def calculate_perturbation_factor(self,
experiment,
gene,
pathway,
visited=None):
visited = [] if not visited else visited
pf = 0
if len(set(gene.split(',')) & set(self.experiment_genes)) != 0:
for name in gene.split(','):
if name.strip() in self.experiment_genes:
# get ΔE
pf = self.FC['FC'][Gene(name)] if not isnan(
self.FC['FC'][Gene(name)]) else MAX_IF
break
# genes directly upstream
for edge in pathway.in_edges(gene):
if edge[0] not in visited:
beta = mean([
interaction_weights[t]
if t in interaction_weights.keys() else 0
for t in get_edge_attributes(pathway, 'type')[edge]
])
# genes directly downstream
dstream = len(pathway.out_edges(edge[0]))
pf += self.calculate_perturbation_factor(
experiment, edge[0], pathway,
visited + [edge[1]]) * beta / dstream
return pf
def test_file_with_description(test_files, tmpdir):
create_files(
tmpdir, {
'control_with_descriptions.tsv': (
'Gene Description Control_1 Control_2',
'TP53 Tumour protein 53 6 6',
'BRCA2 Breast cancer type 2 s. protein 6 7',
)
})
expected_warning = (
'First line of your file contains "description" column '
'but you did not provide "--description_column" argument.')
# user forgot
with pytest.warns(UserWarning, match=expected_warning):
opts = p_parse('case t.tsv control control_with_descriptions.tsv')
assert len(opts.control.sample_collection.samples) == 3
# user remembered
opts = p_parse('case t.tsv control control_with_descriptions.tsv -d')
assert len(opts.control.sample_collection.samples) == 2
assert set(opts.control.sample_collection.samples[0].genes) == {
Gene('TP53', description='Tumour protein 53'),
Gene('BRCA2', description='Breast cancer type 2 s. protein')
}
def create_test_models():
protein = Protein(refseq='NM_0001',
gene=Gene(name='SOMEGENE'),
sequence='ABCD')
mutation = Mutation(protein=protein, position=1, alt='E')
protein.gene.preferred_isoform = protein
MC3Mutation(mutation=mutation,
cancer=Cancer(code='CAN'),
samples='Sample A,Sample B',
count=2)
InheritedMutation(mutation=mutation,
clin_data=[
ClinicalData(disease=Disease(name='Some disease'),
sig_code=5),
ClinicalData(disease=Disease(name='Other disease'),
sig_code=2)
])
protein_kinase = Protein(refseq='NM_0002',
gene=Gene(name='OTHERGENE'),
sequence='ABCD')
kinase = Kinase(name='Kinase name', protein=protein_kinase)
site = Site(protein=protein,
position=1,
residue='A',
kinases={kinase},
pmid={1, 2},
types={SiteType(name='glycosylation')})
protein.sites = [site]
return locals()
def test_sample_init():
genes = {Gene('BAD'): 1.2345, Gene('FUCA2'): 6.5432}
sample = Sample('Tumour_1', genes)
assert sample.name == 'Tumour_1'
assert all(isinstance(k, Gene) for k in sample.data.keys())
assert sample.data == genes
def test_mapping(self):
gene_a = Gene(
name='A',
isoforms=[
# the full isoform of gene A
Protein(refseq='NM_01', sequence='AAAAAAAAAXAA'),
# a trimmed isoform of gene A
Protein(refseq='NM_02', sequence='AAAXAA'),
])
gene_b = Gene(name='B',
isoforms=[
Protein(refseq='NM_03', sequence='BBBBBBBBBYBB'),
Protein(refseq='NM_04', sequence='BBBYBB'),
])
db.session.add_all([gene_a, gene_b])
# whoops, NM_03 has be accidentally removed (!)
db.session.delete(Protein.query.filter_by(refseq='NM_03').one())
db.session.commit()
mapper = SiteMapper(create_key_model_dict(Protein, 'refseq'),
lambda s: f'{s.position}{s.residue}')
sites = DataFrame.from_dict(data={
'good site A': ('A', 'NM_01', 10, 'AXA', 'X', 1),
'lost isoform': ('B', 'NM_03', 10, 'BYB', 'Y', 1)
},
orient='index')
sites.columns = [
'gene', 'refseq', 'position', 'sequence', 'residue',
'left_sequence_offset'
]
mapped_sites = mapper.map_sites_by_sequence(sites)
sites_by_isoform = group_by_isoform(mapped_sites)
# one from NM_01 (defined), from NM_02 (mapped), from NM_04 (mapped)
assert len(mapped_sites) == 3
assert set(sites_by_isoform) == {'NM_01', 'NM_02', 'NM_04'}
assert sites_by_isoform['NM_01'].residue == sites_by_isoform[
'NM_02'].residue == 'X'
assert sites_by_isoform['NM_01'].position == 10
assert sites_by_isoform['NM_02'].position == 4
assert sites_by_isoform['NM_04'].residue == 'Y'
