x=1
for i in contigs:
#print "A contig, ", i
if crf:
string_seq = i[1]
#print "String seq is", string_seq
nuc_index = i[0][0]
dict_seq = {}
# the sequence string at
for nuc in string_seq:
dict_seq[nuc_index] = nuc
nuc_index += 1
#print "original dict_seq is", dict_seq
# add info for consensus dictionary
mut_events = mtvcf_main_analysis(mt_table, sam_file, sample_name, tail=tail)
consensus_single = get_consensus_single(mut_events[mut_events.keys()[0]],hf=hf)
#print consensus_single
# alter dict_seq keys for the implementation
# of the consensus information
#
#print "CONSENSUS SINGLE: ", consensus_single
for p_info in consensus_single:
if p_info[0] in dict_seq.keys():
#print "P_INFO: ", p_info
# maybe I don't need to consider mismatch but I'll do anyway
if p_info[-1] == 'mism':
dict_seq[p_info[0]] = p_info[1][0] # check THIS
elif p_info[-1] == 'ins':
# in the consensus, the ins is reported as the nuc of pos of the ins + the inserted bases
dict_seq[p_info[0]+'.1'] = p_info[1][0][1:]
# alternatively it could be
if crf:
string_seq = i[1]
#print "String seq is", string_seq
nuc_index = i[0][0]
dict_seq = {}
# the sequence string at
for nuc in string_seq:
dict_seq[nuc_index] = nuc
nuc_index += 1
#print "original dict_seq is", dict_seq
# add info for consensus dictionary
mut_events = mtvcf_main_analysis(mt_table,
sam_file,
sample_name,
tail=tail)
consensus_single = get_consensus_single(
mut_events[mut_events.keys()[0]], hf=hf)
#print consensus_single
# alter dict_seq keys for the implementation
# of the consensus information
#
#print "CONSENSUS SINGLE: ", consensus_single
for p_info in consensus_single:
if p_info[0] in dict_seq.keys():
#print "P_INFO: ", p_info
# maybe I don't need to consider mismatch but I'll do anyway
if p_info[-1] == 'mism':
dict_seq[p_info[0]] = p_info[1][0] # check THIS
elif p_info[-1] == 'ins':
# in the consensus, the ins is reported as the nuc of pos of the ins + the inserted bases
dict_seq[p_info[0] + '.1'] = p_info[1][0][1:]
# alternatively it could be
new_i = i
#write fasta header
f.write('>Contig.%i|%i-%i\n' % (x, new_i[0][0], new_i[0][1]))
#print "A contig, ", i
if crf:
string_seq = i[1]
#print "String seq is", string_seq
nuc_index = i[0][0]
dict_seq = {}
# the sequence string at
for nuc in string_seq:
dict_seq[nuc_index] = nuc
nuc_index += 1
#print "original dict_seq is", dict_seq
# add info for consensus dictionary
consensus_single = get_consensus_single(
mut_events[mut_events.keys()[0]], hf_max=hf_max, hf_min=hf_min)
#print consensus_single
# alter dict_seq keys for the implementation
# of the consensus information
#
#print "CONSENSUS SINGLE: ", consensus_single
#check if there are repeated positions with different mut type
if len(consensus_single) == 0:
print 'no variants found in this contig {0}\n'.format(x)
pass
else:
df = pd.DataFrame(consensus_single)
positions = df[0]
dup_positions = positions[positions.duplicated()].values
for x in dup_positions:
d = df[df[0] == x][
mut_events_cellar.close()
if crf:
position=1
f=open(contigfile,'w')
print "Generating fasta output..."
for i in contigs:
string_seq = i[1]
nuc_index = i[0][0]
dict_seq = {}
for nuc in string_seq:
dict_seq[nuc_index] = nuc
nuc_index += 1
# This only gathers consensus bases for the mut_events
consensus_single = mtVariantCaller.get_consensus_single(mut_events[mut_events.keys()[0]],hf=hf)
for p_info in consensus_single:
if p_info[0] in dict_seq.keys():
if p_info[-1] == 'mism':
dict_seq[p_info[0]] = p_info[1][0] # check THIS
elif p_info[-1] == 'ins':
dict_seq[p_info[0]+'.1'] = p_info[1][0][1:]
elif p_info[-1] == 'del':
for deleted_pos in p_info[1]:
if deleted_pos < len(dict_seq):
del(dict_seq[deleted_pos])
# sort positions in dict_seq and join to have the sequence
contig_seq = ''
for j in sorted(dict_seq.keys()):
contig_seq += dict_seq[j]
new_i = ((i[0][0], i[0][1]), contig_seq)