def make_background_file(bgseqs, use_revcomp, bgorder):
"""create a meme background file and returns its name and the model itself as
a tuple"""
def make_seqs(seqs):
"""prepare the input sequences for feeding into meme.
This means only taking the unique sequences and their reverse
complement if desired"""
meme_input_seqs = []
for locseq in seqs.values():
seq = locseq[1]
if seq not in meme_input_seqs:
meme_input_seqs.append(seq)
if use_revcomp:
revseq = st.revcomp(seq)
if revseq not in meme_input_seqs:
meme_input_seqs.append(revseq)
return meme_input_seqs
filename = None
bgmodel = st.markov_background(make_seqs(bgseqs), bgorder)
with tempfile.NamedTemporaryFile(mode='w+', prefix='memebg',
delete=False) as outfile:
filename = outfile.name
#logging.info("make background file '%s'", filename)
outfile.write("# %s order Markov background model\n" %
util.order2string(len(bgmodel) - 1))
for order_row in bgmodel:
for seq, frequency in order_row.items():
outfile.write('%s %10s\n' % (seq, str(round(frequency, 8))))
return (filename, bgmodel)
def make_background_file(bgseqs, use_revcomp, bgorder):
"""create a meme background file and returns its name and the model itself as
a tuple"""
def make_seqs(seqs):
"""prepare the input sequences for feeding into meme.
This means only taking the unique sequences and their reverse
complement if desired"""
meme_input_seqs = []
for locseq in seqs.values():
seq = locseq[1]
if seq not in meme_input_seqs:
meme_input_seqs.append(seq)
if use_revcomp:
revseq = st.revcomp(seq)
if revseq not in meme_input_seqs:
meme_input_seqs.append(revseq)
return meme_input_seqs
filename = None
bgmodel = st.markov_background(make_seqs(bgseqs), bgorder)
with tempfile.NamedTemporaryFile(mode='w+', prefix='memebg',
delete=False) as outfile:
filename = outfile.name
#logging.info("make background file '%s'", filename)
outfile.write("# %s order Markov background model\n" %
util.order2string(len(bgmodel) - 1))
for order_row in bgmodel:
for seq, frequency in order_row.items():
outfile.write('%s %10s\n' %
(seq, str(round(frequency, 8))))
return (filename, bgmodel)
def test_markov_background_1(self):
"""test markov_background() with order 1"""
background = st.markov_background(["ACCGTATA", "CACAT"], 1)
self.assertEquals(2, len(background))
self.assertEquals(4, len(background[0]))
self.assertEquals(7, len(background[1]))
def test_markov_background_0(self):
"""test markov_background() with order 0"""
background = st.markov_background(["ACCGTATA", "CACAT"], 0)
self.assertEquals(1, len(background))
self.assertEquals(4, len(background[0]))
def test_markov_background_1(self):
"""test markov_background() with order 1"""
background = st.markov_background(["ACCGTATA", "CACAT"], 1)
self.assertEquals(2, len(background))
self.assertEquals(4, len(background[0]))
self.assertEquals(7, len(background[1]))
def test_markov_background_0(self):
"""test markov_background() with order 0"""
background = st.markov_background(["ACCGTATA", "CACAT"], 0)
self.assertEquals(1, len(background))
self.assertEquals(4, len(background[0]))
