def generatePSP(positives, negatives, outfile):
''' generate a discrimitative PSP file from
the positives and negatives that can be used
to do descriminative MEME '''
psp_options = PARAMS["psp_options"]
nseqs_pos = int(FastaIterator.count(positives))
nseqs_neg = int(FastaIterator.count(negatives))
if nseqs_pos < 2 or nseqs_neg < 2:
E.warn("%s: input files do not have sufficent sequences"
"to run psp-gen, skipping" % outfile)
P.touch(outfile)
return
# get appropriate options from meme options
if PARAMS.get("meme_revcomp", True):
psp_options += " -revcomp"
statement = '''psp-gen -pos %(positives)s
-neg %(negatives)s
%(psp_options)s
> %(outfile)s '''
P.run(statement)
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 | python %(scriptsdir)s/fasta2table.py
-s cpg -s length
--log=%(outfile)s.log > %(outfile)s'''
P.run()
def maskSequences(self, sequences):
'''mask a collection of sequences.'''
outfile, infile = tempfile.mkstemp()
for x, s in enumerate(sequences):
os.write(outfile, ">%i\n%s\n" % (x, s))
os.close(outfile)
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.StringIO(out))]
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]
fasta, identifiers = infile[0], "identifiers.tsv"
inf = IOTools.openFile(fasta)
E.info("Reading ids for primer design")
ids = readIdentifiers(identifiers)
E.info("collecting sequences")
for f in FastaIterator.iterate(IOTools.openFile(fasta)):
if f.title in ids:
outf = IOTools.openFile(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.iteritems():
if "constraints" in key:
outf.write("%s=%s\n" % (key.replace("constraints_", "").upper(), value))
outf.write("SEQUENCE_TEMPLATE=%s\n=\n" % seq)
outf.close()
def segmentWithCpG(infile, with_contig_sizes=False):
'''segment a fasta file, output locations of CpG.'''
ninput, nskipped, noutput = 0, 0, 0
iterator = FastaIterator.FastaIterator(infile)
segments, contig_sizes = [], collections.OrderedDict()
for cur_record in iterator:
ninput += 1
contig = re.sub("\s.*", "", cur_record.title)
last = None
contig_sizes[contig] = (0, len(cur_record.sequence))
for pos, this in enumerate(cur_record.sequence.upper()):
if last == "C" and this == "G":
segments.append((contig, pos - 1, pos + 1, 1.0))
last = this
E.info("ninput=%i, noutput=%i, nskipped=%i" % (ninput, noutput, nskipped))
if with_contig_sizes:
return segments, contig_sizes
return segments
def countMotifs(infile, motifs):
'''find regular expression *motifs* in
sequences within fasta formatted *infile*.
'''
it = FastaIterator.FastaIterator(infile)
positions = []
while 1:
try:
seq = next(it)
except StopIteration:
break
if not seq:
break
rseq = Genomics.complement(seq.sequence)
lsequence = len(seq.sequence)
pos = []
for motif, pattern in motifs:
for x in pattern.finditer(seq.sequence):
pos.append((motif, "+", x.start(), x.end()))
for x in pattern.finditer(rseq):
pos.append(
(motif, "-", lsequence - x.end(), lsequence - x.start()))
positions.append((seq.title, pos))
return positions
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 = optparse.OptionParser(version="%prog version: $Id: script_template.py 2871 2010-03-03 10:20:44Z andreas $",
usage=globals()["__doc__"])
parser.add_option("-b", "--bamfile", dest="bamfile", type="string",
help="supply bam file")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# read in contigs
E.info("reading in contig file")
contigs = {}
for fasta in FastaIterator.iterate(options.stdin):
contigs[fasta.title] = (1, len(fasta.sequence) - 1)
E.info("read %i contigs" % len(contigs.keys()))
# read in bamfile
E.info("reading bam file")
samfile = pysam.Samfile(options.bamfile)
E.info("iterating over contigs")
c = 0
for contig, coords in contigs.iteritems():
coords = list(coords)
#################################
# NB this is specific for my data!
contig = contig.split(" ")[0]
#################################
species_counts = collections.defaultdict(int)
for alignment in samfile.fetch(contig, coords[0], coords[1]):
species_id = alignment.qname.split("|")[1]
species_counts[species_id] += 1
# at the moment ignore if there are no counts
if len(species_counts.values()) == 0:
E.warn("no reads map to %s" % contig)
continue
for species, count in species_counts.iteritems():
if species_counts[species] == max(species_counts.values()):
top_dog = species
c += 1
break
E.info("species %s assigned to contig number %i" % (top_dog, c))
options.stdout.write("%s\t%s\n" % (contig, top_dog))
# write footer and output benchmark information.
E.Stop()
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 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 maskSequences(self, sequences):
'''mask a collection of sequences.'''
outfile, infile = tempfile.mkstemp()
for x, s in enumerate(sequences):
os.write(outfile, ">%i\n%s\n" % (x, s))
os.close(outfile)
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))]
os.remove(infile)
return result
def countCompleteGenes(infile, outfile):
'''
count the number of genes that are classed
as complete based on having a start and stop codon
'''
start = "ATG"
stop = ["TAG", "TAA", "TGA"]
ntotal = 0
nstart = 0
nstop = 0
nstart_nstop = 0
for fasta in FastaIterator.iterate(IOTools.openFile(infile)):
ntotal += 1
if fasta.sequence.startswith(start):
nstart += 1
if fasta.sequence[-3:len(fasta.sequence)] in stop:
nstop += 1
if fasta.sequence.startswith(
start) and fasta.sequence[-3:len(fasta.sequence)] in stop:
nstart_nstop += 1
outf = open(outfile, "w")
outf.write("total_genes\tpstart\tpstop\tpstart_stop\n")
outf.write("\t".join(
map(str, [
ntotal,
float(nstart) / ntotal,
float(nstop) / ntotal,
float(nstart_nstop) / ntotal
])) + "\n")
outf.close()
def removeContaminants(infiles, outfile):
'''remove adaptor contamination from fastq files.
This method uses cutadapt.
'''
infile, contaminant_file = infiles
adaptors = []
for entry in FastaIterator.FastaIterator(
IOTools.openFile(contaminant_file)):
adaptors.append("-a %s" % entry.sequence)
adaptors = " ".join(adaptors)
to_cluster = True
statement = '''
cutadapt
%(adaptors)s
--overlap=%(contamination_min_overlap_length)i
--format=fastq
%(contamination_options)s
<( zcat < %(infile)s )
2> %(outfile)s.log
| gzip > %(outfile)s
'''
P.run()
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 = optparse.OptionParser(version="%prog version: $Id: script_template.py 2871 2010-03-03 10:20:44Z andreas $",
usage=globals()["__doc__"])
parser.add_option("-g", "--genome-dir", dest="genome_dir", type="string",
help="supply help")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
contigs_map = {}
for genome in glob.glob(os.path.join(options.genome_dir, "*")):
for fasta in FastaIterator.iterate(IOTools.openFile(genome)):
identifier = fasta.title.split("|")
gi = identifier[1]
contigs_map[gi] = fasta.title
for line in options.stdin.readlines():
data = line[:-1].split("\t")
gi = data[1]
assert gi in contigs_map, "cannot find genome with id gi|%s in genomes directory" % gi
options.stdout.write("%s\t%s\n" % (data[0], contigs_map[gi]))
# write footer and output benchmark information.
E.Stop()
def filterByCoverage(infiles, outfile):
fcoverage = PARAMS["coverage_filter"]
contig_file = infiles[0]
dbh = sqlite3.connect(
os.path.join(PARAMS["results_resultsdir"], PARAMS["database"]))
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 filterByCoverage(infiles, outfile):
fcoverage = PARAMS["coverage_filter"]
contig_file = infiles[0]
dbh = sqlite3.connect(
os.path.join(PARAMS["results_resultsdir"], PARAMS["database"]))
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 createOTUs(infiles, outfile):
'''Make OTUs from the template sequences of all samples that passed
filtering'''
fasta_files = []
for infile in infiles:
fasta_file = P.snip(infile, '_true_snps.tsv', strip_path=True)\
+ '_template.fasta'
fasta_file = os.path.join('05_mapped_fastas.dir', fasta_file)
assert os.path.exists(fasta_file)
fasta_files.append(fasta_file)
tmpf = P.getTempFilename('.')
