def iterativeEM(fm,
b,
alphabet,
reads,
starts,
errors,
maxIters,
readLen=50,
genomeLen=5000,
prop=1):
unmatched = [1] * len(reads)
numUnmatched = len(reads)
prevSize = 2 * len(reads)
currIter = 0
sizes = []
correct = 0
incorrect = 0
while numUnmatched > 0 and currIter < maxIters and float(
prevSize - numUnmatched) / prevSize > 0.1:
threshold = 0.25 * prop * numUnmatched * readLen / genomeLen
currIter += 1
prevSize = numUnmatched
# Match reads against t2
mutations = dict()
# match all reads to genome, collect mutations
for i in xrange(len(reads)):
if unmatched[i] == 1:
m = bwt.findApproximate(fm, b, alphabet, ''.join(reads[i]),
errors)
if len(m) > 0:
unmatched[i] = 0
if i < prop * len(reads):
for k, edits in m.items():
for v in edits:
if v[0] == 2:
vnew = (v[0], v[1] + k)
else:
vnew = (v[0], v[1] + k, v[2])
if vnew in mutations:
mutations[vnew] += 1
else:
mutations[vnew] = 1
found = False
for j in xrange(-errors, errors + 1):
if starts[i] + j in m and not found:
correct += 1
found = True
if not found:
incorrect += 1
mutationsString = cPickle.dumps(mutations)
sizes += [sys.getsizeof(mutationsString)]
# apply mutations to fm index
for k, v in mutations.items():
if v >= threshold:
if k[0] == 1:
fm = bwt.insert(fm, b, alphabet, k[1], k[2])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] += 1
elif k[0] == 0:
fm = bwt.substitute(fm, b, alphabet, k[1], k[2])
elif k[0] == 2:
fm = bwt.delete(fm, b, alphabet, k[1])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] -= 1
else:
print 'Error: k[0] = ' + str(k[0])
numUnmatched = sum(unmatched)
#print " Iter " + str(currIter) + " - " + str(correct) + " correct, " + str(incorrect) + " incorrect, " + str(len(reads)-correct-incorrect) + ' unmatched, length = ' + str(len(mutations)) + ', size = ' + str(sys.getsizeof(mutationsString))
#print " Accuracy: " + str(float(correct) / len(reads))
return float(correct) / len(reads), sizes[0]
#!/usr/bin/env python3
import bwt
import random
alpha = ['A', 'C', 'G', 'T']
t = ['$'] + [random.choice(alpha) for x in range(10000)]
alphabet = alpha + ['$']
b = 5
print('OldRow, First, Checkpt, NewRow, SA, Checkpt, Reord')
fm = bwt.constructFM(t, b, alphabet)
bwt.insert(fm, b, alphabet, 1, random.choice(alpha), timing=True)
bwt.insert(fm, b, alphabet, 5, random.choice(alpha), timing=True)
bwt.insert(fm, b, alphabet, 10, random.choice(alpha), timing=True)
bwt.insert(fm, b, alphabet, 50, random.choice(alpha), timing=True)
bwt.insert(fm, b, alphabet, 100, random.choice(alpha), timing=True)
bwt.insert(fm, b, alphabet, 500, random.choice(alpha), timing=True)
bwt.insert(fm, b, alphabet, 1000, random.choice(alpha), timing=True)
bwt.insert(fm, b, alphabet, 5000, random.choice(alpha), timing=True)
print()
fm = bwt.constructFM(t, b, alphabet)
bwt.insert(fm, b, alphabet, 1, 'A', timing=True)
bwt.insert(fm, b, alphabet, 5, 'A', timing=True)
bwt.insert(fm, b, alphabet, 10, 'A', timing=True)
bwt.insert(fm, b, alphabet, 50, 'A', timing=True)
bwt.insert(fm, b, alphabet, 100, 'A', timing=True)
bwt.insert(fm, b, alphabet, 500, 'A', timing=True)
bwt.insert(fm, b, alphabet, 1000, 'A', timing=True)
bwt.insert(fm, b, alphabet, 5000, 'A', timing=True)
bwt.insert(fm, b, alphabet, 10000, 'A', timing=True)
print()
t2 = t[:subId] + [newChar] + t[subId+1:]
startUpdate = time.time()
fm_new = bwt.substitute(fm, b, alphabet, subId, newChar)
