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generate_normal.py
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import tracemalloc
import psutil
import os
import msprime
import tskit
import numpy as np
import gzip
import glob
import string
import random
import pandas as pd
import time
import pickle
import sys
from Bio import SeqIO
from datasketch import *
import math
import re
#CONSTANTS
tab = str.maketrans("ACTG", "TGAC")
list_of_bases = ['A', 'C', 'T', 'G']
list_of_pairs = ['CA', 'CG', 'CT', 'TA', 'TC', 'TG']
#signature parameters
sig_df = pd.read_csv('./data/signatures.txt', sep = '\t', header = None)
sig_df = sig_df.iloc[1:]
sig_df = sig_df.iloc[:,1:]
signatures_matrix = sig_df.to_numpy()
num_signatures = 78
use_signatures = False
signature_alpha = 10
signature_distributions = [float(1/num_signatures)]*num_signatures
#genome and exome delimiters
full_genome = '/projects/schwartzlabscratch/DesignOpt/data/fakegenome.fa'
EXON_FILE = '/projects/schwartzlabscratch/DesignOpt/data/fakeexonsegments.txt'
#CHANGE THIS TO WHERE YOU WANT TO STORE THE DATA!
reversemap = {'chr1':0, 'chr10':2, 'chr11':4, 'chr12':6, 'chr13':8, 'chr14':10, 'chr15':12, 'chr16':14, 'chr17':16, 'chr18':18, 'chr19':20, 'chr2':22, 'chr20':24, 'chr21':26, 'chr22':28, 'chr3':30, 'chr4':32, 'chr5':34, 'chr6':36, 'chr7':38, 'chr8':40, 'chr9':42, 'chrX':44, 'chrY':45}
numchrommap = {0: 'chr1', 1: 'chr1', 2: 'chr10', 3: 'chr10', 4: 'chr11', 5: 'chr11', 6: 'chr12', 7: 'chr12', 8: 'chr13', 9: 'chr13', 10: 'chr14', 11: 'chr14', 12: 'chr15', 13: 'chr15', 14: 'chr16', 15: 'chr16', 16: 'chr17', 17: 'chr17', 18: 'chr18', 19: 'chr18', 20: 'chr19', 21: 'chr19', 22: 'chr2', 23: 'chr2', 24: 'chr20', 25: 'chr20', 26: 'chr21', 27: 'chr21', 28: 'chr22', 29: 'chr22', 30: 'chr3', 31: 'chr3', 32: 'chr4', 33: 'chr4', 34: 'chr5', 35: 'chr5', 36: 'chr6', 37: 'chr6', 38: 'chr7', 39: 'chr7', 40: 'chr8', 41: 'chr8', 42: 'chr9', 43: 'chr9', 44: 'chrX', 45: 'chrY'}
chrom_dict = ['chr1', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19',
'chr2', 'chr20', 'chr21', 'chr22', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chrX', 'chrY']
reduced_chrom_dict = ['chr1', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17',
'chr18', 'chr19', 'chr2', 'chr20', 'chr21', 'chr22', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9']
sex_chroms = ['chrX', 'chrY']
def getmemory():
process = psutil.Process(os.getpid())
print(process.memory_info().rss)
GLOBAL_CHROM_NUM = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]
def getSVSize(string_size):
very_small_criterion = max(2, int(0.000001*string_size))
small_criterion = max(2, int(0.00001*string_size))
medium_criterion = max(2, int(0.001*string_size))
medium_large_criterion = max(2, int(0.1*string_size))
large_criterion = max(2, int(0.5*string_size))
if(string_size < 15):
return string_size
if(string_size < 1000):
small_size = 10 #minimal size of a structural variant
elif(string_size >= 1000 and string_size < 1e6):
small_size = 40
else:
small_size = 60
maximal_size = int(0.9*string_size)
return_val0 = np.random.negative_binomial(1, 1/(very_small_criterion))
return_val1 = np.random.negative_binomial(1, 1/(small_criterion))
return_val2 = np.random.negative_binomial(1, 1/(medium_criterion))
return_val3 = np.random.negative_binomial(1, 1/(medium_large_criterion))
return_val4 = np.random.negative_binomial(1, 1/(large_criterion))
zed = random.random()
if(zed < 0.2):
o_return_val = return_val0
elif(zed >= 0.2 and zed < 0.75):
o_return_val = return_val1
