def test_run_advanced(self):
sultan = Sultan()
try:
sultan.mkdir("-p /tmp/mytestdir")\
.mkdir("-p /tmp/mytestdir/foobar")\
.touch("/tmp/mytestdir/a")\
.touch("/tmp/mytestdir/b")\
.run()
response = sultan.ls("-1 /tmp/mytestdir/").run()
self.assertEqual(response, ['a', 'b', 'foobar'])
finally:
if os.path.exists('/tmp/mytestdir'):
shutil.rmtree('/tmp/mytestdir')
def generate_profiles(in_dataframe, out_path):
"""Rather complicated and quite honetly ugly looking function used
for generating the profiles from a given set of sequences. Intended to be used internally.
"""
out_path = Path(out_path)
dataset = in_dataframe
s = Sultan()
print('Unpacking and generating Uniprot DB.')
s.gunzip('-fk ../data/swiss-prot/uniprot_sprot.fasta.gz').run()
cmd = NcbimakeblastdbCommandline(
input_file='../data/swiss-prot/uniprot_sprot.fasta', dbtype='prot')
cmd()
if not (out_path / 'profile').exists():
s.mkdir(out_path / 'profile').run()
with TemporaryDirectory() as psi_temp:
for _, sample in tqdm(dataset.iterrows(),
total=len(dataset),
desc='Generating profiles'):
with NamedTemporaryFile(mode='w') as blast_in:
if isinstance(sample.name, tuple):
sample_id, chain = sample.name[0], sample.name[1]
out_name = f'{sample_id}_{chain}'
dump_path = out_path / 'full_test_summary.joblib'
else:
sample_id = sample.name
out_name = sample_id
dump_path = out_path / 'jpred_summary.joblib'
sequence, structure = sample[['Sequence', 'Structure']]
structure = ' ' + structure
print(f'>{out_name}', file=blast_in)
print(sequence, file=blast_in)
blast_in.seek(0)
cmd = NcbipsiblastCommandline(
query=blast_in.name,
db='../data/swiss-prot/uniprot_sprot.fasta',
evalue=0.01,
num_iterations=3,
out_ascii_pssm=f'{psi_temp}/{out_name}.pssm',
num_descriptions=10000,
num_alignments=10000,
# out=f'{psi_temp}{out_name}.alns.blast',
num_threads=8)
cmd()
if not os.path.exists(
os.path.join(psi_temp, out_name + '.pssm')):
tqdm.write(
f'Unable to generate profile for {out_name}. No hits in the database.'
)
dataset.drop(index=sample.name, inplace=True)
continue
with open(f'{psi_temp}/{out_name}.pssm', 'r') as pssm_file:
pssm_file.readline()
pssm_file.readline()
profile = []
offset = False
position = 0
for line in pssm_file:
line = line.rstrip()
if not line:
break
line = line.split()
line.append(structure[position])
position += 1
if not offset:
for i in range(2):
line.insert(0, '')
offset = True
profile.append(line)
profile = pd.DataFrame(profile)
profile.drop(
(profile.columns[col] for col in range(2, 22)),
axis=1,
inplace=True)
profile.drop((profile.columns[-3:-1]),
axis=1,
inplace=True)
profile.drop((profile.columns[0]), axis=1, inplace=True)
profile.columns = profile.iloc[0]
profile = profile[1:]
profile.rename(columns={profile.columns[0]: "Sequence"},
inplace=True)
profile.rename(columns={profile.columns[-1]: "Structure"},
inplace=True)
profile = profile[
['Structure'] +
[col for col in profile.columns if col != 'Structure']]
profile.loc[:, 'A':'V'] = profile.loc[:, 'A':'V'].astype(
float).divide(100)
profile.to_csv(out_path / 'profile' /
(out_name + '.profile'),
sep='\t',
index=False)
print(
f'Dumping clean test to {dump_path}. Profiles are generated in {out_path}/profile'
)
dump(dataset, dump_path)
