def simulateReads(infileName,outfileName,mean,std,desired_cov,trialCount,names):
#Initialize variables
y=0
loopController=True
#Open the index file (chrdist.td)
with open("SiLiCO_Scratch/chrdist.td",'r') as infile:
lengths = []
names = []
for x in infile:
length = x.split('\t')[1]
name = x.split('\t')[0]
lengths.append(int(length))
names.append(str(name))
infile.close()
lengthDict = dict(zip(names,lengths))
genomeLength=0
names=natsorted(names) #A sorted list of chromosome names.
#Calculate the total genome length
for n in lengths:
genomeLength += n
chrCount = len(lengths)
thresholdDict={} #A dictionary of chromome thresholds (defined as end of a chromsome as determined by length from chrdist.td)
correctionDict={} #A correction to get the start of the chromosome. Important for generating bed file.
#Generate the threshold dictionary.
print("Building threshold dictionaries...")
for chrom in range(0,len(names)): #might have to change this because it will change chrX to chr24 (build an array of names and use an index)
name = str(names[chrom])#this name will go as key in dict
print name
if name =="chr1":
thresholdDict[name]=lengthDict[name]
#correctionDict["chr1"]=0
else:
threshVal = 0
correctedVal = 0
i=1
while (i<=chrom+1):
name2 = "chr"+str(i)
threshVal += lengthDict[name2]
i += 1
correctedVal = threshVal - lengthDict[name2]
thresholdDict[name2] = threshVal
#print("Done!")
#print thresholdDict
#Some calculations for the log-normal distribution.
print("Calculating distribution parameters...")
sigma = (math.log(1+(float(mean)/(float(std))**2)))**0.5
mu = math.log(float(mean))-0.5*sigma**2
req_reads = int((int(desired_cov)*genomeLength)/float(mean))
#print("Sigma:" + str(sigma))
#print("Mu: " + str(mu))
#print(str(req_reads) + " in-silico reads will be generated per trial.")
#Begin generating in-silico reads
trial_counter = 0
while trial_counter < int(trialCount):
print("This is trial " + str(trial_counter))
read_length_counter = 0
read_pos_counter = 0
readlengths = None
readlengths=np.random.lognormal(mu,sigma,req_reads) #Read lengths are randomly determined from the calculated log-normal distribution.
read_pos=[]
name_counter = 0
outfile = gzip.open(str(outfileName) + '/simulated_read_positions_trial_'+str(trial_counter) +'.bed.gz','wb')
for length in readlengths:
x = int(round(length))
buf = math.ceil(x/2) #protects against end selection bias and simulated read bridging two chromosomes, in the event of a .5, rounds up to the whole
y=getRandomPosition(buf,genomeLength,thresholdDict,names)
start_pos = int(y-buf)
end_pos = int(y+buf)
print("start = " + str(start_pos))
print("end = " + str(end_pos))
#Figure out which chromosome this is in
selected_chrom = findChromosome(start_pos,names,thresholdDict)
#Build correction dictionary
for j in range(0,chrCount):
chromName = str(names[j])
if j-1 < 0: #chr1
correctionDict[chromName] = 0
elif j-1 >= 0:
prevChromName = str(names[j-1])
correctionDict[chromName] = thresholdDict[prevChromName]
outfile.write(str(selected_chrom) + '\t' + str(start_pos-correctionDict[str(selected_chrom)]) + '\t' + str(end_pos-correctionDict[str(selected_chrom)]) + '\t' + 'trial_'+str(trial_counter) +'_sim_read_' + str(name_counter) + '\n')
#count this run and reset reused variables.