elif(zed >= 0.75 and zed < 0.95):
o_return_val = return_val2
elif(zed >= 0.95 and zed < 0.995):
o_return_val = return_val3
else:
o_return_val = return_val4
if (o_return_val < small_size):
return small_size
elif(o_return_val > maximal_size):
return maximal_size
else:
return int(o_return_val)
def getDirichletCloneFromDistn(num_clones, alpha_dir, the_distn):
cumulate_product = 1.0
current_weights = []
ep = 0.001
epsil = 1-ep
tot = 0.0
while(tot < epsil):
beta = random.betavariate(1, alpha_dir)
pi_app = beta*cumulate_product
cumulate_product = cumulate_product * (1-beta)
current_weights.append(pi_app)
tot += pi_app
sum_current_weights = sum(current_weights)
weights = []
for i in current_weights:
weights.append(i/sum_current_weights)
print(weights)
distn = [0.0]*(num_clones+1)
for i in weights:
all_choices = list(range(num_clones+1))
pos = random.choices(all_choices, weights=the_distn)[0]
distn[pos] = distn[pos] + i
return distn
def getKmers(sequence, size):
return {sequence[x:x+size].upper() for x in range(len(sequence) - size + 1)}
def getDirichletClone(num_clones, alpha_dir):
cumulate_product = 1.0
current_weights = []
ep = 0.001
epsil = 1-ep
tot = 0.0
while(tot < epsil):
beta = random.betavariate(1, alpha_dir)
pi_app = beta*cumulate_product
cumulate_product = cumulate_product * (1-beta)
current_weights.append(pi_app)
tot += pi_app
sum_current_weights = sum(current_weights)
weights = []
for i in current_weights:
weights.append(i/sum_current_weights)
distn = [0.0]*(num_clones+1)
for i in weights:
pos = random.randint(0, num_clones)
distn[pos] = distn[pos] + i
return distn
def pickdclone(prob_list, num_clones):
return np.random.choice(np.arange(num_clones+1), p=prob_list)
def SNPSig(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
seq = seqs[chrom[0]]
target = GLOBAL_CHROM_NUM[chrom[0]]
signature_drawn_dist = getDirichletCloneFromDistn(
num_signatures-1, signature_alpha, signature_distributions)
signature_drawn = pickdclone(
signature_drawn_dist, len(signature_drawn_dist)-1)
distribution_over_mutations = list(signatures_matrix[:, signature_drawn])
position = pickdclone(distribution_over_mutations,
len(distribution_over_mutations)-1)
pos = 0
first_letter = int(position/24)
last_letter = position % 24 % 4 # if == 0 > A if 1 > C if 2 > G if 3 >T
middle_two = int((position % 24)/4)
if random.random() < 0.5:
fullstring = list_of_bases[first_letter] + \
list_of_pairs[middle_two][0] + list_of_bases[last_letter]
mutated_base = list_of_pairs[middle_two][1]
indices = [m.start() for m in re.finditer(fullstring, seq)]
if(len(indices) > 0):
pos = random.choice(indices)+1
seq = seq[:pos] + mutated_base + seq[pos+1:]
else:
pos = -1
else:
altstring = list_of_bases[first_letter] + \
list_of_pairs[middle_two][0].translate(
tab) + list_of_bases[last_letter]
mutated_base = list_of_pairs[middle_two][1].translate(tab)
indices = [m.start() for m in re.finditer(altstring, seq)]
if(len(indices) > 0):
pos = random.choice(indices)+1
seq = seq[:pos] + mutated_base + seq[pos+1:]
else:
pos = -1
seqs[chrom[0]] = seq
return [-1], [pos, chrom[0], target, pos+1, numchrommap[target]]
def speedSNP(seqs):
BASES = ['A', 'C', 'T', 'G']
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
seq = seqs[chrom[0]]
target = GLOBAL_CHROM_NUM[chrom[0]]
n = len(seq)
pos = 0
all_positions = []
if (n == 0):
return [-1], [-1, chrom[0]]
elif(n == 1):
n_repeats = 1
all_positions = [0]
for i in range(n_repeats):
oldseq = seq
char = oldseq[pos]
while(char == seq[pos]):
