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_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 = "ACCGTGGACCGTTAGGATTGGACCCAAGGTTG"
seq2 = "T"*25 + "ACCGTGGACCGTAGGATTGGACCAAGGTTA" + "A"*25
aln1 = ("-------------------------ACCGTGGACCGTTAGGA"
"TTGGACCCAAGGTTG-------------------------")
aln2 = ("TTTTTTTTTTTTTTTTTTTTTTTTTACCGTGGACCGT-AGGA"
"TTGGACC-AAGGTTAAAAAAAAAAAAAAAAAAAAAAAAAA")
expected = (aln1, aln2, 131.0)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
actual = 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(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
aln1 = ("-------------------------ACCGTGGACCGTTAGGA"
"TTGGACCCAAGGTT-------------------------G")
aln2 = ("TTTTTTTTTTTTTTTTTTTTTTTTTACCGTGGACCGT-AGGA"
"TTGGACC-AAGGTTAAAAAAAAAAAAAAAAAAAAAAAAAA")
expected = (aln1, aln2, 97.0)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
actual = 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(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
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 = "ACCGTGGACCGTTAGGATTGGACCCAAGGTTG"
seq2 = "T"*25 + "ACCGTGGACCGTAGGATTGGACCAAGGTTA" + "A"*25
aln1 = ("-------------------------ACCGTGGACCGTTAGGA"
"TTGGACCCAAGGTTG-------------------------")
aln2 = ("TTTTTTTTTTTTTTTTTTTTTTTTTACCGTGGACCGT-AGGA"
"TTGGACC-AAGGTTAAAAAAAAAAAAAAAAAAAAAAAAAA")
expected = (aln1, aln2, 131.0)
actual = 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(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
aln1 = ("-------------------------ACCGTGGACCGTTAGGA"
"TTGGACCCAAGGTT-------------------------G")
aln2 = ("TTTTTTTTTTTTTTTTTTTTTTTTTACCGTGGACCGT-AGGA"
"TTGGACC-AAGGTTAAAAAAAAAAAAAAAAAAAAAAAAAA")
expected = (aln1, aln2, 97.0)
actual = 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(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
def test_global_pairwise_align_nucleotide(self):
obs_msa, obs_score, obs_start_end = global_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC"), DNA("GAACTTTGACGTAAC"),
gap_open_penalty=5., gap_extend_penalty=0.5, match_score=5,
mismatch_score=-4)
self.assertEqual(obs_msa, TabularMSA([DNA("G-ACCTTGACCAGGTACC"),
DNA("GAACTTTGAC---GTAAC")]))
self.assertEqual(obs_score, 41.0)
self.assertEqual(obs_start_end, [(0, 16), (0, 14)])
obs_msa, obs_score, obs_start_end = global_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC"), DNA("GAACTTTGACGTAAC"),
gap_open_penalty=10., gap_extend_penalty=0.5, match_score=5,
mismatch_score=-4)
self.assertEqual(obs_msa, TabularMSA([DNA("-GACCTTGACCAGGTACC"),
DNA("GAACTTTGAC---GTAAC")]))
self.assertEqual(obs_score, 32.0)
self.assertEqual(obs_start_end, [(0, 16), (0, 14)])
# DNA sequences with metadata
obs_msa, obs_score, obs_start_end = global_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC", metadata={'id': "s1"}),
DNA("GAACTTTGACGTAAC", metadata={'id': "s2"}),
gap_open_penalty=10., gap_extend_penalty=0.5, match_score=5,
mismatch_score=-4)
self.assertEqual(
obs_msa,
TabularMSA([DNA("-GACCTTGACCAGGTACC", metadata={'id': "s1"}),
DNA("GAACTTTGAC---GTAAC", metadata={'id': "s2"})]))
self.assertEqual(obs_score, 32.0)
self.assertEqual(obs_start_end, [(0, 16), (0, 14)])
# Align one DNA sequence and one TabularMSA, score computed manually
obs_msa, obs_score, obs_start_end = global_pairwise_align_nucleotide(
TabularMSA([DNA("GACCTTGACCAGGTACC", metadata={'id': "s1"}),
DNA("GACCATGACCAGGTACC", metadata={'id': "s2"})]),
DNA("GAACTTTGACGTAAC", metadata={'id': "s3"}),
gap_open_penalty=10., gap_extend_penalty=0.5, match_score=5,
mismatch_score=-4)
self.assertEqual(
obs_msa,
TabularMSA([DNA("-GACCTTGACCAGGTACC", metadata={'id': "s1"}),
DNA("-GACCATGACCAGGTACC", metadata={'id': "s2"}),
DNA("GAACTTTGAC---GTAAC", metadata={'id': "s3"})]))
self.assertEqual(obs_score, 27.5)
self.assertEqual(obs_start_end, [(0, 16), (0, 14)])
# TypeError on invalid input
self.assertRaises(TypeError, global_pairwise_align_nucleotide,
42, DNA("ACGT"))
self.assertRaises(TypeError, global_pairwise_align_nucleotide,
DNA("ACGT"), 42)
def test_nucleotide_aligners_use_substitution_matrices(self):
alt_sub = make_identity_substitution_matrix(10, -10)
# alternate substitution matrix yields different alignment (the
# aligned sequences and the scores are different) with local alignment
actual_no_sub = local_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4)