assert sites_by_isoform['NM_04'].position == 4
# will the mapping to NM_02 still work if we remove 'gene' column?
sites.drop(columns=['gene'], inplace=True)
mapped_sites = mapper.map_sites_by_sequence(sites)
sites_by_isoform = group_by_isoform(mapped_sites)
assert len(mapped_sites) == 2
assert set(sites_by_isoform) == {'NM_01', 'NM_02'}
def minimal_data():
tp53 = Gene('TP53')
map2k1 = Gene('MAP2K1')
case = SampleCollection('case', [Sample('1', {tp53: 2, map2k1: 1})])
control = SampleCollection('control', [Sample('1', {tp53: 1, map2k1: 1})])
return tp53, map2k1, case, control
def test_init():
genes1 = {Gene('BAD'): 1.2345, Gene('FUCA2'): 6.5432}
genes2 = {Gene('BAD'): 2.3456, Gene('FUCA2'): 7.6543}
samples = [Sample('Tumour_1', genes1), Sample('Tumour_2', genes2)]
sample_collection = SampleCollection('Tumour', samples)
assert sample_collection.name == 'Tumour'
assert all(isinstance(k, Sample) for k in sample_collection.samples)
def test_train_model(self):
phosphorylation = SiteType(name='phosphorylation')
# non-phosphorylated serine residues are needed to generate negative sites
p = Protein(refseq='NM_007',
sequence='--------SLPA-----------SVIT-------')
g = Gene(isoforms=[p], preferred_isoform=p)
db.session.add(g)
# phosphorylated, with sites
p = Protein(refseq='NM_001',
sequence='--------SPAK-----------SPAR-------')
g = Gene(isoforms=[p], preferred_isoform=p)
db.session.add(g)
k = Kinase(name='CDK1', is_involved_in={phosphorylation})
for pos in [9, 24]:
s = Site(position=pos,
types={phosphorylation},
residue='S',
protein=p,
kinases={k})
db.session.add(s)
db.session.commit()
with TemporaryDirectory() as temp_dir:
model = train_model(phosphorylation,
sequences_dir=temp_dir,
sampling_n=2,
threshold=2)
# the model should have one set of params - for CDK1 kinase
assert len(model) == 1
cdk_params = model.rx2('CDK1')
pwm = cdk_params.rx2('pwm')
# and the position-specific weight matrix should be created
assert pwm
# the very detailed testing should be performed by rMIMP,
# but why not test the basics?
weights_of_central_aa = {
aa: value
for aa, value in zip(pwm.rownames, pwm.rx(True, 8))
}
assert weights_of_central_aa['S'] == max(
weights_of_central_aa.values())
def test_gene_init():
gene = Gene('BAD')
assert gene.name == 'BAD'
same = Gene('BAD')
assert same == gene
assert same is gene
g = Gene('TP53', 'Tumour suppressor p53')
assert g.name == 'TP53'
assert g.description == 'Tumour suppressor p53'
from copy import copy
assert copy(g).id is g.id
def test_search_proteins(self):
from views.search import search_proteins
# create 15 genes and proteins
mock_proteins_and_genes(15)
# control: do we start with the mocked proteins not others?
assert not search_proteins('TP53')
# does respect limit? does symbol search work?
results = search_proteins('Gene', 10)
assert results
assert len(results) == 10
assert results[0].name.startswith('Gene')
# are results sorted?
db.session.add_all([
Gene(name=name, preferred_isoform=Protein(refseq='NM_%s' % 20 * i))
for i, name in enumerate(['TPK', 'TPKK'])
])
results = search_proteins('TPK', 2)
assert results[0].name == 'TPK'
assert results[0].best_score < results[1].best_score
# does include both: refseq and symbol search?
assert search_proteins('NM_0003', 1)
# can we change subset of searched features?
assert not search_proteins('NM_0003', 1, features=['gene_symbol'])
assert not search_proteins('Gene', 1, features=['refseq'])
def genes():
if request.method == "GET":
genes = Gene.query.all()
return render_template('display_genes.html', genes=genes)
elif request.method == "POST":
description = request.form.get("description")
dna_sequence = request.form.get("dna_sequence")
created_by = request.form.get("created_by")
creation_date = request.form.get("creation_date")
notes = request.form.get("notes")
files = request.files.getlist('files[]')
snapgene_files = ""
for file in files:
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
snapgene_files += filename + ","
file.save(
os.path.join(app.config['UPLOAD_FOLDER'], "plasmids",
filename))
contains_genes = request.form.get("contains_genes")
new_gene = Gene(description, dna_sequence, created_by, creation_date,
notes, snapgene_files)
db.session.add(new_gene)
db.session.commit()
flash("Success!")