char = random.choice(BASES)
seq = seq[:0]+char
else:
n_repeats = 1
for i in range(n_repeats):
pos = random.randint(0, n-1)
all_positions.append(pos)
oldseq = seq
char = oldseq[pos]
while(char == seq[pos]):
char = random.choice(BASES)
seq = seq[:pos] + char + seq[pos+1:]
seqs[chrom[0]] = seq
return [-1], [pos, chrom[0], target, pos+1, numchrommap[target]]
def insertionsmall(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
target = GLOBAL_CHROM_NUM[chrom[0]]
BASES = ['A', 'C', 'T', 'G']
seq = seqs[chrom[0]]
n = len(seq)
pos = random.randint(0, n)
number_of_bases_to_insert = random.randint(1, 15)
ins_str = ''
ins_list = [pos]*number_of_bases_to_insert
for i in range(number_of_bases_to_insert):
ins_str = ins_str + random.choice(BASES)
new_seq = seq[:pos] + ins_str + seq[pos:]
seqs[chrom[0]] = new_seq
return [ins_str], [pos, number_of_bases_to_insert, chrom[0], target, pos+1, numchrommap[target]]
def deletionsmall(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
seq = seqs[chrom[0]]
target = GLOBAL_CHROM_NUM[chrom[0]]
oldseq = seq
n = len(seq)
pos = 0
if (n == 0):
pos = -1
elif (n == 1):
pos = 0
else:
pos = random.randint(0, n-1)
deletion_range = random.randint(1, 15)
new_seq = seq[:pos] + seq[pos+deletion_range:]
seqs[chrom[0]] = new_seq
position = int(pos)
chromosome = int(chrom[0])
return [-1], [position, deletion_range, chromosome, target, position+1, numchrommap[target]]
def CNV(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
seq = seqs[chrom[0]]
target = GLOBAL_CHROM_NUM[chrom[0]]
n = len(seq)
if (n == 1 or n == 0):
return[-1], [-1, chrom[0]]
#lowbd = int(0.0001*n)
#upbd = int(0.1*n)
#dist = int(random.randint(lowbd, upbd))
dist = n
while(dist > 0.2*n):
dist = getSVSize(n)
if(dist < 50):
break
stidx = random.randint(0, n-1)
edidx = min(stidx + dist, n-1)
max_reps = 4
rep_num = random.randint(2, max_reps)
subseq = seq[stidx:edidx]
repeatedsubseq = subseq*rep_num
start_string = seq[:stidx]
end_string = seq[edidx:]
full_string = start_string + repeatedsubseq + end_string
seqs[chrom[0]] = full_string
return [-1], [stidx, edidx, rep_num, chrom[0], target, stidx+1, edidx+1, numchrommap[target]]
def aneuploidy(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
seq = seqs[chrom[0]]
target = GLOBAL_CHROM_NUM[chrom[0]]
max_reps = 3
rep_num = random.randint(2, max_reps)
if (random.random() < 0.2):
del seqs[chrom[0]]
else:
for i in range(rep_num-1):
GLOBAL_CHROM_NUM.append(target)
seqs.append(seq)
return [-1], [rep_num, chrom[0], target, numchrommap[target]]
def deletion(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
target = GLOBAL_CHROM_NUM[chrom[0]]
seq = seqs[chrom[0]]
n = len(seq)
if(n == 0):
return [-1], [-1, chrom[0]]
elif(n == 1):
seqs[chrom[0]] = ''
return [-1], [0, chrom[0], target]
else:
#lowbd = int(0.0001*n)
#upbd = int(0.01*n)
#dist = int(random.randint(lowbd, upbd))
dist = getSVSize(n)
stidx = random.randint(0, n-1)
edidx = min(stidx + dist, n-1)
start_string = seq[:stidx]
end_string = seq[edidx:]
full_string = start_string + end_string
seqs[chrom[0]] = full_string
return [-1], [stidx, edidx, chrom[0], target, stidx+1, edidx+1, numchrommap[target]]
def inversion(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
target = GLOBAL_CHROM_NUM[chrom[0]]
seq = seqs[chrom[0]]
n = len(seq)
if (n == 0 or n == 1):
return [-1], [-1, chrom[0]]
dist = getSVSize(n)
stidx = random.randint(0, n-1)
edidx = min(stidx + dist, n-1)
subseq = seq[stidx:edidx]
reversed_subseq = subseq[::-1]
start_string = seq[:stidx]
end_string = seq[edidx:]