timeUpdate = time.time() - startUpdate
updateTimes[0] += timeUpdate
# Insertion of a character
insertId = random.randint(0,length-1)
newChar = random.choice(['A', 'C', 'T', 'G'])
t2 = t[:insertId] + [newChar] + t[insertId:]
startUpdate = time.time()
fm_new = bwt.insert(fm, b, alphabet, insertId, newChar)
timeUpdate = time.time() - startUpdate
updateTimes[1] += timeUpdate
# Deletion of a character
deleteId = random.randint(0,length-1)
t2 = t[:deleteId] + t[deleteId+1:]
startUpdate = time.time()
fm_new = bwt.delete(fm, b, alphabet, deleteId)
timeUpdate = time.time() - startUpdate
updateTimes[2] += timeUpdate
def iterativeUpdate(fm, b, alphabet, reads, starts, errors, maxIters, threshold=False, readLen=50, genomeLen=5000):
unmatched = [1]*len(reads)
numUnmatched = len(reads)
prevSize = 2*len(reads)
currIter = 0
firstIter = True
initialAcc = 0.0
correct = 0
incorrect = 0
while numUnmatched > 0 and currIter < maxIters and float(prevSize - numUnmatched) / prevSize > 0.1:
threshold = 0.5 * numUnmatched * readLen / genomeLen
currIter += 1
prevSize = numUnmatched
# Match reads against t2
mutations = dict()
# match all reads to genome, collect mutations
for i in xrange(len(reads)):
if unmatched[i] == 1:
m = bwt.findApproximate(fm, b, alphabet, ''.join(reads[i]), errors)
if len(m) > 0:
unmatched[i] = 0
for k,edits in m.items():
for v in edits:
if v[0] == 2:
vnew = (v[0],v[1]+k)
else:
vnew = (v[0],v[1]+k,v[2])
if vnew in mutations:
mutations[vnew] += 1
else:
mutations[vnew] = 1
found = False
for j in xrange(-errors, errors+1):
if starts[i]+j in m and not found:
correct += 1
found = True
if not found:
incorrect += 1
if firstIter:
firstIter = False
initialAcc = float(correct) / len(reads)
# apply mutations to fm index
for k,v in mutations.items():
if v >= threshold:
if k[0] == 1:
fm = bwt.insert(fm, b, alphabet, k[1], k[2])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] += 1
elif k[0] == 0:
fm = bwt.substitute(fm, b, alphabet, k[1], k[2])
elif k[0] == 2:
fm = bwt.delete(fm, b, alphabet, k[1])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] -= 1
else:
print 'Error: k[0] = ' + str(k[0])
numUnmatched = sum(unmatched)
print " Iter " + str(currIter) + " - " + str(correct) + " correct, " + str(incorrect) + " incorrect, " + str(len(reads)-correct-incorrect) + ' unmatched'
#print " Accuracy: " + str(float(correct) / len(reads))
return initialAcc, float(correct) / len(reads)
def iterativeEM(fm, b, alphabet, reads, starts, errors, maxIters, readLen=50, genomeLen=5000, prop=1):
unmatched = [1] * len(reads)
numUnmatched = len(reads)
prevSize = 2 * len(reads)
currIter = 0
sizes = []
correct = 0
incorrect = 0
while numUnmatched > 0 and currIter < maxIters and float(prevSize - numUnmatched) / prevSize > 0.1:
threshold = 0.25 * prop * numUnmatched * readLen / genomeLen
currIter += 1
prevSize = numUnmatched
# Match reads against t2
mutations = dict()
# match all reads to genome, collect mutations
for i in xrange(len(reads)):
if unmatched[i] == 1:
m = bwt.findApproximate(fm, b, alphabet, "".join(reads[i]), errors)
if len(m) > 0:
unmatched[i] = 0
if i < prop * len(reads):
for k, edits in m.items():
for v in edits:
if v[0] == 2:
vnew = (v[0], v[1] + k)
else:
vnew = (v[0], v[1] + k, v[2])
if vnew in mutations:
mutations[vnew] += 1
else:
mutations[vnew] = 1
found = False
for j in xrange(-errors, errors + 1):
