interactors.append(
[[metabolite, metabolite.id],
[ortholog.protein, ortholog.ortholog_id]])
# for each interactor pair, create interaction if it doesnt exist, otherwise update attributes
for interactor_pair in interactors:
homogenous = (interactor_pair[0][0] == interactor_pair[1][0])
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_pair[0][0]),
Interaction.interactors.contains(interactor_pair[1][0]),
Interaction.homogenous == homogenous).first()
if interaction is None:
# since one of the interactors may be a metabolite, set strain to match strain of protein
strain = None
if interactor_pair[0][0].type == 'p':
strain = interactor_pair[0][0].strain
else:
strain = interactor_pair[1][0].strain
# if interaction did not exist, set it to Ecoli ortholog derived
interaction = Interaction(
strain=strain,
interactors=[
interactor_pair[0][0], interactor_pair[1][0]
],
type=(interactor_pair[0][0].type + '-' +
interactor_pair[1][0].type),
ortholog_derived='Ecoli')
session.add(interaction), session.commit()
ref_parameter_list = get_psimi_ref_list(row)
# in case the interaction already existed, make sure interactor_a and interactor_b variables for
# new interaction reference match up with the first and second interactors of the existing
# interaction (so it's easy to see which Pseudomonas interactor matches up with which Ecoli
# ortholog)
interactor_a, interactor_b = None, None
if interaction.interactors[0] == interactor_pair[0][0]:
interactor_a = interactor_pair[0][1]
interactor_b = interactor_pair[1][1]
else:
interactor_b = interactor_pair[0][1]
interactor_a = interactor_pair[1][1]
is_experimental = is_experimental_interaction(row)
# check to see if source exists
nsource = session.query(InteractionSource).filter_by(
data_source=source,
is_experimental=is_experimental).first()
# if source doesn't exist, create and add it to the interaction's sources
if nsource is None:
nsource = InteractionSource(
data_source=source, is_experimental=is_experimental)
interaction.sources.append(nsource)
# if the source does exist, add it to the interaction's sources if it isn't already
elif nsource not in interaction.sources:
interaction.sources.append(nsource)
# go through each reference in the ref_parameter list, search for it, and if it doesnt exist create it
for ref in ref_parameter_list:
nref = session.query(InteractionReference).filter_by(
detection_method=ref[0],
author_ln=ref[1],
pub_date=ref[2],
pmid=ref[3],
interaction_type=ref[4],
source_db=ref[5],
confidence=ref[6],
interactor_a=interactor_a,
interactor_b=interactor_b).first()
# if nref doesn't exist, create and add it to the interaction's reference list,
# and add the source to the reference's sources
if nref is None:
nref = InteractionReference(detection_method=ref[0],
author_ln=ref[1],
pub_date=ref[2],
pmid=ref[3],
interaction_type=ref[4],
source_db=ref[5],
confidence=ref[6],
interactor_a=interactor_a,
interactor_b=interactor_b)
interaction.references.append(nref)
nref.sources.append(nsource)
# if nref does exist, add the interaction and source to it's attributes if they aren't added
else:
if interaction not in nref.interactions:
nref.interactions.append(interaction)
if nsource not in nref.sources:
nref.sources.append(nsource)
session.commit()
print(source, session.query(Interaction).count())
def parse_ecoli_bindingdb(session):
with open('Data/Ecoli/PSICQUIC/BindingDB.txt') as csvfile:
reader = csv.DictReader(csvfile)
# iterate through each interaction
for row in reader:
uniprot_protein = None
# check if interactor B has uniprot ID
if 'uniprotkb' in row['ID(s) interactor B']:
uniprot_protein = row['ID(s) interactor B'].split(
'uniprotkb:')[1].split('|')[0]
if uniprot_protein is None: continue
orthologs = []
for ecoli_ortholog in session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == uniprot_protein).all():