return redirect("/")
def create_test_data():
mappings_filename = make_named_gz_file(raw_mappings)
# create proteins from first three data rows
protein_data = [
('NM_002749',
'MAEPLKEEDGEDGSAEPPGPVKAEPAHTAASVAAKNLALLKARSFDVTFDVGDEYEIIETIGNGAYGVVSSARRRLTGQQVAIKKIPNAFDVVTNAKRTLRELKILKHFKHDNIIAIKDILRPTVPYGEFKSVYVVLDLMESDLHQIIHSSQPLTLEHVRYFLYQLLRGLKYMHSAQVIHRDLKPSNLLVNENCELKIGDFGMARGLCTSPAEHQYFMTEYVATRWYRAPELMLSLHEYTQAIDLWSVGCIFGEMLARRQLFPGKNYVHQLQLIMMVLGTPSPAVIQAVGAERVRAYIQSLPPRQPVPWETVYPGADRQALSLLGRMLRFEPSARISAAAALRHPFLAKYHDPDDEPDCAPPFDFAFDREALTRERIKEAIVAEIEDFHARREGIRQQIRFQPSLQPVASEPGCPDVEMPSPWAPSGDCAMESPPPAPPPCPGPAPDTIDLTLQPPPPVSEPAPPKKDGAISDNTKAALKAALLKSLRSRLRDGPSAPLEAPEPRKPVTAQERQREREEKRRRRQERAKEREKRRQERERKERGAGASGGPSTDPLAGLVLSDNDRSLLERWTRMARPAAPALTSVPAPAPAPTPTPTPVQPTSPPPGPVAQPTGPQPQSAGSTSGPVPQPACPPPGPAPHPTGPPGPIPVPAPPQIATSTSLLAAQSLVPPPGLPGSSTPGVLPYFPPGLPPPDAGGAPQSSMSESPDVNLVTQQLSKSQVEDPLPPVFSGTPKGSGAGYGVGFDLEEFLNQSFDMGVADGPQDGQADSASLSASLLADWLEGHGMNPADIESLQREIQMDSPMLLADLPDLQDP*'
),
('NM_139033',
'MAEPLKEEDGEDGSAEPPGPVKAEPAHTAASVAAKNLALLKARSFDVTFDVGDEYEIIETIGNGAYGVVSSARRRLTGQQVAIKKIPNAFDVVTNAKRTLRELKILKHFKHDNIIAIKDILRPTVPYGEFKSVYVVLDLMESDLHQIIHSSQPLTLEHVRYFLYQLLRGLKYMHSAQVIHRDLKPSNLLVNENCELKIGDFGMARGLCTSPAEHQYFMTEYVATRWYRAPELMLSLHEYTQAIDLWSVGCIFGEMLARRQLFPGKNYVHQLQLIMMVLGTPSPAVIQAVGAERVRAYIQSLPPRQPVPWETVYPGADRQALSLLGRMLRFEPSARISAAAALRHPFLAKYHDPDDEPDCAPPFDFAFDREALTRERIKEAIVAEIEDFHARREGIRQQIRFQPSLQPVASEPGCPDVEMPSPWAPSGDCAMESPPPAPPPCPGPAPDTIDLTLQPPPPVSEPAPPKKDGAISDNTKAALKAALLKSLRSRLRDGPSAPLEAPEPRKPVTAQERQREREEKRRRRQERAKEREKRRQERERKERGAGASGGPSTDPLAGLVLSDNDRSLLERWTRMARPAAPALTSVPAPAPAPTPTPTPVQPTSPPPGPVAQPTGPQPQSAGSTSGPVPQPACPPPGPAPHPTGPPGPIPVPAPPQIATSTSLLAAQSLVPPPGLPGSSTPGVLPYFPPGLPPPDAGGAPQSSMSESPDVNLVTQQLSKSQVEDPLPPVFSGTPKGSGAGYGVGFDLEEFLNQSFDMGVADGPQDGQADSASLSASLLADWLEGHGMNPADIESLQREIQMDSPMLLADLPDLQDP*'
),
('NM_139034',
'MAEPLKEEDGEDGSAEPPGPVKAEPAHTAASVAAKNLALLKARSFDVTFDVGDEYEIIETIGNGAYGVVSSARRRLTGQQVAIKKIPNAFDVVTNAKRTLRELKILKHFKHDNIIAIKDILRPTVPYGEFKSVYVVLDLMESDLHQIIHSSQPLTLEHVRYFLYQLLRGLKYMHSAQVIHRDLKPSNLLVNENCELKIGDFGMARGLCTSPAEHQYFMTEYVATRWYRAPELMLSLHEYTQAIDLWSVGCIFGEMLARRQLFPGKNYVHQLQLIMMVLGTPSPAVIQAVGAERVRAYIQSLPPRQPVPWETVYPGADRQALSLLGRMLRFEPSARISAAAALRHPFLAKYHDPDDEPDCAPPFDFAFDREALTRERIKEAIVAEIEDFHARREGIRQQIRFQPSLQPVASEPGCPDVEMPSPWAPSGDCAMESPPPAPPPCPGPAPDTIDLTLQPPPPVSEPAPPKKDGAISDNTKAALKAALLKSLRSRLRDGPSAPLEAPEPRKPVTAQERQREREEKRRRRQERAKEREKRRQERERKERGAGASGGPSTDPLAGLVLSDNDRSLLERWTRMARPAAPALTSVPAPAPAPTPTPTPVQPTSPPPGPVAQPTGPQPQSAGSTSGPVPQPACPPPGPAPHPTGPPGPIPVPAPPQIATSTSLLAAQSLVPPPGLPGSSTPGVLPYFPPGLPPPDAGGAPQSSMSESPDVNLVTQQLSKSQVEDPLPPVFSGTPKGSGAGYGVGFDLEEFLNQSFDMGVADGPQDGQADSASLSASLLADWLEGHGMNPADIESLQREIQMDSPMLLADLPDLQDP*'
),
]
gene = Gene(name='MAPK7')
proteins = {
refseq_nm: Protein(refseq=refseq_nm, sequence=sequence, gene=gene)
for refseq_nm, sequence in protein_data
}
# we need to have proteins with id in session - hence commit
db.session.add(gene)
db.session.commit()
return mappings_filename, gene, proteins
def test_remove_unwanted_proteins(self):
sequences = {
'NM_1': 'MAKS*', # all right
'NM_2': 'MKFR', # lack of stop codon
'NM_3': 'MK*A', # premature stop codon
}
proteins = create_test_proteins(['NM_1', 'NM_2', 'NM_3'])
gene = Gene(isoforms=list(proteins.values()),
preferred_isoform=proteins['NM_3'])
