def maskSequences(self, sequences):
'''mask a collection of sequences.'''
with tempfile.NamedTemporaryFile(mode="w+t", delete=False) as outf:
for x, s in enumerate(sequences):
outf.write(">%i\n%s\n" % (x, s))
infile = outf.name
statement = self.mCommand % locals()
E.debug("statement: %s" % statement)
s = subprocess.Popen(statement,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
close_fds=True)
(out, err) = s.communicate()
if s.returncode != 0:
raise RuntimeError(
"Error in running %s \n%s\nTemporary directory" %
(statement, err))
result = [
x.sequence for x in FastaIterator.iterate(StringIO(out.decode()))]
os.remove(infile)
return result
def buildInputFiles(infile, outfiles):
'''
build input file based on parameters and fasta sequences
that primers are to be designed for
'''
PARAMS["constraints_primer_mispriming_library"] = glob.glob("mispriming.dir/*.lib")[0]
primer_thermodynamics_parameters_path = PARAMS["general_primer_thermodynamics_parameters_path"]
fasta, identifiers = infile[0], "identifiers.tsv"
inf = IOTools.open_file(fasta)
E.info("Reading ids for primer design")
ids = readIdentifiers(identifiers)
E.info("collecting sequences")
for f in FastaIterator.iterate(IOTools.open_file(fasta)):
if f.title in ids:
outf = IOTools.open_file(os.path.join(
"input.dir",f.title.replace(" ", "_").replace("/","_") + ".input").replace('"', ''), "w")
seq = f.sequence
outf.write("SEQUENCE_ID=%s\n" % f.title)
for key, value in PARAMS.items():
if "constraints" in key:
outf.write("%s=%s\n" % (key.replace("constraints_", "").upper(), value))
outf.write("SEQUENCE_TEMPLATE=%s\n" % seq)
outf.write("PRIMER_THERMODYNAMIC_PARAMETERS_PATH=%s\n=\n" % primer_thermodynamics_parameters_path)
outf.close()
def calculateSequenceComposition(interval_names,
sequence_file,
outfile,
header_line=True):
'''
given a set of interval names that are present in a
fasta file, return CpG content file
'''
interval_file = open(interval_names)
if header_line:
interval_file.readline()
sequence_file = open(sequence_file)
interval_set = set()
for line in interval_file.readlines():
interval_set.add(line[:-1])
temp = P.getTempFile("/ifs/scratch")
for record in FastaIterator.iterate(sequence_file):
seq_id = record.title.split(" ")[0]
if seq_id in interval_set:
temp.write(">%s\n%s\n" % (record.title, record.sequence))
temp.close()
inf = temp.name
statement = '''
cat %(inf)s | cgat fasta2table
-s na -s cpg -s length
--log=%(outfile)s.log > %(outfile)s'''
P.run()
def filterByCoverage(infiles, outfile):
fcoverage = PARAMS["coverage_filter"]
contig_file = infiles[0]
dbh = sqlite3.connect(
os.path.join(PARAMS["results_resultsdir"], PARAMS["database_name"]))
cc = dbh.cursor()
contigs = set()
for infile in infiles[1:]:
dirsplit = infile.split("/")
infile = os.path.join(
PARAMS["results_resultsdir"],
dirsplit[-2].split(".dir")[0] + "-" + dirsplit[-1])
tablename = P.toTable(os.path.basename(infile))
if P.snip(contig_file, ".fa") == P.snip(os.path.basename(infile),
".coverage.load"):
statement = """SELECT contig_id ave FROM
(SELECT contig_id, AVG(coverage) as ave FROM %s GROUP BY contig_id)
WHERE ave > %i""" % (tablename,
PARAMS["coverage_filter"])
for data in cc.execute(statement).fetchall():
contigs.add(data[0])
outf = open(outfile, "w")
print(contigs)
for fasta in FastaIterator.iterate(iotools.openFile(contig_file)):
identifier = fasta.title.split(" ")[0]
if identifier in contigs:
outf.write(">%s\n%s\n" % (identifier, fasta.sequence))
outf.close()
def RenameFastaTitle(fastafile, file2tax, outfile):
taxonomy = file2tax[fastafile]
suffix=1
for fasta in fastaiterator.iterate(iotools.open_file(fastafile)):
suffix_str = "(" + str(suffix) + ")"
new_title = taxonomy + suffix_str
suffix += 1
outfile.write(">" + new_title + "\n" + fasta.sequence + "\n")