with IOTools.openFile(tmpf, 'w') as outf:
for fasta_file in fasta_files:
for fasta in FI.FastaIterator(IOTools.openFile(fasta_file)):
outf.write('>' + fasta.title + ';size=1\n' + fasta.sequence +
'\n')
out_up = P.snip(outfile, '.fasta') + '_up.txt'
statement = ("usearch"
" -cluster_otus %(tmpf)s"
" -otus %(outfile)s"
" -uparseout %(out_up)s"
" -otu_radius_pct 1.0"
" -uparse_break -999"
" -relabel OTU_"
" &> %(outfile)s.log")
to_cluster = False
P.run()
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]
fasta, identifiers = infile[0], "identifiers.tsv"
inf = IOTools.openFile(fasta)
E.info("Reading ids for primer design")
ids = readIdentifiers(identifiers)
E.info("collecting sequences")
for f in FastaIterator.iterate(IOTools.openFile(fasta)):
if f.title in ids:
outf = IOTools.openFile(
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.iteritems():
if "constraints" in key:
outf.write(
"%s=%s\n" %
(key.replace("constraints_", "").upper(), value))
outf.write("SEQUENCE_TEMPLATE=%s\n=\n" % seq)
outf.close()
def segmentUngapped(infile, gap_char, min_gap_size=0):
iterator = FastaIterator.FastaIterator(infile)
while 1:
try:
cur_record = iterator.next()
except StopIteration:
break
if cur_record is None:
break
contig = re.sub("\s.*", "", cur_record.title)
size = len(cur_record.sequence)
last_end = 0
for start, end in gapped_regions(cur_record.sequence, gap_char):
if end - start < min_gap_size:
continue
if last_end != 0:
yield(contig, last_end, start, 0)
last_end = end
if last_end < size:
yield(contig, last_end, size, 0)
def listAdaptors(infile):
adaptors = []
for entry in FastaIterator.FastaIterator(IOTools.openFile(infile)):
adaptors.append("%s %s" %
(PARAMS["contamination_trim_type"], entry.sequence))
adaptors = " ".join(adaptors)
return adaptors
def iterate_double_fasta ( fn1, fn2 ):
iterator = FastaIterator.iterate_together( fn1, fn2 )
for seq1, seq2 in iterator:
yield AlignedPairs.UnalignedPair(
token1 = seq1.title,
sequence1 = seq1.sequence,
token2 = seq2.title,
sequence2 = seq2.sequence )
def main( argv = None ):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv == None: argv = sys.argv
# setup command line parser
parser = E.OptionParser( version = "%prog version: $Id$",
usage = globals()["__doc__"] )
parser.add_option("-n", dest="N", type="int",
help="e.g N50 - the length at which 50% of contigs are equal or above")
parser.add_option("-f", "--filter-length", dest="filter_length", type="int",
help="calculate stats on contigs longer than -f")
parser.set_defaults(N = 50,
filter_length = 0)
## add common options (-h/--help, ...) and parse command line
(options, args) = E.Start( parser, argv = argv )
f = options.filter_length
# iterate over the contigs/scaffolds and return stats
number_of_contigs = 0
N = options.N
contig_lengths = []
for record in FastaIterator.iterate(options.stdin):
contig_length = len(list(record.sequence))
if contig_length >= f:
number_of_contigs += 1
contig_lengths.append(contig_length)
# mean, median and max contig/scaffold lengths
mean_length = np.mean(contig_lengths)
median_length = np.median(contig_lengths)
max_length = max(contig_lengths)
# iterate over contigs/scaffolds sorted by longest
# and caculate the NX
index = 0
cum_length = 0
total_length = sum(contig_lengths)
for length in sorted(contig_lengths, reverse = True):
while cum_length <= total_length*(float(N)/100):
index += 1
cum_length += length
# output the results
options.stdout.write("nscaffolds\tscaffold_length\tN%i\tmedian_length\tmean_length\tmax_length\n" % N)
options.stdout.write("%s\t%s\t%s\t%s\t%s\t%s\n" % (number_of_contigs, total_length, sorted(contig_lengths, reverse = True)[index], str(median_length), str(mean_length), str(max_length)))
