def get_gene_distance(seq_1, seq_2):
"""
Returns hamming distance between two DNA sequences
Alignment based on Striped Smith-Waterman algorithm
"""
query = StripedSmithWaterman(seq_1.upper())
alignment = query(seq_2.upper())
q = DNA(alignment.aligned_query_sequence)
t = DNA(alignment.aligned_target_sequence)
return q.distance(t)
def test_transcribe_preserves_all_metadata(self):
im = IntervalMetadata(4)
im.add([(0, 2)], metadata={'gene': 'p53'})
exp = RNA('AGUU', metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)},
interval_metadata=im)
seq = DNA('AGTT', metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)},
interval_metadata=im)
self.assertEqual(seq.transcribe(), exp)
def _process_roi(roi, samdata, amplicon_ref, reverse_comp=False):
roi_dict = {'region':roi.position_range}
range_match = re.search('(\d*)-(\d*)', roi.position_range)
if not range_match:
return roi_dict
start = int(range_match.group(1)) - 1
end = int(range_match.group(2))
aa_sequence_counter = Counter()
nt_sequence_counter = Counter()
depth = 0
for read in samdata.fetch(amplicon_ref, start, end):
rstart = read.reference_start
if rstart <= start:
nt_sequence = DNA(read.query_alignment_sequence[start-rstart:end-rstart])
if reverse_comp:
nt_sequence = nt_sequence.reverse_complement()
#scikit-bio doesn't support translating degenerate bases currently, so we will just throw out reads with degenerates for now
if nt_sequence.has_degenerates():
continue
aa_sequence = nt_sequence.translate()
aa_string = str(aa_sequence).replace('*', 'x')
if aa_string:
nt_sequence_counter.update([str(nt_sequence)])
aa_sequence_counter.update([aa_string])
depth += 1
if len(aa_sequence_counter) == 0:
roi_dict['flag'] = "region not found"
return roi_dict
aa_consensus = aa_sequence_counter.most_common(1)[0][0]
nt_consensus = nt_sequence_counter.most_common(1)[0][0]
num_changes = 0
reference = roi.aa_sequence
consensus = aa_consensus
if roi.nt_sequence:
reference = roi.nt_sequence
consensus = nt_consensus
for i in range(len(reference)):
if len(consensus) <= i or reference[i] != consensus[i]:
num_changes += 1
roi_dict['most_common_aa_sequence'] = aa_consensus
roi_dict['most_common_nt_sequence'] = nt_consensus
roi_dict['reference'] = reference
roi_dict['changes'] = str(num_changes)
roi_dict['aa_sequence_distribution'] = aa_sequence_counter
roi_dict['nt_sequence_distribution'] = nt_sequence_counter
roi_dict['depth'] = str(depth)
return roi_dict
def test_distances(self):
"""distances functions as expected
"""
s1 = SequenceCollection([DNA("ACGT", "d1"), DNA("ACGG", "d2")])
expected = [[0, 0.25], [0.25, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(hamming)
self.assertEqual(actual, expected)
# alt distance function provided
def dumb_distance(s1, s2):
return 42.
expected = [[0, 42.], [42., 0]]
expected = DistanceMatrix(expected, ['d1', 'd2'])
actual = s1.distances(dumb_distance)
self.assertEqual(actual, expected)
def reformat_egid(genbank_fp,
output_dir):
""" Reformat input genome to the formats accepted by EGID.
Parameters
----------
genbank_fp: string
file path to genome in GenBank format
output_dir: string
output directory path
Notes
-----
Input to EGID are five obsolete NCBI standard files: gbk, fna, faa, ffn
and ptt.
