def test_aligned_dna_fasta_format_validate_positive(self):
filepath = self.get_data_path('aligned-dna-sequences.fasta')
format = AlignedDNAFASTAFormat(filepath, mode='r')
format.validate()
def test_aligned_dna_fasta_format_validate_negative(self):
filepath = self.get_data_path('not-dna-sequences')
format = AlignedDNAFASTAFormat(filepath, mode='r')
with self.assertRaisesRegex(ValueError, 'AlignedDNAFASTA'):
format.validate()
def test_aligned_dna_fasta_format_validate_positive(self):
filepath = self.get_data_path('aligned-dna-sequences.fasta')
format = AlignedDNAFASTAFormat(filepath, mode='r')
format.validate()
def test_aligned_dna_fasta_format_validate_negative(self):
filepath = self.get_data_path('not-dna-sequences')
format = AlignedDNAFASTAFormat(filepath, mode='r')
with self.assertRaisesRegex(ValidationError, 'AlignedDNAFASTA'):
format.validate()
def _dna_iterator_to_aligned_fasta(iterator):
ff = AlignedDNAFASTAFormat()
skbio.io.write(iter(iterator), format='fasta', into=str(ff))
return ff
def _mafft(sequences_fp, alignment_fp, n_threads, parttree, addfragments):
# Save original sequence IDs since long ids (~250 chars) can be truncated
# by mafft. We'll replace the IDs in the aligned sequences file output by
# mafft with the originals.
#
# https://github.com/qiime2/q2-alignment/issues/37
aligned_seq_ids = {}
unaligned_seq_ids = {}
if alignment_fp is not None:
for seq in skbio.io.read(alignment_fp, format='fasta',
constructor=skbio.DNA):
id_ = seq.metadata['id']
if id_ in aligned_seq_ids:
raise ValueError(
"A sequence ID is duplicated in the aligned sequences: "
"%r" % id_)
else:
aligned_seq_ids[id_] = True
for seq in skbio.io.read(sequences_fp, format='fasta',
constructor=skbio.DNA):
id_ = seq.metadata['id']
if id_ in unaligned_seq_ids:
raise ValueError(
"A sequence ID is duplicated in the unaligned sequences: "
"%r" % id_)
elif id_ in aligned_seq_ids:
raise ValueError(
"A sequence ID is present in both the aligned and unaligned "
"sequences: %r" % id_)
else:
unaligned_seq_ids[id_] = True
result = AlignedDNAFASTAFormat()
result_fp = str(result)
ids = {**aligned_seq_ids, **unaligned_seq_ids}
# mafft will fail if the number of sequences is larger than 1 million.
# mafft requires using parttree which is an algorithm to build an
# approximate tree from a large number of unaligned sequences.
# By catching the error below if a user has not used parttree flag, we are
# eliminating the need for the mafft error to be shown to the user which
# can be confusing and intimidating.
if not parttree and len(ids) > 1000000:
raise ValueError(
"The number of sequences in your feature table is larger than "
"1 million, please use the parttree parameter")
# mafft's signal for utilizing all cores is -1. We want to our users
# to enter auto for using all cores. This is to prevent any confusion and
# to keep the UX consisent.
if n_threads == 'auto':
n_threads = -1
# `--inputorder` must be turned on because we need the input and output in
# the same sequence order to replace the IDs below. This is mafft's default
# behavior but we pass the flag in case that changes in the future.
cmd = ["mafft", "--preservecase", "--inputorder",
"--thread", str(n_threads)]
if parttree:
cmd += ['--parttree']
if alignment_fp is not None:
add_flag = '--addfragments' if addfragments else '--add'
cmd += [add_flag, sequences_fp, alignment_fp]
else:
cmd += [sequences_fp]
run_command(cmd, result_fp)
# Read output alignment into memory, reassign original sequence IDs, and
# write alignment back to disk.
msa = skbio.TabularMSA.read(result_fp, format='fasta',
constructor=skbio.DNA)
# Using `assert` because mafft would have had to add or drop sequences
# while aligning, which would be a bug on mafft's end. This is just a
# sanity check and is not expected to trigger in practice.
assert len(ids) == len(msa)
for id, seq in zip(ids, msa):