def specformatter(Infile, Outfile):
infile = iotools.open_file(Infile)
fastas = fastaiterator.iterate(infile)
outfile = open(Outfile, "w")
for fasta in fastas:
name = fasta.title.split(";")[6]
specID = name.split("(")[1]
specID = specID[:-1]
genusspecies = name.split("(")[0]
genus = genusspecies.split("_")[0]
species = genusspecies.split("_")[1]
newtitle = " ".join([specID, genus, species])
outfile.write(">" + newtitle + "\n" + fasta.sequence + "\n")
def buildMisprimingLib(infiles, outfile):
'''
build fasta file of sequences to check for mispriming
'''
fasta, identifiers = infiles
inf = IOTools.open_file(fasta)
E.info("reading ids for sequences to keep")
ids = readIdentifiers(identifiers)
outf = IOTools.open_file(outfile, "w")
E.info("collecting sequences")
for f in FastaIterator.iterate(IOTools.open_file(fasta)):
if f.title not in ids:
outf.write(">%s\n%s\n" % (f.title, f.sequence))
outf.close()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if not argv:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-d",
"--outputdir",
dest="outdir",
type="string",
help="output directory to save plots")
parser.add_option("-f",
"--fasta",
dest="fasta_file",
type="string",
help="fasta file containing tRNA cluster fasta seqs")
parser.set_defaults(fasta_file=None, outdir=None)
(options, args) = E.start(parser, argv=argv)
if len(args) == 0:
args.append("-")
E.info(options.stdin)
dict_trna = {}
for record in FastaIterator.iterate(IOTools.open_file(options.fasta_file)):
title = record.title.strip("-")
length = len(record.sequence)
dict_trna[title] = length
# For each read in bamfile find end position and then plot this using length of tRNA cluster
samfile = pysam.AlignmentFile(options.stdin.name, "rb")
refname = ""
values = []
n = 0
for line in samfile:
if line.reference_name == refname:
if line.reference_end is None:
pass
else:
end = int(line.reference_end) - int(line.reference_start)
values.append(end)
elif line.reference_name != refname:
n += 1
if n > 1:
values = pd.Series(values)
percent = values.value_counts() / values.count() * 100
percent = percent.sort_index()
percent = pd.DataFrame(percent)
percent.rename(columns={0: 'Percent'}, inplace=True)
# length of each tRNA from fasta
length = dict_trna[refname.strip("-")] + 1
temp_df = pd.DataFrame(0,
index=range(1, length),
columns=['A'])
temp_df = pd.concat([temp_df, percent], axis=1)
percent = temp_df.fillna(0)
refname = options.outdir + refname.strip("-")
outfile = refname + ".csv"
outfig = refname + ".eps"
percent.to_csv(outfile)
g = sns.factorplot(x=percent.index,
y="Percent",
data=percent,
size=8,
kind="bar",
palette="Blues")
g.set_xlabels('position from 5\' end')
g.set_xticklabels(rotation=90)
g.savefig(outfig, format='eps')
values = []
refname = line.reference_name
else:
refname = line.reference_name
E.stop()
def main(argv=None):
if argv is None:
argv = sys.argv
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-s",
"--correct-gap-shift",
dest="correct_shift",
action="store_true",
help="correct gap length shifts in alignments. "
"Requires alignlib_lite.py [%default]")
parser.add_option(
"-1",
"--pattern1",
dest="pattern1",
type="string",
help="pattern to extract identifier from in identifiers1. "
"[%default]")
parser.add_option(
"-2",
"--pattern2",
dest="pattern2",
type="string",
help="pattern to extract identifier from in identifiers2. "
"[%default]")
parser.add_option("-o",
"--output-section",
dest="output",
type="choice",
action="append",
choices=("diff", "missed", "seqdiff"),
help="what to output [%default]")
parser.set_defaults(correct_shift=False,
pattern1="(\S+)",
pattern2="(\S+)",
output=[])
(options, args) = E.start(parser)
if len(args) != 2:
raise ValueError("two files needed to compare.")
