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bayesian_simulator.py
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bayesian_simulator.py
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import os, sys, subprocess, re
from argparse import ArgumentParser, FileType
import random
import numpy as np
def rev_comp(seq):
rev = ""
for nt in seq[::-1]:
nt = nt.upper()
if nt == 'A':
rev += 'T'
elif nt == 'T':
rev += 'A'
elif nt == 'G':
rev += 'C'
elif nt == 'C':
rev += 'G'
return rev
def simulation (ref_seq,
out_fname,
num,
left_bound,
right_bound,
dyad_offset,
mist_prob,
indel_prob):
def dyad_sampling(template, left_bound, right_bound, N):
dyadmap = [0.0]*len(template)
st, ed = left_bound, len(template) - 1 - right_bound
for i in range(N):
pos = random.randint(st,ed)
dyadmap[pos] += 1
return dyadmap
def get_lambda(template, dyadmap, tlist, blist, eplist, delist, offset):
tlamb, blamb = [0.0]*len(template), [0.0]*len(template)
for i in range(len(template)):
if i + offset < len(template):
tlamb[i] += dyadmap[i+offset]*tlist[i]
if i - offset >= 0:
blamb[i] += dyadmap[i-offset]*blist[i]
total1, total2 = 0.0, 0.0
for j in range(len(template)):
if j != i + offset:
total1 += dyadmap[j]
if j != i - offset:
total2 += dyadmap[j]
tlamb[i] += total1*eplist[i]
blamb[i] += total2*delist[i]
return tlamb, blamb
def make_cleavage(template, tlamb, blamb):
tnum_list, bnum_list = [], []
frags_list = []
for i in range(len(template)):
tnum = np.random.poisson(tlamb[i],1)
bnum = np.random.poisson(blamb[i],1)
tnum_list.append(tnum)
bnum_list.append(bnum)
right_frag = template[i:]
left_frag = rev_comp(template[:i+1])
for k in range(tnum):
frags_list.append(right_frag)
for k in range(bnum):
frags_list.append(left_frag)
return tnum_list, bnum_list, frags_list
def mutations (seq_list, mist_prob, indel_prob):
def mismatch(seq, prob):
nts = set(['A','T','C','G'])
new_seq = ""
for i in range(len(seq)):
if random.random() < prob:
subs = nts - set(seq[i])
new_seq += random.choice(list(subs))
else:
new_seq += seq[i]
return new_seq
def indel(seq, prob):
nts = ['A','T','C','G']
new_seq = ""
for i in range(len(seq)):
if random.random() < prob:
if random.random() < 0.5 or i == len(seq):
new_seq += random.choice(nts)
else:
continue
new_seq += seq[i]
return new_seq
new_list = []
for seq in seq_list:
new_seq = indel(mismatch(seq, prob=mist_prob), prob=indel_prob)
new_list.append(new_seq)
return new_list
f = open(out_fname + '.fastq', 'w')
for ref_id, template in ref_seq.items():
#if not (ref_id.startswith('AAA-46') or ref_id.startswith("AAAAAAAAAAAA-157")):
# continue
print ref_id
dyadmap = dyad_sampling(template, left_bound, right_bound, N=num)
#dyadmap = [0.0]*(225/2) + [100] + [0.0]*(225/2)
tlist, blist = [1.0]*len(template), [0.5]*len(template)
eplist, delist = [0.001]*len(template), [0.0005]*len(template)
tlamb, blamb = get_lambda(template, dyadmap, tlist, blist, eplist, delist, offset=dyad_offset)
tnum_list, bnum_list, frags_list = make_cleavage(template, tlamb, blamb)
seqs_list = mutations(frags_list, mist_prob, indel_prob)
for i in range(len(seqs_list)):
seq = seqs_list[i]
print >> f, "@M01556:71:000000000-BHYK4:1:1101:11804:1000 1:N:0:CTTGTA" # read ID
print >> f, seq # read seq
print >> f, '+' # optional
print >> f, 'G'*len(seq) # quality score
f.close()
if __name__ == '__main__':
parser = ArgumentParser(description='make simulated data set for slide-seq')
parser.add_argument(metavar='-x',
dest='ref_fname',
type=str,
help='reference sequence prefix filename')
parser.add_argument('-o',
dest='out_fname',
type=str,
help='output prefix filename')
parser.add_argument('-n',
dest='num',
type=int,
default = 10000,
help='dyad sampling number for each sequence')
parser.add_argument('--left_bound',
dest='left_bound',
type=int,
default = 147/2,
help='left bound length for dyad sampling')
parser.add_argument('--right_bound',
dest='right_bound',
type=int,
default = 147/2,
help='right bound length for dyad sampling')
parser.add_argument('--dyad-offset',
dest="dyad_offset",
default=52,
type=int,
help='off-set length from cut site to dyad position')
parser.add_argument('--mist_prob',
dest='mist_prob',
type=float,
default = 0.02,
help='mismatch probabilty')
parser.add_argument('--indel_prob',
dest='indel_prob',
type=float,
default = 0.03,
help='insertion/deletion probabilty')
args = parser.parse_args()
ref_seq = {}
for line in open(args.ref_fname + '.ref'):
line = line.strip()
if line.startswith('>'):
ref_id = line[1:].strip()
continue
if line:
assert ref_id not in ref_seq
line = line.strip()
ref_seq[ref_id] = line
if not args.out_fname:
out_fname = args.ref_fname
else:
out_fname = args.out_fname
simulation (ref_seq,
out_fname,
args.num,
args.left_bound,
args.right_bound,
args.dyad_offset,
args.mist_prob,
args.indel_prob
)