diff --git a/noisyplotype.py b/noisyplotype.py
index 1388a0f6f3f5758b04ec09a87fc2b893793c2caa..055715f8b3a992f3519bdc6a4b51e64a6aa36348 100644
--- a/noisyplotype.py
+++ b/noisyplotype.py
@@ -1,56 +1,65 @@
import random
-import csv
import math
import numpy as np
#def generate_chr_for_one_ind (chromosome_size, marker_density, err_rate, mean_depth, max_depth, markers_positions, conversion_factor):
-def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor):
+def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor, errA, errB):
'''
Generate a chromosome for a given size, density of markers, and depth (follwing a gaussian distrib with a mean_depth and sd_depth).
Args:
- chromosome_size (int): The size of the chromosome.
- marker_density (float): The density of markers.
- err_rate (float): The error rate of the chromosome.
- mean_depth (float): The mean depth of the chromosome.
- max_depth
- markers_positions
+ mean_depth (float): The mean sequencing depth of the chromosome
+ markers_positions (lst) : liste of marker positions (picked on a grid)
+ conversion_factor (float) : Value of the bp per cM conversion (bp chromosome size / cM chromosome size)
+ errA & errB (float): Respectively the error rate of observing a B whereas the genotype is truely a A and a A whereas the genotype is truely a B
Returns:
- list: The chromosome.
+ segment: A chromosome with each position having a given genotype with a given site depth and allele depth
+ segment_error : A chromosome with each position having a given genotype with a given site depth and allele depth considering the error rates errA and errB
'''
-
+
+ # Create an empty list to store the chromosome
segment = []
+ # Create an empty list to store the chromosome with error
+ segment_error = []
+ breakpoints = []
- # Iterate through the list of markers positions
+ # Iterate through the list of markers positions (fixed)
+ previousMarkerGenotype = ""
for i in range(0, len(markers_positions)):
+ recomb1 = False
+ recomb2 = False
# If the current marker is not the first one
if i > 0:
# Calculate the interval between the current marker and the previous one
- #TODO
- #Kosambi inverse function
+ #using the Kosambi inverse function to estimate the chance of recombination
IntervalWithPreviousMarker = markers_positions[i] - markers_positions[i-1]
- tempcM = 2 * (IntervalWithPreviousMarker / 100) / conversion_factor
+ tempcM = 2 * (IntervalWithPreviousMarker / 100) / conversion_factor ## Conversion factor is used to have the bp per cM
IntervalWithPreviousMarkerInRF = 0.5 * ((math.exp(tempcM) - math.exp(-tempcM)) / (math.exp(tempcM) + math.exp(-tempcM)))
rnd = random.random()
- # If the random number is greater than the probability of a recombination
- #print("before",genotype1, genotype2)
- if rnd > 1 - IntervalWithPreviousMarkerInRF: # a recombination occurs
+
+ # If the random number is greater than the probability of a recombination a recombination occurs
+ # Done for genotype 1
+ if rnd > 1 - IntervalWithPreviousMarkerInRF:
+ recomb1 = True
# If the previous marker is A, change the genotype to B
if genotype1 == "A":
- genotype1 = "B";
+ genotype1 = "B"
else :
- genotype1 = "A";
+ genotype1 = "A"
+
+ # Done for genotype 2
rnd = random.random()
if rnd > 1 - IntervalWithPreviousMarkerInRF:
+ recomb2 = True
# If the previous marker is B, change the genotype to A
if genotype2 == "A":
- genotype2 = "B";
+ genotype2 = "B"
else :
- genotype2 = "A";
- #print("after",genotype1, genotype2)
- # If the current marker is the first one, fix the two first genotypes.
+ genotype2 = "A"
+
+ # If the current marker is the first one, Set the two first genotypes (random).