if options.correct_shift:
try:
import alignlib_lite
except ImportError:
raise ImportError(
"option --correct-shift requires alignlib_lite.py_ "
"but alignlib not found")
seqs1 = dict([
(x.title, x.sequence)
for x in FastaIterator.iterate(iotools.open_file(args[0], "r"))
])
seqs2 = dict([
(x.title, x.sequence)
for x in FastaIterator.iterate(iotools.open_file(args[1], "r"))
])
if not seqs1:
raise ValueError("first file %s is empty." % (args[0]))
if not seqs2:
raise ValueError("second file %s is empty." % (args[1]))
MapIdentifiers(seqs1, options.pattern1)
MapIdentifiers(seqs2, options.pattern2)
nsame = 0
nmissed1 = 0
nmissed2 = 0
ndiff = 0
ndiff_first = 0
ndiff_last = 0
ndiff_prefix = 0
ndiff_selenocysteine = 0
ndiff_masked = 0
nfixed = 0
found2 = {}
write_missed1 = "missed" in options.output
write_missed2 = "missed" in options.output
write_seqdiff = "seqdiff" in options.output
write_diff = "diff" in options.output or write_seqdiff
for k in sorted(seqs1):
if k not in seqs2:
nmissed1 += 1
if write_missed1:
options.stdout.write("---- %s ---- %s\n" % (k, "missed1"))
continue
found2[k] = 1
s1 = seqs1[k].upper()
s2 = seqs2[k].upper()
m = min(len(s1), len(s2))
if s1 == s2:
nsame += 1
else:
status = "other"
ndiff += 1
if s1[1:] == s2[1:]:
ndiff_first += 1
status = "first"
elif s1[:m] == s2[:m]:
ndiff_prefix += 1
status = "prefix"
elif s1[:-1] == s2[:-1]:
ndiff_last += 1
status = "last"
else:
if len(s1) == len(s2):
# get all differences: the first and last residues
# can be different for peptide sequences when
# comparing my translations with ensembl peptides.
differences = []
for x in range(1, len(s1) - 1):
if s1[x] != s2[x]:
differences.append((s1[x], s2[x]))
l = len(differences)
# check for Selenocysteins
if len(
[x for x in differences
if x[0] == "U" or x[1] == "U"]) == l:
ndiff_selenocysteine += 1
status = "selenocysteine"
# check for masked residues
elif len([
x for x in differences
if x[0] in "NX" or x[1] in "NX"
]) == l:
ndiff_masked += 1
status = "masked"
# correct for different gap lengths
if options.correct_shift:
map_a2b = alignlib_lite.py_makeAlignmentVector()
a, b = 0, 0
keep = False
x = 0
while x < m and not (a == len(s1) and b == len(s2)):
try:
if s1[a] != s2[b]:
while s1[a] == "N" and s2[b] != "N":
a += 1
while s1[a] != "N" and s2[b] == "N":
b += 1
if s1[a] != s2[b]:
break
except IndexError:
print(
"# index error for %s: x=%i, a=%i, b=%i, l1=%i, l2=%i"
% (k, x, a, b, len(s1), len(s2)))
break
a += 1
b += 1
map_a2b.addPairExplicit(a, b, 0.0)
# check if we have reached the end:
else:
keep = True
nfixed += 1
f = alignlib_lite.py_AlignmentFormatEmissions(map_a2b)
print("fix\t%s\t%s" % (k, str(f)))
if not keep:
print("# warning: not fixable: %s" % k)
if write_diff:
options.stdout.write("---- %s ---- %s\n" % (k, status))
if write_seqdiff:
options.stdout.write("< %s\n> %s\n" % (seqs1[k], seqs2[k]))
for k in sorted(list(seqs2.keys())):
if k not in found2:
nmissed2 += 1
if write_missed2:
options.stdout.write("---- %s ---- %s\n" % (k, "missed2"))
options.stdlog.write("""# Legend:
""")
E.info("seqs1=%i, seqs2=%i, same=%i, ndiff=%i, nmissed1=%i, nmissed2=%i" %
(len(seqs1), len(seqs2), nsame, ndiff, nmissed1, nmissed2))
E.info(
"ndiff=%i: first=%i, last=%i, prefix=%i, selenocysteine=%i, masked=%i, fixed=%i, other=%i"
% (ndiff, ndiff_first, ndiff_last, ndiff_prefix, ndiff_selenocysteine,
ndiff_masked, nfixed, ndiff - ndiff_first - ndiff_last -
ndiff_prefix - ndiff_selenocysteine - ndiff_masked - nfixed))
E.stop()
twoA = 0
twoG = 0
twoT = 0
twoC = 0
twoO = 0
thrA = 0
thrG = 0
thrT = 0
thrC = 0
thrO = 0
i = 0
###Iterate over and change titles if they are matched.
