###grab slice of sequence fom this window.
targetRec = rec[slice_start:slice_end]
matching_feature = [f for f in targetRec.features if f.id == mytarget.id]
if matching_feature:
target_feat = matching_feature[0]
my_target_dict={} # re-initialize target dictionary
if target_feat.location.start.position == 0:
target_feat.location.start.position = 1
#get the mask features by removing target...all features are masked as just using snp and indels, a smarter filter could be added
exclude_feat = list(targetRec.features) ##list copy to avoid possible side-effects
exclude_feat.remove(target_feat)
my_target_dict={'SEQUENCE_ID' : rec.name,\
'SEQUENCE_TEMPLATE': targetRec.seq.tostring().upper(),\
'SEQUENCE_TARGET': [target_feat.location.start.position,1],\
'SEQUENCE_EXCLUDED_REGION': [[x.location.start.position,x.location.end.position -x.location.start.position] for x in exclude_feat]}
result=P3.run_P3(target_dict=my_target_dict,global_dict=def_dict)
if my_args.run_uMelt:
amp_seq=targetRec.seq ##need to make this conditional on getting a result >0 and melt=True
mutamp_seq=targetRec.seq.tomutable()
mutamp_seq[target_feat.location.start:target_feat.location.end]=target_feat.qualifiers['Variant_seq'][0] #mutate to variant
other_SNP=[f for f in targetRec.features if f.id != mytarget.id]
if other_SNP:
for snp in other_SNP:
mutamp_seq[snp.location.start:snp.location.end]=snp.qualifiers['Variant_seq'][0]
for primerset in result:
amp_start=int(primerset['PRIMER_LEFT'][0])
amp_end=int(primerset['PRIMER_RIGHT'][0])
ref_melt_Tm=0
var_melt_Tm=0
diff_melt=0
if my_args.run_uMelt:
str(x.location.end.position -
x.location.start.position)
for x in exclude_feat
])
my_target_dict = {
'SEQUENCE_ID':
rec.name,
'SEQUENCE_TEMPLATE':
targetRec.seq.tostring(),
'SEQUENCE_TARGET':
str(target_feat.location.start.position) + ',1',
'SEQUENCE_INTERNAL_EXCLUDED_REGION':
excludes_str
}
my_target_dict.update(def_dict) ##add in defaults
result = P3.run_P3(target_dict=my_target_dict)
if my_args.run_uMelt:
amp_seq = targetRec.seq ##need to make this conditional on getting a result >0 and melt=True
mutamp_seq = targetRec.seq.tomutable()
mutamp_seq[target_feat.location.start:target_feat.
location.end] = target_feat.qualifiers[
'Variant_seq'][
0] #mutate to variant
for primerset in result:
amp_start = int(
primerset['PRIMER_LEFT'].split(',')[0])
amp_end = int(
primerset['PRIMER_RIGHT'].split(',')[0])
ref_melt_Tm = 0
var_melt_Tm = 0
diff_melt = 0
]
if matching_feature:
target_feat = matching_feature[0]
my_target_dict = {} # re-initialize target dictionary
if target_feat.location.start.position == 0:
target_feat.location.start.position = 1
#get the mask features by removing target...all features are masked as just using snp and indels, a smarter filter could be added
exclude_feat = list(
targetRec.features
) ##list copy to avoid possible side-effects
exclude_feat.remove(target_feat)
my_target_dict={'SEQUENCE_ID' : rec.name,\
'SEQUENCE_TEMPLATE': targetRec.seq.tostring().upper(),\
'SEQUENCE_TARGET': [target_feat.location.start.position,1],\
'SEQUENCE_EXCLUDED_REGION': [[x.location.start.position,x.location.end.position -x.location.start.position] for x in exclude_feat]}
result = P3.run_P3(target_dict=my_target_dict,
global_dict=def_dict)
if my_args.run_uMelt:
amp_seq = targetRec.seq ##need to make this conditional on getting a result >0 and melt=True
mutamp_seq = targetRec.seq.tomutable()
mutamp_seq[target_feat.location.start:target_feat.
location.end] = target_feat.qualifiers[
'Variant_seq'][
0] #mutate to variant
other_SNP = [
f for f in targetRec.features
if f.id != mytarget.id
]
if other_SNP:
for snp in other_SNP:
mutamp_seq[snp.location.start:snp.location.
end] = snp.qualifiers[
slice_end = featLocation + my_args.prod_max_size
###grab slice of sequence fom this window.
targetRec = rec[slice_start:slice_end]
matching_feature = [f for f in targetRec.features if f.id == mytarget.id]
if matching_feature:
target_feat = matching_feature[0]
my_target_dict={} # re-initialize target dictionary
if target_feat.location.start.position == 0:
target_feat.location.start.position = 1
#get the mask features by removing target...all features are masked as just using snp and indels, a smarter filter could be added
exclude_feat = list(targetRec.features) ##list copy to avoid possible side-effects
exclude_feat.remove(target_feat)
excludes_str=' '.join([str(x.location.start.position)+','+str(x.location.end.position -x.location.start.position) for x in exclude_feat])
my_target_dict={'SEQUENCE_ID' : rec.name, 'SEQUENCE_TEMPLATE': targetRec.seq.tostring(),'SEQUENCE_TARGET': str(target_feat.location.start.position) + ',1','SEQUENCE_INTERNAL_EXCLUDED_REGION': excludes_str}
my_target_dict.update(def_dict) ##add in defaults
result=P3.run_P3(target_dict=my_target_dict)
if my_args.run_uMelt:
amp_seq=targetRec.seq ##need to make this conditional on getting a result >0 and melt=True
mutamp_seq=targetRec.seq.tomutable()
mutamp_seq[target_feat.location.start:target_feat.location.end]=target_feat.qualifiers['Variant_seq'][0] #mutate to variant
for primerset in result:
amp_start=int(primerset['PRIMER_LEFT'].split(',')[0])
amp_end=int(primerset['PRIMER_RIGHT'].split(',')[0])
ref_melt_Tm=0
var_melt_Tm=0
diff_melt=0
if my_args.run_uMelt:
try:
ref_melt_Tm=umelts.getTm(umelts.getmelt(amp_seq.tostring()[amp_start:amp_end+1]))
var_melt_Tm=umelts.getTm(umelts.getmelt(mutamp_seq.tostring()[amp_start:amp_end+1]))
diff_melt=abs(ref_melt_Tm - var_melt_Tm)