if ecoli_ortholog is not None:
orthologs.append(
[ecoli_ortholog.protein, ecoli_ortholog.ortholog_id])
if len(orthologs) == 0: continue
ids_metabolite = row['#ID(s) interactor A'].split('|')
chebi_metabolite, pubchem_metabolite = None, None
# check if interactor A has ChEBI id
for id in ids_metabolite:
if id.split(':')[0] == 'chebi':
chebi_metabolite = id.split(':')[1][1:-1]
metabolite = None
# if interactor A has ChEBI id, query for matching metabolite
if chebi_metabolite is not None:
metabolite = session.query(Metabolite).filter(
Metabolite.chebi == chebi_metabolite).first()
# if unable to identify metabolite based on ChEBI id, try using pubchem id
if metabolite is None:
alt_ids_metabolite = row['Alt. ID(s) interactor A'].split('|')
for id in alt_ids_metabolite:
if id.split(':')[0] == 'pubchem':
pubchem_metabolite = id.split(':')[1]
metabolite = session.query(Metabolite).filter(
Metabolite.id == pubchem_metabolite).first()
# if unable to find interactor A in database, create new metabolite
if metabolite is None:
metabolite = Metabolite(id=pubchem_metabolite,
pubchem=pubchem_metabolite,
chebi=chebi_metabolite)
session.add(metabolite), session.commit()
for interactor in orthologs:
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor[0]),
Interaction.interactors.contains(metabolite)).first()
if interaction is not None:
if interaction.ortholog_derived is None:
interaction.ortholog_derived = 'cfe'
elif 'fe' not in interaction.ortholog_derived:
interaction.ortholog_derived += ', cfe'
session.commit()
else:
interaction = Interaction(
strain=interactor.strain,
interactors=[metabolite, interactor[0]],
type='p-m',
ortholog_derived='fe')
# should ortholog interactions be marked as experimental?
if is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
session.add(interaction), session.commit()
interactor_a, interactor_b = '', ''
if interaction.interactors[0] == metabolite:
interactor_a = metabolite.id
interactor_b = interactor[1]
else:
interactor_b = metabolite.id
interactor_a = interactor[1]
author, date, pmid = None, None, None
if row['Publication 1st author(s)'] != '-':
author = row['Publication 1st author(s)'].split(' ')[0]
date = row['Publication 1st author(s)'].split('(')[1][:-1]
if 'pubmed:' in row['Publication Identifier(s)']:
pmid = row['Publication Identifier(s)'].split(
'pubmed:')[1][:8]
reference = InteractionReference(
interaction_id=interaction.id,
detection_method=row['Interaction detection method(s)'].
split('(')[1][:-1],
author_ln=author,
pmid=pmid,
pub_date=date,
interaction_type=row['Interaction type(s)'].split(
'(')[1][:-1],
source_db=row['Source database(s)'].split('(')[1][:-1],
confidence=row['Confidence value(s)'].split('(')[0],
interactor_a=interactor_a,
interactor_b=interactor_b)
source = session.query(InteractionSource).filter(
InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'BindingDB').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id,
data_source='BindingDB')
session.add(source)
session.add(reference)
session.commit()
def parse_psimi(file, strain, source, session):
with open(file) as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t', fieldnames=cols)
next(reader)
for row in reader:
uniprot_A, refseq_A, interactor_A, uniprot_B, refseq_B, interactor_B = None, None, None, None, None, None
# check if interactor A has uniprot or refseq id, store these values
if 'uniprotkb' in row['interactor_A']:
uniprot_A = row['interactor_A'].split('uniprotkb:')[1].split(
'|')[0]
if 'refseq' in row['interactor_A']:
refseq_A = row['interactor_A'].split('refseq:')[1].split(
'|')[0]
# if a uniprot id was found, try to find the interactor in the database
if uniprot_A is not None:
# check if there is a protein-complex with this uniprot id
interactor_A = session.query(Interactor).get(uniprot_A)
# if no protein complex, check for protein matching the uniprot id
if interactor_A is None:
interactor_A = session.query(Protein).filter_by(
uniprotkb=uniprot_A).first()
# if no interactor A was found but there was also a refseq id, try to find the protein based on
# it's refseq
if (interactor_A is None) and (refseq_A is not None):
interactor_A = session.query(Protein).filter_by(
ncbi_acc=refseq_A).first()
# if no interactor A was found, move on to next interaction
if interactor_A is None: continue
# same as for A above
if 'uniprotkb' in row['interactor_B']:
uniprot_B = row['interactor_B'].split('uniprotkb:')[1].split(
'|')[0]
if 'refseq' in row['interactor_B']:
refseq_B = row['interactor_B'].split('refseq:')[1].split(
'|')[0]
if uniprot_B is not None:
interactor_B = session.query(Interactor).get(uniprot_B)
if interactor_B is None:
interactor_B = session.query(Protein).filter_by(
uniprotkb=uniprot_B).first()
if (interactor_B is None) and (refseq_B is not None):
interactor_B = session.query(Protein).filter_by(
ncbi_acc=refseq_B).first()
if interactor_B is None: continue
homogenous = (interactor_A == interactor_B)
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_A),
Interaction.interactors.contains(interactor_B),
Interaction.homogenous == homogenous).first()
# if no interaction was found with the interactors, create a new interaction
if interaction is None:
interaction = Interaction(
strain=strain,
type='p-p',
homogenous=homogenous,
interactors=[interactor_A, interactor_B])
session.add(interaction), session.commit()
ref_parameter_list = get_psimi_ref_list(row)
is_experimental = is_experimental_interaction(row)
# check to see if source exists
nsource = session.query(InteractionSource).filter_by(
data_source=source, is_experimental=is_experimental).first()
# if source doesn't exist, create and add it to the interaction's sources
if nsource is None:
nsource = InteractionSource(data_source=source,
is_experimental=is_experimental)
interaction.sources.append(nsource)
# if the source does exist, add it to the interaction's sources if it isn't already
elif nsource not in interaction.sources:
interaction.sources.append(nsource)
# go through each reference in the ref_parameter list, search for it, and if it doesnt exist create it
for ref in ref_parameter_list:
nref = session.query(InteractionReference).filter_by(
detection_method=ref[0],
author_ln=ref[1],
pub_date=ref[2],
pmid=ref[3],
interaction_type=ref[4],
source_db=ref[5],
confidence=ref[6],
interactor_a=None,
interactor_b=None).first()
# if nref doesn't exist, create and add it to the interaction's reference list,
# and add the source to the reference's sources
if nref is None:
nref = InteractionReference(detection_method=ref[0],
author_ln=ref[1],
pub_date=ref[2],
pmid=ref[3],
interaction_type=ref[4],
source_db=ref[5],
confidence=ref[6])
interaction.references.append(nref)
nref.sources.append(nsource)
# if nref does exist, add the interaction and source to it's attributes if they aren't added
else:
if interaction not in nref.interactions:
nref.interactions.append(interaction)
if nsource not in nref.sources:
nref.sources.append(nsource)
#collect all the cross references for the interaction
for xref in row['identifier'].split('|'):
xref_field = xref.split(':')
# check if the cross reference exists for this interaction, if it doesnt create it
xref = session.query(InteractionXref).filter_by(
accession=xref_field[1],
interaction_id=interaction.id).first()
if xref is None:
xref = InteractionXref(interaction_id=interaction.id,
accession=xref_field[1],
data_source=xref_field[0])
session.add(xref)
session.commit()
print(source, session.query(Interaction).count())
def parse_ecoli_ebi_goa_nonintact(session):
with open('Ecoli/PSICQUIC/EBI-GOA-nonIntAct.txt') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
interactors = []
uniprot_A, uniprot_B = None, None
