def main(filename):
file = open(filename)
rna0 = file.readline().strip()
secStr0 = computeViennaSecStr(rna0)
D = {}
n = len(rna0); num = 0
line = file.readline()
numMut = 0; numSamples = 0
while line:
if line[0]=='>':
if numMut == 0: #first time, get number of samples
numSamples = int(line.split()[2])
#Now add information for new number of mutations
words = line.split()
numMut = int(words[-2]) #new number of mutations
D[numMut] = {}
for i in range(n):
D[numMut][i] = {}
for ch in NUCL: D[numMut][i][ch] = 0.0
num = 0 #start over counter of number of mutations
line = file.readline()
continue
num += 1
rna = line.strip().upper()
secStr = file.readline().strip()
for i in range(n):
D[numMut][i][rna[i]] += 1.0
line = file.readline()
file.close()
#Normalize D values
print "Consensus sequences for k-mutants, each k"
for num in range(1,numMut+1):
sys.stdout.write(">%d\n" % num)
for i in range(n):
consensusNucl = ''; maxFreq = 0
for ch in NUCL:
D[num][i][ch] /= numSamples
nuclFreq = D[num][i][ch] #nucleotide frequency
if nuclFreq>maxFreq:
maxFreq = nuclFreq; consensusNucl = ch
sys.stdout.write("%s" % consensusNucl)
sys.stdout.write("\n")
E = {} #E[i] is consensus nucleotide in position i over ALL mutations
print "Consensus sequences over all mutants"
print "> %d" % (numMut+1)
for i in range(n):
E[i] = {}
for ch in NUCL: E[i][ch] = 0.0
for num in range(1,numMut+1):
for ch in NUCL:
E[i][ch] += D[num][i][ch]
maxFreq = 0; consensusNucl = ''
for ch in NUCL:
E[i][ch] = E[i][ch]/numMut #normalize
if E[i][ch]>maxFreq:
maxFreq = E[i][ch]; consensusNucl = ch
sys.stdout.write("%s" % consensusNucl)
sys.stdout.write("\n")
def main(filename):
file = open(filename)
rna0 = file.readline().strip()
secStr0 = computeViennaSecStr(rna0)
D = {}
SS = {}
H = {} #H is entropy
n = len(rna0)
num = 0
line = file.readline()
numMut = 0
numSamples = 0
while line:
if line[0] == '>':
if numMut == 0: #first time, get number of samples
numSamples = int(line.split()[2])
else: #update entropy H
H[numMut] = {}
for i in range(1, n + 1): #indices 1<=i<=n
sumEntropyForI = 0.0 #compute H[i]
probII = 1.0 #compute p(i,i) by 1.0 - p(i,j) all j
for j in range(1, n + 1):
if i != j:
probIJ = SS[numMut][i][j] / float(numSamples)
sumEntropyForI += -xLogX(probIJ)
probII -= probIJ
H[numMut][i] = sumEntropyForI + -xLogX(probII)
#Now add information for new number of mutations
words = line.split()
numMut = int(words[-2]) #new number of mutations
D[numMut] = {}
SS[numMut] = {}
for i in range(1, n + 1):
SS[numMut][i] = {}
for j in range(1, n + 1):
SS[numMut][i][j] = 0.0
D[numMut][i - 1] = 0.0
#D[numMut][i] is number of mutations in position i
#WARNING: indices in D are 0<=i<n, while those in SS are 1<=i<=n
num = 0 #start over counter of number of mutations
line = file.readline()
continue
num += 1
if DEBUG: print numMut, num
rna = line.strip().upper()
secStr = file.readline().strip()
bps = basePairList(secStr)
for i in range(n):
if rna0[i] != rna[i]: D[numMut][i] += 1.0
baseI = i + 1 #warning 1<=baseI<=n, but 0<=i<n for accessing RNA string
for j in range(1, n + 1):
if baseI < j and (baseI, j) in bps:
SS[numMut][baseI][j] += 1
elif j < baseI and (j, baseI) in bps:
SS[numMut][baseI][j] += 1
line = file.readline()
file.close()
#Must complete computation of H for last value of numMut
#As well, we normalize D values to be between [0,1]
H[numMut] = {}
for i in range(1, n + 1): #indices 1<=i<=n
sumEntropyForI = 0.0 #compute H[i]
probII = 1.0 #compute p(i,i) by 1.0 - p(i,j) all j
for j in range(1, n + 1):
if i != j:
probIJ = SS[numMut][i][j] / float(num)
sumEntropyForI += -xLogX(probIJ)
probII -= probIJ
H[numMut][i] = sumEntropyForI + -xLogX(probII)
#Normalize D values
for num in range(1, numMut + 1):
for i in range(n):
D[num][i] /= numSamples
#Now compute correlation coefficient
corrCoeffList = []
print "k\tcorrCoeff\tmean1\t\tstdev1\t\tmean2\t\tstdev2"
for num in range(1, numMut + 1):
L1 = []
L2 = []
for i in range(n):
L1.append(D[num][i])
L2.append(H[num][i + 1]) #WARNING: indices in H are from 1 to n
mean1, stdev1, max1, min1 = getSampleStats(L1)
mean2, stdev2, max2, min2 = getSampleStats(L2)
print "%d\t%f\t%f\t%f\t%f\t%f" % (num, corrCoeff(
