def loadcond(infoname,barcodefn,datafnbase):
infodict = readinfo(infoname)
numbins = 4
condbase = int(infodict['condbase'])
condident = infodict['condident']
mut_region_start = int(infodict['mut_region_start'])
mut_region_length = int(infodict['mut_region_length'])
expname = infodict['exp_name']
fnnames = glob.glob(datafnbase + expname + '*.fasta')
fnnames.sort()
barcode_dict = {}
reverse_dict = {}
csvfile = open(barcodefn,'r')
reader = csv.DictReader(csvfile)
for row in reader:
barcode_dict[row['experiment_name']] = row['fwd_barcode']
reverse_dict[row['experiment_name']] = row['rev_barcode']
sequences = [readuniqueseqssingleend.collatedmat(fn) for fn in fnnames]
seq_start = len(barcode_dict[expname])
seq_end = sequences[0][0].find(reverse_dict[expname][0:5])
print 'sequences loaded'
print len(sequences[0])
for i in range(0,numbins):
sequences[i] = [sequences[i][z][seq_start:seq_end] for z in range(0,len(sequences[i]))]
batch_vec_temp = []
seqs = []
for i in range(0,numbins):
tempseqs = list(set(sequences[i]))
seqs = seqs + [tempseqs[z][mut_region_start:mut_region_start + mut_region_length] for z in range(0,len(tempseqs)) if tempseqs[z][condbase] == condident]
batch_vec_temp = batch_vec_temp + [i for z in range(0,len(tempseqs)) if tempseqs[z][condbase] == condident]
batch_vec_temp = np.array(batch_vec_temp)
#batch_vec_temp = [batch_vec_temp[i] for i in range(0,len(seqs)) if seqs[i].count('A') > 2 and len(seqs[i]) == mut_region_length]
print len(batch_vec_temp)
#seqs = [seqs[i] for i in range(0,len(seqs)) if seqs[i].count('A') > 2 and len(seqs[i]) == mut_region_length]
print len(seqs)
seq_mat_temp = np.empty([4,len(seqs[1]),len(seqs)])
if condbase >= mut_region_start and condbase < mut_region_start + mut_region_length:
for i, line in enumerate(seqs):
seq_mat_temp[:,:,i] = MCMC_utils.seq2mat(line)
seq_mat_temp[:,condbase-mut_region_start,i] = MCMC_utils.seq2mat(np.random.choice(['A','C','G','T'])).transpose()
else:
for i, line in enumerate(seqs):
seq_mat_temp[:,:,i] = MCMC_utils.seq2mat(line)
return seq_mat_temp, batch_vec_temp
def loadseqsuniquemut(infoname,barcodefn,datafnbase):
infodict = readinfo(infoname)
numbins = 4
mut_region_start = int(infodict['mut_region_start'])
mut_region_length = int(infodict['mut_region_length'])
expname = infodict['exp_name']
fnnames = glob.glob(datafnbase + expname + '*.fasta')
fnnames.sort()
barcode_dict = {}
reverse_dict = {}
csvfile = open(barcodefn,'r')
reader = csv.DictReader(csvfile)
for row in reader:
barcode_dict[row['experiment_name']] = row['fwd_barcode']
reverse_dict[row['experiment_name']] = row['rev_barcode']
sequences = [readuniqueseqssingleend.collatedmat(fn) for fn in fnnames]
seq_start = len(barcode_dict[expname])
seq_end = sequences[0][0].find(reverse_dict[expname][0:5])
print 'sequences loaded'
print len(sequences[0])
for i in range(0,numbins):
sequences[i] = [sequences[i][z][seq_start:seq_end] for z in range(0,len(sequences[i]))]
batch_vec_temp = []
seqs = []
for i in range(0,numbins):
#tempseqs = list(set(sequences[i])) I instead added this to the next line to make sure only sequences with unique mutated regions are counted.
tempseqs = sequences[i]
s2 = list(set([tempseqs[z][mut_region_start:mut_region_start + mut_region_length] for z in range(0,len(tempseqs))]))
seqs = seqs + s2
batch_vec_temp = batch_vec_temp + [i for z in range(len(s2))]
batch_vec_temp = np.array(batch_vec_temp)
#batch_vec_temp = [batch_vec_temp[i] for i in range(0,len(seqs)) if seqs[i].count('A') > 2 and len(seqs[i]) == mut_region_length]
print len(batch_vec_temp)
#seqs = [seqs[i] for i in range(0,len(seqs)) if seqs[i].count('A') > 2 and len(seqs[i]) == mut_region_length]
print len(seqs)
seq_mat_temp = np.empty([4,len(seqs[1]),len(seqs)])
for i, line in enumerate(seqs):
seq_mat_temp[:,:,i] = MCMC_utils.seq2mat(line)
return seq_mat_temp, batch_vec_temp
fnnames.sort()
barcodefn = config.get('Input','barcodefn')
barcode_dict = {}
reverse_dict = {}
csvfile = open(barcodefn,'r')
reader = csv.DictReader(csvfile)
for row in reader:
barcode_dict[row['experiment_name']] = row['fwd_barcode']
reverse_dict[row['experiment_name']] = row['rev_barcode']
numseq = [[] for i in range(0,4)]
seq_mat = [[] for i in range(0,4)]
sequences = [readuniqueseqssingleend.collatedmat(fn) for fn in fnnames]
seq_start = len(barcode_dict[expname])
seq_end = sequences[0][0].find(reverse_dict[expname][0:5])
print 'sequences loaded'
print len(sequences[0])
for i in range(0,numbins):
sequences[i] = [sequences[i][z][seq_start:seq_end] for z in range(0,len(sequences[i]))]
batch_vec_temp = []
seqs = []
for i in range(0,numbins):
tempseqs = list(set(sequences[i]))
seqs = seqs + [tempseqs[z][mut_region_start:mut_region_start + mut_region_length] for z in range(0,len(tempseqs))]
batch_vec_temp = batch_vec_temp + [i for z in range(0,len(tempseqs))]
batch_vec_temp = np.array(batch_vec_temp)
reverse_dict = {}
start_dict = {}
end_dict = {}
csvfile = open(barcodefn,'r')
reader = csv.DictReader(csvfile)
for row in reader:
barcode_dict[row['experiment_name']] = row['fwd_barcode']
reverse_dict[row['experiment_name']] = row['rev_barcode']
start_dict[row['experiment_name']] = row['startseq']
end_dict[row['experiment_name']] = row['endseq']
numseq = [[] for i in range(0,4)]
seq_mat = [[] for i in range(0,4)]
#read in sequences from files
sequences = [readuniqueseqssingleend.collatedmat(fn) for fn in fnnames]
seq_start = len(barcode_dict[expname])
seq_end = seq_start + int(end_dict[expname]) - int(start_dict[expname])
#seq_end = sequences[0][0].find(reverse_dict[expname][0:5])
print 'sequences loaded'
print len(sequences[0])
for i in range(0,numbins):
sequences[i] = [sequences[i][z][seq_start:seq_end] for z in range(0,len(sequences[i]))]
batch_vec_temp = []
seqs = []
#filter sequences for only unique sequences in the target range.
for i in range(0,numbins):
#tempseqs = list(set(sequences[i])) I instead added this to the next line to make sure only sequences with unique mutated regions are counted.
tempseqs = sequences[i]
s2 = list(set([tempseqs[z][mut_region_start:mut_region_start + mut_region_length] for z in range(0,len(tempseqs))]))