for transcript in FastaIterator.iterate(infile):
if transcript.sequence[0] == "A":
oneA += 1
elif transcript.sequence[0] == "G":
oneG += 1
elif transcript.sequence[0] == "T":
oneT += 1
elif transcript.sequence[0] == "C":
oneC += 1
else:
oneO += 1
if transcript.sequence[1] == "A":
twoA += 1
elif transcript.sequence[1] == "G":
twoG += 1
def main(argv=None):
parser = E.ArgumentParser(description=__doc__)
parser.add_argument("--version", action='version', version="1.0")
parser.add_argument("-c", "--is-cds", dest="is_cds", action="store_true",
help="input are cds (nucleotide) sequences ")
parser.set_defaults(
is_cds=False,
)
(args) = E.start(parser, argv=argv)
args.stdout.write(
"snpid\tidentifier\tpos\treference\tvariant\tcounts\tweight\n")
alphabet = "ACDEFGHIKLMNPQRSTVWY"
snpid = 0
for entry in FastaIterator.iterate(args.stdin):
identifier = entry.title
if args.is_cds:
cds_sequence = entry.sequence.upper()
assert len(cds_sequence) % 3 == 0, \
"length of sequence '%s' is not a multiple of 3" % entry.title
sequence = Genomics.translate(cds_sequence)
weights = []
for pos, cds_pos in enumerate(range(0, len(cds_sequence), 3)):
codon = cds_sequence[cds_pos:cds_pos + 3]
counts = collections.defaultdict(int)
for x in range(0, 3):
rna = codon[x]
for na in "ACGT":
if na == rna:
continue
taa = Genomics.translate(
codon[:x] + na + codon[x + 1:])
counts[taa] += 1
weights.append(counts)
else:
sequence = entry.sequence.upper()
counts = {}
for x in alphabet:
counts[x] = 1
weights = [counts] * len(sequence)
for pos, ref in enumerate(sequence):
if ref not in alphabet:
continue
w = weights[pos]
t = float(sum(w.values()))
for variant in alphabet:
if variant == ref:
continue
snpid += 1
args.stdout.write(
"%s\n" % "\t".join(
("%010i" % snpid,
identifier,
str(pos + 1),
ref,
variant,
"%i" % w[variant],
"%6.4f" % (w[variant] / t),
)))
E.stop()
def findTATABox(infiles, outfile):
'''find TATA box in promotors. There are several matrices to choose from:
M00216 V$TATA_C Retroviral TATA box
M00252 V$TATA_01 cellular and viral TATA box elements
M00311 V$ATATA_B Avian C-type TATA box
M00320 V$MTATA_B Muscle TATA box
'''
# 1. create fasta file - look for TATA box
#
bedfile, genomefile = infiles
statement = '''
slopBed -i %(bedfile)s
-l %(tata_search_upstream)i
-r %(tata_search_downstream)i
-s
-g %(genomefile)s
| cgat bed2fasta
--use-strand
--genome=%(genome_dir)s/%(genome)s
--log=%(outfile)s.log
> %(outfile)s.fasta
'''
P.run()
match_executable = '/ifs/data/biobase/transfac/match/bin/match_linux64'
match_matrix = '/ifs/data/biobase/transfac/dat/matrix.dat'
match_profile = 'minFP_good.prf'
match_profile = outfile + ".prf"
prf = '''tata.prf
prf to minimize sum of both errors - derived from minSUM.prf
MIN_LENGTH 300
0.0
1.000 0.716 0.780 M00216 V$TATA_C
1.000 0.738 0.856 M00252 V$TATA_01
1.000 0.717 0.934 M00311 V$ATATA_B
1.000 0.711 0.784 M00320 V$MTATA_B
//
'''
with iotools.openFile(match_profile, "w") as outf:
outf.write(prf)
# -u : uniq - only one best match per sequence
statement = '''
%(match_executable)s
%(match_matrix)s
%(outfile)s.fasta
%(outfile)s.match
%(match_profile)s
-u
>> %(outfile)s.log
'''
P.run()
transcript2pos = {}
for entry in FastaIterator.iterate(iotools.openFile(outfile + ".fasta")):
transcript_id, contig, start, end, strand = re.match(
"(\S+)\s+(\S+):(\d+)..(\d+)\s+\((\S)\)", entry.title).groups()
transcript2pos[transcript_id] = (contig, int(start), int(end), strand)
MATCH = collections.namedtuple(
"MATCH",
"pid transfac_id pos strand core_similarity matrix_similarity sequence"
)
def _grouper(infile):
r = []
keep = False
for line in infile:
if line.startswith("Inspecting sequence ID"):
