def determineAncestor(op1, op2, startPosition, endPosition, aligned1, aligned2, sequence1, sequence2, opIndex1, opIndex2, event, strain1, strain2):
ancestralOperon = copy.deepcopy(event.operon1Alignment)
updatedUnaligned = []
unaligned1 = []
unaligned2 = []
deletionSizes = None
duplicationSizes = None
print "Aligned Sections"
print aligned1
print aligned2
unaligned = getUnaligned(op1, startPosition[0]-1, endPosition[0]-1)
print "Unaligned sections"
print unaligned
#numUnique, unaligned, numDuplicateFound = compareDuplicates(aligned2, unaligned)
numUnique = len(op1)-len(aligned2)
numDuplicateFound = 0
alignedRange = (startPosition[0]-1, endPosition[0]-1)
for geneList in unaligned:
if geneList:
numUniqueFound, deletionSizes, duplicationSizes, updatedGeneList = findUniqueGenes(geneList, sequence1, opIndex1, alignedRange)
numUnique -= numUniqueFound
numDuplicateFound += numUniqueFound
updatedUnaligned.append(updatedGeneList)
else:
updatedUnaligned.append([])
unaligned = getUnaligned(op2, startPosition[1]-1, endPosition[1]-1)
print "Unaligned sections"
print unaligned
# numUnique, unaligned, numDuplicateFound = compareDuplicates(aligned1, unaligned)
numUnique = len(op2)-len(aligned1)
numDuplicateFound = 0
alignedRange = (startPosition[1]-1, endPosition[1]-1)
for geneList in unaligned:
if geneList:
numUniqueFound, deletionSizes, duplicationSizes, updatedGeneList = findUniqueGenes(geneList, sequence2, opIndex2, alignedRange)
numUnique -= numUniqueFound
numDuplicateFound += numUniqueFound
updatedUnaligned.append(updatedGeneList)
else:
updatedUnaligned.append([])
print updatedUnaligned
while updatedUnaligned[0] or updatedUnaligned[2]:
if updatedUnaligned[0]:
gene = updatedUnaligned[0].pop()
if gene != '-':
unaligned1.insert(0, gene)
if updatedUnaligned[2]:
gene = updatedUnaligned[2].pop()
if gene != '-':
unaligned1.insert(0, gene)
while updatedUnaligned[1] or updatedUnaligned[3]:
if updatedUnaligned[1]:
gene = updatedUnaligned[1].pop()
if gene != '-':
unaligned2.insert(0, gene)
if updatedUnaligned[3]:
gene = updatedUnaligned[3].pop()
if gene != '-':
unaligned2.insert(0, gene)
print('Differences detected between these two operons!')
operon1Gaps = event.operon1Gaps
operon2Gaps = event.operon2Gaps
operon1GapIndexes = event.operon1GapIndexes
operon2GapIndexes = event.operon2GapIndexes
print operon1Gaps
print operon1GapIndexes
print operon2Gaps
print operon2GapIndexes
print('These are the extra genes for operon 1: %s' %(operon1Gaps))
print('These are the indexes for extra genes in operon 1: %s' %(operon1GapIndexes))
print('These are the extra genes for operon 2: %s' %(operon2Gaps))
print('These are the indexes for extra genes in operon 2: %s' %(operon2GapIndexes))
#Checks if these extra genes are duplicates by checking if they exist within the alignment and removes them if they do
operon1Gaps, duplicateSizesWithinAlignment1 = checkForMatchesInAlignment(operon1Gaps, event.operon1Alignment)
operon2Gaps, duplicateSizesWithinAlignment2 = checkForMatchesInAlignment(operon2Gaps, event.operon2Alignment)
#increment the duplicate counters
#incrementDuplicateSizeCounters(duplicateSizesWithinAlignment1)
#incrementDuplicateSizeCounters(duplicateSizesWithinAlignment2)
strain1 = addDuplicationEventsToStrain(duplicateSizesWithinAlignment1, strain1)
strain2 = addDuplicationEventsToStrain(duplicateSizesWithinAlignment2, strain2)
i = len(operon1Gaps)
j = len(operon2Gaps)
while (i > 0) or (j > 0):
#Select the gap with the biggest index b/c we will be performing the insertion rear to front of operon to avoid messing up the indexes of the other gaps
if i > 0 and j > 0 and operon1GapIndexes[i-1] > operon2GapIndexes[j-1]:
#This means both queues have gaps however the index in queue 1 is bigger so we'll deal with that one first
#print('Gap being processed: %s' % (operon1Gaps[i]))
numUniqueFound, deletionSizes, duplicationSizes, _ = findUniqueGenes(operon1Gaps[i-1], sequence1, opIndex1)
strain1 = addDuplicationEventsToStrain(duplicationSizes, strain1)