"""
(gb, genes) = _merge_genbank_seqs(genbank_fp)
DNA.write(gb, join(output_dir, 'id.fna'), format='fasta')
DNA.write(gb, join(output_dir, 'id.gbk'), format='genbank')
nucl_seq = str(gb)
output_f = {}
for x in ('faa', 'ffn', 'ptt'):
output_f[x] = open(join(output_dir, 'id.' + x), 'w')
output_f['ptt'].write('locus001\n' + str(len(genes)) + ' proteins\n')
# a ptt file contains the following columns:
fields = ('Location', 'Strand', 'Length', 'PID', 'Gene', 'Synonym',
'Code', 'COG', 'Product')
output_f['ptt'].write('\t'.join(fields) + '\n')
gid = 1 # assign an incremental integer to the current gene
for (gene, l) in sorted(genes.items(), key=lambda x: x[1][1]):
output_f['faa'].write('>' + gene + '\n' + l[0] + '\n')
output_f['ptt'].write(str(l[1]) + '..' + str(l[2]) + '\t' +
l[3] + '\t' + str(len(l[0])) + '\t' +
str(gid) + '\t-\tgene' + str(gid) +
'\t-\t-\t-\n')
if l[3] == '+': # positive strand
output_f['ffn'].write('>locus001:' + str(l[1]) + '-' +
str(l[2]) + '\n' +
nucl_seq[l[1]-1:l[2]] + '\n')
else: # negative strand (reverse complement)
rc_seq = str(DNA(nucl_seq[l[1]-1:l[2]]).reverse_complement())
output_f['ffn'].write('>locus001:c' + str(l[2]) + '-' +
str(l[1]) + '\n' + rc_seq + '\n')
gid += 1
for x in output_f:
output_f[x].close()
def test_embl_to_dna(self):
i = 1
exp = self.multi[i]
obs = _embl_to_dna(self.multi_fp, seq_num=i+1)
exp = DNA(exp[0], metadata=exp[1], lowercase=True,
interval_metadata=exp[2])
self.assertEqual(exp, obs)
def reformat_genemark(genbank_fp, output_dir):
""" Reformat input genome to the formats accepted by GeneMark.
Parameters
----------
genbank_fp: string
file path to genome in GenBank format
output_dir: string
output directory path
Notes
-----
GeneMark's acceptable input file format is FASTA (genome sequence).
"""
gb = _merge_genbank_seqs(genbank_fp)[0]
DNA.write(gb, join(output_dir, 'id.fna'), format='fasta')
DNA.write(gb, join(output_dir, 'id.gbk'), format='genbank')
def test_reverse_transcribe_preserves_all_metadata(self):
seq = RNA('AGUU',
metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)})
exp = DNA('AGTT',
metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)})
self.assertEqual(seq.reverse_transcribe(), exp)
def generateReference(assay_list):
from skbio import DNA
for assay in assay_list:
name = assay.name
if assay.AND:
for operand in assay.AND:
if isinstance(operand, Target):
name = name + "_%s" % operand.gene_name if operand.gene_name else name
for amplicon in operand.amplicons:
name = name + "_%s" % amplicon.variant_name if amplicon.variant_name else name
seq = DNA(amplicon.sequence, id=name)
yield seq
else:
for amplicon in assay.target.amplicons:
name = assay.name + "_%s" % amplicon.variant_name if amplicon.variant_name else name
seq = DNA(amplicon.sequence, {'id':name})
yield seq
def test_translate_ncbi_table_id(self):
for seq in RNA('AAAUUUAUGCAU'), DNA('AAATTTATGCAT'):
# default
obs = seq.translate()
self.assertEqual(obs, Protein('KFMH'))
obs = seq.translate(9)
self.assertEqual(obs, Protein('NFMH'))
def _find_approx_reverse(args):
"""
Finds an approximate match for a reverse primer
"""
[sequence, primer] = args
primer = DNA(primer)
align_ = _local_aln(sequence, primer)
return align_[2][0][0]
def _find_approx_forward(args):
"""
Finds an approximate match for a forward primer
"""
[sequence, primer] = args
primer = DNA(primer)
align_ = _local_aln(sequence, primer)
return align_[2][0][1] + 1
def gene_distance(A,B):
'''compute sequence distance between two genes A and B
'''
X,Y = '','' # new sequence removing common gaps
for a,b in izip(A.values,B.values):
if (a in A.gap_chars) and (b in B.gap_chars):
continue
if a in A.degenerate_chars:
X += random_choice(list(A.degenerate_map[a]))
else:
X += a
if b in B.degenerate_chars:
Y += random_choice(list(B.degenerate_map[b]))
else:
Y += b
newA = DNA(X,metadata={})
newB = DNA(Y,metadata={})
return newA.distance(newB)
def reformat_genemark(genbank_fp,
output_dir):
""" Reformat input genome to the formats accepted by GeneMark.