actual_alt_sub = local_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4,
substitution_matrix=alt_sub)
self.assertNotEqual(str(actual_no_sub[0]), str(actual_alt_sub[0]))
self.assertNotEqual(str(actual_no_sub[1]), str(actual_alt_sub[1]))
self.assertNotEqual(actual_no_sub.score(),
actual_alt_sub.score())
# alternate substitution matrix yields different alignment (the
# aligned sequences and the scores are different) with global alignment
actual_no_sub = local_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4)
actual_alt_sub = global_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4,
substitution_matrix=alt_sub)
self.assertNotEqual(str(actual_no_sub[0]), str(actual_alt_sub[0]))
self.assertNotEqual(str(actual_no_sub[1]), str(actual_alt_sub[1]))
self.assertNotEqual(actual_no_sub.score(),
actual_alt_sub.score())
def test_nucleotide_aligners_use_substitution_matrices(self):
alt_sub = make_identity_substitution_matrix(10, -10)
# alternate substitution matrix yields different alignment (the
# aligned sequences and the scores are different) with local alignment
with warnings.catch_warnings():
warnings.simplefilter("ignore")
actual_no_sub = local_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4)
actual_alt_sub = local_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4,
substitution_matrix=alt_sub)
self.assertNotEqual(str(actual_no_sub[0]), str(actual_alt_sub[0]))
self.assertNotEqual(str(actual_no_sub[1]), str(actual_alt_sub[1]))
self.assertNotEqual(actual_no_sub.score(),
actual_alt_sub.score())
# alternate substitution matrix yields different alignment (the
# aligned sequences and the scores are different) with global alignment
with warnings.catch_warnings():
warnings.simplefilter("ignore")
actual_no_sub = local_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4)
actual_alt_sub = global_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=5., match_score=5, mismatch_score=-4,
substitution_matrix=alt_sub)
self.assertNotEqual(str(actual_no_sub[0]), str(actual_alt_sub[0]))
self.assertNotEqual(str(actual_no_sub[1]), str(actual_alt_sub[1]))
self.assertNotEqual(actual_no_sub.score(),
actual_alt_sub.score())
def global_alignments(ref, q):
s1 = DNA(ref)
s2 = DNA(q)
alignment, score, start_end_positions = global_pairwise_align_nucleotide(
s1, s2, match_score=4, mismatch_score=1)
return alignments_to_cigar(alignment[0]._string.decode("utf-8"),
alignment[1]._string.decode("utf-8"))
def test_nucleotide_aligners_use_substitution_matrices(self):
alt_sub = make_identity_substitution_matrix(10, -10)
# alternate substitution matrix yields different alignment (the
# aligned sequences and the scores are different) with local alignment
msa_no_sub, score_no_sub, start_end_no_sub = \
local_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC"), DNA("GAACTTTGACGTAAC"),
gap_open_penalty=10., gap_extend_penalty=5., match_score=5,
mismatch_score=-4)
msa_alt_sub, score_alt_sub, start_end_alt_sub = \
local_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC"), DNA("GAACTTTGACGTAAC"),
gap_open_penalty=10., gap_extend_penalty=5., match_score=5,
mismatch_score=-4, substitution_matrix=alt_sub)
self.assertNotEqual(msa_no_sub, msa_alt_sub)
self.assertNotEqual(score_no_sub, score_alt_sub)
self.assertNotEqual(start_end_no_sub, start_end_alt_sub)
# alternate substitution matrix yields different alignment (the
# aligned sequences and the scores are different) with global alignment
msa_no_sub, score_no_sub, start_end_no_sub = \
global_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC"), DNA("GAACTTTGACGTAAC"),
gap_open_penalty=10., gap_extend_penalty=5., match_score=5,
mismatch_score=-4)
msa_alt_sub, score_alt_sub, start_end_alt_sub = \
global_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC"), DNA("GAACTTTGACGTAAC"),
gap_open_penalty=10., gap_extend_penalty=5., match_score=5,
mismatch_score=-4, substitution_matrix=alt_sub)
self.assertNotEqual(msa_no_sub, msa_alt_sub)
self.assertNotEqual(score_no_sub, score_alt_sub)
self.assertEqual(start_end_no_sub, start_end_alt_sub)
def global_align(seq1_1hot, seq2_1hot):
"""Align two 1-hot encoded sequences."""