else:
rnd = random.random()
# If the random number is greater than 0.5
@@ -64,72 +73,157 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor):
genotype2 = "B"
else :
genotype2 = "A"
+
+ # initialize previousMarkerGenotype
+ if genotype1 == "A" and genotype2 == "A":
+ previousMarkerGenotype = "A"
+ elif genotype1 == "B" and genotype2 == "B":
+ previousMarkerGenotype = "B"
+ else :
+ previousMarkerGenotype = "H"
+ #Breakpoints
+ if recomb1 or recomb2:
+ #this is a breakpoint [beforeBkpAfter, transition]
+ transition = previousMarkerGenotype + " => "
- #TODO : ajouter erreur de séquençage
+ if genotype1 == "A" and genotype2 == "A":
+ previousMarkerGenotype = "A"
+ elif genotype1 == "B" and genotype2 == "B":
+ previousMarkerGenotype = "B"
+ else :
+ previousMarkerGenotype = "H"
+
+ transition = transition + previousMarkerGenotype
- # Calculate the depth of the current marker
+ breakpoints.append([i,transition])
+
+ # Calculate the site depth of the current marker
g = np.random.poisson(mean_depth)
# If the depth of the current marker is less than 0 (safeguard)
if g < 0:
g = 0
# Round the depth of the current marker
site_depth = round(g)
- # Initialize x and y
+ # Initialize x and y (with and without error)
x = 0
y = 0
+ x_error = 0
+ y_error = 0
+
# If the depth of the current marker is 0, genotype is Missing Data
if site_depth == 0:
genotype = "./."
+ genotype_error = "./."
else :
- # If the current marker is A and the previous marker is A, genotype if homozygote A (ref, 0/0)
+ # If the site is homozygous A (ref, 0/0)
if genotype1 == "A" and genotype2 == "A":
x = site_depth
y = 0
genotype = "0/0"
+ x_error = 0
+ y_error = 0
+ # Considering the sequencing and mapping error at each site for a given site depth
+ for j in range(0,site_depth):
+ rnd = random.random()
+ # If the random number is smaller the error rate, we increase the wronge allele depth
+ if rnd < errA:
+ x_error = x_error + 0
+ y_error = y_error + 1
+ else :
+ x_error = x_error + 1
+ y_error = y_error + 0
+ if y_error == site_depth:
+ genotype_error = "1/1"
+ elif y_error > 0:
+ genotype_error = "0/1"
+ else :
+ genotype_error = "0/0"
+
# If the current marker is B and the previous marker is B, genotype if homozygote B (alt, 1/1)
elif genotype1 == "B" and genotype2 == "B":
- x = 1
+ x = 0
y = site_depth
genotype = "1/1"
+ x_error = 0
+ y_error = 0
+ # Considering the sequencing and mapping error at each site for a given site depth
+ for j in range(0,site_depth):
+ rnd = random.random()
+ # If the random number is smaller the error rate, we increase the wronge allele depth
+ if rnd < errB:
+ x_error = x_error + 1
+ y_error = y_error + 0
+ else :
+ x_error = x_error + 0
+ y_error = y_error + 1
+ if x_error == site_depth:
+ genotype_error = "0/0"
+ elif x_error > 0:
+ genotype_error = "0/1"
+ else :
+ genotype_error = "1/1"
+
# If the current marker is neither A nor B, genotype if heterozygous H (0/1)
else:
# Generate a random number between 0 and the depth of the current marker
x = random.randint(0,site_depth)
y = site_depth - x
- # If the depth of x of the current marker is 0, seen as homozygous
+ # Error of A and B compensate themselves in heterozygous site so no need to change the x_error and y_error
+ x_error = x
+ y_error = y
+ # If the depth of x of the current marker is 0, it's seen as homozygous site (alt, 1/1)
if x == 0:
genotype = "1/1"
- # If the depth of y of the current marker is not 0, seen as homozygous
+ genotype_error = "1/1"
+ # If the depth of x of the current marker is 0, it's seen as homozygous site (ref, 0/0)
elif y == 0:
- genotype = "0/0"
- # If the depth x and y of the current marker is not 0, seen as heterozygous
+ genotype = "0/0"
+ genotype_error = "0/0"
+ # If the depth x and y of the current marker is not 0, it's seen as heterozygous (0/1)
else :
- genotype = "0/1"
+ genotype = "0/1"
+ genotype_error = "0/1"
+
#print("reads",genotype1, genotype2)
#print(genotype)
# Append the genotype and the depth of the current marker to the segment list
- finalGenotype = str(genotype) + ":" + str(site_depth) + ":" + str(x) + "," + str(y) + ":.:.:.:.:."
+ finalGenotype = str(genotype) + ":" + str(site_depth) + ":" + str(x) + "," + str(y) + ":.:.:.:.:."
segment.append(finalGenotype)
+
+ # Append the genotype and the depth of the current marker to the segment_error list
+ finalGenotype_error = str(genotype_error) + ":" + str(site_depth) + ":" + str(x_error) + "," + str(y_error) + ":.:.:.:.:."