Parameters
----------
genbank_fp: string
file path to genome in GenBank format
output_dir: string
output directory path
Notes
-----
GeneMark's acceptable input file format is FASTA (genome sequence).
"""
gb = _merge_genbank_seqs(genbank_fp)[0]
DNA.write(gb, join(output_dir, 'id.fna'), format='fasta')
DNA.write(gb, join(output_dir, 'id.gbk'), format='genbank')
def test_constructor_non_empty_no_keys(self):
# 1x3
seqs = [DNA('ACG')]
msa = TabularMSA(seqs)
self.assertIs(msa.dtype, DNA)
self.assertEqual(msa.shape, (1, 3))
with self.assertRaises(OperationError):
msa.keys
self.assertEqual(list(msa), seqs)
# 3x1
seqs = [DNA('A'), DNA('C'), DNA('G')]
msa = TabularMSA(seqs)
self.assertIs(msa.dtype, DNA)
self.assertEqual(msa.shape, (3, 1))
with self.assertRaises(OperationError):
msa.keys
self.assertEqual(list(msa), seqs)
def test_constructor_empty_no_keys(self):
# sequence empty
msa = TabularMSA([])
self.assertIsNone(msa.dtype)
self.assertEqual(msa.shape, (0, 0))
with self.assertRaises(OperationError):
msa.keys
with self.assertRaises(StopIteration):
next(iter(msa))
# position empty
seqs = [DNA(''), DNA('')]
msa = TabularMSA(seqs)
self.assertIs(msa.dtype, DNA)
self.assertEqual(msa.shape, (2, 0))
with self.assertRaises(OperationError):
msa.keys
self.assertEqual(list(msa), seqs)
def setUp(self):
self.d1 = DNA('GATTACA', metadata={'id': "d1"})
self.d2 = DNA('TTG', metadata={'id': "d2"})
self.d3 = DNA('GTATACA', metadata={'id': "d3"})
self.r1 = RNA('GAUUACA', metadata={'id': "r1"})
self.r2 = RNA('UUG', metadata={'id': "r2"})
self.r3 = RNA('U-----UGCC--', metadata={'id': "r3"})
self.seqs1 = [self.d1, self.d2]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs4 = [self.d1, self.d3]
self.s1 = SequenceCollection(self.seqs1)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.s4 = SequenceCollection(self.seqs4)
self.empty = SequenceCollection([])
def test_genbank_to_dna(self):
i = 1
exp = self.multi[i]
obs = _genbank_to_dna(self.multi_fp, seq_num=i + 1)
exp = DNA(exp[0],
metadata=exp[1],
lowercase=True,
positional_metadata=exp[2])
self.assertEqual(exp, obs)
def setUp(self):
self.seq_array = pd.Series(
data=[DNA('AGTC', metadata={'id': 'A'}),
DNA('ARWS', metadata={'id': 'B'}),
DNA('CTWK', metadata={'id': 'C'}),
DNA('GTCM', metadata={'id': 'D'}),
DNA('ATGN', metadata={'id': 'E'})],
index=['A', 'B', 'C', 'D', 'E']
)
self.seq_array.index = self.seq_array.index.astype(str)
self.in_mer = pd.Series(
data=np.array(['AGTCCATGC', 'TACGAGTGA',
'ACTCCATGC', 'AAAAAAAGT'])
)
self.reads2 = pd.Series(
data=np.array(['AGTC', 'WGWN', 'AGTT']),
index=['r2.0', 'r2.1', 'r2.2'],
)
def reformat_egid(genbank_fp, output_dir):
""" Reformat input genome to the formats accepted by EGID.