align_opts = {
'gap_open_penalty': 10,
'gap_extend_penalty': 1,
'match_score': 5,
'mismatch_score': -4
}
seq1_dna = DNA(dna_io.hot1_dna(seq1_1hot))
seq2_dna = DNA(dna_io.hot1_dna(seq2_1hot))
# seq_align = global_pairwise_align_nucleotide(seq1_dna, seq2_dna, *align_opts)[0]
seq_align = global_pairwise_align_nucleotide(seq1_dna,
seq2_dna,
gap_open_penalty=10,
gap_extend_penalty=1,
match_score=5,
mismatch_score=-4)[0]
seq1_align = str(seq_align[0])
seq2_align = str(seq_align[1])
return seq1_align, seq2_align
def dnaAlign(seq1, seq2, gap_open_penalty, gap_extend_penalty, local=False):
seq1 = seq1.upper()
seq2 = seq2.upper()
if local:
aln, score, _ = local_pairwise_align_nucleotide(
DNA(seq1), DNA(seq2), gap_open_penalty, gap_extend_penalty)
else:
aln, score, _ = global_pairwise_align_nucleotide(
DNA(seq1), DNA(seq2), gap_open_penalty, gap_extend_penalty)
response = {
'aln1':
str(aln[0]),
'aln2':
str(aln[1]),
'score':
score,
'similarity':
float('{:.2f}'.format(aln[0].match_frequency(aln[1], relative=True) *
100))
}
return response
def test_global_pairwise_align_nucleotide_invalid_dtype(self):
with self.assertRaisesRegex(TypeError,
"TabularMSA with DNA or RNA dtype.*dtype "
"'Protein'"):
global_pairwise_align_nucleotide(TabularMSA([DNA('ACGT')]),
TabularMSA([Protein('PAW')]))
def test_global_pairwise_align_nucleotide(self):
expected = ("G-ACCTTGACCAGGTACC", "GAACTTTGAC---GTAAC", 41.0, 0, 0)
actual = global_pairwise_align_nucleotide("GACCTTGACCAGGTACC",
"GAACTTTGACGTAAC",
gap_open_penalty=5.,
gap_extend_penalty=0.5,
match_score=5,
mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), list('01'))
expected = ("-GACCTTGACCAGGTACC", "GAACTTTGAC---GTAAC", 32.0, 0, 0)
actual = global_pairwise_align_nucleotide("GACCTTGACCAGGTACC",
"GAACTTTGACGTAAC",
gap_open_penalty=10.,
gap_extend_penalty=0.5,
match_score=5,
mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), list('01'))
# DNA (rather than str) as input
expected = ("-GACCTTGACCAGGTACC", "GAACTTTGAC---GTAAC", 32.0, 0, 0)
actual = global_pairwise_align_nucleotide(DNA("GACCTTGACCAGGTACC",
"s1"),
DNA("GAACTTTGACGTAAC", "s2"),
gap_open_penalty=10.,
gap_extend_penalty=0.5,
match_score=5,
mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), ["s1", "s2"])
# Align one DNA sequence and one Alignment, score computed manually
expected = ("-GACCTTGACCAGGTACC", "-GACCATGACCAGGTACC",
"GAACTTTGAC---GTAAC", 27.5, 0, 0)
actual = global_pairwise_align_nucleotide(Alignment(
[DNA("GACCTTGACCAGGTACC", "s1"),
DNA("GACCATGACCAGGTACC", "s2")]),
DNA("GAACTTTGACGTAAC", "s3"),
gap_open_penalty=10.,
gap_extend_penalty=0.5,
match_score=5,
mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(str(actual[2]), expected[2])
self.assertEqual(actual.score(), expected[3])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), ["s1", "s2", "s3"])
# ids are provided if they're not passed in
actual = global_pairwise_align_nucleotide(DNA("GACCTTGACCAGGTACC"),
DNA("GAACTTTGACGTAAC"),
gap_open_penalty=10.,
gap_extend_penalty=0.5,
match_score=5,
mismatch_score=-4)
self.assertEqual(actual.ids(), list('01'))
# TypeError on invalid input
self.assertRaises(TypeError, global_pairwise_align_nucleotide, 42,
"HEAGAWGHEE")
self.assertRaises(TypeError, global_pairwise_align_nucleotide,
"HEAGAWGHEE", 42)
def test_global_pairwise_align_nucleotide(self):
expected = ("G-ACCTTGACCAGGTACC", "GAACTTTGAC---GTAAC", 41.0, 0, 0)
actual = global_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=5.,
