def create_orf_annotator(parameters):
'It creates a function that annotates orfs'
runner = create_runner(tool='estscan', parameters=parameters)
def annotate_orf(sequence):
'It adds the orf to the SeqFeatures'
if sequence is None:
return
results = runner(sequence)
dna_fhand = results['dna']
prot_fhand = results['protein']
description, seq = get_content_from_fasta(dna_fhand)[1:]
pep = get_content_from_fasta(prot_fhand)[-1]
prot_fhand.close()
dna_fhand.close()
# If there is no description, ther is no org
if description is None:
return sequence
items = description.split()
start = int(items[1])
end = int(items[2])
seq = Seq(seq, generic_dna)
pep = Seq(pep, generic_protein)
qualifiers = {'dna':seq, 'pep':pep}
if start < end:
qualifiers['strand'] = 'forward'
else:
qualifiers['strand'] = 'reverse'
feature = SeqFeature(location=FeatureLocation(start, end), type='orf',
qualifiers=qualifiers)
sequence.features.append(feature)
return sequence
return annotate_orf
def __init__(self, subject=None, database=None, program='blastn',
parameters=None, filters=None):
'''It inits the class.
Query should be a sequence and subject can be one or several.
subject could be an fhand (fasta) or an string
'''
if subject is None and database is None:
raise ValueError('Either subject or database should be given')
if subject is not None and database is not None:
msg = 'subject and database can not be given at the same time'
raise ValueError(msg)
if parameters is None:
parameters = {}
self._filters = filters
if subject is not None:
parameters['alig_format'] = 0
self._parser = get_alignment_parser('blast_text')
self._subject_fhand = _seq_to_fasta_fhand(subject)
parameters['subject'] = self._subject_fhand.name
if database is not None:
parameters['database'] = database
parameters['alig_format'] = 5
self._parser = get_alignment_parser('blast')
self._program = program
self._aligner = create_runner(tool=program, parameters=parameters)
def test_build_water_relations():
'''it test the function that makes the relations between two sequences
using a markx10 format file'''
seq = 'ATGGCTTCATCCATTCTCTCATCCGCCGNTGTGGCCTTTGNCAACAGGGCTTCCCCTGCTCA'
seq += 'AGCTAGCATGGGGGCACCATTCACTGGCCTAAAATCCGCCGCTGCTTTCCCNGTTTTATGTA'
seq += 'CTGTTTTNACTCGCANGACCAACGACATCACCACTTTGGTTAGCAATGGGGGAAGAGTTCAG'
seq += 'GGCNTGAAGGTGTGCCCACCACTTGGATTGAAGAAGTTCGAGACTCTTTCTTACCTTCCTGA'
seq += 'TATGAGTAACGAGCAATTGGGAAAGGAAGTTGACTACCTTCTCAGGAAGGGATGGATTCCCT'
seq += 'GCATTGAATTCGACATTCACAGTGGATTCGTTTACCGTGAGACCCACAGGTCACCAGGATAC'
seq += 'TTCGATGGACGCTACTGGACCATGTGGAAGCTGCCCATGTTTGGCTGCACCGAT'
seq2 = 'ATGGCTTCATCCATTCTCTCATCCGCCGNTGTGGCCTTTGNCAACAGGGCTTCCCTGCTCAA'
seq2 += 'GCTAGCATGGGGGCACCATTCACTGGCCTAAAATCCGCCGCTGCTTTCCCNGTNACTCGCAN'
seq2 += 'GACCAACGACATCACCACTTTGGTTAGCAATGGGGGAAGAGTTCAGGGCNTGAAGGTGTGCC'
seq2 += 'CACCACTTGGATTGAAGAAGTTCGAGACTCTTTCTTACCTTCCTGATATGAGTAACGAGCAA'
seq2 += 'TTGGGAAAGGAAGTTGACTACCTTCTCAGGAAGGGATGGATTCCCTGCATTGAATTCGACAT'
seq2 += 'TCACAGTGGATTCGTTTACCGTGAGACCCACAGGTCACCAGGATACTTCGATGGACGCTAC'
seq2 += 'TGGACCATGTGGAAGCTGCCCATGTTTGGCTGCACCGAT'
subject_seq = SeqWithQuality(seq=Seq(seq), name='subject')
query_seq = SeqWithQuality(seq=Seq(seq2), name='query')
subject_fhand = temp_fasta_file(subject_seq)
parameters = {'subject':subject_fhand.name}
aligner = create_runner(tool='water', parameters=parameters)
result_fhand = aligner(query_seq)['water']
relations = build_relations_from_aligment(result_fhand,
query_name=query_seq.name,
subject_name=subject_seq.name)
assert relations == {'query': [(0, 50), (51, 112), (113, 409)],
'subject': [(0, 50), (52, 113), (129, 425)]}
def create_aligner_filter(aligner_cmd, cmd_parameters, match_filters=None,
environment=None):
'''A function factory factory that creates aligner filters.