if 'uniprotkb:' in row['#ID(s) interactor A']:
uniprot_A = row['#ID(s) interactor A'].split('uniprotkb:')[1]
if 'uniprotkb:' in row['ID(s) interactor B']:
uniprot_B = row['ID(s) interactor B'].split('uniprotkb:')[1]
if (uniprot_A is None) | (uniprot_B is None): continue
orthologs_A = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == uniprot_A).all()
orthologs_B = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == uniprot_B).all()
for ortholog_A in orthologs_A:
for ortholog_B in orthologs_B:
if (ortholog_A is not None) and (ortholog_B is not None):
if ortholog_A.strain_protein == ortholog_B.strain_protein:
interactors.append(
[[ortholog_A.protein, ortholog_A.ortholog_id],
[ortholog_B.protein, ortholog_B.ortholog_id]])
for interactor_pair in interactors:
homogenous = (interactor_pair[0][0] == interactor_pair[1][0])
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_pair[0][0]),
Interaction.interactors.contains(interactor_pair[1][0]),
Interaction.homogenous == homogenous).first()
if interaction is not None:
if interaction.ortholog_derived is None:
interaction.ortholog_derived = 'cfe'
elif 'fe' not in interaction.ortholog_derived:
interaction.ortholog_derived += ', cfe'
session.commit()
else:
interaction = Interaction(
strain=interactor_pair[0][0].strain,
interactors=interactor_pair,
type='p-p',
ortholog_derived='fe')
if is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
session.add(interaction), session.commit()
interactor_a, interactor_b = '', ''
if interaction.interactors[0] == interactor_pair[0][0]:
interactor_a = interactor_pair[0][1]
interactor_b = interactor_pair[1][1]
else:
interactor_b = interactor_pair[0][1]
interactor_a = interactor_pair[1][1]
reference = InteractionReference(
interaction_id=interaction.id,
detection_method=row['Interaction detection method(s)'].
split('(')[1][:-1],
author_ln=row['Publication 1st author(s)'].split(' ')[0],
pub_date=row['Publication 1st author(s)'].split('(')[1],
pmid=row['Publication Identifier(s)'].split('pubmed:')[1],
interaction_type=row['Interaction type(s)'].split(
'(')[1][:-1],
source_db=row['Source database(s)'].split('(')[1][:-1],
interactor_a_id=row['#ID(s) interactor A'].split(':')[1],
interactor_b_id=row['ID(s) interactor B'].split(':')[1])
session.add(reference)
source = session.query(InteractionSource).filter(
InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source ==
'EBI-GOA non-IntAct').first()
if source is None:
source = InteractionSource(
interaction_id=interaction.id,
data_source='EBI-GOA non-IntAct')
session.add(source)
session.commit()
print(session.query(Interaction).count())
def parse_irefindex(file, strain, taxid, session):
with open(file) as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
interactors = []
if ((row['Taxid interactor A'].split('|')[0] != taxid) |
(row['Taxid interactor B'].split('|')[0] != taxid)): continue
A_id = row['#ID(s) interactor A'].split(':')
B_id = row['ID(s) interactor B'].split(':')
if A_id[0] == 'uniprotkb':
if session.query(Interactor).filter(Interactor.id == A_id[1]).first() is not None:
interactors.append(session.query(Interactor).filter(Interactor.id == A_id[1]).one())
elif session.query(Protein).filter(Protein.uniprotkb == A_id[1]).first() is not None:
interactors.append(session.query(Protein).filter(Protein.uniprotkb == A_id[1]).one())
elif A_id[0] == 'refseq':
if session.query(Protein).filter(Protein.ncbi_acc == A_id[1]).first() is not None:
interactors.append(session.query(Protein).filter(Protein.ncbi_acc == A_id[1]).one())
if B_id[0] == 'uniprotkb':
if session.query(Interactor).filter(Interactor.id == B_id[1]).first() is not None:
interactors.append(session.query(Interactor).filter(Interactor.id == B_id[1]).one())
elif session.query(Protein).filter(Protein.uniprotkb == B_id[1]).first() is not None:
interactors.append(session.query(Protein).filter(Protein.uniprotkb == B_id[1]).one())