gap_extend_penalty=0.5, match_score=5, mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), list('01'))
expected = ("-GACCTTGACCAGGTACC", "GAACTTTGAC---GTAAC", 32.0, 0, 0)
actual = global_pairwise_align_nucleotide(
"GACCTTGACCAGGTACC", "GAACTTTGACGTAAC", gap_open_penalty=10.,
gap_extend_penalty=0.5, match_score=5, mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), list('01'))
# DNA (rather than str) as input
expected = ("-GACCTTGACCAGGTACC", "GAACTTTGAC---GTAAC", 32.0, 0, 0)
actual = global_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC", metadata={'id': "s1"}),
DNA("GAACTTTGACGTAAC", metadata={'id': "s2"}),
gap_open_penalty=10., gap_extend_penalty=0.5, match_score=5,
mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(actual.score(), expected[2])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), ["s1", "s2"])
# Align one DNA sequence and one Alignment, score computed manually
expected = ("-GACCTTGACCAGGTACC", "-GACCATGACCAGGTACC",
"GAACTTTGAC---GTAAC", 27.5, 0, 0)
actual = global_pairwise_align_nucleotide(
Alignment([DNA("GACCTTGACCAGGTACC", metadata={'id': "s1"}),
DNA("GACCATGACCAGGTACC", metadata={'id': "s2"})]),
DNA("GAACTTTGACGTAAC", metadata={'id': "s3"}),
gap_open_penalty=10., gap_extend_penalty=0.5, match_score=5,
mismatch_score=-4)
self.assertEqual(str(actual[0]), expected[0])
self.assertEqual(str(actual[1]), expected[1])
self.assertEqual(str(actual[2]), expected[2])
self.assertEqual(actual.score(), expected[3])
self.assertEqual(actual.start_end_positions(), [(0, 16), (0, 14)])
self.assertEqual(actual.ids(), ["s1", "s2", "s3"])
# ids are provided if they're not passed in
actual = global_pairwise_align_nucleotide(
DNA("GACCTTGACCAGGTACC"),
DNA("GAACTTTGACGTAAC"),
gap_open_penalty=10., gap_extend_penalty=0.5, match_score=5,
mismatch_score=-4)
self.assertEqual(actual.ids(), list('01'))
# TypeError on invalid input
self.assertRaises(TypeError, global_pairwise_align_nucleotide,
42, "HEAGAWGHEE")
self.assertRaises(TypeError, global_pairwise_align_nucleotide,
"HEAGAWGHEE", 42)
def pairwise_similarity(seq, query):
alignment = global_pairwise_align_nucleotide(seq, query)
return alignment[0].fraction_same(alignment[1])
def get_meth_profile(args, seg_chrom, seg_start, seg_end, seg_name,
seg_strand):
logger.info('profiling %s %s:%d-%d:%s' %
(seg_name, seg_chrom, seg_start, seg_end, seg_strand))
te_ref_seq = single_seq_fa(args.teref)
ref = pysam.Fastafile(args.ref)
meth_tbx = pysam.Tabixfile(args.meth)
tmp_methdata = str(uuid4()) + '.tmp.methdata.tsv'
with open(tmp_methdata, 'w') as meth_out:
# header
with gzip.open(args.meth, 'rt') as _:
for line in _:
assert line.startswith('chromosome')
meth_out.write(line)
break
assert seg_chrom in meth_tbx.contigs
for rec in meth_tbx.fetch(seg_chrom, seg_start, seg_end):
meth_out.write(str(rec) + '\n')
# index by read_name
methdata = pd.read_csv(tmp_methdata, sep='\t', header=0, index_col=4)
os.remove(tmp_methdata)
reads = []
if args.excl_ambig:
reads = exclude_ambiguous_reads(args.bam, seg_chrom, seg_start,
seg_end)
else:
reads = get_reads(args.bam, seg_chrom, seg_start, seg_end)
reads = list(set(reads).intersection(set(methdata.index)))
methdata = methdata.loc[reads]
seg_reads = {}
for index, row in methdata.iterrows():
r_start = row['start']
r_end = row['end']
llr = row['log_lik_ratio']
seq = row['sequence']
# get per-CG position (nanopolish/calculate_methylation_frequency.py)
cg_pos = seq.find("CG")
first_cg_pos = cg_pos