## write footer and output benchmark information.
E.Stop()
def iterate_single_fasta ( fn1 ):
iterator = FastaIterator.FastaIterator( fn1 )
while 1:
seq1, seq2 = iterator.next(), iterator.next()
yield AlignedPairs.UnalignedPair(
token1 = seq1.title,
sequence1 = seq1.sequence,
token2 = seq2.title,
sequence2 = seq2.sequence )
def filterContigs(infile, outfile, length):
'''
filter contigs by length
'''
outf = open(outfile, "w")
for fasta in FastaIterator.iterate(IOTools.openFile(infile)):
seq_length = len(fasta.sequence)
if seq_length < length: continue
outf.write(">%s\n%s\n" % (fasta.title, fasta.sequence))
outf.close()
def filterContigs(infile, outfile, length):
'''
filter contigs by length
'''
outf = open(outfile, "w")
for fasta in FastaIterator.iterate(IOTools.openFile(infile)):
seq_length = len(fasta.sequence)
if seq_length < length: continue
outf.write(">%s\n%s\n" % (fasta.title, fasta.sequence))
outf.close()
def extract16SSequences(infile, outfile):
'''Extract any RNA fasta entry with the term 16S in the title'''
outf = IOTools.openFile(outfile, 'w')
for fasta in FastaIterator.FastaIterator(IOTools.openFile(infile)):
if re.search('16S', fasta.title, re.IGNORECASE):
outf.write('\n'.join(['>' + fasta.title, fasta.sequence]) + '\n')
outf.close()
def contig_to_stats(contigs_file, stats_file, params):
"""
calculate descriptive stats for a set
of contigs / scaffolds
"""
PARAMS = params
if PARAMS["filter"]:
f = PARAMS["filter"]
else:
f = 0
# iterate over the contigs/scaffolds and return stats
number_of_scaffolds = 0
N = PARAMS["scaffold_n"]
scaffold_lengths = []
inf = open(contigs_file)
for record in FastaIterator.iterate(inf):
scaffold_length = len(list(record.sequence))
if scaffold_length >= f:
number_of_scaffolds += 1
scaffold_lengths.append(scaffold_length)
# mean, median and max contig/scaffold lengths
mean_length = np.mean(scaffold_lengths)
median_length = np.median(scaffold_lengths)
max_length = max(scaffold_lengths)
# iterate over contigs/scaffolds sorted by longest
# and caculate the NX
index = 0
cum_length = 0
total_length = sum(scaffold_lengths)
for length in sorted(scaffold_lengths, reverse=True):
while cum_length <= total_length * (float(N) / 100):
index += 1
cum_length += length
# output the results
outf = open(stats_file, "w")
outf.write("nscaffolds\tscaffold_length\tN%i\tmedian_length\tmean_length\tmax_length\n" % N)
outf.write(
"%s\t%s\t%s\t%s\t%s\t%s\n"
% (
number_of_scaffolds,
total_length,
sorted(scaffold_lengths, reverse=True)[index],
str(median_length),
str(mean_length),
str(max_length),
)
)
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: sequence2alignment.py 2782 2009-09-10 11:40:29Z andreas $",
usage=globals()["__doc__"])
parser.set_defaults()
(options, args) = E.Start(parser)
iterator = FastaIterator.FastaIterator(sys.stdin)
ninput, noutput, nskipped = 0, 0, 0
options.stdout.write(
"query\tsbjct\tquery_from\tquery_to\tsbjct_from\tsbjct_to\tquery_starts\tsbjct_starts\tblock_sizes\n"
)
while 1:
try:
cur_record = iterator.next()
except StopIteration:
break
ninput += 1
sequence = re.sub(" ", "", cur_record.sequence)
l = len(sequence)
map_sequence2mali = alignlib_lite.py_makeAlignmentVector()
alignlib_lite.py_AlignmentFormatExplicit(0, sequence, 0, "X" *
l).copy(map_sequence2mali)
options.stdout.write("\t".join(
(cur_record.title, "ref",
str(alignlib_lite.py_AlignmentFormatBlocks(map_sequence2mali)))) +
"\n")
noutput += 1
if options.loglevel >= 1:
options.stdlog.write("# ninput=%i, noutput=%i, nskipped=%i.\n" %
(ninput, noutput, nskipped))
E.Stop()
def build_scaffold_lengths(contigs_file, outfile, params):
'''
output the distribution of scaffold lengths
'''
inf = open(contigs_file)
outf = open(outfile, "w")
outf.write("scaffold_name\tlength\n")
for record in FastaIterator.iterate(inf):
scaffold_length = len(list(record.sequence))
outf.write("%s\t%i\n" % (record.title, scaffold_length))
outf.close()
def build_scaffold_lengths(contigs_file, outfile, params):
'''
output the distribution of scaffold lengths
'''
inf = open(contigs_file)
outf = open(outfile, "w")
outf.write("scaffold_name\tlength\n")
for record in FastaIterator.iterate(inf):
scaffold_length = len(list(record.sequence))
outf.write("%s\t%i\n" % (record.title, scaffold_length))
outf.close()
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: fastas2fasta.py 2782 2009-09-10 11:40:29Z andreas $",
usage=globals()["__doc__"])
(options, args) = E.Start(parser)
if len(args) < 2:
raise ValueError(
"please supply at least two filenames to concatenate.")
iterators = []
for a in args:
iterators.append(FastaIterator.FastaIterator(IOTools.openFile(a, "r")))
ninput, noutput, nerrors = 0, 0, 0
while 1:
sequences = []
ids = []
for iterator in iterators:
try:
cur_record = iterator.next()
except StopIteration:
break
sequences.append(re.sub(" ", "", cur_record.sequence))
ids.append(cur_record.title)
if not sequences:
break
ninput += 1
if len(sequences) != len(iterators):
raise "unequal number of sequences in files."
noutput += 1
options.stdout.write(">%s\n%s\n" % (ids[0], "".join(sequences)))
E.info("ninput=%i, noutput=%i, nerrors=%i" % (ninput, noutput, nerrors))
E.Stop()
def collectGenomeSizes(infile, outfile):
'''
output the genome sizes for each genome
'''
to_cluster = True
outf = open(outfile, "w")
outf.write("genome\tlength\n")
# assume single fasta entry
for fasta in FastaIterator.iterate(IOTools.openFile(infile)):
name = P.snip(os.path.basename(infile), ".fna")
length = len(list(fasta.sequence))
outf.write("%s\t%s\n" % (name, str(length)))
outf.close()
def collectGenomeSizes(infile, outfile):
'''
output the genome sizes for each genome
'''
to_cluster = True
outf = open(outfile, "w")
outf.write("genome\tlength\n")
# assume single fasta entry
for fasta in FastaIterator.iterate(IOTools.openFile(infile)):
name = P.snip(os.path.basename(infile), ".fna")
length = len(list(fasta.sequence))
outf.write("%s\t%s\n" % (name, str(length)))
outf.close()
def contig_to_stats(contigs_file, stats_file, params):
'''
calculate descriptive stats for a set
of contigs / scaffolds
'''
PARAMS = params
if PARAMS["filter"]:
f = PARAMS["filter"]
else:
f = 0
# iterate over the contigs/scaffolds and return stats
number_of_scaffolds = 0
N = PARAMS["scaffold_n"]
scaffold_lengths = []
inf = open(contigs_file)
for record in FastaIterator.iterate(inf):
scaffold_length = len(list(record.sequence))
if scaffold_length >= f:
number_of_scaffolds += 1
scaffold_lengths.append(scaffold_length)
# mean, median and max contig/scaffold lengths
mean_length = np.mean(scaffold_lengths)
median_length = np.median(scaffold_lengths)
max_length = max(scaffold_lengths)
# iterate over contigs/scaffolds sorted by longest
# and caculate the NX
index = 0
cum_length = 0
total_length = sum(scaffold_lengths)
for length in sorted(scaffold_lengths, reverse=True):
while cum_length <= total_length * (float(N) / 100):
index += 1
cum_length += length
# output the results
outf = open(stats_file, "w")
outf.write(
"nscaffolds\tscaffold_length\tN%i\tmedian_length\tmean_length\tmax_length\n"
% N)
outf.write("%s\t%s\t%s\t%s\t%s\t%s\n" %
(number_of_scaffolds, total_length,
sorted(scaffold_lengths, reverse=True)[index],
str(median_length), str(mean_length), str(max_length)))
def runDREME(infile, outfile, neg_file="", options=""):
''' Run DREME on fasta file. If a neg_file is passed
then DREME will use this as the negative set, otherwise
the default is to shuffle the input '''
nseqs_pos = int(FastaIterator.count(infile))
if nseqs_pos < 2:
E.warn("%s: less than 2 sequences - dreme skipped" % outfile)
P.touch(outfile)
return
if neg_file:
nseqs_neg = int(FastaIterator.count(neg_file))
if nseqs_neg < 2:
E.warn(
"%s: less than 2 sequences in negatives file - dreme skipped" %
outfile)
P.touch(outfile)
return
else:
neg_file = "-n %s" % neg_file
logfile = outfile + ".log"
target_path = os.path.join(os.path.abspath(PARAMS["exportdir"]), outfile)
tmpdir = P.get_temp_dir(".")