elif B_id[0] == 'refseq':
if session.query(Protein).filter(Protein.ncbi_acc == B_id[1]).first() is not None:
interactors.append(session.query(Protein).filter(Protein.ncbi_acc == B_id[1]).one())
if len(interactors) != 2: continue
homogenous = (interactors[0] == interactors[1])
interaction = session.query(Interaction).filter((Interaction.interactors.contains(interactors[0])),
(Interaction.interactors.contains(interactors[1])),
(Interaction.homogenous == homogenous)).first()
if interaction is None:
type = interactors[0].type + '-' + interactors[1].type
interaction = Interaction(strain=strain, type=type, homogenous=homogenous, interactors=interactors)
if row['Interaction detection method(s)'] != '-':
if is_experimental_psimi(row['Interaction detection method(s)'].split('MI:')[1][:4]):
interaction.is_experimental = 1
else:
interaction.is_experimental = 0
else:
if is_experimental_psimi(row['Interaction detection method(s)'].split('MI:')[1][:4]):
interaction.is_experimental = 1
elif (row['Interaction detection method(s)'] == '-') and (interaction.is_experimental == 0):
interaction.is_experimental = None
author, date, type= None, None, None
pmids, detections = [None], [None]
if row['Interaction detection method(s)'] != '-':
del detections[0]
for method in row['Interaction detection method(s)'].split('|'):
detections.append(method.split('(')[1][:-1])
if (row['Interaction type(s)'] != '-'):
type = row['Interaction type(s)'].split('(')[1][:-1]
if (row['Publication 1st author(s)'] != '-'):
author = row['Publication 1st author(s)'].split('-')[0][0].upper() + \
row['Publication 1st author(s)'].split('-')[0][1:]
date = row['Publication 1st author(s)'].split('-')[1]
if (row['Publication Identifier(s)'] != '-'):
del pmids[0]
for pmid in row['Publication Identifier(s)'].split('|'):
pmids.append(pmid.split('pubmed:')[1][:8])
for pmid in pmids:
for detection in detections:
reference = InteractionReference(interaction_id=interaction.id,
detection_method=detection,
author_ln=author,
pub_date=date,
pmid=pmid,
interaction_type=type,
source_db=row['Source database(s)'].split('(')[1][:-1],
confidence_score=row['Confidence value(s)'])
session.add(reference)
for xref in row['Interaction identifier(s)'].split('|'):
xref_field = xref.split(':')
xref = session.query(InteractionXref).filter(InteractionXref.accession == xref_field[1],
InteractionXref.interaction_id == interaction.id).first()
if xref is None:
xref = InteractionXref(interaction_id=interaction.id, accession=xref_field[1],
data_source=xref_field[0])
session.add(xref)
source = session.query(InteractionSource).filter(InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'iRefIndex').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id, data_source='iRefIndex')
session.add(source)
print(session.query(Interaction).count())
def parse_ecoli_dip(session):
with open('Ecoli/DIP.txt') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
interactors = []
ids_A = row['ID interactor A'].split('|')
ids_B = row['ID interactor B'].split('|')
refseq_A, uniprotkb_A, refseq_B, uniprotkb_B = '', '', '', ''
for id in ids_A:
fields = id.split(':')
if fields[0] == 'refseq':
refseq_A = fields[1]
elif fields[0] == 'uniprotkb':
uniprotkb_A = fields[1]
for id in ids_B:
fields = id.split(':')
if fields[0] == 'refseq':
refseq_B = fields[1]
elif fields[0] == 'uniprotkb':
uniprotkb_B = fields[1]
orthologs_A, orthologs_B = [], []
if uniprotkb_A != '':
orthologs_A = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == uniprotkb_A).all()
if (len(orthologs_A) == 0) & (refseq_A != ''):
orthologs_A = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_refseq == refseq_A).all()
if uniprotkb_B != '':
orthologs_B = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == uniprotkb_B).all()
if (len(orthologs_B) == 0) & (refseq_B != ''):