keep = True
if r:
yield pid, r
r = []
pid = re.match("Inspecting sequence ID\s+(\S+)",
line).groups()[0]
continue
elif line.startswith(" Total"):
break
if not keep:
continue
if line[:-1].strip() == "":
continue
transfac_id, v, core_similarity, matrix_similarity, sequence = [
x.strip() for x in line[:-1].split("|")
]
pos, strand = re.match("(\d+) \((\S)\)", v).groups()
r.append(
MATCH._make((pid, transfac_id, int(pos), strand,
float(core_similarity), float(matrix_similarity),
sequence)))
yield pid, r
offset = PARAMS["tata_search_upstream"]
outf = iotools.openFile(outfile + ".table.gz", "w")
outf.write("\t".join(("transcript_id", "strand", "start", "end",
"relative_start", "relative_end", "transfac_id",
"core_similarity", "matrix_similarity",
"sequence")) + "\n")
bedf = iotools.openFile(outfile, "w")
c = E.Counter()
found = set()
for transcript_id, matches in _grouper(iotools.openFile(outfile +
".match")):
contig, seq_start, seq_end, strand = transcript2pos[transcript_id]
c.promotor_with_matches += 1
nmatches = 0
found.add(transcript_id)
for match in matches:
c.matches_total += 1
lmatch = len(match.sequence)
if match.strand == "-":
c.matches_wrong_strand += 1
continue
# get genomic location of match
if strand == "+":
genome_start = seq_start + match.pos
else:
genome_start = seq_end - match.pos - lmatch
genome_end = genome_start + lmatch
# get relative location of match
if strand == "+":
tss_start = seq_start + offset
relative_start = genome_start - tss_start
else:
tss_start = seq_end - offset
relative_start = tss_start - genome_end
relative_end = relative_start + lmatch
outf.write("\t".join(
map(str, (transcript_id, strand, genome_start, genome_end,
relative_start, relative_end, match.transfac_id,
match.core_similarity, match.matrix_similarity,
match.sequence))) + "\n")
c.matches_output += 1
nmatches += 1
bedf.write("\t".join(
map(str, (contig, genome_start, genome_end, transcript_id,
strand, match.matrix_similarity))) + "\n")
if nmatches == 0:
c.promotor_filtered += 1
else:
c.promotor_output += 1
c.promotor_total = len(transcript2pos)
c.promotor_without_matches = len(
set(transcript2pos.keys()).difference(found))
outf.close()
bedf.close()
with iotools.openFile(outfile + ".summary", "w") as outf:
outf.write("category\tcounts\n")
outf.write(c.asTable() + "\n")
E.info(c)
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if not argv:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-k",
"--kmer-size",
dest="kmer",
type="int",
help="supply kmer length")
parser.add_option("-p",
"--output-proportion",
dest="proportion",
action="store_true",
help="output proportions - overides the default output")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.start(parser, argv=argv)
# do not allow greater than octonucleotide
assert options.kmer <= 8, "cannot handle kmer of length %i" % options.kmer
# how we deal with the nucleotides depends on the kmer length
nucleotides = []
for nucleotide in ["A", "C", "T", "G"]:
nucleotides = nucleotides + \
[x for x in itertools.repeat(nucleotide, options.kmer)]
E.info("retrieving %imer sequences" % options.kmer)
# get all kmer sequences to query
kmers = set()
for kmer in itertools.permutations(nucleotides, options.kmer):
kmers.add(kmer)
E.info("matching %imers in file" % options.kmer)
# count the number of kmers in each sequence
result = {}
# NB assume that non fasta files are caught by FastaIterator
total_entries = 0
for fasta in FastaIterator.iterate(options.stdin):
total_entries += 1
result[fasta.title] = {}
for kmer in kmers:
counts = [
m.start() for m in re.finditer("".join(kmer), fasta.sequence)
]