db.session.add_all(proteins.values())
db.session.add(gene)
for fake_refseq, protein in proteins.items():
protein.sequence = sequences[fake_refseq]
clean_from_wrong_proteins()
# NM_2 and NM_3 should be removed, NM_1 should still be there
assert len(proteins) == 1
assert 'NM_1' in proteins
for refseq in ['NM_2', 'NM_3']:
assert refseq not in proteins
# NM_1 should become a preferred isoform of gene (in place of NM_3)
assert gene.preferred_refseq == 'NM_1'
def test_select_preferred_isoform(self):
proteins_data = [
('NM_001', 'MA', False),
('NM_002', 'MAA', True),
('NM_003', 'MAAA', True), # we want this one:
# canonical according to uniprot, then longest, then oldest in refseq
('NM_004', 'MAAA', True),
('NM_005', 'MAAAA', False)
]
preferred_refseq = 'NM_003'
gene = Gene(name='Gene X')
for refseq, seq, is_uniprot_canonical in proteins_data:
protein = Protein(refseq=refseq, sequence=seq, gene=gene)
if is_uniprot_canonical:
protein_references = ProteinReferences(
uniprot_entries=[UniprotEntry(isoform=1, reviewed=True)])
protein.external_references = protein_references
db.session.add(gene)
isoform = select_preferred_isoform(gene)
assert isoform
assert isoform.refseq == preferred_refseq
def test_edge_cases_mapping(self):
gene_t = Gene(
name='T',
isoforms=[
# 123456789
Protein(refseq='NM_01', sequence='AXAXAYAYA'),
# C-terminal part was trimmed
Protein(refseq='NM_02', sequence='AXAXA'),
# N-terminal part was trimmed
Protein(refseq='NM_03', sequence='AYAYA'),
])
db.session.add(gene_t)
db.session.commit()
mapper = SiteMapper(create_key_model_dict(Protein, 'refseq'),
lambda s: f'{s.position}{s.residue}')
# all sites in NM_01, the idea is to test
sites = DataFrame.from_dict(data={
'site at N-terminus edge': ('T', 'NM_01', 1, '^AX', 'A', 2),
'site at C-terminus edge': ('T', 'NM_01', 9, 'YA$', 'A', 2),
},
orient='index')
sites.columns = [
'gene', 'refseq', 'position', 'sequence', 'residue',
'left_sequence_offset'
]
mapped_sites = mapper.map_sites_by_sequence(sites)
assert len(mapped_sites) == 4
def test_prepare_dataset(self):
from views.mutation import prepare_datasets
p = Protein(refseq='NM_000123',
sequence='TRAN',
gene=Gene(name='TP53'))
mutation = Mutation(protein=p, position=2, alt='K')
details = MC3Mutation(mutation=mutation, count=2)
db.session.add(mutation)
datasets, user_datasets = prepare_datasets(mutation)
expected_datasets = [{
'filter': 'Mutation.sources:in:' + source.name,
'name': source.display_name,
'mutation_present': False
} if source is not MC3Mutation else {
'filter': 'Mutation.sources:in:' + MC3Mutation.name,
'name': MC3Mutation.display_name,
'mutation_present': [details]
} for source in source_manager.confirmed]
assert datasets == expected_datasets
assert not user_datasets
def _set_genes(self, gene_ids):
if gene_ids is None:
return
conn = Pgsql.Common.connect()
cnt = 0
for gnid in gene_ids:
# get sequence information
#gene_seq_pep_min = GeneSequence(gnid=gnid, seq_type=self.seq_type, is_max_seq_len=False, conn=conn)
gene_seq_pep_min = None
gene_seq_pep_max = GeneSequence(gnid=gnid,
seq_type=self.seq_type,
is_max_seq_len=True,
conn=conn,
k=self.k)
gene_seq_dna_max = None
# set Gene instance for each gnid
gene = Gene(gnid=gnid,
pep_seq_min=gene_seq_pep_min,
pep_seq_max=gene_seq_pep_max,
dna_seq=gene_seq_dna_max)