full_string = start_string + reversed_subseq + end_string
seqs[chrom[0]] = full_string
return [-1], [stidx, edidx, chrom[0], target, stidx+1, edidx+1, numchrommap[target]]
def kataegis(seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
target = GLOBAL_CHROM_NUM[chrom[0]]
seq = seqs[chrom[0]]
n = len(seq)
if (n == 0 or n == 1):
return [-1], [-1, chrom[0]]
#lowbd = int(0.1*n)
#upbd = int(0.2*n)
#dist = int(random.randint(lowbd, upbd))
dist = getSVSize(n)
stidx = random.randint(0, n-1)
edidx = min(stidx + dist, n-1)
type_of_variation = random.randint(0, 2)
subseq = seq[stidx:edidx]
new_seq = ''
if(type_of_variation == 0):
def rand_replace(c): return 'T' if random.random(
) < 0.8 and c == 'C' else c
new_seq = ''.join([rand_replace(c) for c in subseq])
elif(type_of_variation == 1):
def rand_replace(c): return 'A' if random.random(
) < 0.8 and c == 'C' else c
new_seq = ''.join([rand_replace(c) for c in subseq])
else:
def rand_replace(c): return 'G' if random.random(
) < 0.8 and c == 'C' else c
new_seq = ''.join([rand_replace(c) for c in subseq])
start_string = seq[:stidx]
end_string = seq[edidx:]
full_string = start_string + new_seq + end_string
# Pick mutational type
seqs[chrom[0]] = full_string
return [-1], [stidx, edidx, type_of_variation, chrom[0], target, stidx+1, edidx+1, numchrommap[target]]
def translocation(seqs):
try:
c = list(range(len(seqs)))
chrom = random.sample(c, 2)
seq1 = seqs[chrom[0]]
seq2 = seqs[chrom[1]]
target1 = GLOBAL_CHROM_NUM[chrom[0]]
target2 = GLOBAL_CHROM_NUM[chrom[1]]
l1 = len(seq1)
l2 = len(seq2)
bkpt1 = random.randint(0, l1-1)
bkpt2 = random.randint(0, l1-1)
rng_recip = random.random()
if(rng_recip < 0.95):
first_part = seq1[:bkpt1]
second_part = seq1[bkpt1:]
fp2 = seq2[:bkpt2]
sp2 = seq2[bkpt2:]
string1 = first_part + sp2
string2 = fp2 + second_part
seqs[chrom[0]] = string1
seqs[chrom[1]] = string2
return [-1], [bkpt1, bkpt2, 1, chrom[0], chrom[1], target1, target2, numchrommap[target1], numchrommap[target2]]
else:
first_part = seq1[:bkpt1]
second_part = seq1[bkpt1:]
fp2 = seq2[:bkpt2]
sp2 = seq2[bkpt2:]
string1 = first_part
string2 = fp2 + sp2 + second_part
seqs[chrom[0]] = string1
seqs[chrom[1]] = string2
return [-1], [bkpt1, bkpt2, 0, chrom[0], chrom[1], target1, target2, numchrommap[target1], numchrommap[target2]]
except:
return [-1], [-1]
def numpy_choices(seq_len, splits):
breakpoints = np.random.choice(seq_len, splits-1, replace=False)
breakpoints.sort()
return breakpoints.tolist()
def numpy_permute(splits):
return np.random.permutation(splits).tolist()
def chromothripsis(seqs):
keep_frequency = random.random()
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
target = GLOBAL_CHROM_NUM[chrom[0]]
orig_seq = seqs[chrom[0]]
n = len(orig_seq)
if (n == 0 or n == 1):
return [-1], [-1, chrom[0]]
NotLongEnough = True
attempts = 0
stidx = 0
edidx = 0
splits = 0
while(NotLongEnough):
splits = random.randint(2, 100)
#lowbd = int(0.1*n)
#upbd = int(0.9*n)
#distance = int(random.randint(lowbd, upbd))
dist = getSVSize(n)
stidx = random.randint(0, n-1)
edidx = min(stidx + dist, n-1)
dist = edidx - stidx
attempts += 1
if(dist > splits):
NotLongEnough = False
break
if(attempts > 10):
return [-1], [-1, chrom[0]]
first_part = orig_seq[:stidx]
last_part = orig_seq[edidx:]
seq = orig_seq[stidx:edidx]
seq_len = len(seq)
breakpoints = numpy_choices(seq_len, splits)
subseq = []
curridx = 0
for i in breakpoints:
subseq.append(seq[curridx:i])
curridx = i
subseq.append(seq[breakpoints[-1]:])
rearrange = numpy_permute(splits)