result[fasta.title][kmer] = len(counts)
E.info("writing results")
# write out the results
headers = sorted(result.keys())
rows = set()
for kmer_counts in list(result.values()):
for kmer, count in kmer_counts.items():
rows.add("".join(kmer))
# write header row
options.stdout.write("kmer\t" + "\t".join(headers) + "\n")
# output proportions if required - normalises by
# sequence length
E.info("computing total counts")
totals = {}
for header in headers:
totals[header] = sum([result[header][tuple(row)] for row in rows])
for row in sorted(rows):
if options.proportion:
options.stdout.write("\t".join([row] + [
str(float(result[header][tuple(row)]) / totals[header])
for header in headers
]) + "\n")
else:
options.stdout.write("\t".join(
[row] +
[str(result[header][tuple(row)])
for header in headers]) + "\n")
E.info("written kmer counts for %i contigs" % total_entries)
# write footer and output benchmark information.
E.stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version=
"%prog version: $Id: split_fasta.py 1714 2007-12-11 16:51:12Z andreas $"
)
parser.add_option("-f",
"--file",
dest="input_filename",
type="string",
help="input filename. If not given, stdin is used.",
metavar="FILE")
parser.add_option(
"-i",
"--input-pattern",
dest="input_pattern",
type="string",
help="input pattern. Parses description line in order to extract id.")
parser.add_option(
"-o",
"--output-filename-pattern",
dest="output_pattern",
type="string",
help="output pattern. Gives filename for a given sequence.")
parser.add_option(
"-n",
"--num-sequences",
dest="num_sequences",
type="int",
help="split by number of sequences (not implemented yet).")
parser.add_option("-m",
"--map",
dest="map_filename",
type="string",
help="map filename. Map identifiers to filenames",
metavar="FILE")
parser.add_option("-s",
"--skip-identifiers",
dest="skip_identifiers",
action="store_true",
help="do not write identifiers.",
metavar="FILE")
parser.add_option("--min-size",
dest="min_size",
type="int",
help="minimum cluster size.")
parser.set_defaults(input_filename=None,
map_filename=None,
skip_identifiers=False,
input_pattern="^(\S+)",
min_size=0,
num_sequences=None,
output_pattern="%s")
(options, args) = E.start(parser)
if options.input_filename:
infile = iotools.open_file(options.input_filename, "r")
else:
infile = sys.stdin
if options.map_filename:
map_id2filename = iotools.ReadMap(open(options.map_filename, "r"))
else:
map_id2filename = {}
if options.num_sequences:
files = FilesChunks(chunk_size=options.num_sequences,
output_pattern=options.output_pattern,
skip_identifiers=options.skip_identifiers)
else:
files = Files(output_pattern=options.output_pattern,
skip_identifiers=options.skip_identifiers)
if options.input_pattern:
rx = re.compile(options.input_pattern)
else:
rx = None
ninput = 0
noutput = 0
identifier = None
chunk = 0
for seq in FastaIterator.iterate(infile):
ninput += 1
if rx:
try:
identifier = rx.search(seq.title).groups()[0]
except AttributeError:
print("# parsing error in description line %s" % (seq.title))
else:
identifier = seq.title
if map_id2filename:
if identifier in map_id2filename:
identifier = map_id2filename[identifier]
else:
continue
files.Write(identifier, seq)
noutput += 1
if options.input_filename:
infile.close()
# delete all clusters below a minimum size
# Note: this has to be done at the end, because
# clusters sizes are only available once both the fasta
# file and the map has been parsed.
if options.min_size:
ndeleted = files.DeleteFiles(min_size=options.min_size)
else:
ndeleted = 0
if options.loglevel >= 1:
print("# input=%i, output=%i, ndeleted=%i" %
(ninput, noutput, ndeleted))
E.stop()