def check_threshold(outdir, genome, scoring="count"):
# gimme_motifs config, to get defaults
config = MotifConfig()
threshold_file = None
if scoring == "count":
# Motif scanning threshold
threshold_file = os.path.join(outdir, "threshold.{}.txt".format(genome))
if not os.path.exists(threshold_file):
# Random sequences from genome
index_dir = os.path.join(config.get_index_dir(), genome)
bg_file = os.path.join(outdir, "background.{}.fa".format(genome))
if not os.path.exists(bg_file):
m = RandomGenomicFasta(index_dir, BG_LENGTH, BG_NUMBER)
m.writefasta(bg_file)
pwmfile = config.get_default_params().get("motif_db")
pwmfile = os.path.join(config.get_motif_dir(), pwmfile)
cmd = "gimme threshold {} {} {} > {}".format(
pwmfile,
bg_file,
FDR,
threshold_file)
sp.call(cmd, shell=True)
return threshold_file
def check_threshold(outdir, genome, scoring="count"):
# gimme_motifs config, to get defaults
config = MotifConfig()
threshold_file = None
if scoring == "count":
# Motif scanning threshold
threshold_file = os.path.join(outdir,
"threshold.{}.txt".format(genome))
if not os.path.exists(threshold_file):
# Random sequences from genome
index_dir = os.path.join(config.get_index_dir(), genome)
bg_file = os.path.join(outdir, "background.{}.fa".format(genome))
if not os.path.exists(bg_file):
m = RandomGenomicFasta(index_dir, BG_LENGTH, BG_NUMBER)
m.writefasta(bg_file)
pwmfile = config.get_default_params().get("motif_db")
pwmfile = os.path.join(config.get_motif_dir(), pwmfile)
cmd = "gimme threshold {} {} {} > {}".format(
pwmfile, bg_file, FDR, threshold_file)
sp.call(cmd, shell=True)
return threshold_file
def _create_background(self, bg_type, bedfile, fafile, outfile, organism="hg18", width=200, nr_times=10):
fg = Fasta(fafile)
if bg_type == "random":
if int(self.markov_model) >= 6:
self.logger.warn("Are you sure about the Markov model? It seems too high!")
else:
order = {"1":"1st","2":"2nd", "3":"3rd", "4":"4th", "5":"5th"}[str(self.markov_model)]
self.logger.debug("Creating random background (%s order Markov)" % order)
m = MarkovFasta(fg, k=int(self.markov_model), n=nr_times * len(fg))
m.writefasta(outfile)
self.logger.debug("Random background: %s", outfile)
# return the number of random sequences created
return len(m)
elif bg_type == "genomic":
self.logger.debug("Creating genomic background")
index_dir = os.path.join(self.config.get_index_dir(), organism)
f = RandomGenomicFasta(index_dir, width, nr_times * len(fg))
f.writefasta(outfile)
return len(f)
elif bg_type == "gc":
self.logger.debug("Creating GC matched background")
f = MatchedGcFasta(fafile, organism, nr_times * len(fg))
f.writefasta(outfile)
self.logger.debug("GC matched background: %s", outfile)
return len(f)
elif bg_type == "promoter":
gene_file = os.path.join(self.config.get_gene_dir(), "%s.bed" % organism)
index_dir = os.path.join(self.config.get_index_dir(), organism)
self.logger.info(
"Creating random promoter background (%s, using genes in %s)",
organism, gene_file)
f = PromoterFasta(gene_file, index_dir, width, nr_times * len(fg))
f.writefasta(outfile)
self.logger.debug("Random promoter background: %s", outfile)
return len(f)
elif bg_type == "user":
bg_file = self.params["user_background"]
if not os.path.exists(bg_file):
self.logger.error(
"User-specified background file %s does not exist!",
bg_file)
sys.exit(1)
else:
self.logger.info("Copying user-specified background file %s to %s.",
bg_file, outfile)
fa = Fasta(bg_file)
l = median([len(seq) for seq in fa.seqs])
if l < width * 0.95 or l > width * 1.05:
self.logger.warn("The user-specified background file %s contains sequences with a median length of %s, while GimmeMotifs predicts motifs in sequences of length %s. This will influence the statistics! It is recommended to use background sequences of the same length.", bg_file, l, width)
fa.writefasta(outfile)
return len(fa)
def _create_background(self,
bg_type,
bedfile,
fafile,
outfile,
organism="hg18",
width=200,
nr_times=10):
fg = Fasta(fafile)
if bg_type == "random":
if int(self.markov_model) >= 6:
self.logger.warn(
"Are you sure about the Markov model? It seems too high!")
else:
order = {
"1": "1st",
"2": "2nd",
"3": "3rd",
"4": "4th",
"5": "5th"
}[str(self.markov_model)]
self.logger.debug(
"Creating random background (%s order Markov)" % order)
m = MarkovFasta(fg, k=int(self.markov_model), n=nr_times * len(fg))
m.writefasta(outfile)
self.logger.debug("Random background: %s", outfile)
# return the number of random sequences created
return len(m)
elif bg_type == "genomic":
self.logger.debug("Creating genomic background")
index_dir = os.path.join(self.config.get_index_dir(), organism)
f = RandomGenomicFasta(index_dir, width, nr_times * len(fg))
f.writefasta(outfile)
return len(f)
elif bg_type == "gc":
self.logger.debug("Creating GC matched background")
f = MatchedGcFasta(fafile, organism, nr_times * len(fg))
f.writefasta(outfile)
self.logger.debug("GC matched background: %s", outfile)
return len(f)
elif bg_type == "promoter":
gene_file = os.path.join(self.config.get_gene_dir(),
"%s.bed" % organism)
index_dir = os.path.join(self.config.get_index_dir(), organism)
self.logger.info(
"Creating random promoter background (%s, using genes in %s)",
organism, gene_file)
f = PromoterFasta(gene_file, index_dir, width, nr_times * len(fg))
f.writefasta(outfile)
self.logger.debug("Random promoter background: %s", outfile)
return len(f)
elif bg_type == "user":
bg_file = self.params["user_background"]
if not os.path.exists(bg_file):
self.logger.error(
"User-specified background file %s does not exist!",
bg_file)
sys.exit(1)
else:
self.logger.info(
"Copying user-specified background file %s to %s.",
bg_file, outfile)
fa = Fasta(bg_file)
l = median([len(seq) for seq in fa.seqs])
if l < width * 0.95 or l > width * 1.05:
self.logger.warn(
"The user-specified background file %s contains sequences with a median length of %s, while GimmeMotifs predicts motifs in sequences of length %s. This will influence the statistics! It is recommended to use background sequences of the same length.",
bg_file, l, width)
fa.writefasta(outfile)
return len(fa)