Parameters
----------
genbank_fp: string
file path to genome in GenBank format
output_dir: string
output directory path
Notes
-----
Input to EGID are five obsolete NCBI standard files: gbk, fna, faa, ffn
and ptt.
"""
(gb, genes) = _merge_genbank_seqs(genbank_fp)
DNA.write(gb, join(output_dir, 'id.fna'), format='fasta')
DNA.write(gb, join(output_dir, 'id.gbk'), format='genbank')
nucl_seq = str(gb)
output_f = {}
for x in ('faa', 'ffn', 'ptt'):
output_f[x] = open(join(output_dir, 'id.' + x), 'w')
output_f['ptt'].write('locus001\n' + str(len(genes)) + ' proteins\n')
# a ptt file contains the following columns:
fields = ('Location', 'Strand', 'Length', 'PID', 'Gene', 'Synonym', 'Code',
'COG', 'Product')
output_f['ptt'].write('\t'.join(fields) + '\n')
gid = 1 # assign an incremental integer to the current gene
for (gene, l) in sorted(genes.items(), key=lambda x: x[1][1]):
output_f['faa'].write('>' + gene + '\n' + l[0] + '\n')
output_f['ptt'].write(
str(l[1]) + '..' + str(l[2]) + '\t' + l[3] + '\t' +
str(len(l[0])) + '\t' + str(gid) + '\t-\tgene' + str(gid) +
'\t-\t-\t-\n')
if l[3] == '+': # positive strand
output_f['ffn'].write('>locus001:' + str(l[1]) + '-' + str(l[2]) +
'\n' + nucl_seq[l[1] - 1:l[2]] + '\n')
else: # negative strand (reverse complement)
rc_seq = str(DNA(nucl_seq[l[1] - 1:l[2]]).reverse_complement())
output_f['ffn'].write('>locus001:c' + str(l[2]) + '-' + str(l[1]) +
'\n' + rc_seq + '\n')
gid += 1
for x in output_f:
output_f[x].close()
def test_stockholm_runon_gs(self):
fp = get_data_path('stockholm_runon_gs_no_whitespace')
msa = _stockholm_to_tabular_msa(fp, constructor=DNA)
exp = TabularMSA(
[DNA('ATCGTTCAGTG', metadata={'LN': 'This is a runon GS line.'})],
index=['seq1'])
self.assertEqual(msa, exp)
fp = get_data_path('stockholm_runon_gs_with_whitespace')
msa = _stockholm_to_tabular_msa(fp, constructor=DNA)
self.assertEqual(msa, exp)
def test_msa_to_stockholm_minimal(self):
fp = get_data_path('stockholm_minimal')
msa = TabularMSA([DNA('TGTGTCGCAGTTGTCGTTTG')], index=['0235244'])
fh = io.StringIO()
_tabular_msa_to_stockholm(msa, fh)
obs = fh.getvalue()
fh.close()
with io.open(fp) as fh:
exp = fh.read()
self.assertEqual(obs, exp)
def test_sort_on_key_with_some_repeats(self):
msa = TabularMSA([
DNA('TCCG', metadata={'id': 10}),
DNA('TAGG', metadata={'id': 10}),
DNA('GGGG', metadata={'id': 8}),
DNA('ACGT', metadata={'id': 0}),
DNA('TAGG', metadata={'id': 10})
],
keys=range(5))
msa.sort(key='id')
self.assertEqual(
msa,
TabularMSA([
DNA('ACGT', metadata={'id': 0}),
DNA('GGGG', metadata={'id': 8}),
DNA('TCCG', metadata={'id': 10}),
DNA('TAGG', metadata={'id': 10}),
DNA('TAGG', metadata={'id': 10})
],
keys=[3, 2, 0, 1, 4]))
def test_translate_six_frames_ncbi_table_id(self):
# rc = CAAUUU
for seq in RNA('AAAUUG'), DNA('AAATTG'):
# default
obs = list(seq.translate_six_frames())
self.assertEqual(obs, [Protein('KL'), Protein('N'), Protein('I'),
Protein('QF'), Protein('N'), Protein('I')])
obs = list(seq.translate_six_frames(9))
self.assertEqual(obs, [Protein('NL'), Protein('N'), Protein('I'),
Protein('QF'), Protein('N'), Protein('I')])
def test_omit_gap_sequences(self):