It returns a function that will accept a sequence and it will return
True or False depending on the exonerate outcome.
parameters is a dictionary and key are defined in ExonerateRunner.
Required is only the target fasta file
'''
#runners = {'blast':BlastRunner, 'exonerate':ExonerateRunner}
parser = get_alignment_parser(aligner_cmd)
run_align_for_seq = create_runner(tool=aligner_cmd, environment=environment,
parameters=cmd_parameters)
def _filter(sequence):
'Giving a sequence it returns true or False depending on the exonerate'
if sequence is None:
return False
source_result = run_align_for_seq(sequence)[aligner_cmd]
results = parser(source_result)
filtered_results = filter_alignments(results, config=match_filters)
try:
#only one sequence -> only one result
filtered_results.next()
except StopIteration:
#there was no result for this sequence
return False
return True
return _filter
def create_striper_by_quality_lucy(parameters=None):
"""It creates a function that removes bad quality regions using lucy.
The function will take a sequence iterator and it will return a new sequence
iterator with the processed sequences in it."""
run_lucy_for_seqs = create_runner(tool="lucy", parameters=parameters)
def strip_seq_by_quality_lucy(sequences):
"""It trims the bad quality regions from the given sequences.
It uses lucy external program. It returns a sequence iterator and a,
list of output files. (A seq file and a qual file). We return the files
because the iterator feeds from them and they are temporary files that
will be removed as soon as they get out of scope.
"""
# pylint: disable-msg=W0612
# now we run lucy
sequences, sequences_for_lucy = tee(sequences, 2)
seq_out_fhand = run_lucy_for_seqs(sequences_for_lucy)["sequence"][0]
# index the lucy result
result_index = SeqIO.index(seq_out_fhand.name, "fasta")
# process each sequence and
for sequence in sequences:
yield _lucy_mapper(sequence, result_index)
seq_out_fhand.close()
return strip_seq_by_quality_lucy
def look_for_similar_sequences(sequence, database, blast_program, filters=None):
'It return a list with the similar sequences in the database'
parameters = {'database': database}
blast_runner = create_runner(tool=blast_program, parameters=parameters)
blast_fhand = blast_runner(sequence)[blast_program]
return similar_sequences_for_blast(blast_fhand, filters=filters)
def test_create_mdust_runner():
'We can create a runner class for mdust'
run_mdust__for_seq = create_runner(tool='mdust')
seq = 'AACTACGTAGCTATGCTGATGCTAGTCTAGAAAAAAAAAAAAAAAAAAAAAAAAAAA'
seq = Seq(seq)
seq1 = SeqWithQuality(seq)
result = run_mdust__for_seq(seq1)['sequence']
assert "57\t31\t57" in result.read()
def xtest_run_iprscan():
'It test the runner of iprscan'
protfile = os.path.join(TEST_DATA_DIR, 'prot.fasta')
seq = 'MLVNRILKHGKKSLAYQIIYRAMKKIQQKTETNPLSVLRQAIRGVTPDIAVKARRVGGSTH'
seq += 'QVPIEIGSTQGKALAIRWLLGASRKRPGRNMAFKLSSELVDAAKGSGDAIRKKEETHRMAEAN'
seq += 'RAFAHFR*'
seq1 = Seq(seq)
run_iprscan_for_seq = create_runner(tool='iprscan', parameters={'format':'html'})
run_iprscan_for_seq(seq1)['result']
def look_for_similar_sequences(sequence, database, blast_program, filters=None):
"""It return a list with the similar sequences in the database.