self.genes[gnid] = gene
cnt += 1
# for test
if cnt % 100 == 0:
print('%s gene has been added.' % gnid)
conn.close() # for single adding
def mock_proteins_and_genes(count):
from database import db
from models import Gene, Protein
for i in range(count):
g = Gene(name='Gene_%s' % i, full_name='Full name of gene %s' % i)
p = Protein(refseq='NM_000%s' % i, gene=g)
g.preferred_isoform = p
db.session.add(g)
def test_domains(self):
proteins = [
Protein(
refseq='NM_018163',
sequence=
'MAVTKELLQMDLYALLGIEEKAADKEVKKAYRQKALSCHPDKNPDNPRAAELFHQLSQALEVLTDAAARAAYDKVRKAKKQAAERTQKLDEKRKKVKLDLEARERQAQAQESEEEEESRSTRTLEQEIERLREEGSRQLEEQQRLIREQIRQERDQRLRGKAENTEGQGTPKLKLKWKCKKEDESKGGYSKDVLLRLLQKYGEVLNLVLSSKKPGTAVVEFATVKAAELAVQNEVGLVDNPLKISWLEGQPQDAVGRSHSGLSKGSVLSERDYESLVMMRMRQAAERQQLIARMQQEDQEGPPT*',
gene=Gene(chrom='15')),
Protein(
refseq='NM_004671',
sequence=
'MADFEELRNMVSSFRVSELQVLLGFAGRNKSGRKHDLLMRALHLLKSGCSPAVQIKIRELYRRRYPRTLEGLSDLSTIKSSVFSLDGGSSPVEPDLAVAGIHSLPSTSVTPHSPSSPVGSVLLQDTKPTFEMQQPSPPIPPVHPDVQLKNLPFYDVLDVLIKPTSLVQSSIQRFQEKFFIFALTPQQVREICISRDFLPGGRRDYTVQVQLRLCLAETSCPQEDNYPNSLCIKVNGKLFPLPGYAPPPKNGIEQKRPGRPLNITSLVRLSSAVPNQISISWASEIGKNYSMSVYLVRQLTSAMLLQRLKMKGIRNPDHSRALIKEKLTADPDSEIATTSLRVSLMCPLGKMRLTIPCRAVTCTHLQCFDAALYLQMNEKKPTWICPVCDKKAAYESLILDGLFMEILNDCSDVDEIKFQEDGSWCPMRPKKEAMKVSSQPCTKIESSSVLSKPCSVTVASEASKKKVDVIDLTIESSSDEEEDPPAKRKCIFMSETQSSPTKGVLMYQPSSVRVPSVTSVDPAAIPPSLTDYSVPFHHTPISSMSSDLPGLDFLSLIPVDPQYCPPMFLDSLTSPLTASSTSVTTTSSHESSTHVSSSSSRSETGVITSSGSNIPDIISLD*',
gene=Gene(chrom='18'))
]
db.session.add_all(proteins)
filename = make_named_temp_file(domains_data)
new_domains = load_domains(filename)
assert len(new_domains) == 6
assert len(proteins[0].domains) == 2
domains = defaultdict(list)
for domain in proteins[1].domains:
domains[domain.interpro.short_description].append(domain)
def assert_ranges(domain, start, end):
assert domain.start == start and domain.end == end
# two SAP domains should be merged for representation purposes due to similarity criteria
# (these two domain annotation overlap so the smaller one is contained in the bigger)
sap_domain = domains['SAP_dom'][0]
assert_ranges(sap_domain, 1, 65)
intepro_domain = sap_domain.interpro
assert intepro_domain.accession == 'IPR003034'
assert intepro_domain.description == 'SAP domain'
# here the two annotations overlap with more than 75% of common
assert_ranges(domains['PINIT'][0], 141, 299)
# and here overlap was too small to merge those domains
assert len(domains['Znf_MIZ']) == 2
def test_refseq(self):
from search.gene import RefseqGeneSearch
# create 15 genes and proteins
mock_proteins_and_genes(10)
search = RefseqGeneSearch().search
# negative control
for phase in ['9999', 'NM_00000', 'Gene']:
assert not search(phase)
# limiting
results = search('NM_', limit=5)
assert len(results) == 5
assert results[0].name.startswith('Gene')
# test the search itself
for refseq in ['NM_0003', 'nm_0003', '0003']:
results = search(refseq)
assert len(results) == 1
assert results[0].name == 'Gene_3'
isoforms = results[0].matched_isoforms
assert len(isoforms) == 1
assert isoforms.pop().refseq == 'NM_0003'
db.session.add_all([
Gene(name='Gene X',
isoforms=[
Protein(refseq='NM_000301'),
Protein(refseq='NM_000302'),
]),
Gene(name='Gene Y', isoforms=[Protein(refseq='NM_000309')])
])