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
# Test to ensure floating point precision bug isn't present. See the
# tests for Alignment.position_frequencies for more details.
aln = Alignment([
DNA('.' * 33, metadata={'id': 'abc'}),
DNA('-' * 33, metadata={'id': 'def'})
])
self.assertEqual(aln.omit_gap_sequences(1 - np.finfo(float).eps),
Alignment([]))
def test_translate_preserves_metadata(self):
metadata = {'foo': 'bar', 'baz': 42}
positional_metadata = {'foo': range(3)}
for seq in (RNA('AUG', metadata=metadata,
positional_metadata=positional_metadata),
DNA('ATG', metadata=metadata,
positional_metadata=positional_metadata)):
obs = seq.translate()
# metadata retained, positional metadata dropped
self.assertEqual(obs,
Protein('M', metadata={'foo': 'bar', 'baz': 42}))
def test_reverse_transcribe_preserves_all_metadata(self):
im = IntervalMetadata(4)
im.add([(0, 2)], metadata={'gene': 'p53'})
seq = RNA('AGUU', metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)},
interval_metadata=im)
exp = DNA('AGTT', metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)},
interval_metadata=im)
self.assertEqual(seq.reverse_transcribe(), exp)
def remove_gapped_columns(msa, site_threshold=0.95):
msa_dict = msa.to_dict()
msa_df = pd.DataFrame(msa_dict)
gapped_columns = msa_df.apply(gap_dectector, axis=1)
nogaps_df = msa_df[gapped_columns < len(msa_df.columns) * site_threshold]
nogap_seqs = [
DNA(nogaps_df[i].str.decode("utf-8").str.cat(), metadata={"id": i})
for i in nogaps_df
]
msa_nogap = TabularMSA(nogap_seqs)
return msa_nogap
def generateReference(assay_list):
from skbio import DNA
from skbio import SequenceCollection
reference = []
for assay in assay_list:
name = assay.name
if assay.AND:
for operand in assay.AND:
if isinstance(operand, Target):
name = name + "_%s" % operand.gene_name if operand.gene_name else name
for amplicon in operand.amplicons:
name = name + "_%s" % amplicon.variant_name if amplicon.variant_name else name
seq = DNA(amplicon.sequence, id=name)
reference.append(seq)
else:
for amplicon in assay.target.amplicons:
name = assay.name + "_%s" % amplicon.variant_name if amplicon.variant_name else name
seq = DNA(amplicon.sequence, {'id': name})
reference.append(seq)
return SequenceCollection(reference)
def dnaLocalAlignSsw(seq1, seq2):
seq1 = seq1.upper()
seq2 = seq2.upper()
msa, score, _ = local_pairwise_align_ssw(DNA(seq1), DNA(seq2))
response = {
'seq1':
str(seq1),
'aln1':
str(msa[0]),
'aln2':
str(msa[1]),
'score':
score,
'similarity':
float('{:.2f}'.format(msa[0].match_frequency(msa[1], relative=True) *
100))
}
return response
def test_stockholm_runon_gf(self):
fp = get_data_path('stockholm_runon_gf_no_whitespace')
msa = _stockholm_to_tabular_msa(fp, constructor=DNA)
exp = TabularMSA([DNA('ACTGGTTCAATG')],
metadata={'CC': 'CBS domains are small intracellular'
' modules mostly found in 2 or four '
'copies within a protein.'},
index=['GG1344'])
self.assertEqual(msa, exp)
fp = get_data_path('stockholm_runon_gf_with_whitespace')
