def test04(self):
to_prune = filter_sequences(self.tax_id,
distmat=self.distmat,
taxa=self.taxa,
strategy='cluster',
cutoff=0.015)
self.assertEqual(sum(to_prune['is_out']), 5)
def test05(self):
to_prune = filter_sequences(self.tax_id,
distmat=self.distmat,
taxa=self.taxa,
strategy='cluster',
percentile=90)
self.assertEqual(sum(to_prune['is_out']), 0)
def test03(self):
to_prune = filter_sequences(self.tax_id,
sequence_file=self.fa,
strategy='radius',
cutoff=0.015,
aligner='muscle')
self.assertEqual(sum(to_prune['is_out']), 5)
def test06(self):
to_prune = filter_sequences(
self.tax_id, distmat=self.distmat, taxa=self.taxa, strategy="cluster", percentile=90, min_radius=0.1
)
self.assertEqual(sum(to_prune["is_out"]), 0)
def test04(self):
to_prune = filter_sequences(self.tax_id, distmat=self.distmat, taxa=self.taxa, strategy="cluster", cutoff=0.015)
self.assertEqual(sum(to_prune["is_out"]), 5)
def test03(self):
to_prune = filter_sequences(
self.tax_id, sequence_file=self.fa, strategy="radius", cutoff=0.015, aligner="muscle"
)
self.assertEqual(sum(to_prune["is_out"]), 5)
def main():
args_parser = argparse.ArgumentParser()
args_parser.add_argument('genus_dir')
args_parser.add_argument('--minimum_length', '-m', default=1000)
args_parser.add_argument('--percentile', '-p', default=90)
args_parser.add_argument('--check_min_n', '-cm', default=3)
args = args_parser.parse_args()
minimum_length = int(args.minimum_length)
# 1. Get all the files in this genus's directory, filtering for FASTA files.
genus_fasta = [
fn for fn in os.listdir(args.genus_dir) if '.fasta' in fn.lower()
]
"""
2. We should make an effort to filter out dubiously named files here. Things that do not start with the genus name and look like crap
Also need to treat the grab-bag Genus sp or Genus spp file that is likely made up of a mix of novel species.
"""
# 3. Open each file
for fn in genus_fasta:
print fn
sp_sr = SeqIO.parse(args.genus_dir + "/" + fn, 'fasta')
# An associative dict for sequences that past muster
species_sr = {}
# 4. And loop through it's records
for sr in sp_sr:
# i. Check to see if the sequence length is over some goal minimum
if len(sr.seq) < minimum_length:
print sr.id, "Does not meet minimum length at ", len(sr.seq)
continue
# ii. Check for ambiguous bases. Don't bother if there are
if not no_ambiguous_bases(sr.seq):
print sr.id, "Has ambiguous bases"
continue
# This assignment also takes care of multiple sequences with the same ID
# The last sequence of sufficient quality of this ID in the file is the one that is kept,
species_sr[sr.id] = sr
# 5. See how many sequences made the cut at this point. If less than one, we can delete the fasta file and move on.
if len(species_sr) < 1:
print "Removing", fn
os.remove(args.genus_dir + "/" + fn)
continue
# 6. Use the deenurp package to cluster and determine if there are outlier sequences
if len(species_sr) >= int(args.check_min_n):
with tempfile.NamedTemporaryFile() as filtered_tf:
for seq_id in species_sr:
SeqIO.write(species_sr[seq_id], filtered_tf, 'fasta')
filtered_tf.flush()
filtered_df = filter_outliers.filter_sequences(
'',
sequence_file=filtered_tf.name,
taxa=fn,
strategy='cluster',
#percentile=args.percentile,
percentile=90,
min_radius=0.0,
max_radius=None,
cluster_type='single',
aligner='cmalign',
executable=None,
)
filtered_tf.close()
not_outliers = filtered_df[filtered_df.is_out == False].seqname
else:
not_outliers = species_sr.keys()
# Again, if nobody made the cut, delete the file
if len(not_outliers) < 1:
print "Removing", fn
os.remove(args.genus_dir + "/" + fn)
continue
# Now save back to the file, removing what was there before
with open(args.genus_dir + "/" + fn, 'w') as species_f:
for seq_id in not_outliers:
SeqIO.write(species_sr[seq_id], species_f, 'fasta')
species_f.close()