# so there are three genes with isoforms starting with NM_0003
# (those are Gene_3, Gene X, Gene Y). Let see if limiting work
# well when applied per-gene.
queries = {'NM_0003': 2, 'NM_00030': 2, 'NM_000301': 1, 'NM_000302': 1}
for query, expected_result in queries.items():
assert len(search(query, limit=2)) == expected_result
def get_gene_chrom(conn, gene_id):
"""return chromsome which the gene is on
"""
query = select([gene]).where(gene.c.id == gene_id)
results = conn.execute(query)
result = next(results, None)
if not result:
return None
return Gene(*result).chrom
def test_show(self):
g = Gene(**test_gene_data)
db.session.add(g)
response = self.client.get('/gene/show/BRCA1')
assert response.status_code == 200
assert b'BRCA1' in response.data
assert b'NM_000123' in response.data
def gene_from_isoforms(all_proteins, chosen_isoforms):
"""Just for testing: in normal settings the bi-directional initialization is performed required"""
isoforms = [
protein for refseq, protein in all_proteins.items()
if refseq in chosen_isoforms
]
gene = Gene(isoforms=isoforms)
for isoform in isoforms:
isoform.gene = gene
return gene
def test_gene_full_name(self):
gene = Gene(name='TP53', entrez_id=7157)
db.session.add(gene)
filename = make_named_temp_file(full_gene_names, mode='wt', opener=gzip.open)
load_full_gene_names(filename)
assert gene.full_name == 'tumor protein p53'
def get_generator(latent_dim, input_dim, ngf):
# Initialise generator
G = Gene(latent_dim, output_dim = input_dim, ngf = ngf)
G.apply(weights_init)
G = G.to(device)
return G
def create_test_kinase(name, refseq):
interactor = Kinase(name=name)
kinase_gene = Gene(name='Gene of ' + interactor.name)
kinase_protein = Protein(refseq=refseq, gene=kinase_gene)
interactor.protein = kinase_protein
return interactor
def test_conservation(self):
proteins = create_test_proteins(['NM_002749', 'NM_000600'])
proteins['NM_002749'].gene = Gene(name='MAPK7', chrom='17')
proteins['NM_000600'].gene = Gene(name='IL6', chrom='7')
for refseq, protein in proteins.items():
protein.sequence = sequences_for_proteins[refseq]
# Generated with:
"""
import pyBigWig
test_data = test_data.loc[('chr17', 'NM_002749')].iloc[0]
full_bw = pyBigWig.open('data/hg19.100way.phyloP100way.bw')
test_bw = pyBigWig.open('chr17_NM_002749.test_data.bw', 'w')
needed_chrom_length = max(max(test_data.exonStarts), max(test_data.exonEnds))
test_bw.addHeader([('chr17', needed_chrom_length)])
coordinates = sorted(zip(test_data.exonStarts, test_data.exonEnds), key=lambda x: x[0])
for start, end in zip(test_data.exonStarts, test_data.exonEnds):
values = full_bw.values('chr17', start, end)
for i, value in enumerate(values):
test_bw.addEntries(['chr17'], starts=[start + i], ends=[start + i + 1], values=[value])
test_bw.close()
"""
conservation_big_wig = 'tests/test_imports/chr17_NM_002749.test_data.bw'