First it makes a blast of the sequences against the database and then
in joins the similar sequences
"""
parameters = {"database": database}
blast_runner = create_runner(tool=blast_program, parameters=parameters)
blast_fhand = blast_runner(sequence)[blast_program]
return similar_sequences_for_blast(blast_fhand, filters=filters)
def _locate_codons_in_orf(sequence, orf, snv):
'It locates the snv in the orf coordinate system'
query_name = sequence.name
orf_seq = orf.qualifiers['dna']
subject_name = 'subject'
subject_fhand = NamedTemporaryFile(suffix='.fasta')
subject_fhand.write('>%s\n%s\n' % (subject_name, orf_seq))
subject_fhand.flush()
parameters = {'subject':subject_fhand.name}
aligner = create_runner(tool='water', parameters=parameters)
result_fhand = aligner(sequence)['water']
relations = build_relations_from_aligment(result_fhand,
query_name=sequence.name,
subject_name=subject_name)
#print relations
coord = CoordSystem(relations=[relations])
# snv .positions
snv_pos = snv.location.start.position
try:
snv_in_orf = coord.transform(from_mol=query_name, to_mol=subject_name,
position=snv_pos)
except RuntimeError:
snv_in_orf = None
#print snv_in_orf_start, snv_in_orf_end
orf_start = 0
orf_end = len(orf.qualifiers['dna']) - 1
orf_start_limit_in_seq = coord.transform(from_mol=subject_name,
to_mol=query_name,
position=orf_start)
orf_end_limit_in_seq = coord.transform(from_mol=subject_name,
to_mol=query_name,
position=orf_end)
if snv_in_orf is None:
# it can be utr3, utr5, or None
if snv_pos < orf_start_limit_in_seq:
position = 'utr5'
elif snv_pos > orf_end_limit_in_seq:
position = 'in utr3'
else:
position = None
codon_start = None
snv_in_orf = None
else:
start_codon_pos = snv_in_orf % 3
codon_start = snv_in_orf - start_codon_pos
position = 'orf'
return (position, codon_start, snv_in_orf)
def test_create_blast_runner():
'We can create a runner class for blast'
blastpath = os.path.join(TEST_DATA_DIR, 'blast')
run_blast_for_seq = create_runner(tool='blastn',
parameters={'database':'arabidopsis_genes+'},
environment={'BLASTDB':blastpath})
seq = 'AACTACGTAGCTATGCTGATGCTAGTCTAGCTAGTCGTAGTCTGATCGTAGTCAGTT'
seq = Seq(seq)
seq1 = SeqWithQuality(seq)
result = run_blast_for_seq(seq1)['blastn']
assert result.read()[0] == '<'
def test_create_lucy_runner():
'We can create a runner class for lucy'
fastafile = os.path.join(TEST_DATA_DIR, 'seq2.fasta')
run_lucy_for_seq = create_runner(tool='lucy',
parameters={'vector':(fastafile,fastafile)})
seq = 'AACTACGTAGCTATGCTGATGCTAGTCTAGAAAAAAAAAAAAAAAAAAAAAAAAAAA'
qual = [30] * len(seq)
seq = Seq(seq)
seq1 = SeqWithQuality(seq, qual=qual)
seqs = [seq1, seq1]
result = run_lucy_for_seq(seqs)['sequence']
assert result[0].read() == ''
def __init__(self, subject, parameters=None, filters=None):
'It inits the class'
if parameters is None:
parameters = {}
self._filters = filters
self._parser = get_alignment_parser('exonerate')
self._subject_fhand = _seq_to_fasta_fhand(subject)
parameters['target'] = self._subject_fhand.name
self._aligner = create_runner(tool='exonerate', parameters=parameters)
def create_microsatellite_annotator():
'It creates a function that'
runner = create_runner(tool='sputnik')
def search_ssr(sequence):
'Do the actual search'
if sequence is None:
return
srrs_out_fhand = runner(sequence)['sputnik']
for feature in _get_features_from_sputnik(srrs_out_fhand):
sequence.features.append(feature)
return sequence
return search_ssr
def create_masker_for_polia():
'It creates a masker function that will mask poly-A tracks'
parameters = {'min_score':'10', 'end':'x', 'incremental_dist':'20',
'fixed_dist':None}
mask_polya_by_seq = create_runner(tool='trimpoly', parameters=parameters)
def mask_polya(sequence):
'''It adds a mask to the sequence where the poly-A is found.
It uses trimpoly from seqclean package
'''
if sequence is None:
return None
fhand = mask_polya_by_seq(sequence)['sequence']
segments = _segments_from_trimpoly(fhand, sequence)
_add_trim_segments(segments, sequence, trim=False)
return sequence
return mask_polya
def strip_seq_by_quality_trimpoly(sequence):
"""It strips the sequence where low quality is found
It uses trimpoly from seqclean package.