strain2 = addDeletionEventsToStrain(deletionSizes, strain2)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon1Gaps[i-1]) > 0:
#Insert gap into operon
operon1Gaps[i-1].reverse()
for gene in operon1Gaps[i-1]:
ancestralOperon.insert(operon1GapIndexes[i-1], gene)
i = i - 1
elif i > 0 and j > 0 and operon1GapIndexes[i-1] < operon2GapIndexes[j-1]:
#This means both queues have gaps however the index in queue 2 is bigger so we'll insert that one first
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
numUniqueFound, deletionSizes, duplicationSizes, _ = findUniqueGenes(operon2Gaps[j-1], sequence2, opIndex2)
strain2 = addDuplicationEventsToStrain(duplicationSizes, strain2)
strain1 = addDeletionEventsToStrain(deletionSizes, strain1)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon2Gaps[j-1]) > 0:
#Insert gap into operon
operon2Gaps[j-1].reverse()
for gene in operon2Gaps[j-1]:
ancestralOperon.insert(operon2GapIndexes[j-1], gene)
j = j - 1
elif i > 0:
#This means that queue 2 has no more gaps so we process the remaining gaps in queue 1
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
numUniqueFound, deletionSizes, duplicationSizes, _ = findUniqueGenes(operon1Gaps[i-1], sequence1, opIndex1)
strain1 = addDuplicationEventsToStrain(duplicationSizes, strain1)
strain2 = addDeletionEventsToStrain(deletionSizes, strain2)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon1Gaps[i-1]) > 0:
#Insert gap into operon
operon1Gaps[i-1].reverse()
for gene in operon1Gaps[i-1]:
ancestralOperon.insert(operon1GapIndexes[i-1], gene)
i = i - 1
elif j > 0:
#This means that queue 1 has no more gaps to process so we deal with the remaining gaps in queue 2
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
numUniqueFound, deletionSizes, duplicationSizes, _ = findUniqueGenes(operon2Gaps[j-1], sequence2, opIndex2)
strain2 = addDuplicationEventsToStrain(duplicationSizes, strain2)
strain1 = addDeletionEventsToStrain(deletionSizes, strain1)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon2Gaps[j-1]) > 0:
#Insert gap into operon
operon2Gaps[j-1].reverse()
for gene in operon2Gaps[j-1]:
ancestralOperon.insert(operon2GapIndexes[j-1], gene)
j = j - 1
#Set ancestral operon
ancestralOperon = unaligned1 + ancestralOperon + unaligned2
event.setAncestralOperonGeneSequence(ancestralOperon)
return ancestralOperon, strain1, strain2
def reconstructOperonSequence(event, strain1, strain2):
ancestralOperon = copy.deepcopy(
event.operon1Alignment
) #The alignments will be identical except when codon mismatches or substitutions occur
if event.score == 0:
print('No differences detected between these two operons')
event.setAncestralOperonGeneSequence(ancestralOperon)
else:
print('Differences detected between these two operons!')
operon1Gaps = event.operon1Gaps
operon2Gaps = event.operon2Gaps
operon1GapIndexes = event.operon1GapIndexes
operon2GapIndexes = event.operon2GapIndexes
operon1GapPositions = event.operon1GapPositions
operon2GapPositions = event.operon2GapPositions
print('These are the extra genes for operon 1: %s' % (operon1Gaps))
print('These are the indexes for extra genes in operon 1: %s' %
(operon1GapIndexes))
print('These are the positions of the extra genes in operon 1: %s' %
(operon1GapPositions))
print('These are the extra genes for operon 2: %s' % (operon2Gaps))
print('These are the indexes for extra genes in operon 2: %s' %
(operon2GapIndexes))
print('These are the positions of the extra genes in operon 2: %s' %
(operon2GapPositions))
#Step 1: Check if these extra genes are the result of a duplicate event within the alignment, remove them if they are
operon1Gaps, operon1GapPositions, duplicateSizesWithinAlignment1, duplicationDetails1 = checkForMatchesWithinAlignment(
operon1Gaps, event.operon1Alignment, operon1GapPositions,
event.fragmentDetails1)
operon2Gaps, operon2GapPositions, duplicateSizesWithinAlignment2, duplicationDetails2 = checkForMatchesWithinAlignment(
operon2Gaps, event.operon2Alignment, operon2GapPositions,