msa = _stockholm_to_tabular_msa(fp, constructor=DNA)
self.assertEqual(msa, exp)
def test_translate_six_frames_genetic_code_object(self):
gc = GeneticCode('M' * 64, '-' * 64)
for seq in RNA('AAAUUG'), DNA('AAATTG'):
obs = list(seq.translate_six_frames(gc))
self.assertEqual(obs, [
Protein('MM'),
Protein('M'),
Protein('M'),
Protein('MM'),
Protein('M'),
Protein('M')
])
def test_embl_to_gb(self):
# EMBL records have more features than genbank, (ex more than one date,
# embl class, DOI cross references) so I can't convert an embl to gb
# and then to embl keeping all those data. But I can start from
# genbank record
# do embl file -> embl object -> gb file -> gb object ->
# embl file. Ensure that first and last files are identical
embl = DNA.read(self.single_rna_simple_fp, format="embl")
# "write" genbank record in a embl file
with io.StringIO() as fh:
DNA.write(embl, format="genbank", file=fh)
# read genbank file
fh.seek(0)
genbank = DNA.read(fh, format="genbank")
# "write" genbank record in a embl file
with io.StringIO() as fh:
DNA.write(genbank, format="embl", file=fh)
# read file object
obs = fh.getvalue()
# test objects
with open(self.single_rna_simple_fp) as fh:
exp = fh.read()
self.assertEqual(exp, obs)
def test_update_ids_sequence_attributes_propagated(self):
# 1 seq
exp_sc = Alignment([
DNA('ACGT', id="abc", description='desc', quality=range(4))
])
exp_id_map = {'abc': 'seq1'}
obj = Alignment([
DNA('ACGT', id="seq1", description='desc', quality=range(4))
])
obs_sc, obs_id_map = obj.update_ids(ids=('abc',))
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
# 2 seqs
exp_sc = Alignment([
DNA('ACGT', id="abc", description='desc1', quality=range(4)),
DNA('TGCA', id="def", description='desc2', quality=range(4)[::-1])
])
exp_id_map = {'abc': 'seq1', 'def': 'seq2'}
obj = Alignment([
DNA('ACGT', id="seq1", description='desc1', quality=(0, 1, 2, 3)),
DNA('TGCA', id="seq2", description='desc2', quality=(3, 2, 1, 0))
])
obs_sc, obs_id_map = obj.update_ids(ids=('abc', 'def'))
self._assert_sequence_collections_equal(obs_sc, exp_sc)
self.assertEqual(obs_id_map, exp_id_map)
def test_embl_to_gb(self):
# EMBL records have more features than genbank, (ex more than one date,
# embl class, DOI cross references) so I can't convert an embl to gb
# and then to embl keeping all those data. But I can start from
# genbank record
# do embl file -> embl object -> gb file -> gb object ->
# embl file. Ensure that first and last files are identical
embl = DNA.read(self.single_rna_simple_fp, format="embl")
# "write" genbank record in a embl file
with io.StringIO() as fh:
DNA.write(embl, format="genbank", file=fh)
# read genbank file
fh.seek(0)
genbank = DNA.read(fh, format="genbank")
# "write" genbank record in a embl file
with io.StringIO() as fh:
DNA.write(genbank, format="embl", file=fh)
# read file object
obs = fh.getvalue()
# test objects
with open(self.single_rna_simple_fp) as fh:
exp = fh.read()
self.assertEqual(exp, obs)
def test_global_pairwise_align_nucleotide_penalize_terminal_gaps(self):