while cg_pos != -1:
cg_start = r_start + cg_pos - first_cg_pos
cg_pos = seq.find("CG", cg_pos + 1)
cg_seg_start = cg_start - seg_start
if cg_start >= seg_start and cg_start <= seg_end:
if index not in seg_reads:
seg_reads[index] = Read(index, cg_seg_start, llr)
else:
seg_reads[index].add_cpg(cg_seg_start, llr)
meth_table = dd(dict)
sample = '.'.join(args.bam.split('.')[:-1])
for name, read in seg_reads.items():
for loc in read.llrs.keys():
uuid = str(uuid4())
meth_table[uuid]['loc'] = loc
meth_table[uuid]['llr'] = read.llrs[loc]
meth_table[uuid]['read'] = name
meth_table[uuid]['sample'] = sample
meth_table[uuid]['call'] = read.meth_calls[loc]
meth_table = pd.DataFrame.from_dict(meth_table).T
meth_table['loc'] = pd.to_numeric(meth_table['loc'])
meth_table['llr'] = pd.to_numeric(meth_table['llr'])
meth_table['orig_loc'] = meth_table['loc']
meth_table['loc'] = ss.rankdata(meth_table['loc'], method='dense')
coord_to_cpg = {}
cpg_to_coord = {}
for orig_loc, new_loc in zip(meth_table['orig_loc'], meth_table['loc']):
coord_to_cpg[orig_loc] = new_loc
cpg_to_coord[new_loc] = orig_loc
windowed_methfrac, meth_n = slide_window(meth_table,
sample,
width=int(args.slidingwindowsize),
slide=int(args.slidingwindowstep))
if len(windowed_methfrac) <= int(args.smoothwindowsize):
logger.warning('too few sites after windowing: %s:%d-%d' %
(seg_chrom, seg_start, seg_end))
return [], []
smoothed_methfrac = smooth(np.asarray(list(windowed_methfrac.values())),
window_len=int(args.smoothwindowsize))
coord_meth_pos = []
cpg_meth_pos = list(windowed_methfrac.keys())
for cpg in cpg_meth_pos:
if seg_strand == '+':
coord_meth_pos.append(cpg_to_coord[cpg])
if seg_strand == '-':
coord_meth_pos.append((seg_end - seg_start) - cpg_to_coord[cpg])
# alignment to ref elt
elt_seq = ref.fetch(seg_chrom, seg_start, seg_end)
if seg_strand == '-':
elt_seq = rc(elt_seq)
te_ref_seq = te_ref_seq.upper()
elt_seq = elt_seq.upper()
s_ref = skseq.DNA(te_ref_seq)
s_elt = skseq.DNA(elt_seq)
aln_res = []
try:
if args.globalign:
aln_res = skalign.global_pairwise_align_nucleotide(s_ref, s_elt)
else:
aln_res = skalign.local_pairwise_align_ssw(s_ref, s_elt)
except IndexError: # scikit-bio throws this if no bases align >:|
logger.warning('no align on seg: %s:%d-%d' %
(seg_chrom, seg_start, seg_end))
return [], []
coord_ref, coord_elt = aln_res[2]
len_ref = coord_ref[1] - coord_ref[0]
len_elt = coord_elt[1] - coord_elt[0]
if len_ref / len(te_ref_seq) < float(args.lenfrac):
logger.warning(
'ref align too short on seg: %s:%d-%d (%f)' %
(seg_chrom, seg_start, seg_end, len_ref / len(te_ref_seq)))
return [], []
if len_elt / len(elt_seq) < float(args.lenfrac):
logger.warning('elt align too short on seg: %s:%d-%d (%f)' %
(seg_chrom, seg_start, seg_end, len_elt / len(elt_seq)))
return [], []
tab_msa = aln_res[0]
elt_to_ref_coords = {}
pos_ref = coord_ref[0]
pos_elt = coord_elt[0]
for pos in tab_msa.iter_positions():
pos = list(pos)
b_ref = pos[0]
b_elt = pos[1]
if '-' not in pos:
elt_to_ref_coords[pos_elt] = pos_ref
pos_ref += 1
pos_elt += 1
if b_elt == '-':
pos_ref += 1
if b_ref == '-':
elt_to_ref_coords[pos_elt] = 'na'
pos_elt += 1
revised_coord_meth_pos = []
meth_profile = []
for pos, meth in zip(coord_meth_pos, smoothed_methfrac):
if pos not in elt_to_ref_coords:
continue
revised_pos = elt_to_ref_coords[pos]
if revised_pos != 'na':
revised_coord_meth_pos.append(revised_pos)
meth_profile.append(meth)
return revised_coord_meth_pos, meth_profile