n_to_select = int(splits * keep_frequency)
n_to_select = len(rearrange)
rearrange = random.sample(list(rearrange), n_to_select)
build_seq = ''
for i in rearrange:
build_seq += subseq[i]
seqs[chrom[0]] = first_part + build_seq + last_part
breakpoints = list(breakpoints)
return [-1], [stidx, edidx, splits, chrom[0], target, stidx+1, edidx+1, numchrommap[target]]
def BFB(seqs):
try:
n_repeats = random.randint(2, 4)
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
target = GLOBAL_CHROM_NUM[chrom[0]]
seq = seqs[chrom[0]]
curr_seq = seq
all_bkpt = []
for i in range(n_repeats):
bkpoint = random.randint(1, len(curr_seq)-2)
all_bkpt.append(bkpoint)
first_portion = curr_seq[:bkpoint]
next_portion = first_portion[::-1]
curr_seq = first_portion + next_portion
seqs[chrom[0]] = curr_seq
return all_bkpt, [n_repeats, chrom[0], target, numchrommap[target]]
except:
return [-1], [-1]
def getsummation(seq_len, splits):
breakpoints = np.random.choice(seq_len, splits-1, replace=False)
breakpoints.sort()
zed = breakpoints.tolist()
new_list = []
idx = 0
for i in zed:
new_list.append(i-idx)
idx = i
new_list.append(seq_len - idx)
return new_list
# this is the slowest one, understandably
def chromoplexy(seqs):
try:
n_chroms = random.randint(2, 6)
c = list(range(len(seqs)))
chrom = random.sample(c, n_chroms)
list_of_start_tels = []
list_of_end_tels = []
middle_segs = []
total_splits = 0
for i in range(n_chroms):
curr_sequence = seqs[chrom[i]]
num_splits = random.randint(2, 20)
breakpoints = numpy_choices(len(curr_sequence), num_splits)
total_splits += num_splits
curridx = 0
for i in breakpoints[1:]:
middle_segs.append(curr_sequence[curridx:i])
curridx = i
list_of_end_tels.append(curr_sequence[breakpoints[-1]:])
list_of_start_tels.append(curr_sequence[0:breakpoints[0]])
usable_splits = total_splits - 2*(n_chroms)
splitter = getsummation(usable_splits, n_chroms)
for i in range(len(splitter)):
build_str = list_of_start_tels.pop(
random.randrange(len(list_of_start_tels)))
for segs in range(splitter[i]):
build_str = build_str + \
middle_segs.pop(random.randrange(len(middle_segs)))
build_str = build_str + \
list_of_end_tels.pop(random.randrange(len(list_of_end_tels)))
seqs[chrom[i]] = build_str
return [-1], [total_splits, n_chroms]
except:
return [-1], [-1]
def generateMatrix(the_matrix, list_of_weights, time_matrix):
rate_matrix = getRateMatrix(time_matrix, list_of_weights)
pop = time_matrix.shape[0]
avg_rate = 0.0
num_rates = 0
for i in range(pop):
for j in range(pop):
if(time_matrix[i, j] != 0):
time = 0
list_of_times = []
while(time < time_matrix[i, j]):
num_rates += 1
avg_rate += rate_matrix[i, j]
mutations_per_branch = rate_matrix[i, j]*time_matrix[i, j]
wait_time = np.random.exponential(1/rate_matrix[i, j])
time += wait_time
if(time < time_matrix[i, j]):
list_of_times.append(time)
the_matrix[i, j] = list_of_times
avg_rate = avg_rate/num_rates
return the_matrix, avg_rate
def getTree(num_clones, pop, working_dir):
tree_sequence = msprime.simulate(
sample_size=num_clones, Ne=pop, recombination_rate=0)
tree_sequence.dump(working_dir + '/tree_sequence.tree')
tree = tree_sequence.first()
return tree
def getPaths(tree, num_clones):
list_of_paths = []
time_matrix = np.zeros((tree.root + 1, tree.root+1))
for u in range(num_clones):
path = []
while u != tskit.NULL:
path.insert(0, u)
time_matrix[u, tree.parent(u)] = tree.get_branch_length(u)
u = tree.parent(u)
list_of_paths.append(path)
time_matrix = np.transpose(time_matrix)
return list(list_of_paths)