if starts[i] + j in m and not found:
correct += 1
found = True
if not found:
incorrect += 1
mutationsString = cPickle.dumps(mutations)
sizes += [sys.getsizeof(mutationsString)]
# apply mutations to fm index
for k, v in mutations.items():
if v >= threshold:
if k[0] == 1:
fm = bwt.insert(fm, b, alphabet, k[1], k[2])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] += 1
elif k[0] == 0:
fm = bwt.substitute(fm, b, alphabet, k[1], k[2])
elif k[0] == 2:
fm = bwt.delete(fm, b, alphabet, k[1])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] -= 1
else:
print "Error: k[0] = " + str(k[0])
numUnmatched = sum(unmatched)
# print " Iter " + str(currIter) + " - " + str(correct) + " correct, " + str(incorrect) + " incorrect, " + str(len(reads)-correct-incorrect) + ' unmatched, length = ' + str(len(mutations)) + ', size = ' + str(sys.getsizeof(mutationsString))
# print " Accuracy: " + str(float(correct) / len(reads))
return float(correct) / len(reads), sizes[0]
def iterativeEMDist(fm, b, alphabet, reads, starts, errors, maxIters, readLen=50, genomeLen=5000, depth=1, chunkSize=1):
unmatched = [1]*len(reads)
numUnmatched = len(reads)
prevSize = 2*len(reads)
currIter = 0
numChunks = int(math.ceil(float(genomeLen) / chunkSize))
sizes = []
correct = 0
incorrect = 0
while numUnmatched > 0 and currIter < maxIters and float(prevSize - numUnmatched) / prevSize > 0.1:
coverage = [0]*numChunks
threshold = 0.25 * depth
currIter += 1
prevSize = numUnmatched
# Match reads against t2
mutations = dict()
# match all reads to genome, collect mutations
for i in xrange(len(reads)):
if unmatched[i] == 1:
m = bwt.findApproximate(fm, b, alphabet, ''.join(reads[i]), errors)
if len(m) > 0:
unmatched[i] = 0
for k,edits in m.items():
# Add mutations to list
for v in edits:
chunk = (v[1]+k) / chunkSize
if coverage[chunk] < depth*chunkSize:
if v[0] == 2:
vnew = (v[0],v[1]+k)
else:
vnew = (v[0],v[1]+k,v[2])
if vnew in mutations:
mutations[vnew] += 1
else:
mutations[vnew] = 1
# Update coverage for matched reads
chunk = k / chunkSize + 1
while chunk*chunkSize < k+readLen:
coverage[chunk-1] += chunk*chunkSize - max((chunk-1)*chunkSize, k)
chunk += 1
coverage[chunk-1] += min(chunk*chunkSize, k+readLen) - max((chunk-1)*chunkSize, k)
# Test whether any of the matches are correct
found = False
for j in xrange(-errors, errors+1):
if starts[i]+j in m and not found:
correct += 1
found = True
if not found:
incorrect += 1
mutationsString = cPickle.dumps(mutations)
sizes += [sys.getsizeof(mutationsString) + sys.getsizeof(coverage)]
# apply mutations to fm index
for k,v in mutations.items():
if v >= threshold:
if k[0] == 1:
fm = bwt.insert(fm, b, alphabet, k[1], k[2])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] += 1
elif k[0] == 0:
fm = bwt.substitute(fm, b, alphabet, k[1], k[2])
elif k[0] == 2:
fm = bwt.delete(fm, b, alphabet, k[1])
for i in xrange(len(starts)):
if starts[i] >= k[1]:
starts[i] -= 1
else:
print 'Error: k[0] = ' + str(k[0])
numUnmatched = sum(unmatched)
#print " Iter " + str(currIter) + " - " + str(correct) + " correct, " + str(incorrect) + " incorrect, " + str(len(reads)-correct-incorrect) + ' unmatched, length = ' + str(len(mutations)) + ', size = ' + str(sys.getsizeof(mutationsString))
#print " Accuracy: " + str(float(correct) / len(reads))
return float(correct) / len(reads), sizes[0]