L1, L2), mean1, stdev1, mean2, stdev2)
def main(filename):
file = open(filename)
rna0 = file.readline().strip()
secStr0 = computeViennaSecStr(rna0)
D = {}
n = len(rna0)
num = 0
line = file.readline()
numMut = 0
numSamples = 0
while line:
if line[0] == '>':
if numMut == 0: #first time, get number of samples
numSamples = int(line.split()[2])
#Now add information for new number of mutations
words = line.split()
numMut = int(words[-2]) #new number of mutations
D[numMut] = {}
for i in range(n):
D[numMut][i] = {}
for ch in NUCL:
D[numMut][i][ch] = 0.0
num = 0 #start over counter of number of mutations
line = file.readline()
continue
num += 1
rna = line.strip().upper()
secStr = file.readline().strip()
for i in range(n):
D[numMut][i][rna[i]] += 1.0
line = file.readline()
file.close()
#Normalize D values
print "Consensus sequences for k-mutants, each k"
for num in range(1, numMut + 1):
sys.stdout.write(">%d\n" % num)
for i in range(n):
consensusNucl = ''
maxFreq = 0
for ch in NUCL:
D[num][i][ch] /= numSamples
nuclFreq = D[num][i][ch] #nucleotide frequency
if nuclFreq > maxFreq:
maxFreq = nuclFreq
consensusNucl = ch
sys.stdout.write("%s" % consensusNucl)
sys.stdout.write("\n")
E = {} #E[i] is consensus nucleotide in position i over ALL mutations
print "Consensus sequences over all mutants"
print "> %d" % (numMut + 1)
for i in range(n):
E[i] = {}
for ch in NUCL:
E[i][ch] = 0.0
for num in range(1, numMut + 1):
for ch in NUCL:
E[i][ch] += D[num][i][ch]
maxFreq = 0
consensusNucl = ''
for ch in NUCL:
E[i][ch] = E[i][ch] / numMut #normalize
if E[i][ch] > maxFreq:
maxFreq = E[i][ch]
consensusNucl = ch
sys.stdout.write("%s" % consensusNucl)
sys.stdout.write("\n")
def main(filename):
file = open(filename)
rna0 = file.readline().strip()
secStr0 = computeViennaSecStr(rna0)
D = {}; SS = {}; H = {} #H is entropy
n = len(rna0); num = 0
line = file.readline()
numMut = 0; numSamples = 0
while line:
if line[0]=='>':
if numMut == 0: #first time, get number of samples
numSamples = int(line.split()[2])
else: #update entropy H
H[numMut] = {}
for i in range(1,n+1): #indices 1<=i<=n
sumEntropyForI = 0.0 #compute H[i]
probII = 1.0 #compute p(i,i) by 1.0 - p(i,j) all j
for j in range(1,n+1):
if i!=j:
probIJ = SS[numMut][i][j]/float(numSamples)
sumEntropyForI += -xLogX(probIJ)
probII -= probIJ
H[numMut][i] = sumEntropyForI + -xLogX(probII)
#Now add information for new number of mutations
words = line.split()
numMut = int(words[-2]) #new number of mutations
D[numMut] = {}
SS[numMut] = {}
for i in range(1,n+1):
SS[numMut][i] = {}
for j in range(1,n+1): SS[numMut][i][j] = 0.0
D[numMut][i-1] = 0.0
#D[numMut][i] is number of mutations in position i
#WARNING: indices in D are 0<=i<n, while those in SS are 1<=i<=n
num = 0 #start over counter of number of mutations
line = file.readline()
continue
num += 1
if DEBUG: print numMut,num
rna = line.strip().upper()
secStr = file.readline().strip()
bps = basePairList(secStr)
for i in range(n):
if rna0[i]!=rna[i]: D[numMut][i] += 1.0
baseI = i+1 #warning 1<=baseI<=n, but 0<=i<n for accessing RNA string
for j in range(1,n+1):
if baseI<j and (baseI,j) in bps:
SS[numMut][baseI][j] += 1
elif j<baseI and (j,baseI) in bps:
SS[numMut][baseI][j] += 1
line = file.readline()
file.close()
#Must complete computation of H for last value of numMut
#As well, we normalize D values to be between [0,1]
H[numMut] = {}
for i in range(1,n+1): #indices 1<=i<=n
sumEntropyForI = 0.0 #compute H[i]
probII = 1.0 #compute p(i,i) by 1.0 - p(i,j) all j
for j in range(1,n+1):
if i!=j:
probIJ = SS[numMut][i][j]/float(num)
sumEntropyForI += -xLogX(probIJ)
probII -= probIJ
H[numMut][i] = sumEntropyForI + -xLogX(probII)
#Normalize D values
for num in range(1,numMut+1):
for i in range(n): D[num][i] /= numSamples
#Now compute correlation coefficient
corrCoeffList = []
print "k\tcorrCoeff\tmean1\t\tstdev1\t\tmean2\t\tstdev2"
for num in range(1,numMut+1):
L1 = []; L2 = []
for i in range(n):
L1.append(D[num][i])
L2.append(H[num][i+1]) #WARNING: indices in H are from 1 to n
mean1,stdev1,max1,min1 = getSampleStats(L1)
mean2,stdev2,max2,min2 = getSampleStats(L2)
print "%d\t%f\t%f\t%f\t%f\t%f" % (num,corrCoeff(L1,L2),mean1,stdev1,mean2,stdev2)