def getTimeMatrix(tree, num_clones):
time_matrix = np.zeros((tree.root + 1, tree.root+1))
dep = 0
for u in range(num_clones):
while u != tskit.NULL:
time_matrix[u, tree.parent(u)] = tree.get_branch_length(u)
dep += tree.get_branch_length(u)
u = tree.parent(u)
time_matrix = np.transpose(time_matrix)
return time_matrix, dep
def getRateMatrix(time_matrix, list_of_weights):
the_shape = np.shape(time_matrix)[0]
rate_matrix = np.zeros((the_shape, the_shape))
for i in range(the_shape):
for j in range(the_shape):
if(time_matrix[i, j] > 0):
rate_matrix[i, j] = np.random.choice(list_of_weights)
return rate_matrix
def generateOrder(tree, time_matrix, list_of_rates):
pop = time_matrix.shape[0]
SNV_matrix = np.zeros((pop, pop), dtype=object)
CNV_matrix = np.zeros((pop, pop), dtype=object)
DELETION_matrix = np.zeros((pop, pop), dtype=object)
DELETIONSMALL_matrix = np.zeros((pop, pop), dtype=object)
INVERSION_matrix = np.zeros((pop, pop), dtype=object)
TRANSLOCATION_matrix = np.zeros((pop, pop), dtype=object)
BFB_matrix = np.zeros((pop, pop), dtype=object)
CHROMOTHRIPSIS_matrix = np.zeros((pop, pop), dtype=object)
CHROMOPLEXY_matrix = np.zeros((pop, pop), dtype=object)
INSERTIONSMALL_matrix = np.zeros((pop, pop), dtype=object)
KATAEGIS_matrix = np.zeros((pop, pop), dtype=object)
ANEUPLOIDY_matrix = np.zeros((pop, pop), dtype=object)
SNV_matrix, snv_rate = generateMatrix(
SNV_matrix, list_of_rates[0], time_matrix)
CNV_matrix, cnv_rate = generateMatrix(
CNV_matrix, list_of_rates[1], time_matrix)
DELETION_matrix, deletion_rate = generateMatrix(
DELETION_matrix, list_of_rates[2], time_matrix)
DELETIONSMALL_matrix, delsmall_rate = generateMatrix(
DELETIONSMALL_matrix, list_of_rates[3], time_matrix)
INVERSION_matrix, inv_rate = generateMatrix(
INVERSION_matrix, list_of_rates[4], time_matrix)
TRANSLOCATION_matrix, trans_rate = generateMatrix(
TRANSLOCATION_matrix, list_of_rates[5], time_matrix)
BFB_matrix, bfb_rate = generateMatrix(
BFB_matrix, list_of_rates[6], time_matrix)
CHROMOTHRIPSIS_matrix, chromothripsis_rate = generateMatrix(
CHROMOTHRIPSIS_matrix, list_of_rates[7], time_matrix)
CHROMOPLEXY_matrix, chromoplexy_rate = generateMatrix(
CHROMOPLEXY_matrix, list_of_rates[8], time_matrix)
INSERTIONSMALL_matrix, inssmall_rate = generateMatrix(
INSERTIONSMALL_matrix, list_of_rates[9], time_matrix)
KATAEGIS_matrix, kat_rate = generateMatrix(
KATAEGIS_matrix, list_of_rates[10], time_matrix)
ANEUPLOIDY_matrix, an_rate = generateMatrix(
ANEUPLOIDY_matrix, list_of_rates[11], time_matrix)
mutationedge_list = []
fin_rate_list = [snv_rate, cnv_rate, deletion_rate, delsmall_rate, inv_rate, trans_rate,
bfb_rate, chromothripsis_rate, chromoplexy_rate, inssmall_rate, kat_rate, an_rate]
for i in range(pop-1):
# gives mutations from i to its parent ONLY
parent_node = tree.parent(i)
l1 = SNV_matrix[parent_node, i]
l2 = CNV_matrix[parent_node, i]
l3 = DELETION_matrix[parent_node, i]
l4 = DELETIONSMALL_matrix[parent_node, i]
l5 = INVERSION_matrix[parent_node, i]
l6 = TRANSLOCATION_matrix[parent_node, i]
l7 = BFB_matrix[parent_node, i]
l8 = CHROMOTHRIPSIS_matrix[parent_node, i]
l9 = CHROMOPLEXY_matrix[parent_node, i]
l10 = INSERTIONSMALL_matrix[parent_node, i]
l11 = KATAEGIS_matrix[parent_node, i]
l12 = ANEUPLOIDY_matrix[parent_node, i]
merged_list = l1+l2+l3+l4+l5+l6+l7+l8+l9+l10
merged_list.sort()
ordered_muts = []
for k in merged_list:
if(k in l1):
ordered_muts.append(0)
elif(k in l2):
ordered_muts.append(1)
elif(k in l3):
ordered_muts.append(2)
elif(k in l4):
ordered_muts.append(3)