name_counter+=1
x=None
y=None
selected_chrom=None
start_pos=None
end_pos=None
outfile.close()
trial_counter+=1
print("Completed trial " + str(trial_counter) + " of " + str(trialCount) + ". ("+ str(100*(float(trial_counter)/trialCount)) + "%)")
def simulateReads(argv):
#parse args and intialize variables
global mean, outfileName, std, desired_cov,y
outfileName = 'simulationResults.bed'
mean = 10000
std = 2050
desired_cov = 8
y=0
loopController=True
try:
opts, args = getopt.getopt(argv,"i:o:m:s:c:h",["infile=","outfile=","mean_read_length=-","standard_dev=", "coverage="])
except getopt.GetoptError:
print("Usage: python simulation.py --infile </path/to/chrdist.td> --outfile </path/to/outfile.bed> -m <mean read length> -s <standard dev of read lenghts> -c <coverage>")
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print("Usage: python simulation.py --infile </path/to/ingenome.fa> --outfile </path/to/outfile.bed> -m <mean read length> -s <standard dev of read lenghts> -c <coverage>")
sys.exit(2)
elif opt in ("-i","--infile"):
infileName = arg
elif opt in ("-o","--outfile"):
outfileName = arg
elif opt in ("-m", "--mean_read_length"):
mean = arg
elif opt in ("-s", "--standard_dev"):
std = arg
elif opt in ("-c", "--coverage"):
desired_cov = arg
print("SiLiCO will simulate sequencing results with the following paramters:" + '\n' + "Mean Read Length = " + str(mean) + '\n' + "Standard Deviation of Read Length = " + str(std) + '\n' + 'Coverage = ' + str(desired_cov) + '\n')
#generate chrdist.td file
with open("SiLiCO_Scratch/chrdist.td",'r') as infile:
lengths = []
names = []
for x in infile:
length = x.split('\t')[1]
name = x.split('\t')[0]
lengths.append(int(length))
names.append(str(name))
infile.close()
lengthDict = dict(zip(names,lengths))
genomeLength=0
names=natsorted(names)
#calculating total genome length
for n in lengths:
genomeLength += n
chrCount = len(lengths)
thresholdDict={} #dictionary of chromome thresholds (defined as end of a chromsome as determined by length from chrdist.td)
correctionDict={} #A correction to get the start of the chromosome as well. Important for generating bed file. (Maybe can get rid of this eventually?)
#Generate a dictionary of chromosome thresholds (replaces hard-coded previous version)
print("Building threshold dictionaries...")
for chrom in range(0,len(names)): #might have to change this because it will change chrX to chr24 (build an array of names and use an index)
name = str(names[chrom])#this name will go as key in dict
print name
if name =="chr1":
thresholdDict[name]=lengthDict[name]
#correctionDict["chr1"]=0
else:
threshVal = 0
correctedVal = 0
i=1
while (i<=chrom+1):
name2 = "chr"+str(i)
threshVal += lengthDict[name2]
i += 1
correctedVal = threshVal - lengthDict[name2]
thresholdDict[name2] = threshVal
#correctionDict[name2] = correctedVal
print("Done!")
print thresholdDict
print("Calculating distribution parameters...")
#Some calculations for the log-normal distribution.
sigma = (math.log(1+(float(mean)/(float(std))**2)))**0.5
mu = math.log(float(mean))-0.5*sigma**2
req_reads = int((int(desired_cov)*genomeLength)/float(mean))
print("sigma:" + str(sigma))
print("mu: " + str(mu))
print(str(req_reads) + " in-silico reads will be generated per trial.")
trial_counter=0
trials = 1
while trial_counter < trials:
print("This is trial " + str(trial_counter))
read_length_counter = 0
read_pos_counter = 0
readlengths = None
readlengths=np.random.lognormal(mu,sigma,req_reads)
read_pos=[]
name_counter = 0
print(len(readlengths))
outfile = gzip.open('simulated_read_positions_trial_'+str(trial_counter) +'.bed.gz','wb')
for length in readlengths:
x = int(round(length))
buf = math.ceil(x/2) #protects against end selection bias and simulated read bridging two chromosomes, in the event of a .5, rounds up to the whole
y=getRandomPosition(buf,genomeLength,thresholdDict,names)
start_pos = int(y-buf)
end_pos = int(y+buf)
print("start = " + str(start_pos))
print("end = " + str(end_pos))
#Figure out which chromosome this is in
selected_chrom = findChromosome(start_pos,names,thresholdDict)
print("This is on " + str(selected_chrom))
#build correction dictionary
print("correcting positions...")
for j in range(0,chrCount):
#print("j= " + str(j))
chromName = str(names[j])
if j-1 < 0: #chr1
correctionDict[chromName] = 0
elif j-1 >= 0:
prevChromName = str(names[j-1])
correctionDict[chromName] = thresholdDict[prevChromName]
print("Writing this read to the outfile for this trial...")
outfile.write(str(selected_chrom) + '\t' + str(start_pos-correctionDict[str(selected_chrom)]) + '\t' + str(end_pos-correctionDict[str(selected_chrom)]) + '\t' + 'trial_'+str(trial_counter) +'_sim_read_' + str(name_counter) + '\n')
#count this run and reset reused variables.
name_counter+=1
x=None
y=None
selected_chrom=None
start_pos=None
end_pos=None
outfile.close()
trial_counter+=1