statement = '''
dreme -p %(infile)s %(neg_file)s -png
-oc %(tmpdir)s
%(dreme_options)s
%(options)s
> %(logfile)s
'''
P.run(statement)
collectMEMEResults(tmpdir, target_path, outfile, method="dreme")
def segmentGaps(infile, gap_char):
iterator = FastaIterator.FastaIterator(infile)
while 1:
try:
cur_record = iterator.next()
except StopIteration:
break
if cur_record is None: break
contig = re.sub("\s.*", "", cur_record.title)
for start, end in gapped_regions(cur_record.sequence, gap_char):
yield (contig, start, end, 0)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = E.OptionParser(
version=
"%prog version: $Id: analyze_sequences.py 2865 2010-03-03 10:18:28Z andreas $",
usage=globals()["__doc__"])
parser.add_option("-s",
"--species",
dest="species",
type="string",
help="species to use.")
parser.add_option("-p",
"--prefix",
dest="prefix",
type="string",
help="prefix to use for temporary files.")
parser.set_defaults(filename_map=None, )
(options, args) = E.Start(parser, add_mysql_options=True)
iterator = FastaIterator.FastaIterator(options.stdin)
print "id\t" + SequenceProperties().GetHeader()
while 1:
cur_record = iterator.next()
if cur_record is None:
break
sequence = re.sub(" ", "", cur_record.sequence)
if len(sequence) % 3:
raise "sequence %s is not multiples of 3: length=%i!" % (
cur_record.title, len(sequence))
s = SequenceProperties()
s.Load(sequence)
print cur_record.title + "\t" + str(s)
E.Stop()
def buildMisprimingLib(infiles, outfile):
'''
build fasta file of sequences to check for mispriming
'''
fasta, identifiers = infiles
inf = IOTools.openFile(fasta)
E.info("reading ids for sequences to keep")
ids = readIdentifiers(identifiers)
outf = IOTools.openFile(outfile, "w")
E.info("collecting sequences")
for f in FastaIterator.iterate(IOTools.openFile(fasta)):
if f.title not in ids:
outf.write(">%s\n%s\n" % (f.title, f.sequence))
outf.close()
def buildMisprimingLib(infiles, outfile):
'''
build fasta file of sequences to check for mispriming
'''
fasta, identifiers = infiles
inf = IOTools.openFile(fasta)
E.info("reading ids for sequences to keep")
ids = readIdentifiers(identifiers)
outf = IOTools.openFile(outfile, "w")
E.info("collecting sequences")
for f in FastaIterator.iterate(IOTools.openFile(fasta)):
if f.title not in ids:
outf.write(">%s\n%s\n" % (f.title, f.sequence))
outf.close()
def segmentFixedWidthWindows(infile, window_size, window_shift):
"""return a list of fixed contig sizes."""
ninput, nskipped, noutput = 0, 0, 0
iterator = FastaIterator.FastaIterator(infile)
window_shift = window_size
# at most 50% can be gap
gap_cutoff = int(window_size // 2)
segments = []
while 1:
ninput += 1
try:
cur_record = iterator.next()
except StopIteration:
break
if cur_record is None:
break
contig = re.sub("\s.*", "", cur_record.title)
seq = cur_record.sequence
size = len(cur_record.sequence)
for x in range(0, size, window_shift):
s = seq[x:x + window_size].upper()
gc, at = 0, 0
for c in s:
if c in "GC":
gc += 1
elif c in "AT":
at += 1
# skip segments containing mostly gaps
if window_size - (gc + at) > gap_cutoff:
nskipped += 1
continue
segments.append(
(contig, x, x + window_size, float(gc) / (gc + at)))
noutput += 1
E.info("ninput=%i, noutput=%i, nskipped_windows=%i" %
(ninput, noutput, nskipped))
return segments
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-pm",
"--profilematrix",
dest="matrixfile",
type="string",
help="name of profile file you want to convert")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
#outf = IOTools.openFile("my_output", "w")
for line in options.matrixfile:
line = line.strip()
fields = line.split()
total = sum([float(col) for col in fields[1:]])
if total == 0:
continue
else:
for i, col in enumerate(fields):
if i == 0: continue
fields[i] = col / total
options.stdout.write("\t".join(map(str, fields)))
for fasta_read in FastaIterator.iterate(IOTools.openFile(
options.fastafile)):
read_sequence = fasta_read.sequence
read_name = fasta_read.title
quals = '.' * len(read_sequence)
new_fastq = Fastq.Record(identifier=read_name,
seq=read_sequence,
quals=quals)
new_fastq.fromPhred([30] * len(read_sequence), format='illumina-1.8')
options.stdout.write(str(new_fastq) + "\n")
# write footer and output benchmark information.
E.Stop()
def build_scaffold_lengths(contigs_file, outfile, params):
'''
output the distribution of scaffold lengths
'''
PARAMS = params
if PARAMS["filter"]:
f = PARAMS["filter"]
else:
f = 0
inf = open(contigs_file)
outf = open(outfile, "w")
outf.write("scaffold_name\tlength\n")
for record in FastaIterator.iterate(inf):
scaffold_length = len(list(record.sequence))
if scaffold_length > f:
# rename sequences if they have a space in them
outf.write("%s\t%i\n" % (record.title.replace(" ", "_"), scaffold_length))
outf.close()
def filter16SGenes(infiles, outfile):
'''Drop anything with gaps and drop anything with sequence length
below 1400bp'''
fasta_file, stat_file = infiles
# fetch a list of those entries to be filtered
to_drop = set()
df = pd.read_table(stat_file, index_col=0)
to_drop.update(df[df['ngap_regions'] > 0].index.tolist())
to_drop.update(df[df['length'] < 1400].index.tolist())
# drop from fasta file
with IOTools.openFile(outfile, 'w') as outf:
for fasta in FastaIterator.FastaIterator(IOTools.openFile(fasta_file)):
if fasta.title in to_drop:
continue
else:
outf.write('\n'.join(['>' + fasta.title, fasta.sequence]) + '\n')
def runMEMEOnSequences(infile, outfile):
'''run MEME to find motifs.
In order to increase the signal/noise ratio,
MEME is not run on all intervals but only the
top 10% of intervals (peakval) are used.
Also, only the segment of 200 bp around the peak
is used and not the complete interval.
* Softmasked sequence is converted to hardmasked
sequence to avoid the detection of spurious motifs.
* Sequence is run through dustmasker
'''
to_cluster = True
# job_options = "-l mem_free=8000M"
nseqs = int(FastaIterator.count(infile))
if nseqs == 0:
E.warn("%s: no sequences - meme skipped" % outfile)
P.touch(outfile)
return
target_path = os.path.join(
os.path.abspath(PARAMS["exportdir"]), "meme", outfile)
tmpdir = P.getTempDir(".")