event.fragmentDetails2)
#Add the details to the respective strain
strain1 = addDuplicationEventsToStrain(strain1,
duplicateSizesWithinAlignment1,
duplicationDetails1)
strain2 = addDuplicationEventsToStrain(strain2,
duplicateSizesWithinAlignment2,
duplicationDetails2)
#Step 2: Check if these extra genes are the result of a duplication event within another operon, remove them if they are, else insert them
i = len(operon1Gaps)
j = len(operon2Gaps)
while (i > 0) or (j > 0):
#Select the gap with the biggest index b/c we will be performing the insertion rear to front of operon to avoid messing up the indexes of the other gaps
if i > 0 and j > 0 and operon1GapIndexes[
i - 1] > operon2GapIndexes[j - 1]:
#This means both queues have gaps however the index in queue 1 is bigger so we'll deal with that one first
#print('Gap being processed: %s' % (operon1Gaps[i]))
duplicationSizes, duplicationDetails, operon1Gaps[
i -
1], operon1GapPositions[i -
1] = checkForMatchesWithinOperons(
strain1.genomeFragments,
event.fragmentDetails1,
operon1Gaps[i - 1],
operon1GapPositions[i - 1])
strain1 = addDuplicationEventsToStrain(
strain1, duplicationSizes,
duplicationDetails) #Adds duplication details to strain
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon1Gaps[i - 1]) > 0:
deletionDetails = ''
deletionSizes = []
#Insert gap into operon
operon1Gaps[i - 1].reverse()
for k in range(0, len(operon1Gaps[i - 1])):
ancestralOperon.insert(operon1GapIndexes[i - 1],
operon1Gaps[i - 1][k])
if event.fragmentDetails1.isNegativeOrientation == False: #This compute the correct gene position based on whether operon was in the negative orientation or not originally
genePos = operon1GapPositions[i - 1][
k] + event.fragmentDetails1.startPositionInGenome
else:
genePos = event.fragmentDetails1.startPositionInGenome + len(
event.fragmentDetails1.sequence
) - operon1GapPositions[i - 1][k] - 1
deletionDetails += operon1Gaps[i - 1][k] + ' ' + str(
genePos) + ', '
deletionDetails = deletionDetails[0:(len(deletionDetails) -
2)]
deletionDetails += ';' #End of deleted segment
deletionSizes.append(len(operon1Gaps[i -
1])) #Size of segment
strain2 = addDeletionEventsToStrain(
strain2, deletionSizes, deletionDetails
) #Remember, if the genes are detected a deletions, it means it was lost in the other strain!!
i = i - 1
elif i > 0 and j > 0 and operon1GapIndexes[
i - 1] < operon2GapIndexes[j - 1]:
#This means both queues have gaps however the index in queue 2 is bigger so we'll insert that one first
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
duplicationSizes, duplicationDetails, operon2Gaps[
j -
1], operon2GapPositions[j -
1] = checkForMatchesWithinOperons(
strain2.genomeFragments,
event.fragmentDetails2,
operon2Gaps[j - 1],
operon2GapPositions[j - 1])
strain2 = addDuplicationEventsToStrain(
strain2, duplicationSizes,
duplicationDetails) #Adds duplication details to strain
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon2Gaps[j - 1]) > 0:
deletionDetails = ''
deletionSizes = []
#Insert gap into operon
operon2Gaps[j - 1].reverse()
for k in range(0, len(operon2Gaps[j - 1])):
ancestralOperon.insert(operon2GapIndexes[j - 1],
operon2Gaps[j - 1][k])
if event.fragmentDetails2.isNegativeOrientation == False: #This compute the correct gene position based on whether operon was in the negative orientation or not originally
genePos = operon2GapPositions[j - 1][
k] + event.fragmentDetails2.startPositionInGenome
else:
genePos = event.fragmentDetails2.startPositionInGenome + len(
event.fragmentDetails2.sequence
) - operon2GapPositions[j - 1][k] - 1
deletionDetails += operon2Gaps[j - 1][k] + ' ' + str(
genePos) + ', '
deletionDetails = deletionDetails[0:(len(deletionDetails) -
2)]
deletionDetails += ';' #End of deleted segment
deletionSizes.append(len(operon2Gaps[j -
1])) #Size of segment
strain1 = addDeletionEventsToStrain(
strain1, deletionSizes, deletionDetails
) #Remember, if the genes are detected a deletions, it means it was lost in the other strain!!