seq.metadata['id'] = id
# Turning off roundtripping options to speed up writing. We can safely turn
# these options off because we know the sequence IDs are rountrip-safe
# since we read them from a FASTA file above.
#
# http://scikit-bio.org/docs/latest/generated/
# skbio.io.format.fasta.html#writer-specific-parameters
msa.write(result_fp, id_whitespace_replacement=None,
description_newline_replacement=None)
return result
def setUp(self):
super().setUp()
self.aligned_dna_path = pkg_resources.resource_filename(
'rescript.tests', 'data/trim-test-alignment.fasta')
self.aligned_dna_seqs = AlignedDNAFASTAFormat(
self.aligned_dna_path, mode='r').view(AlignedDNAIterator)
def setUp(self):
super().setUp()
input_fp = self.get_data_path('degap-test-alignment.fasta')
self.alignedseqs = AlignedDNAFASTAFormat(
input_fp, mode='r').view(AlignedDNAIterator)
def setUp(self):
super().setUp()
aligned_seqs_fp = self.get_data_path('trim-test-alignment.fasta')
aligned_with_primers_fp = self.get_data_path(
'trim-test-alignment-with-primers.fasta')
self.aligned_seqs = qiime2.Artifact.import_data(
'FeatureData[AlignedSequence]', aligned_seqs_fp)
self.aligned_seqs_fasta = AlignedDNAFASTAFormat(aligned_seqs_fp,
mode='r')
self.primers_dict = {
"forward": "GGGAATCTTCCACAATGG",
"reverse": "GTGTTCTTCTCTAACAACAG"
}
self.aligned_with_primers = qiime2.Artifact.import_data(
'FeatureData[AlignedSequence]', aligned_with_primers_fp)
self.aligned_with_primers_fasta = AlignedDNAFASTAFormat(
aligned_with_primers_fp, mode='r')
self.aligned_mess_fasta = AlignedDNAFASTAFormat(
self.get_data_path('trim-test-alignment-with-primers-mess.fasta'),
mode='r')
self.aligned_with_fwd_fasta = AlignedDNAFASTAFormat(
self.get_data_path('trim-test-alignment-fwd.fasta'), mode='r')
self.aligned_with_rev_fasta = AlignedDNAFASTAFormat(
self.get_data_path('trim-test-alignment-rev.fasta'), mode='r')
self.trimmed_fasta = AlignedDNAFASTAFormat(
self.get_data_path('trim-test-sequences-trimmed.fasta'), mode='r')
self.fake_ctx = FakeCtx({
1: self.aligned_with_primers_fasta,
2: self.aligned_with_fwd_fasta,
3: self.aligned_with_rev_fasta
})
self.exp_seqs_both_primers = {
's1': ('GGGAATCTTCCACAATGGGTGCAAACCTGATGGAGCAATGCCGCGTGAG'
'TGAAGANAGGTCTTCGGATCGTAAAGCTCTGTTGTTAGAGAAGAACAC'),
's2': ('GGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGCCGCGTGGG'
'GGATGA-CGGCCTTCGGGTTGTAAACTCCTTTCGCCAGGGACGAAGCG'),
's3': ('GGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTG'
'TGAAGA-AGGCCTTTTGGTTGTAAAGCACTTTAAGTGGGGAGGAAAAG'),
's4': ('GGGAATTTTGGACAATGGGGGCAACCCTGATCCAGCCATGCCGCGTGCG'
'GGAAGANAGGCCTTCGGGTTGTAAACCGCTTTTGTTCGGGAAGAAATC'),
's5': ('GGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTG'
'TGAAGA-AGGCCTTTTGGTTGTAAAGCACTTTAAGTGGGGAGGAAAAG'),
}
self.exp_seqs_only_fwd = {
's1': ('GGGAATCTTCCACAATGGGTGCAAACCTGATGGAGCAATGCCGCGTGAG'
'TGAAGANAGGTCTTCGGATCGTAAAGCTCTGTTGTTAGAGAAGAACACG'
'TGCTAGG--------'),
's2': ('GGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGCCGCGTGGG'
'GGATGA-CGGCCTTCGGGTTGTAAACTCCTTTCGCCAGGGACGAAGCGT'
'TTTG-----------'),
's3': ('GGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTG'
'TGAAGA-AGGCCTTTTGGTTGTAAAGCACTTTAAGTGGGGAGGAAAAGC'
'TTATGGTTAAAAAAA'),
's4': ('GGGAATTTTGGACAATGGGGGCAACCCTGATCCAGCCATGCCGCGTGCG'
'GGAAGANAGGCCTTCGGGTTGTAAACCGCTTTTGTTCGGGAAGAAATCC'
'TCTGGGCTAAAAAAA'),
's5': ('GGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTG'
'TGAAGA-AGGCCTTTTGGTTGTAAAGCACTTTAAGTGGGGAGGAAAAGC'
'TTGTGGTTAA-----'),
}
self.exp_seqs_only_rev = {
's1': ('-----TAGGGAATCTTCCACAATGGGTGCAAACCTGATGGAGCAATGC'
'CGCGTGAGTGAAGANAGGTCTTCGGATCGTAAAGCTCTGTTGTTAGAG'
'AAGAACAC'),
's2': ('AATTTTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGC'
'CGCGTGGGGGATGA-CGGCCTTCGGGTTGTAAACTCCTTTCGCCAGGG'
'ACGAAGCG'),
's3': ('-----TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGC'
'CGCGTGTGTGAAGA-AGGCCTTTTGGTTGTAAAGCACTTTAAGTGGGG'
'AGGAAAAG'),
's4': ('-----TGGGGAATTTTGGACAATGGGGGCAACCCTGATCCAGCCATGC'
'CGCGTGCGGGAAGANAGGCCTTCGGGTTGTAAACCGCTTTTGTTCGGG'
'AAGAAATC'),
's5': ('---AATGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGC'
'CGCGTGTGTGAAGA-AGGCCTTTTGGTTGTAAAGCACTTTAAGTGGGG'
'AGGAAAAG'),
}