statement = '''
meme %(infile)s -dna -revcomp
-mod %(meme_model)s
-nmotifs %(meme_nmotifs)s
-oc %(tmpdir)s
-maxsize %(motifs_max_size)s
%(meme_options)s
> %(outfile)s.log
'''
P.run()
collectMEMEResults(tmpdir, target_path, outfile)
def subsampleNReadsFromFasta(infile, outfile, nreads, logfile=""):
checkParams()
nseqs = FastaIterator.count(infile)
if nreads > nseqs:
prop = 1
else:
prop = float(nreads)/float(nseqs)
if logfile:
logfile = "-L %s" % logfile
statement = ''' python %(scriptsdir)s/fasta2fasta.py
-I %(infile)s
%(logfile)s
-m sample
--sample-proportion=%(prop)s
-S %(outfile)s '''
P.run()
def countCompleteGenes(infile, outfile):
'''
count the number of genes that are classed
as complete based on having a start and stop codon
'''
start = "ATG"
stop = ["TAG", "TAA", "TGA"]
ntotal = 0
nstart = 0
nstop = 0
nstart_nstop = 0
for fasta in FastaIterator.iterate(IOTools.openFile(infile)):
ntotal += 1
if fasta.sequence.startswith(start):
nstart += 1
if fasta.sequence[-3:len(fasta.sequence)] in stop:
nstop += 1
if fasta.sequence.startswith(start) and fasta.sequence[-3:len(fasta.sequence)] in stop:
nstart_nstop += 1
outf = open(outfile, "w")
outf.write("total_genes\tpstart\tpstop\tpstart_stop\n")
outf.write("\t".join(map(str,[ntotal, float(nstart)/ntotal, float(nstop)/ntotal, float(nstart_nstop)/ntotal])) + "\n")
outf.close()
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-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.openFile(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()
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.openFile(args[0], "r"))])
seqs2 = dict([
(x.title, x.sequence) for x in FastaIterator.iterate(
IOTools.openFile(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()
def main( argv = None ):
parser = E.OptionParser( version = "%prog version: $Id: analyze_codonbias_shannon.py 2864 2010-03-03 10:18:16Z andreas $",
usage = globals()["__doc__"] )
parser.add_option( "-c", "--is-cds", dest="is_cds", action="store_true",
help = "input are cds (nucleotide) sequences [%default]" )
parser.set_defaults(
is_cds = False,
)
(options, args) = E.Start( parser, argv = argv )
options.stdout.write( "snpid\tidentifier\tpos\treference\tvariant\tcounts\tweight\n" )
alphabet = "ACDEFGHIKLMNPQRSTVWY"
snpid = 0
for entry in FastaIterator.iterate( options.stdin ):
identifier = entry.title
if options.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
options.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 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 = result.keys()
rows = set()
for kmer_counts in result.values():
for kmer, count in kmer_counts.iteritems():
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 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 not argv:
argv = sys.argv
# setup command line parser
parser = optparse.OptionParser(version="%prog version: $Id: contigs2random_sample.py 2871 2010-03-03 10:20:44Z nicki $",
usage=globals()["__doc__"])
parser.add_option("-m", "--species-map", dest="species_map", type="string",
help="text file specifying the mapping between contig and genome")
parser.add_option("-g", "--genome-dir", dest="genome_dir", type="string",
help="specify directory where genome / genomes are stored")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# read in contig lengths into dictionary
E.info("reading contigs file")
c_contigs = 0
contigs_lengths = {}
for fasta in FastaIterator.iterate(options.stdin):
c_contigs += 1
# titles of fasta records must be single strings with no special
# characters
contigs_lengths[fasta.title.split(" ")[0]] = len(fasta.sequence)
E.info("read %i contigs" % c_contigs)
# read in mapping between spcies and contigs
species_map = {}
for line in open(options.species_map).readlines():
data = line[:-1].split("\t")
contig, species = data[0], data[1]
species_map[contig] = species
# read genomes into memory
# NB this may need optimisin if using large
# genomes or many genomes
E.info("reading genomes from %s" % options.genome_dir)
# The directory must ONLY contain genome files!!
genomes_sequences = {}
c_genomes = 0
for genome_file in glob.glob(os.path.join(options.genome_dir, "*")):
c_genomes += 1
for fasta in FastaIterator.iterate(IOTools.openFile(genome_file)):
genomes_sequences[fasta.title] = fasta.sequence
E.info("read %i genomes from %s" % (c_genomes, options.genome_dir))
# iterate over the contigs and sample from the respective genome
E.info("iterating over contigs")
c_contigs_output = 0
for contig, length in contigs_lengths.iteritems():
if contig not in species_map:
E.warn("contig %s not in species map file" % contig)
else:
c_contigs_output += 1
genome = species_map[contig]
genome_length = len(genomes_sequences[genome])
# get the start position from which to sample
start = random.randint(1, genome_length)
try:
end = start + length - 1
except ValueError:
print "end of sampled contig extends beyond length of genome"
sampled_seq = genomes_sequences[genome][start:end]
options.stdout.write(
">%s_random\n%s\n" % (contig + "_%s" % species_map[contig], sampled_seq))
E.info("written %i contigs" % c_contigs_output)
# write footer and output benchmark information.
E.Stop()
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:
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("--input-fasta", dest="fasta", type="str",
help="name of fasta infile")
parser.add_option("-k", "--kmer-size", dest="kmer", type="int",
help="supply kmer length")
parser.add_option("--subset", dest="subset", type="int",
help="only analyse the first x entries")
parser.set_defaults(
fasta=None,
kmer=10,
subset=None)
(options, args) = E.Start(parser)
E.info("%s\n" % using("start"))
assert options.fasta, "must provide a fasta filename (--input-fasta=)"
k = KmerCounter()
Iterator = FastaIterator.iterate(IOTools.openFile(options.fasta))
# total entries also acts as the index for the entry_id
total_entries = 0
options.stdout.write("%s\n" % "\t".join((
"id", "unique_kmers", "non_unique_kmers", "fraction_unique")))
# iterate transcripts, shred and identify unique kmers
E.info("shredding fasta to identify unique kmers")
for entry in Iterator:
if total_entries % 1000 == 0:
E.info("1st shred complete for %i entries" % total_entries)
if options.subset and total_entries >= options.subset:
break
k.shred(entry.sequence.upper(), options.kmer)
total_entries += 1
total_entries = 0
Iterator = FastaIterator.iterate(IOTools.openFile(options.fasta))
# iterate transcripts, shread and count unique kmers
E.info("re-shredding fasta to count unique kmers")
for entry in Iterator:
if total_entries % 1000 == 0:
E.info("2nd shred complete for %i entries" % total_entries)
transcript_id = entry.title.split()[0]
if options.subset and total_entries >= options.subset:
break
total_entries += 1
unique, non_unique = k.countUniqueKmers(
entry.sequence.upper(), options.kmer)
fraction = np.divide(float(unique), (unique + non_unique))
options.stdout.write("%s\n" % "\t".join(
map(str, (transcript_id, unique, non_unique, fraction))))
E.info("found %i kmers" % len(k.kmer2entry))
E.info("written kmer counts for %i contigs" % total_entries)
# write footer and output benchmark information.
E.info("%s\n" % using("end"))
E.Stop()
parser.set_defaults(
input_format="fasta",
output_format="fasta",
method = None,
parameters = "",
gop = -10.0,
gep = -1.0,
alignment_method = "sw",
)
(options, args) = E.Start( parser )
options.parameters = options.parameters.split(",")
iterator = FastaIterator.iterate( sys.stdin )
if options.method == "add":
mali = Mali.Mali()
mali.readFromFile( open(options.parameters[0], "r"), format = options.input_format )
del options.parameters[0]
old_length = mali.getLength()
new_mali = convertMali2Mali( mali )
if options.alignment_method == "sw":
alignator = alignlib.makeAlignatorFullDP( options.gop, options.gep )
else:
def main(argv=None):
if argv is None:
argv = sys.argv
parser = E.OptionParser(version="%prog version",
usage=globals()["__doc__"])
parser.add_option(
"--output-quality-format", dest="q_format", type="int",
help="sequence quality format, e.g 33 = +33/Sanger"
"[default=%default].")