orthologs_B = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_refseq == refseq_B).all()
for ortholog_A in orthologs_A:
for ortholog_B in orthologs_B:
if (ortholog_A is not None) and (ortholog_B is not None):
if ortholog_A.strain_protein == ortholog_B.strain_protein:
interactors.append(
[[ortholog_A.protein, ortholog_A.ortholog_id],
[ortholog_B.protein, ortholog_B.ortholog_id]])
for interactor_pair in interactors:
is_new = 0
homogenous = (interactor_pair[0][0] == interactor_pair[1][0])
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_pair[0][0]),
Interaction.interactors.contains(interactor_pair[1][0]),
Interaction.homogenous == homogenous).first()
if interaction is not None:
if interaction.ortholog_derived is None:
interaction.ortholog_derived = 'cfe'
elif 'fe' not in interaction.ortholog_derived:
interaction.ortholog_derived += ', cfe'
session.commit()
else:
is_new = 1
interaction = Interaction(
strain=interactor_pair[0][0].strain,
interactors=[
interactor_pair[0][0], interactor_pair[1][0]
],
type='p-p',
ortholog_derived='fe')
session.add(interaction), session.commit()
detections, pmids, types, list = [], [], [], []
if row['Interaction detection method(s)'] != '-':
detections = row['Interaction detection method(s)'].split(
'|')
list.append(detections)
if row['Publication Identifier(s)'] != '-':
pmids = row['Publication Identifier(s)'].split('|')
list.append(pmids)
if row['Interaction type(s)'] != '-':
types = row['Interaction type(s)'].split('|')
list.append(types)
interactor_a, interactor_b = '', ''
if interaction.interactors[0] == interactor_pair[0][0]:
interactor_a = interactor_pair[0][1]
interactor_b = interactor_pair[1][1]
else:
interactor_b = interactor_pair[0][1]
interactor_a = interactor_pair[1][1]
for num in range(0, len(list[0])):
type = types[num].split('(')[1][:-1]
pmid = pmids[num * 2].split('pubmed:')[1]
detection = detections[num].split('(')[1][:-1]
# there are more than one pmid sometimes
reference = InteractionReference(
interaction_id=interaction.id,
detection_method=detection,
pmid=pmid,
source_db=row['Source database(s)'].split('(')[1][:-1],
interactor_a=interactor_a,
interactor_b=interactor_b)
session.add(reference)
if is_new:
if interaction.is_experimental is None:
if is_experimental_psimi(
row['Interaction detection method(s)'].
split('MI:')[1][:4]):
interaction.is_experimental = 1
else:
interaction.is_experimental = 0
elif is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
source = session.query(InteractionSource).filter(
InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'DIP').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id,
data_source='DIP')
session.add(source)
session.commit()
print(session.query(Interaction).count())
def parse_mentha(file, strain, taxid, session):
with open(file) as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
interactors = []
if ((row['Taxid interactor A'].split('|')[0] != taxid) |
(row['Taxid interactor B'].split('|')[0] != taxid)): continue
A_id = row['#ID(s) interactor A'].split(':')[1]
B_id = row['ID(s) interactor B'].split(':')[1]
if session.query(Interactor).filter(Interactor.id == A_id).first() is not None:
interactors.append(session.query(Interactor).filter(Interactor.id == A_id).one())
elif session.query(Protein).filter(Protein.uniprotkb == A_id).first() is not None:
interactors.append(session.query(Protein).filter(Protein.uniprotkb == A_id).one())
if session.query(Interactor).filter(Interactor.id == B_id).first() is not None:
interactors.append(session.query(Interactor).filter(Interactor.id == B_id).one())
elif session.query(Protein).filter(Protein.uniprotkb == B_id).first() is not None:
interactors.append(session.query(Protein).filter(Protein.uniprotkb == B_id).one())
if len(interactors) != 2: continue
homogenous = (interactors[0] == interactors[1])
