diff --git a/noisyplotype.py b/noisyplotype.py
index dd88d4b5d1ba8152396621058cbfa64b29e0c975..14f6ee26ad2fc6f90f39f9482497a5b093d9fd39 100644
--- a/noisyplotype.py
+++ b/noisyplotype.py
@@ -19,9 +19,13 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor,
segment = []
segment_error = []
+ breakpoints = []
# Iterate through the list of markers positions
+ 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
@@ -34,6 +38,7 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor,
# If the random number is greater than the probability of a recombination
#print("before",genotype1, genotype2)
if rnd > 1 - IntervalWithPreviousMarkerInRF: # a recombination occurs
+ recomb1 = True
# If the previous marker is A, change the genotype to B
if genotype1 == "A":
genotype1 = "B";
@@ -41,11 +46,12 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor,
genotype1 = "A";
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";
+ genotype2 = "A"
#print("after",genotype1, genotype2)
# If the current marker is the first one, fix the two first genotypes.
else:
@@ -61,10 +67,31 @@ 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
+ breakpoints.append([i,transition])
+
# Calculate the depth of the current marker
g = np.random.poisson(mean_depth)
# If the depth of the current marker is less than 0 (safeguard)
@@ -91,7 +118,7 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor,
y_error = 0
for j in range(0,site_depth):
rnd = random.random()
- # If the random number is greater than errB, we switch increase the wronge allele depth
+ # If the random number is smaller than errA, we switch increase the wrong allele depth
if rnd < errA:
x_error = x_error + 0
y_error = y_error + 1
@@ -114,7 +141,7 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor,
y_error = 0
for j in range(0,site_depth):
rnd = random.random()
- # If the random number is greater than errB, we switch increase the wronge allele depth
+ # If the random number is smaller than errB, we switch increase the wronge allele depth
if rnd < errB:
x_error = x_error + 1
y_error = y_error + 0
@@ -145,7 +172,8 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor,
# If the depth x and y of the current marker is not 0, seen as heterozygous
else :
genotype = "0/1"
- genotype_error = "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
@@ -155,12 +183,13 @@ def generate_chr_for_one_ind (mean_depth, markers_positions, conversion_factor,
finalGenotype_error = str(genotype_error) + ":" + str(site_depth) + ":" + str(x_error) + "," + str(y_error) + ":.:.:.:.:."
segment_error.append(finalGenotype_error)
- return segment, segment_error
+ return segment, segment_error, breakpoints
# Generate a list of individuals
def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_depth, conversion_factor, errA, errB):
matrix = []
matrix_error = []
+ breakpoints = []
# Calculate the number of marker requiered
markers_nb = round(chromosome_size * marker_density)
@@ -168,11 +197,19 @@ def generate_individuals (nb_individuals, chromosome_size, marker_density, mean_
#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))
- result = generate_chr_for_one_ind(mean_depth, markers_positions, conversion_factor, errA, errB)
- matrix.append(result[0])
- matrix_error.append(result[1])
+ with open('Breakpoints.csv', 'w') as breakpointFile:
+ breakpointFile.write(",".join(["sample","average_bkp_position", "bkp_start_position", "bkp_stop_position", "transition"]) + "\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, matrix_error, markers_positions
@@ -187,7 +224,7 @@ 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 = 100
+nb_individuals = 3
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!