elif(k in l5):
ordered_muts.append(4)
elif(k in l6):
ordered_muts.append(5)
elif(k in l7):
ordered_muts.append(6)
elif(k in l8):
ordered_muts.append(7)
elif(k in l9):
ordered_muts.append(8)
elif(k in l10):
ordered_muts.append(9)
elif(k in l11):
ordered_muts.append(10)
elif(k in l12):
ordered_muts.append(11)
else:
print('error no mut match')
print(ordered_muts)
mutationedge_list.append(ordered_muts)
return list(mutationedge_list), list(fin_rate_list)
def wgz(floc, ob):
with open(floc, 'w') as f:
for i in ob:
f.write(f'{i}\n')
def rgz(floc):
return_list = []
with open(floc, 'r') as file:
return_list = [line.strip() for line in file]
return return_list
def applyMutations(tot_nodes, working_dir, list_of_paths, use_signatures, mutationedge_list):
d = {}
infos = {}
muts = {}
for i in range(tot_nodes):
d[i] = 'na'
d[tot_nodes-1] = working_dir + str(tot_nodes - 1) + '.gz'
infos[tot_nodes - 1] = []
muts[tot_nodes - 1] = []
for path in list_of_paths:
print(path)
for i in range(len(path)-1):
if(d[path[i+1]] == 'na'):
current_genome = []
current_genome = rgz(d[path[i]])
c_infos = infos[path[i]].copy()
c_muts = muts[path[i]].copy()
current_muts = mutationedge_list[path[i+1]]
for k in current_muts:
info = []
if(k == 0):
if(use_signatures):
_, info = SNPSig(current_genome)
else:
_, info = speedSNP(current_genome)
elif(k == 1):
_, info = CNV(current_genome)
elif(k == 2):
_, info = deletion(current_genome)
elif(k == 3):
_, info = deletionsmall(current_genome)
elif(k == 4):
_, info = inversion(current_genome)
elif(k == 5):
_, info = translocation(current_genome)
elif(k == 6):
_, info = BFB(current_genome)
elif(k == 7):
_, info = chromothripsis(current_genome)
elif(k == 8):
_, info = chromoplexy(current_genome)
elif(k == 9):
_, info = insertionsmall(current_genome)
elif(k == 10):
_, info = kataegis(current_genome)
elif(k == 11):
_, info = aneuploidy(current_genome)
else:
print('invalid')
c_infos.append(info)
c_muts.append(k)
d[path[i+1]] = working_dir + str(path[i+1]) + '.gz'
'''
with gzip.open(working_dir + str(path[i+1])+'.gz', 'wb0') as y:
pickle.dump(current_genome, y)
'''
wgz(working_dir + str(path[i+1]) + '.gz', current_genome)
infos[path[i+1]] = c_infos
muts[path[i+1]] = c_muts
else:
donothing = 1
return infos, muts
def clear_dir(working_dir):
files = glob.glob(working_dir+'*')
for f in files:
os.remove(f)
def pickClone(num_clones, dir_conc):
alpha_dir = np.tile(dir_conc, num_clones)
distribution = np.random.dirichlet(alpha_dir)
clone_picked = np.random.choice(range(0, num_clones), p=distribution)
return int(clone_picked)
def writeToFasta(file_loc, curr_string):
import random
with open(file_loc, 'at') as f:
f.write(curr_string)
def getfrag(r):
return_val = 0
while(return_val < r-20):
return_val = np.random.negative_binomial(r, 0.5)
return return_val
def drawPoisson(r):
return_val = 0
return np.random.poisson(r)
def getDNAchunk(length, seqs):
c = list(range(len(seqs)))
chrom = random.sample(c, 1)
seq = seqs[chrom[0]]
n = len(seq)
stidx = random.randint(0, n-1)
edidx = stidx + length
subseq = seq[stidx:edidx]
return subseq
def mutateFrag(frag, err_rate):
num_muts = 0
s = str(frag)
n = len(frag)
q = 1-err_rate
if (err_rate > 0.1):
num_muts = int(random.gauss(n*err_rate, n*err_rate*q))
else:
num_muts = drawPoisson(n*err_rate)
BASES = ['A', 'C', 'T', 'G']
for i in range(num_muts):
pos = random.randint(0, n-1)
oldseq = s
char = oldseq[pos]
while(char == s[pos]):
char = random.choice(BASES)
s = s[:pos] + char + s[pos+1:]
return s
def split(string, length):