interaction = session.query(Interaction).filter(Interaction.interactors.contains(interactors[0]),
Interaction.interactors.contains(interactors[1]),
Interaction.homogenous == homogenous).first()
if interaction is None:
type=(interactors[0].type + '-' + interactors[1].type)
interaction = Interaction(strain=strain, type=type, homogenous=homogenous, interactors=interactors)
if is_experimental_psimi(row['Interaction detection method(s)'].split('MI:')[1][:4]):
interaction.is_experimental = 1
else:
interaction.is_experimental = 0
session.add(interaction), session.commit()
else:
if is_experimental_psimi(row['Interaction detection method(s)'].split('MI:')[1][:4]):
interaction.is_experimental = 1
reference = InteractionReference(interaction_id=interaction.id,
detection_method=row['Interaction detection method(s)'].split('(')[1][:-1],
pmid=row['Publication Identifier(s)'].split('pubmed:')[1][:8],
interaction_type=row['Interaction type(s)'].split('(')[1][:-1],
source_db=row['Source database(s)'].split('(')[1][:-1],
confidence_score=row['Confidence value(s)'])
session.add(reference)
xref_field = row['Interaction identifier(s)'].split(':')
xref = session.query(InteractionXref).filter(InteractionXref.accession == xref_field[1],
InteractionXref.interaction_id == interaction.id).first()
if xref is None:
xref = InteractionXref(interaction_id=interaction.id, accession=xref_field[1],
data_source=xref_field[0])
session.add(xref)
source = session.query(InteractionSource).filter(InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'mentha').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id, data_source='mentha')
session.add(source)
print(session.query(Interaction).count())
def parse_ecoli_mentha(session):
with open('Ecoli/PSICQUIC/mentha.txt') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
if (row['#ID(s) interactor A'] == '-') | (row['ID(s) interactor B']
== '-'):
continue
interactors = []
orthologs_A = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot ==
row['#ID(s) interactor A'].split(':')[1]).all()
orthologs_B = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot ==
row['ID(s) interactor B'].split(':')[1]).all()
for ortholog_A in orthologs_A:
for ortholog_B in orthologs_B:
if (ortholog_A is not None) and (ortholog_B is not None):
if ortholog_A.strain_protein == ortholog_B.strain_protein:
interactors.append(
[[ortholog_A.protein, ortholog_A.ortholog_id],
[ortholog_B.protein, ortholog_B.ortholog_id]])
for interactor_pair in interactors:
homogenous = (interactor_pair[0][0] == interactor_pair[1][0])
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_pair[0][0]),
Interaction.interactors.contains(interactor_pair[1][0]),
Interaction.homogenous == homogenous).first()
if interaction is not None:
if interaction.ortholog_derived is None:
interaction.ortholog_derived = 'cfe'
elif 'fe' not in interaction.ortholog_derived:
interaction.ortholog_derived += ', cfe'
session.commit()
else:
interaction = Interaction(
strain=interactor_pair[0][0].strain,
interactors=[
interactor_pair[0][0], interactor_pair[1][0]
],
type=(interactor_pair[0][0].type + '-' +
interactor_pair[1][0]),
ortholog_derived='fe')
#ask about marking ecoli ortholog interactions as experimental!!
if 'MI:' in row['Interaction detection method(s)']:
#iterate through all methods
if is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
session.add(interaction), session.commit()
interactor_a, interactor_b = '', ''
if interaction.interactors[0] == interactor_pair[0][0]:
interactor_a = interactor_pair[0][1]
interactor_b = interactor_pair[1][1]
else:
interactor_b = interactor_pair[0][1]
interactor_a = interactor_pair[1][1]
reference = InteractionReference(
interaction_id=interaction.id,
psimi_detection=row['Interaction detection method(s)'].
split('MI:')[1][:4],
detection_method=row['Interaction detection method(s)'].