# in these tests one sequence is about 3x the length of the other.
# we toggle penalize_terminal_gaps to confirm that it results in
# different alignments and alignment scores.
seq1 = DNA("ACCGTGGACCGTTAGGATTGGACCCAAGGTTG")
seq2 = DNA("T"*25 + "ACCGTGGACCGTAGGATTGGACCAAGGTTA" + "A"*25)
obs_msa, obs_score, obs_start_end = global_pairwise_align_nucleotide(
seq1, seq2, gap_open_penalty=5., gap_extend_penalty=0.5,
match_score=5, mismatch_score=-4, penalize_terminal_gaps=False)
self.assertEqual(
obs_msa,
TabularMSA([DNA("-------------------------ACCGTGGACCGTTAGGA"
"TTGGACCCAAGGTTG-------------------------"),
DNA("TTTTTTTTTTTTTTTTTTTTTTTTTACCGTGGACCGT-AGGA"
"TTGGACC-AAGGTTAAAAAAAAAAAAAAAAAAAAAAAAAA")]))
self.assertEqual(obs_score, 131.0)
obs_msa, obs_score, obs_start_end = global_pairwise_align_nucleotide(
seq1, seq2, gap_open_penalty=5., gap_extend_penalty=0.5,
match_score=5, mismatch_score=-4, penalize_terminal_gaps=True)
self.assertEqual(
obs_msa,
TabularMSA([DNA("-------------------------ACCGTGGACCGTTAGGA"
"TTGGACCCAAGGTT-------------------------G"),
DNA("TTTTTTTTTTTTTTTTTTTTTTTTTACCGTGGACCGT-AGGA"
"TTGGACC-AAGGTTAAAAAAAAAAAAAAAAAAAAAAAAAA")]))
self.assertEqual(obs_score, 97.0)
def test_motif_pyrimidine_run(self):
seq = DNA("")
self.assertEqual(list(seq.find_motifs("pyrimidine-run")), [])
seq = DNA("AARC--TCRA")
self.assertEqual(list(seq.find_motifs("pyrimidine-run")),
[slice(3, 4), slice(6, 8)])
seq = DNA("AA-RC--TCR-A")
self.assertEqual(list(seq.find_motifs("pyrimidine-run", min_length=3,
ignore=seq.gaps())),
[slice(4, 9)])
def test_motif_purine_run(self):
seq = DNA("")
self.assertEqual(list(seq.find_motifs("purine-run")), [])
seq = DNA("AARC--TCRG")
self.assertEqual(list(seq.find_motifs("purine-run")),
[slice(0, 3), slice(8, 10)])
seq = DNA("AA-RC--TCR-G")
self.assertEqual(list(seq.find_motifs("purine-run", min_length=3,
ignore=seq.gaps())),
[slice(0, 4)])
def test_gb_to_embl(self):
genbank = DNA.read(self.genbank_fp, format="genbank")
with io.StringIO() as fh:
DNA.write(genbank, format="embl", file=fh)
# EMBL can't deal with genbank version (ie M14399.1 GI:145229)
# read embl data and write to gb
fh.seek(0)
embl = DNA.read(fh, format="embl")
with io.StringIO() as fh:
DNA.write(embl, format="genbank", file=fh)
# read gb data
obs = fh.getvalue()
with open(self.genbank_fp) as fh:
exp = fh.read()
self.assertEqual(exp, obs)
def test_transcribe_does_not_modify_input(self):
seq = DNA('ATAT')
self.assertEqual(seq.transcribe(), RNA('AUAU'))
self.assertEqual(seq, DNA('ATAT'))
def test_transcribe_preserves_all_metadata(self):
exp = RNA('AGUU', metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)})
seq = DNA('AGTT', metadata={'foo': 'bar'},
positional_metadata={'foo': range(4)})
self.assertEqual(seq.transcribe(), exp)
from skbio import DNA, read
with open("outfile.fasta", "w") as outfile:
for seq in read('test_sequences.fasta', format='fasta'):
new_seq = DNA(seq)