return (string[0+i:length+i] for i in range(0, len(string), length))
def revc(sequence):
return sequence.translate(tab)[::-1]
def runSim(num_clones, coverage, rl, read_loc, floc, batch, root, alpha, erate, flag=0):
#enforce int
rl = int(rl)
if(flag == 0):
f = open(floc + 'bulk.fasta', 'w')
elif(flag == 1):
f = open(floc + 'ref.fasta', 'w')
else:
randid = random_str = ''.join(random.choice(
string.ascii_lowercase) for _ in range(3))
f = open(floc + f'singlecell.fasta', 'w')
cov = 0.0
ratio = rl
#random_str = 0
ls = []
if(flag == 2):
ri = random.randint(0, num_clones)
ls = rgz(f'{read_loc}{ri}.gz')
if(flag == 1):
ls = rgz(f'{read_loc}{root}.gz')
giga_list = []
while(cov < coverage):
print(giga_list)
print(cov)
for i in range(batch):
distn = getDirichletClone(num_clones, alpha)
clone = pickdclone(distn, num_clones)
if(flag == 0):
ls = rgz(f'{read_loc}{clone}.gz')
for chrom in ls:
frag_len = 0
while(frag_len <= rl):
frag_len = getfrag(rl)
if random.random() < 0.5:
altchrom = chrom
else:
altchrom = revc(chrom)
for sub in split(altchrom, frag_len):
random_str = ''.join(random.choices(
string.ascii_letters, k=15))
sub = mutateFrag(sub, erate) # check type here
qual = 'K'*len(sub)
giga_list.extend([f'@{random_str}', str(sub), '+', qual])
for x in giga_list:
f.write(x)
f.write('\n')
giga_list.clear()
print('first batch write done')
# f.write('\n'.join(giga_list))
cov += 2*batch
#del giga_list
del giga_list
del ls
def runPairedSim(num_clones, coverage, rl, fl, read_loc, floc, batch, root, alpha, erate, flag=0):
#enfroce int
rl = int(rl)
if(flag == 0):
f = open(floc + 'bulkleft.fasta', 'w')
f2 = open(floc + 'bulkright.fasta', 'w')
elif(flag == 1):
f = open(floc + 'refleft.fasta', 'w')
f2 = open(floc + 'refright.fasta', 'w')
else:
randid = random_str = ''.join(
random.choices(string.ascii_lowercase, k=4))
f = open(floc + f'singlecellleft.fasta', 'w')
f2 = open(floc + f'singlecellright.fasta', 'w')
cov = 0.0
ratio = 2*rl/fl
#random_str = 0
ls = []
if(flag == 2):
ri = random.randint(0, num_clones)
ls = rgz(f'{read_loc}{ri}.gz')
if(flag == 1):
ls = rgz(f'{read_loc}{root}.gz')
giga_list = []
giga_list2 = []
while(cov < coverage):
print(giga_list)
print(giga_list2)
print(cov)
for i in range(batch):
distn = getDirichletClone(num_clones, alpha)
clone = pickdclone(distn, num_clones)
print(clone)
if(flag == 0):
ls = rgz(f'{read_loc}{clone}.gz')
for chrom in ls:
frag_len = 0
while (frag_len <= rl):
frag_len = getfrag(fl)
if random.random() < 0.5:
altchrom = chrom
else:
altchrom = revc(chrom)
for sub in split(altchrom, frag_len):
random_str = ''.join(random.choices(
string.ascii_letters, k=15))
#sub = mutateFrag(sub, erate)
write1 = sub[:rl]
write2 = revc(sub[-rl:])
write1 = mutateFrag(write1, erate)
write2 = mutateFrag(write2, erate)
qual = 'K'*len(write1)
qual2 = 'K'*len(write2)
giga_list.extend([f'@{random_str}', write1, '+', qual])
giga_list2.extend(
[f'@{random_str}', write2, '+', qual2])
for x in giga_list:
f.write(x)
f.write('\n')
for x in giga_list2:
f2.write(x)
f2.write('\n')
giga_list.clear()
giga_list2.clear()
print('first batch write done')
cov += 2*batch*ratio
del giga_list
del giga_list2
del ls
def get_random_str(main_str, substr_len):
if(len(main_str) < substr_len):
return main_str, 0
else:
idx = random.randrange(0, len(main_str) - substr_len + 1)
return main_str[idx : (idx+substr_len)], idx
def exonrunSim(num_clones, coverage, rl, rloc, floc, batch, root, exonDict, numchrommap, subblock, alpha, erate, flag=0):
#enforce int
rl = int(rl)
if(flag == 0):