def test_unit_train_classify(tmpdir):
tmpdir = str(tmpdir)
out_path = os.path.join(tmpdir, 'model.pkl')
run([
'train', '--model',
get_test_file('random_forest_test.json'), '--classes',
get_test_file('BGC0000015.classes.csv'), '--output', out_path,
get_test_file('BGC0000015.pfam.csv')
])
assert os.path.exists(out_path)
model = SequenceModelWrapper.load(out_path)
domains = pd.read_csv(get_test_file('BGC0000015.pfam.csv'))
classes = model.predict(
[sample for _, sample in domains.groupby('sequence_id')])
assert isinstance(classes, pd.DataFrame)
assert list(classes.columns) == ['class1', 'class2', 'class3', 'class4']
assert len(classes.index) == 2
assert list(classes.iloc[0] > 0.5) == [True, False, True, False]
assert list(classes.iloc[1] > 0.5) == [False, True, False, True]
def test_integration_pfam_annotator(tmpdir):
tmpdir = str(tmpdir)
tmppath = os.path.join(tmpdir, 'test')
records = SeqIO.parse(get_test_file('BGC0000015.gbk'), format='genbank')
record = next(records)
annotator = HmmscanPfamRecordAnnotator(
record=record,
tmp_path_prefix=tmppath,
db_path=get_test_file('Pfam-A.PF00005.hmm'),
clans_path=get_test_file('Pfam-A.PF00005.clans.tsv'))
annotator.annotate()
pfams = util.get_pfam_features(record)
assert len(pfams) == 2
pfam = pfams[0]
assert pfam.location.start == 249
assert pfam.location.end == 696
assert pfam.location.strand == -1
assert pfam.qualifiers.get('db_xref') == ['PF00005.26']
assert pfam.qualifiers.get('locus_tag') == ['AAK73498.1']
assert pfam.qualifiers.get('description') == ['ABC transporter']
assert pfam.qualifiers.get('database') == ['31.0']
assert_sorted_features(record)
def test_integration_train_detect_fail_fasta():
# Should fail due to unprocessed input sequence
with pytest.raises(NotImplementedError):
run([
'train', '--model',
get_test_file('clusterfinder_geneborder_test.json'), '--output',
'bar.pkl',
get_test_file('BGC0000015.fa')
])
def test_unit_train_detect(model, tmpdir):
tmpdir = str(tmpdir)
out_path = os.path.join(tmpdir, 'model.pkl')
run([
'train', '--model',
get_test_file(model), '--config', 'PFAM2VEC',
get_test_file('pfam2vec.test.tsv'), '--output', out_path,
get_test_file('BGC0000015.pfam.csv'),
get_test_file('negative.pfam.csv')
])
assert os.path.exists(out_path)
model = SequenceModelWrapper.load(out_path)
pos_domains = pd.read_csv(get_test_file('BGC0000015.pfam.csv'))
neg_domains = pd.read_csv(get_test_file('negative.pfam.csv'))
pos_prediction = model.predict(pos_domains)
neg_prediction = model.predict(neg_domains)
assert isinstance(pos_prediction, pd.Series)
assert isinstance(neg_prediction, pd.Series)
assert pos_prediction.index.equals(pos_domains.index)
assert neg_prediction.index.equals(neg_domains.index)
assert pos_prediction.mean() > 0.5
assert neg_prediction.mean() < 0.5
def test_integration_pipeline_labelled(tmpdir):
tmpdir = str(tmpdir)
report_dir = os.path.join(tmpdir, 'report')
run(['pipeline', '--output', report_dir, get_test_file('labelled.gbk')])
evaluation_dir = os.path.join(report_dir, 'evaluation')
files = os.listdir(evaluation_dir)
for file in files:
print(file)
assert 'report.bgc.png' in files
assert 'report.score.png' in files
assert 'report.roc.png' in files
assert 'report.pr.png' in files
def test_integration_prepare_default(tmpdir):
tmpdir = str(tmpdir)
outgbk = os.path.join(tmpdir, 'outfile.gbk')
outtsv = os.path.join(tmpdir, 'outfile.tsv')
run([
'prepare', '--output-gbk', outgbk, '--output-tsv', outtsv,
get_test_file('BGC0000015.fa')
])
records = list(SeqIO.parse(outgbk, 'genbank'))
assert len(records) == 2
record = records[0]
assert_sorted_features(record)
proteins = util.get_protein_features(record)
pfams = util.get_pfam_features(record)
assert len(proteins) == 18
print([util.get_protein_id(f) for f in proteins])
assert len(pfams) == 111
record = records[1]
assert_sorted_features(record)
proteins = util.get_protein_features(record)
pfams = util.get_pfam_features(record)
assert len(proteins) == 27
assert len(pfams) == 36
domains = pd.read_csv(outtsv, sep='\t')
records = domains.groupby('sequence_id')
assert len(records) == 2
record = records.get_group('BGC0000015.1')
print(record['protein_id'].unique())
# some of the proteins do not have any Pfam domains so they are not present
assert len(record['protein_id'].unique()) == 17
assert len(record) == 111
record = records.get_group('BGC0000015.2')
# some of the proteins do not have any Pfam domains so they are not present
assert len(record['protein_id'].unique()) == 11
assert len(record) == 36
def test_integration_protein_annotator(tmpdir):
tmpdir = str(tmpdir)
tmppath = os.path.join(tmpdir, 'test')
records = SeqIO.parse(get_test_file('BGC0000015.fa'), format='fasta')
record = next(records)
annotator = ProdigalProteinRecordAnnotator(record=record, tmp_path_prefix=tmppath)
annotator.annotate()
proteins = util.get_protein_features(record)
assert len(proteins) == 18
protein = proteins[0]
assert protein.location.start == 3
assert protein.location.end == 1824
assert protein.id == 'BGC0000015.1_1'
assert protein.qualifiers.get('locus_tag') == ['BGC0000015.1_BGC0000015.1_1']
assert_sorted_features(record)
def test_integration_pipeline_default(tmpdir, input_file):
tmpdir = str(tmpdir)
report_dir = os.path.join(tmpdir, 'report')
run(['pipeline', '--output', report_dir, get_test_file(input_file)])
files = os.listdir(report_dir)
for file in files:
print(file)
assert 'README.txt' in files
assert 'report.bgc.gbk' in files
assert 'report.bgc.tsv' in files
assert 'report.full.gbk' in files
assert 'report.pfam.tsv' in files
evaluation_dir = os.path.join(report_dir, 'evaluation')
files = os.listdir(evaluation_dir)
for file in files:
print(file)
assert 'report.bgc.png' in files
assert 'report.score.png' in files
records = list(
SeqIO.parse(os.path.join(report_dir, 'report.full.gbk'), 'genbank'))
assert len(records) == 2
record = records[0]
cluster_features = util.get_cluster_features(record)
assert len(cluster_features) >= 1
record = records[1]
cluster_features = util.get_cluster_features(record)
assert len(cluster_features) >= 1
cluster_records = list(
SeqIO.parse(os.path.join(report_dir, 'report.bgc.gbk'), 'genbank'))
assert len(cluster_records) >= 2