for protein in new_seq.translate_six_frames():
if not protein.has_stops():
outfile.write(">" + str(new_seq.metadata['id']) + "\n" + str(protein) + "\n")
from skbio import DNA
from skbio.alignment import global_pairwise_align_nucleotide
s1 = DNA.read("data/seq1")
s2 = DNA.read("data/seq2")
query = DNA("TTTTCTTGTTGATTCTGGTCCAGAGTAATCGCTTGAGTGTTG")
def pairwise_similarity(seq, query):
alignment = global_pairwise_align_nucleotide(seq, query)
return alignment[0].fraction_same(alignment[1])
print "seq1: %s\nseq2: %s" % (s1, s2)
print "seq1-query: %s" % pairwise_similarity(s1, query)
print "seq2-query: %s" % pairwise_similarity(s2, query)
def _merge_genbank_seqs(genbank_fp):
""" Merge one to multiple sequences in a GenBank file into one.
Parameters
----------
genbank_fp: string
file path to genome in GenBank format
Returns
-------
tuple of (
skbio.Sequence,
Genome sequence, genes and metadata
dict of { list of [ string, int, int, string ] }
Gene name : translation, start, end, and strand
)
"""
loci = []
nucl_seq = ''
genes = {}
nseq = 0 # number of nucleotide sequences
with open(genbank_fp, 'r') as input_f:
for line in input_f:
if line.startswith('//'):
nseq += 1
abs_pos = 0 # absolute position in concantenated nucleotide sequence
for i in range(nseq):
gb = Sequence.read(genbank_fp, seq_num=i+1, format='genbank')
locus_name = gb.metadata['LOCUS']['locus_name']
size = gb.metadata['LOCUS']['size']
loci.append([locus_name, size])
nucl_seq += str(gb)
for feature in gb.interval_metadata._intervals:
m = feature.metadata
if m['type'] == 'CDS' and 'protein_id' in m:
protein_id = m['protein_id'].replace('\"', '')
if protein_id not in genes:
translation = m['translation'].replace(' ', '') \
.replace('\"', '')
strand = m['strand']
start = feature.bounds[0][0] + abs_pos + 1
end = feature.bounds[0][1] + abs_pos
genes[protein_id] = [translation, start, end, strand]
abs_pos += int(size)
gb = DNA(nucl_seq)
# generate mock metadata for the merged sequence
gb.metadata['LOCUS'] = {'locus_name': 'locus001', 'size': len(nucl_seq),
'unit': 'bp', 'shape': 'circular',
'division': 'CON', 'mol_type': 'DNA',
'date': '01-JAN-1900'}
gb.metadata['id'] = 'locus001'
gid = 1 # assign an incremental integer to the current gene
gb.interval_metadata._intervals = []
for (gene, l) in sorted(genes.items(), key=lambda x: x[1][1]):
# generate "gene" and "CDS" records for each protein-coding gene
location = str(l[1]) + '..' + str(l[2]) # start and end coordinates
if l[3] == '-': # negative strand
location = 'complement(' + location + ')'
feature = {'type': 'gene', 'locus_tag': 'gene' + str(gid),
'__location': location}
gb.interval_metadata.add([(l[1] - 1, l[2])], metadata=feature)
feature = {'type': 'CDS', 'locus_tag': 'gene' + str(gid),
'__location': location, 'protein_id': gene,
'translation': l[0]}
gb.interval_metadata.add([(l[1] - 1, l[2])], metadata=feature)
gid += 1
return (gb, genes)