gene_coordinates = make_named_temp_file(coordinates_data)
load_conservation(conservation_big_wig, gene_coordinates)
# Protein.query.filter_by(refseq='NM_002749').one().conservation
assert proteins[
'NM_002749'].conservation == '3.47;1.67;2.95;1.23;.9;1.64;4.08;1.72;1.25;1.15;2.26;1.69;1.03;1.05;2.39;1.52;2.88;.5;2.28;-.09;2.24;1.32;-.06;.59;1.2;-.13;.37;.76;.04;1.26;-.2;1.84;.97;2.95;5.67;3.98;2.91;4.5;2.33;3.17;4.66;3.73;4.24;4.01;2.49;4.35;5.29;2.95;4.67;5.66;5.36;5.25;4.23;4.87;5.31;5.18;3.52;4.53;5.59;3.55;5.05;6.65;2.01;5.75;5.06;4.88;5.9;4.9;5.63;3.32;3.52;5.09;4.04;4.24;2.24;1.84;2.13;6.12;6.54;2.75;6.08;5.79;5.7;8.26;6.81;5.82;3.83;4.47;3.08;5.11;5;3.4;2.52;4.67;3.97;3.58;4.87;3.72;5.33;4.48;3.92;6.79;3.7;7.52;5.46;4.05;3.07;4.02;5.47;5.31;5.12;7.11;6.59;6.09;5.37;4.73;5.6;6.3;6.52;5.96;3.32;2.84;4.06;.53;3.5;3.79;3.11;1.81;4.51;4.81;3.35;4.29;5.34;5.31;4.63;6.41;2.39;6.46;3.16;8.2;6.23;2.67;6.63;2.39;4.07;5;3.49;4.71;4.16;3.27;.7;6.43;1.1;3.25;2.31;1.57;6.39;5.62;2.68;4.33;6.16;6;3.28;6.16;6.66;3.66;3.77;4.11;6.52;3.69;6.55;5.97;5.28;5.9;3.63;6.16;5.36;5.92;4.24;6.23;4.27;7.07;5.64;6.62;5.32;1.79;6.59;3.26;4.38;3.92;6.2;3.59;5.22;5.76;6.77;4.58;7.52;4.24;3.91;5.75;5.28;6.51;7.43;5.49;2.98;5.48;2.01;2.62;2.19;1.95;1.01;2.03;5.55;1.47;2.2;2.38;5.52;5.93;3.39;6.62;5.51;6.67;5.67;3.9;6.03;8.99;6.67;4.17;4.7;4.01;6.27;3.7;5.06;3.66;2.06;2.64;3.76;2.56;5.94;3.82;2.89;4.05;5.17;6.75;3.05;8.99;4.25;5.57;6.66;6.22;6.06;3.65;4.13;6.66;8.76;2.77;2.34;1.65;1.84;3.91;3.38;5.3;3.49;6.22;3.21;3.91;5.43;2.8;4.28;6.44;3.25;3.89;2.56;5.75;4.58;1.02;5.09;3.53;5.75;5;3.97;3.24;2.08;.17;3.51;2.96;1.66;2.99;3.71;4.75;3.78;5.8;3.71;5.3;4.16;3.18;4.66;3.56;6.62;4.69;2.96;4.27;3.1;3.49;2.38;2.54;3.18;4.54;5.17;3.34;.46;4.2;3.77;2.53;1.27;3.6;4.46;.83;1.58;4.57;2.92;2.17;2.81;3.17;1.48;1.13;8.45;2.85;1.15;4.09;5.67;3.85;.82;1.15;3.3;3.95;.21;4.67;1.19;2.83;5.43;3.54;.76;5.35;3.81;3.85;3.23;2.46;5.65;5.76;3.97;6.32;3.35;2.86;5.95;6.63;4.5;2.89;5.45;3.95;4.59;4.34;5.09;4.99;5.24;4.02;4.91;5.83;1.59;2.45;1.71;2.51;2.87;2.93;6;3.07;4.58;5.09;5.26;4.44;4.06;3.28;2.1;6.69;4.55;2.58;5.57;4.79;3.75;.58;1.99;2.45;4.16;3.64;1.55;3.8;2.09;3.71;4.36;2.27;4.31;4.26;3.43;1.01;.44;1.57;1.7;2.68;1.46;1.5;1.7;.7;.92;1.94;1.42;2.39;.76;3.24;3.14;1.01;2.85;1.17;1.48;1.22;.67;.8;.75;3.02;1.12;1.34;4.82;3.73;1.26;.92;-.86;1.13;.65;1.18;.06;-.67;.8;.2;.41;.35;.05;.69;1.11;1.09;.06;1.29;2.34;.9;-.06;.36;1.24;-.18;.06;-.14;.87;.86;2.37;1.38;.52;1.97;.97;4.2;1.48;4.38;5.02;3.81;4.14;3.29;4.98;4.21;4.73;5.01;2.84;4.03;4.01;4.89;4.64;2.37;1.66;2.52;5.89;3.6;1.53;1.1;2.19;1.25;1;2.33;2.53;1.89;2.01;1;.84;-.21;.93;2.4;2.06;1.91;4.48;.3;3.03;2.21;2.16;4.76;4.08;4.42;4.07;5.72;3.13;3.52;2.69;5.1;3.98;3.99;5.24;5.81;2.02;1.75;3.18;2.96;4.36;4.26;3.2;3.71;1.85;4.88;2.93;4.94;4.22;2.17;1.67;2.44;4.68;1.22;3.37;1.68;1.73;2.69;1.65;.75;-.94;1.27;.11;-.79;3;1.26;.57;1.71;.45;2.93;1.