+ segment_error.append(finalGenotype_error)
- return segment
-
+ return segment, segment_error, breakpoints
# Generate a list of individuals
-def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor):
+def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor, errA, errB):
matrix = []
+ matrix_error = []
# Calculate the number of marker requiered
markers_nb = round(chromosome_size * marker_density)
+
# Generate a sorted list of markers positions
- # positions = sorted(random.sample(range(chromosome_size),size))
+ #markers_positions = sorted(random.sample(range(chromosome_size),size))
markers_positions = np.linspace(1, chromosome_size, markers_nb, dtype="int")
- for i in range(nb_individuals):
- print("individual " + str(i+1))
- matrix.append(generate_chr_for_one_ind(mean_depth, markers_positions, conversion_factor))
+ with open('Breakpoints.csv', 'w') as breakpointFile:
+ breakpointFile.write(",".join(["sample","average_bkp_position", "bkp_start_position", "bkp_stop_position", "transitionType"]) + "\n");
+ for i in range(nb_individuals):
+ print("individual " + str(i+1))
+ result = generate_chr_for_one_ind(mean_depth, markers_positions, conversion_factor, errA, errB)
+ matrix.append(result[0])
+ matrix_error.append(result[1])
+ for bkp in result[2]:
+ startBkp = markers_positions[bkp[0] - 1]
+ stopBkp = markers_positions[bkp[0]]
+ averageBkpPosition = startBkp + round((stopBkp - startBkp)/2)
+ bkp_row = [str(i), str(averageBkpPosition), str(startBkp), str(stopBkp), bkp[1]]
+ breakpointFile.write(",".join(bkp_row) + "\n");
- return matrix, markers_positions
+ return matrix, matrix_error, markers_positions
# # Check if a row is correct
# def is_correct (row):
@@ -142,20 +236,21 @@ def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_
# return [count_a, count_b, count_h, 0.45 <= count_h <= 0.55 and 0.2 <= count_b <= 0.3 and 0.2 <= count_a <= 0.3]
# Set the parameters for the script
-nb_individuals = 3
+nb_individuals = 100
chromosome_size = 44000000
cMsize = 180 ## Size of genetic map
conversion_factor = chromosome_size/cMsize ## Corresponds to a bpPercM conversion ! Needs to be fixed... Does not produce the correct division!
-#conversion_factor = 233000
-print(conversion_factor)
-marker_density = 255000/44000000
+marker_density = 0.0055
mean_depth = 3
-max_depth = 10 #TODO
-err_rate = 0.02
-pop = generate_individuals(nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor)
-matrix = pop[0]
-markers_positions = pop[1]
+max_depth = 6 #TODO (better estimate of max?)
+errA = 0.005
+errB = 0.005
+## Generate the pop and associate the results to the matrixes with and without errors
+pop = generate_individuals(nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor, errA, errB)
+matrix = pop[0]
+matrix_error = pop[1]
+markers_positions = pop[2]
# Header to add to the VCF for it to be recognized as such file.
header = [
@@ -180,7 +275,8 @@ header = [
]
-with open('Newtest.vcf', 'w') as file:
+## Write the VCF files (with and without errors)
+with open('test.vcf', 'w') as file:
# Write the header of the file
file.writelines(header)
@@ -194,4 +290,20 @@ with open('Newtest.vcf', 'w') as file:
row.append(matrix[i][m])
row.append("0/0:"+ str(mean_depth) + ":" + str(mean_depth) + ",0:.:.:.:.")
row.append("1/1:"+ str(mean_depth) + ":0," + str(mean_depth) + ":.:.:.:.")
+ file.write("\t".join(row) + "\n");
+
+with open('test_error.vcf', 'w') as file:
+ # Write the header of the file
+ file.writelines(header)
+
+ # Write the header of the file
+ file.write("\t".join(["#CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", "FORMAT"] + [str(n) for n in range(nb_individuals)] + ["Parent1", "Parent2"]) + "\n")
+
+ # Iterate over each row in the table
+ for m in range(0, len(matrix_error[0])):
+ row = ["chr01", str(markers_positions[m]), ".", "A", "T", ".", ".", ".", "GT:DP:AD:RO:QR:AO:QA:GL"]
+ for i in range(0, len(matrix_error)):
+ row.append(matrix_error[i][m])
+ row.append("0/0:"+ str(mean_depth) + ":" + str(mean_depth) + ",0:.:.:.:.")
+ row.append("1/1:"+ str(mean_depth) + ":0," + str(mean_depth) + ":.:.:.:.")
file.write("\t".join(row) + "\n");
\ No newline at end of file