split('(')[1][:-1],
pmid=row['Publication Identifier(s)'].split('pubmed:')[1],
psimi_type=row['Interaction type(s)'].split('MI:')[1][:4],
interaction_type=row['Interaction type(s)'].split(
'(')[1][:-1],
psimi_db=row['Source database(s)'].split('MI:')[1][:4],
source_db=row['Source database(s)'].split('(')[1][:-1],
confidence_score=row['Confidence value(s)'])
session.add(reference)
source = session.query(InteractionSource).filter(
InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'mentha').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id,
data_source='mentha')
session.add(source)
session.commit()
print(session.query(Interaction).count())
def parse_ecoli_irefindex(session):
with open('Ecoli/PSICQUIC/iRefIndex.txt') as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
if (row['#ID(s) interactor A'] == '-') | (row['ID(s) interactor B']
== '-'):
continue
interactors = []
orthologs_A = []
id_A = row['#ID(s) interactor A'].split(':')
if id_A[0] == 'uniprotkb':
orthologs_A = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == id_A[1]).all()
elif id_A[0] == 'refseq':
orthologs_A = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_refseq == id_A[1]).all()
if len(orthologs_A) == 0: continue
orthologs_B = []
id_B = row['ID(s) interactor B'].split(':')
if id_B[0] == 'uniprotkb':
orthologs_B = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_uniprot == id_B[1]).all()
elif id_B[0] == 'refseq':
orthologs_B = session.query(OrthologEcoli).filter(
OrthologEcoli.ortholog_refseq == id_B[1]).all()
for ortholog_A in orthologs_A:
for ortholog_B in orthologs_B:
if (ortholog_A is not None) and (ortholog_B is not None):
if ortholog_A.strain_protein == ortholog_B.strain_protein:
interactors.append(
[[ortholog_A.protein, ortholog_A.ortholog_id],
[ortholog_B.protein, ortholog_B.ortholog_id]])
for interactor_pair in interactors:
homogenous = (interactor_pair[0][0] == interactor_pair[1][0])
interaction = session.query(Interaction).filter(
Interaction.interactors.contains(interactor_pair[0][0]),
Interaction.interactors.contains(interactor_pair[1][0]),
Interaction.homogenous == homogenous).first()
if interaction is not None:
if interaction.ortholog_derived is None:
interaction.ortholog_derived = 'cfe'
elif 'fe' not in interaction.ortholog_derived:
interaction.ortholog_derived += ', cfe'
session.commit()
else:
interaction = Interaction(
strain=interactor_pair[0][0].strain,
interactors=[
interactor_pair[0][0], interactor_pair[1][0]
],
type=(interactor_pair[0][0].type + '-' +
interactor_pair[1][0]),
ortholog_derived='fe')
if 'MI:' in row['Interaction detection method(s)']:
#iterate through all methods
if is_experimental_psimi(
row['Interaction detection method(s)'].split(
'MI:')[1][:4]):
interaction.is_experimental = 1
session.add(interaction), session.commit()
interactor_a, interactor_b = '', ''
if interaction.interactors[0] == interactor_pair[0][0]:
interactor_a = interactor_pair[0][1]
interactor_b = interactor_pair[1][1]
else:
interactor_b = interactor_pair[0][1]
interactor_a = interactor_pair[1][1]
author, date, psimi_type, type = None, None, None, None
confidences, psimi_detections, detections, pmids = [None], [
None
], [None], [None]
if row['Publication 1st author(s)'] != '-':
author = row['Publication 1st author(s)'].split(' ')[0]
date = row['Publication 1st author(s)'].split('(')[1][:-1]
if row['Interaction type(s)'] != '-':
type = row['Interaction type(s)'].split('(')[1][:-1]
if 'MI' in row['Interaction type(s)']:
psimi_type = row['Interaction type(s)'].split(
'MI:')[1][:4]
if row['Publication Identifier(s)'] != '-':
del pmids[0]
for pmid in row['Publication Identifier(s)'].split('|'):
pmids.append(pmid.split(':')[1])
if row['Interaction detection method(s)'] != '-':
del detections[0]
del psimi_detections[0]
for detection in row['Publication Identifier(s)'].split(
'|'):
detections.append(detection.split('(')[1][:-1])
psimi_detections.append(detection.split('MI:')[1][:4])
for pmid in pmids:
for confidence in confidences:
for (detection,
psimi_detection) in zip(detections,
psimi_detections):
reference = InteractionReference(
interaction_id=interaction.id,
psimi_detection=psimi_detection,
detection_method=detection,
author_ln=author,
date=date,
psimi_type=psimi_type,
interaction_type=type,
psimi_db=row['Source database(s)'].split(
'MI')[1][:4],
source_db=row['Source database(s)'].split(
'(')[1][:-1],
confidence=confidence,
interactor_a=interactor_a,
interactor_b=interactor_b)
session.add(reference)
source = session.query(InteractionSource).filter(
InteractionSource.interaction_id == interaction.id,
InteractionSource.data_source == 'iRefIndex').first()
if source is None:
source = InteractionSource(interaction_id=interaction.id,
data_source='iRefIndex')
session.add(source)
session.commit()
print(session.query(Interaction).count())