parser.add_option(
"--output-paired-end", dest="paired", action="store_true",
help="generate paired end reads [default = %default].")
parser.add_option(
"--insert-length-mean", dest="insert_mean", type="float",
help="mean insert length [default = %default].")
parser.add_option(
"--insert-length-sd", dest="insert_sd", type="float",
help="insert length standard deviation [default = %default].")
parser.add_option(
"--counts-method", dest="counts_method", type="choice",
choices=("reads", "copies"),
help="simulate a ground truth number of reads per entry or"
"copies per entry [default = %default].")
parser.add_option(
"--counts-min", dest="counts_min", type="float",
help="minimum number of reads/read pairs per fasta entry"
"or copies per entry [default = %default].")
parser.add_option(
"--counts-max", dest="counts_max", type="float",
help="maximum number of reads/read pairs per fasta entry "
"or copies per entry [default = %default].")
parser.add_option(
"--output-read-length", dest="read_length", type="int",
help="read length [default = %default].")
parser.add_option(
"--sequence-error-phred", dest="phred", type="int",
help="phred quality score [default = %default].")
parser.add_option(
"--output-counts", dest="output_counts", type="string",
help="name for counts outfile [default=%default].")
parser.add_option(
"--output-fastq2", dest="fastq2_out", type="string",
help="filename for second fastq outfile [default=%default].")
parser.add_option(
"--premrna-fraction", dest="premrna_fraction", type="float",
help="the fraction of reads to simulate from pre-mRNA"
"[default= % default].")
parser.add_option(
"--infile-premrna-fasta", dest="premrna_fasta", type="string",
help="filename for pre-mRNA fasta[default=%default].")
parser.set_defaults(
q_format=33,
paired=False,
insert_mean=0,
insert_sd=1,
counts_method="reads",
counts_min=1,
counts_max=1,
read_length=50,
fastq2_out=None,
output_counts=None,
phred=30,
premrna_fraction=0,
premrna_fasta=None
)
(options, args) = E.Start(parser)
if options.paired:
assert options.fastq2_out, ("must specify a second fastq outfile for "
"paired end (--output-fastq2)")
outf2 = IOTools.openFile(options.fastq2_out, "w")
if options.premrna_fraction:
assert options.premrna_fasta, ("must specfify the location of the"
"fasta file for the pre-mRNA")
# the sequence quality string will always be the same so define here
sequence_quality = chr(options.q_format + options.phred)
qual = "".join([sequence_quality] * options.read_length)
if options.premrna_fraction:
iterator = FastaIterator.iterate_together(
options.stdin, IOTools.openFile(options.premrna_fasta))
else:
iterator = FastaIterator.FastaIterator(options.stdin)
# set a cut off of twice the read/pair length for short entries
if options.paired:
minimum_entry_length = (
2 * ((options.read_length * 2) + options.insert_mean))
else:
minimum_entry_length = 2 * options.read_length
c = collections.Counter()
counts = collections.Counter()
copies = collections.Counter()
for f_entry in iterator:
if options.premrna_fraction:
assert getTitle(f_entry[0]) == getTitle(f_entry[1]), (
"entry ids do not match: %s != %s" % (
f_entry[0].title, f_entry[1].title))
entry = f_entry[0]
pre_entry = f_entry[1]
else:
entry = f_entry
# reject short fasta entries
if len(entry.sequence) < minimum_entry_length:
E.info("skipping short transcript: %s length=%i"
% (entry.title, len(entry.sequence)))
c['skipped'] += 1
continue
else:
c['not_skipped'] += 1
if options.paired:
fragment_length = (
(2 * options.read_length) + options.insert_mean)
else:
fragment_length = options.read_length
reads_per_entry = float(len(entry.sequence)) / fragment_length
if options.counts_method == "reads":
n_reads = random.randint(options.counts_min,
options.counts_max + 1)
n_copies = float(n_reads) / reads_per_entry
if options.premrna_fraction:
n_reads_pre = int(round(n_reads * options.premrna_fraction))
elif options.counts_method == "copies":
# random float [0-1]
rand = np.random.random_sample()
n_copies = (options.counts_min +
(rand * (options.counts_max - options.counts_min)))
n_reads = int(round(n_copies * reads_per_entry, 0))
# as n_reads must be rounded to int, need to redefine n_copies
n_copies = float(n_reads) / reads_per_entry
if options.premrna_fraction:
reads_per_pre_entry = (float(len(pre_entry.sequence)) /
fragment_length)
n_copies_pre = n_copies * options.premrna_fraction
n_reads_pre = int(round(n_copies_pre * reads_per_pre_entry, 0))
# as n_reads_pre must be rounded to int, need to
# redefine n_copies_pre
n_copies_pre = float(n_reads_pre) / reads_per_pre_entry
entry_id = getTitle(entry)
counts[entry_id] = n_reads
copies[entry_id] = n_copies
if "N" in entry.sequence.upper():
E.warn("fasta entry %s contains unknown bases ('N')" % entry_id)
for i in range(0, n_reads):
read = generateRead(entry=entry.sequence.upper(),
read_length=options.read_length,
error_rate=options.phred,
paired=options.paired,
insert_mean=options.insert_mean,
insert_sd=options.insert_sd)
if options.paired:
r1, r2 = read
h1 = "@%s_%i/1" % (entry_id, i)
h2 = "@%s_%i/2" % (entry_id, i)
options.stdout.write("\n".join((h1, r1, "+", qual)) + "\n")
outf2.write("\n".join((h2, r2, "+", qual)) + "\n")
else:
h = "@%s_%i/1" % (entry_id, i)
options.stdout.write("\n".join((h, read, "+", qual)) + "\n")
if options.premrna_fraction:
c['pre_counts'] += n_reads_pre
c['pre_copies'] += n_copies_pre
for i in range(0, n_reads_pre):
read = generateRead(entry=pre_entry.sequence.upper(),
read_length=options.read_length,
error_rate=options.phred,
paired=options.paired,
insert_mean=options.insert_mean,
insert_sd=options.insert_sd)
if options.paired:
r1, r2 = read
h1 = "@%s_pre-mRNA_%i/1" % (entry_id, i)
h2 = "@%s_pre-mRNA_%i/2" % (entry_id, i)
options.stdout.write("\n".join((h1, r1, "+", qual)) + "\n")
outf2.write("\n".join((h2, r2, "+", qual)) + "\n")
else:
h = "@%s_pre-mRNA_%i/1" % (entry_id, i)
options.stdout.write("\n".join((h, read, "+", qual)) + "\n")
if options.paired:
outf2.close()
with IOTools.openFile(options.output_counts, "w") as counts_out:
counts_out.write("%s\n" % "\t".join(("id", "read_count", "tpm")))
sum_copies = sum(copies.values())
sum_counts = sum(counts.values())
for entry_id, count in counts.items():
tpm = 1000000 * (float(copies[entry_id]) / sum_copies)
counts_out.write(
"%s\n" % "\t".join(map(str, (entry_id, count, tpm))))
E.info("Reads simulated for %i fasta entries, %i entries skipped"
% (c['not_skipped'], c['skipped']))
E.info("Simulated: %i reads (%i mRNA, %i pre-mRNA), "
"%f transcripts (%f mRNA, %f pre-mRNA)" % (
sum_counts + c['pre_counts'], sum_counts, c['pre_counts'],
sum_copies + c['pre_copies'], sum_copies, c['pre_copies']))
E.Stop()
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: run_nubiscan.py 2861 2010-02-23 17:36:32Z andreas $", usage = globals()["__doc__"] )
parser.add_option("-i", "--iterations", dest="iterations", type="int",
help="number of iterations for sampling [default=%default]." )
parser.add_option("-q", "--qvalue", dest="qvalue_threshold", type="float",