This program does not work well with short sequences.
"""
if sequence is None:
return None
if len(sequence) < 80:
return None
parameters = {"only_n_trim": None, "ntrim_above_percent": ntrim_above_percent}
mask_polya_by_seq = create_runner(tool="trimpoly", parameters=parameters)
fhand = mask_polya_by_seq(sequence)["sequence"]
segments = _segments_from_trimpoly(fhand, sequence)
_add_trim_segments(segments, sequence, vector=False)
return sequence
def create_masker_for_polia():
"It creates a masker function that will mask poly-A tracks"
parameters = {"min_score": "10", "end": "x", "incremental_dist": "20", "fixed_dist": None}
mask_polya_by_seq = create_runner(tool="trimpoly", parameters=parameters)
def mask_polya(sequence):
"""It adds a mask to the sequence where the poly-A is found.
It uses trimpoly from seqclean package
"""
if sequence is None:
return None
fhand = mask_polya_by_seq(sequence)["sequence"]
segments = _segments_from_trimpoly(fhand, sequence)
_add_trim_segments(segments, sequence, trim=False)
return sequence
return mask_polya
def create_masker_for_low_complexity():
'It creates a masker function for low complexity sections that uses mdust'
mask_low_complex_by_seq = create_runner(tool='mdust')
def mask_low_complexity(sequence):
'''It adds a mask to the sequence where low complexity is found
It uses mdust from the seqclean package
'''
if sequence is None:
return None
fhand = mask_low_complex_by_seq(sequence)['sequence']
segments = []
for line in fhand:
start, end = line.strip().split()[-2:]
segments.append((int(start) - 1, int(end) - 1))
_add_trim_segments(segments, sequence, trim=False)
return sequence
return mask_low_complexity
def create_unique_contiguous_region_filter(distance, genomic_db,
genomic_seqs_fpath):
'''It returns a filter that removes snv in a region that give more than one
match or more than one match_parts'''
parameters = {'database': genomic_db}
blast_runner = create_runner(tool='blastn', parameters=parameters)
blast_parser = get_alignment_parser('blast')
match_filters = [{'kind' : 'score_threshold',
'score_key': 'similarity',
'min_score': 90,
},
{'kind' : 'min_length',
'min_num_residues': 20,
'length_in_query' : True
}
]
if not genomic_seqs_fpath:
msg = 'No genomic sequence file defined for unique SNV filter'
raise ValueError(msg)
if not genomic_db:
msg = 'No genomic blast database defined for unique SNV filter'
raise ValueError(msg)
genomic_seqs_fhand = open(genomic_seqs_fpath)
genomic_seqs_index = SeqIO.index(genomic_seqs_fhand.name,
guess_seq_file_format(genomic_seqs_fhand))
def unique_contiguous_region_filter(sequence):
'''It filters out the snv in regions repeated in the genome or
discontiguous'''
if sequence is None:
return None
for snv in sequence.get_features(kind='snv'):
# Check if it is already done
previous_result = _get_filter_result(snv, 'uniq_contiguous',
threshold=distance)
if previous_result is not None:
continue
#we make a blast
#with the sequence around the snv
location = snv.location.start.position
start = location - distance
end = location + distance
if start < 0:
start = 0
#print start, end
seq_fragment = sequence[start:end]
blast_fhand = blast_runner(seq_fragment)['blastn']
#now we parse the blast
blast_result = blast_parser(blast_fhand)
alignments = filter_alignments(blast_result, config=match_filters)
#are there any similar sequences?
try:
alignment = alignments.next()
result = True
except StopIteration:
#if there is no similar sequence we assume that is unique
result = False
if result:
#how many matches, it should be only one
num_hits = len(alignment['matches'])
if num_hits > 1:
result = True
else:
#how many match parts have the first match?
#we could do it with the blast result, but blast is not very
#good aligning, so we realign with est2genome
blast_fhand.seek(0)
sim_seqs = similar_sequences_for_blast(blast_fhand)
sim_seq = sim_seqs[0] if sim_seqs else None
introns = infer_introns_for_cdna(sequence=seq_fragment,
genomic_seqs_index=genomic_seqs_index,
similar_sequence=sim_seq,
genomic_db=genomic_db)
if introns:
result = True
else:
result = False
blast_fhand.close()
_add_filter_result(snv, 'uniq_contiguous', result, distance)
return sequence
return unique_contiguous_region_filter