48;1.67;2.62;3.54;.95;3.03;2.33;2.5;3.92;2.41;3.3;2.08;2.16;.87;1.26;3.18;3.02;4.04;1.48;.78;4.1;2.06;.59;1.89;.25;.44;-.23;-.57;-.45;.33;-.12;.22;-1.71;.1;.63;.58;.04;-.21;.5;-2.18;.06;.06;-.46;-.39;-.23;.26;.36;.42;.86;1.85;.56;.27;-.51;.65;.42;-.35;.43;.93;.05;-.66;.28;.09;.41;-.05;.46;.51;.02;.67;.11;-.55;.27;.09;.77;-.18;.11;2.34;.99;.1;.78;.52;1.23;.3;.04;.31;-.42;.13;.78;1.01;-.48;.98;.14;.65;.25;.97;-.67;.8;.49;1.15;.15;-1.05;.52;1.43;.04;.68;-.14;-.15;.24;.73;1.71;.89;-.05;.68;.53;.1;.52;.81;1.56;.28;.07;2.18;.55;1.24;2.33;.78;-.04;.77;-.09;.4;.61;.96;1.59;1.57;1.71;1.52;1.41;1.11;1.48;.52;1.61;.76;2.41;.05;.73;-.38;.4;1.78;.95;.46;2.29;1.05;1.94;1.74;.88;1.32;1.97;1.66;1.64;1.17;2.12;2.38;2.63;2.64;1.63;2.49;3.23;1.49;4.21;5.62;5.72;5.3;.78;3.01;4.23;3.16;3.53;5.02;3.73;4.38;4.53;4.4;6.09;6.37;5.1;5.65;4.64;5.8;4.94;5.24;4.63;6.08;4.74;4.27;4.1;5.34;5.96;3.64;2.03;3.29;5.31;2.6;2.34;2.25;4.26;2.1;2.08;2.48;.6;1.53;.31;1.96;4.53;3.8;3.27;.67;5.5;2.33;4.89;2.31;3.4;4.37;3.71;4.24;2.89;3.27;2.97;5.14;7.61;2.14;5.73;3.45;4.27;3.13;5.77;4.91;2.21;2.5;4.74;3.57;4.6;3.43;3.41;6.01;3.21;5.22;3.11;6.33;4.8;4.29;5.23;3.28;6.47;2.48;6.01;3.17;5.92;1.38;1.79;2.96;2.67;2.21;2.67;1.89'
# no data for this one, lets see if the pipeline handles such cases well
assert proteins['NM_000600'].conservation is None
db.session.add_all(proteins.values())
db.session.commit()
assert Protein.query.filter_by(
refseq='NM_000600').one().conservation == ''
def create_test_models():
protein = Protein(refseq='NM_0001',
gene=Gene(name='SOMEGENE'),
sequence='ABCD')
mutation = Mutation(protein=protein, position=1, alt='E')
MC3Mutation(mutation=mutation,
cancer=Cancer(code='CAN'),
samples='Some sample')
InheritedMutation(
mutation=mutation,
clin_data=[ClinicalData(disease=Disease(name='Some disease'))])
protein_kinase = Protein(refseq='NM_0002',
gene=Gene(name='OTHERGENE'),
sequence='ABCD')
kinase = Kinase(name='Kinase name', protein=protein_kinase)
site = Site(protein=protein, position=1, residue='A', kinases=[kinase])
protein.sites = [site]
return locals()
def mutate(gene: Gene) -> Gene:
items = [item for item in gene.value]
possibilities = list(range(len(items))) + ['add']
if len(items) >= 2:
possibilities.append('remove')
random_choice = random.choice(possibilities)
if random_choice == 'add':
items.append(random.choice(conf.GENE_ITEMS_POOL))
elif random_choice == 'remove':
items.pop(random.randint(0, len(items) - 1))
else:
items[random_choice] = random.choice(conf.GENE_ITEMS_POOL)
return Gene(tuple(items))
def test_show(self):
p = Protein(refseq='NM_000123',
sequence='TRAN',
gene=Gene(name='TP53'))
mutation = Mutation(protein=p, position=2, alt='K')
db.session.add(mutation)
response = self.client.get('/mutation/show/NM_000123/2/K')
assert response.status_code == 200
assert b'TP53' in response.data
assert b'NM_000123' in response.data