help="qvalue threshold [default=%default]." )
parser.add_option("--without-combine", dest="combine", action = "store_false",
help="combine overlapping motifs [default=%default]." )
parser.add_option("-f", "--fdr-control", dest="fdr_control", type="choice",
choices = ("per-sequence", "all", "xall"),
help="qvalue threshold [default=%default]." )
parser.add_option("-m", "--motif", dest="motif", type="choice",
choices=("rxrvdr", "rxrvdr1", "rxrvdr2", "nr"),
help="qvalue threshold [default=%default]." )
parser.add_option("-a", "--arrangements", dest="arrangements", type="string",
help ="',' separated list of repeat arrangements [default=%default]")
parser.add_option("-x", "--mask", dest="mask", type="choice",
choices=("dust","repeatmasker"),
help ="mask sequences before scanning [default=%default]")
parser.add_option("--output-stats", dest="output_stats", action = "store_true",
help="output stats [default=%default]." )
parser.add_option("--add-sequence", dest="add_sequence", action = "store_true",
help="add sequence information [default=%default]." )
parser.set_defaults(
iterations = 100,
qvalue_threshold = 0.05,
motif = "rxrvdr",
fdr_control = "all",
combine = True,
arrangements = None,
mask = None,
output_stats = False,
add_sequence = False,
)
## add common options (-h/--help, ...) and parse command line
(options, args) = E.Start( parser, argv = argv, add_output_options = True )
## do sth
ninput, nskipped, noutput = 0, 0, 0
if options.arrangements == None:
options.arrangements = [ "DR%s" % x for x in range(0,15) ] + [ "ER%s" % x for x in range(0,15) ]
else:
options.arrangements = options.arrangements.split(",")
options.stdout.write( "%s" % "\t".join(Nubiscan.NubiscanMatch._fields) )
if options.add_sequence: options.stdout.write( "\tsequence" )
options.stdout.write("\n")
if options.motif == 'nr': sense_matrix = NR
elif options.motif == "rxrvdr": sense_matrix = RXRVDR
elif options.motif == "rxrvdr1": sense_matrix = RXRVDR1
elif options.motif == "rxrvdr2": sense_matrix = RXRVDR2
else:
raise ValueError("unknown matrix %s" % options.motif)
if options.fdr_control == "all":
seqs = list(FastaIterator.iterate(options.stdin))
if options.mask:
masked_seqs = maskSequences( [x.sequence for x in seqs], options.mask )
else:
masked_seqs = [x.sequence for x in seqs]
ninput = len(seqs)
map_id2title = dict( enumerate( [re.sub("\s.*", "", x.title) for x in seqs] ) )
matcher = Nubiscan.MatcherRandomisationSequences( sense_matrix,
samples = options.iterations )
results = matcher.run( masked_seqs,
options.arrangements,
qvalue_threshold = options.qvalue_threshold )
if options.combine:
results = Nubiscan.combineMotifs( results )
for r in results:
if r.alternatives:
alternatives = ",".join( [x.arrangement for x in r.alternatives ] )
else:
alternatives = ""
options.stdout.write( "\t".join( (
map_id2title[r.id],
"%i" % r.start,
"%i" % r.end,
r.strand,
r.arrangement,
"%6.4f" % r.score,
"%6.4f" % r.zscore,
"%6.4e" % r.pvalue,
"%6.4e" % r.qvalue,
alternatives) ) )
if options.add_sequence:
s = masked_seqs[int(r.id)][r.start:r.end]
if r.strand == "-": s = Genomics.complement( s )
s = s[:6].upper() + s[6:-6].lower() + s[-6:].upper()
options.stdout.write( "\t%s" % s )
options.stdout.write("\n")
noutput += 1
# output stats
if options.output_stats:
outfile = E.openOutputFile( "fdr" )
outfile.write("bin\thist\tnobserved\n" )
for bin, hist, nobs in zip(matcher.bin_edges, matcher.hist, matcher.nobservations):
outfile.write( "%f\t%f\t%f\n" % (bin, hist, nobs))
outfile.close()
elif options.fdr_control == "xall":
matcher = Nubiscan.MatcherRandomisationSequence( sense_matrix,
samples = options.iterations )
# collect all results
matches = []
for seq in FastaIterator.iterate(options.stdin):
ninput += 1
mm = matcher.run( seq.sequence,
options.arrangements,
qvalue_threshold = None )
for m in mm:
matches.append( m._replace( sequence = seq.title ) )
# estimate qvalues for all matches across all sequences
pvalues = [ x.pvalue for x in matches ]
fdr = Stats.doFDR( pvalues )
qvalues = fdr.mQValues
results = []
for m, qvalue in zip(matches, qvalues):
if qvalue > options.qvalue_threshold: continue
results.append( m._replace( qvalue = qvalue ) )
if options.combine:
results = Nubiscan.combineMotifs( results )
# output
for r in results:
options.stdout.write( "\t".join( (
r.id,
"%i" % r.start,
"%i" % r.end,
r.strand,
r.arrangement,
"%6.4f" % r.score,
"%6.4f" % r.zscore,
"%6.4e" % r.pvalue,
"%6.4e" % r.qvalue ) ) + "\n" )
noutput += 1
elif options.fdr_control == "per-sequence":
matcher = Nubiscan.MatcherRandomisationSequence( sense_matrix,
samples = options.iterations )
for seq in FastaIterator.iterate(options.stdin):
ninput += 1
result = matcher.run( seq.sequence,
options.arrangements,
qvalue_threshold = options.qvalue_threshold )
if options.combine:
result = Nubiscan.combineMotifs( result )
t = re.sub(" .*","", seq.title)
for r in result:
options.stdout.write( "\t".join( (
t,
"%i" % r.start,
"%i" % r.end,
r.strand,
r.arrangement,
"%6.4f" % r.score,
"%6.4f" % r.zscore,
"%f" % r.pvalue,
"%f" % r.qvalue ) ) + "\n" )
noutput += 1
E.info( "ninput=%i, noutput=%i, nskipped=%i" % (ninput, noutput,nskipped) )
## 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 not argv:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$", usage=globals()["__doc__"])
parser.add_option("--input-fasta", dest="fasta", type="str", help="name of fasta infile")
parser.add_option(
"--method",
dest="method",
type="choice",
choices=("transcript", "gene"),
help="count unique kmers per transcript or gene",
)
parser.add_option("--genemap", dest="genemap", type="str", help="file mapping transcripts to genes")
parser.add_option("-k", "--kmer-size", dest="kmer", type="int", help="supply kmer length")
parser.add_option("--subset", dest="subset", type="int", help="only analyse the first x entries")
parser.set_defaults(fasta=None, method="transcript", genemap=None, kmer=10, subset=None)
(options, args) = E.Start(parser)
E.info("%s\n" % using("start"))
assert options.fasta, "must provide a fasta filename (--input-fasta=)"
k = KmerCounter()
Iterator = FastaIterator.iterate(IOTools.openFile(options.fasta))
# total entries also acts as the index for the entry_id
total_entries = 0
options.stdout.write("%s\n" % "\t".join(("id", "unique_kmers", "non_unique_kmers", "fraction_unique")))
# iterate fasta entries, shred and identify kmers
if options.method == "gene":
E.info("shredding genes to identify unique kmers")
assert options.genemap, (
"to perform a gene-level unique kmer count, " "you must supply a transcript2gene map (--genemap)"
)
t2g = {}
with IOTools.openFile(options.genemap, "r") as inf:
for line in inf:
transcript, gene = line.strip().split("\t")
t2g[transcript] = gene
genes = set()
current_gene = None
sequences = []
for entry in Iterator:
if options.subset and total_entries >= options.subset:
break
transcript_id = entry.title.split()[0]
gene_id = t2g[transcript_id]
if gene_id != current_gene:
if not current_gene:
current_gene = gene_id
continue
# check this is the first time we've dealt with this gene?
assert current_gene not in genes, (
"the fasta does not appear to be sorted in gene order, the"
" same gene is observed in non-consecutive positions!"
)
genes.add(current_gene)
k.shred(sequences, options.kmer)
if total_entries % 1000 == 0:
E.info("1st shred complete for %i genes" % total_entries)
total_entries += 1
sequences = [entry.sequence.upper()]
current_gene = gene_id
else:
sequences.append(entry.sequence.upper())
# catch last gene
if not options.subset or options.subset and total_entries < options.subset:
k.shred(sequences, options.kmer)
E.info("1st shred complete for %i genes" % total_entries)
elif options.method == "transcript":
E.info("shredding transcripts to identify unique kmers")
for entry in Iterator:
if total_entries % 1000 == 0:
E.info("1st shred complete for %i transcripts" % total_entries)
if options.subset and total_entries >= options.subset:
break
k.shred([entry.sequence.upper()], options.kmer)
total_entries += 1
E.info("1st shred complete for %i transcripts" % total_entries)
total_entries = 0
Iterator = FastaIterator.iterate(IOTools.openFile(options.fasta))
# iterate fasta entries, shread and count unique kmers
if options.method == "gene":
E.info("re-shredding fasta to count gene unique kmers")
genes = set()
current_gene = None
sequences = []
for entry in Iterator:
if options.subset and total_entries >= options.subset:
break
transcript_id = entry.title.split()[0]
gene_id = t2g[transcript_id]
if gene_id != current_gene:
if not current_gene:
current_gene = gene_id
continue
# check this is the first time we've dealt with this gene?
assert current_gene not in genes, (
"the fasta does not appear to be sorted in gene order, the"
" same gene is observed in non-consecutive positions!"
)
genes.add(current_gene)
unique, non_unique = k.countUniqueKmers(sequences, options.kmer)
fraction = np.divide(float(unique), (unique + non_unique))
options.stdout.write("%s\n" % "\t".join(map(str, (current_gene, unique, non_unique, fraction))))
if total_entries % 1000 == 0:
E.info("2nd shred complete for %i genes" % total_entries)
total_entries += 1
sequences = [entry.sequence.upper()]
current_gene = gene_id
total_entries += 1
else:
sequences.append(entry.sequence.upper())
# catch last gene
if not options.subset or options.subset and total_entries < options.subset:
unique, non_unique = k.countUniqueKmers(sequences, options.kmer)
fraction = np.divide(float(unique), (unique + non_unique))
options.stdout.write("%s\n" % "\t".join(map(str, (gene_id, unique, non_unique, fraction))))
if options.method == "transcript":
E.info("re-shredding fasta to count transcript unique kmers")
for entry in Iterator:
if total_entries % 1000 == 0:
E.info("2nd shred complete for %i transcripts" % total_entries)
if options.subset and total_entries >= options.subset:
break
transcript_id = entry.title.split()[0]
total_entries += 1
unique, non_unique = k.countUniqueKmers([entry.sequence.upper()], options.kmer)
fraction = np.divide(float(unique), (unique + non_unique))
options.stdout.write("%s\n" % "\t".join(map(str, (transcript_id, unique, non_unique, fraction))))
E.info("found %i kmers" % len(k.kmer2entry))
E.info("written kmer counts for %i contigs" % total_entries)
# write footer and output benchmark information.
E.info("%s\n" % using("end"))
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: sequences2mali.py 2782 2009-09-10 11:40:29Z andreas $", usage=globals()["__doc__"])
parser.add_option("-i", "--input-format", dest="input_format", type="choice",
choices=(
"plain", "fasta", "clustal", "stockholm", "phylip"),
help="input format of multiple alignment")
parser.add_option("-o", "--output-format", dest="output_format", type="choice",
choices=("plain", "fasta", "stockholm", "phylip"),
help="output format of multiple alignment")
parser.add_option("-m", "--method", dest="method", type="choice",
choices=("add",),
help="""method to use to build multiple alignment.""")
parser.add_option("-p", "--parameters", dest="parameters", type="string",
help="parameter stack for methods that require one.")
parser.add_option("-a", "--alignment-method", dest="alignment_method", type="choice",
choices=("sw", "nw"),
help="alignment_method [%default].")
parser.set_defaults(
input_format="fasta",
output_format="fasta",
method=None,
parameters="",
gop=-10.0,
gep=-1.0,
alignment_method="sw",
)
(options, args) = E.Start(parser)
options.parameters = options.parameters.split(",")
iterator = FastaIterator.iterate(sys.stdin)
if options.method == "add":
mali = Mali.Mali()
mali.readFromFile(
open(options.parameters[0], "r"), format=options.input_format)
del options.parameters[0]
old_length = mali.getLength()
new_mali = convertMali2Mali(mali)
if options.alignment_method == "sw":
alignator = alignlib_lite.py_makeAlignatorFullDP(
options.gop, options.gep)
else:
alignator = alignlib_lite.py_makeAlignatorFullDPGlobal(
options.gop, options.gep)
while 1:
cur_record = iterator.next()
if cur_record is None:
break
map_mali2seq = alignlib_lite.py_makeAlignataVector()
sequence = alignlib_lite.py_makeSequence(cur_record.sequence)
profile = alignlib_lite.py_makeProfileFromMali(new_mali)
if options.loglevel >= 4:
options.stdlog.write(profile.Write())
alignator.Align(profile, sequence, map_mali2seq)
if options.loglevel >= 3:
options.stdlog.write(map_mali2seq.Write())
# add sequence to mali
a = alignlib_lite.py_makeAlignatumFromString(cur_record.sequence)
a.thisown = 0
new_mali.addAlignatum(a, map_mali2seq, 1, 1, 1, 1, 1)
id = cur_record.title
mali.mIdentifiers.append(id)
mali.mMali[id] = Mali.AlignedString(id, 0, len(
cur_record.sequence), new_mali.getRow(new_mali.getWidth() - 1).getString())
# substitute
for x in range(old_length):
mali.mMali[mali.mIdentifiers[x]].mString = new_mali.getRow(
x).getString()
mali.writeToFile(sys.stdout, format=options.output_format)
E.Stop()