j = j - 1
elif i > 0:
#This means that queue 2 has no more gaps so we process the remaining gaps in queue 1
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
duplicationSizes, duplicationDetails, operon1Gaps[
i -
1], operon1GapPositions[i -
1] = checkForMatchesWithinOperons(
strain1.genomeFragments,
event.fragmentDetails1,
operon1Gaps[i - 1],
operon1GapPositions[i - 1])
strain1 = addDuplicationEventsToStrain(
strain1, duplicationSizes,
duplicationDetails) #Adds duplication details to strain
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon1Gaps[i - 1]) > 0:
deletionDetails = ''
deletionSizes = []
#Insert gap into operon
operon1Gaps[i - 1].reverse()
for k in range(0, len(operon1Gaps[i - 1])):
ancestralOperon.insert(operon1GapIndexes[i - 1],
operon1Gaps[i - 1][k])
if event.fragmentDetails1.isNegativeOrientation == False: #This compute the correct gene position based on whether operon was in the negative orientation or not originally
genePos = operon1GapPositions[i - 1][
k] + event.fragmentDetails1.startPositionInGenome
else:
genePos = event.fragmentDetails1.startPositionInGenome + len(
event.fragmentDetails1.sequence
) - operon1GapPositions[i - 1][k] - 1
deletionDetails += operon1Gaps[i - 1][k] + ' ' + str(
genePos) + ', '
deletionDetails = deletionDetails[0:(len(deletionDetails) -
2)]
deletionDetails += ';' #End of deleted segment
deletionSizes.append(len(operon1Gaps[i -
1])) #Size of segment
strain2 = addDeletionEventsToStrain(
strain2, deletionSizes, deletionDetails
) #Remember, if the genes are detected a deletions, it means it was lost in the other strain!!
i = i - 1
elif j > 0:
#This means that queue 1 has no more gaps to process so we deal with the remaining gaps in queue 2
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
duplicationSizes, duplicationDetails, operon2Gaps[
j -
1], operon2GapPositions[j -
1] = checkForMatchesWithinOperons(
strain2.genomeFragments,
event.fragmentDetails2,
operon2Gaps[j - 1],
operon2GapPositions[j - 1])
strain2 = addDuplicationEventsToStrain(
strain2, duplicationSizes,
duplicationDetails) #Adds duplication details to strain
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon2Gaps[j - 1]) > 0:
deletionDetails = ''
deletionSizes = []
#Insert gap into operon
operon2Gaps[j - 1].reverse()
for k in range(0, len(operon2Gaps[j - 1])):
ancestralOperon.insert(operon2GapIndexes[j - 1],
operon2Gaps[j - 1][k])
if event.fragmentDetails2.isNegativeOrientation == False: #This compute the correct gene position based on whether operon was in the negative orientation or not originally
genePos = operon2GapPositions[j - 1][
k] + event.fragmentDetails2.startPositionInGenome
else:
genePos = event.fragmentDetails2.startPositionInGenome + len(
event.fragmentDetails2.sequence
) - operon2GapPositions[j - 1][k] - 1
deletionDetails += operon2Gaps[j - 1][k] + ' ' + str(
genePos) + ', '
deletionDetails = deletionDetails[0:(len(deletionDetails) -
2)]
deletionDetails += ';' #End of deleted segment
deletionSizes.append(len(operon2Gaps[j -
1])) #Size of segment
strain1 = addDeletionEventsToStrain(
strain1, deletionSizes, deletionDetails
) #Remember, if the genes are detected a deletions, it means it was lost in the other strain!!
j = j - 1
#Set ancestral operon
event.setAncestralOperonGeneSequence(ancestralOperon)
#print('This is the resulting ancestral operon: %s' % (ancestralOperon))
#print('\n\n')
#print('These are the extra genes remaining for operon 1: %s' %(operon1Gaps))
#print('These are the extra genes remaining for operon 2: %s' %(operon2Gaps))
#print('These are the duplicate sizes operon 1: %s' %(duplicateSizesWithinAlignment1))
#print('These are the duplicate sizes operon 2: %s\n\n' %(duplicateSizesWithinAlignment2))
return event, strain1, strain2
def findOrthologsBySelfGlobalAlignment(strain, coverageTracker, sibling):
print('Performing self-global alignment on strain: %s' % (strain.name))
if strain.name == 'NC_015634':
print('BREAK')
lossEvents = []
duplicationEvents = []
fragments = strain.genomeFragments
for i in range(0, len(coverageTracker)):
if coverageTracker[i] == False: #Operon has not been marked
bestScore = 1000 #Make the best score some large numer
bestEvent = None #Initialize the event
minDistance = 1000 #Used to track the minimum distance from singleton to operon that has an identical gene
filteredList = iter(
filter(
lambda x: x.fragmentIndex == i,
fragments)) #Get the fragment we need based on the index
unmarkedFragment = next(filteredList, None)
if len(unmarkedFragment.sequence) > 1: #We're processing an operon
for j in range(0, len(coverageTracker)):
filteredList = iter(
filter(lambda x: x.fragmentIndex == j, fragments)
) #Get the fragment we need based on the index
currFragment = next(filteredList, None)
if i != j and coverageTracker[j] == True and len(
currFragment.sequence) > 1:
op1 = unmarkedFragment.sequence
op2 = currFragment.sequence
event = Event(0)
event.setFragmentDetails1(unmarkedFragment)
event.setFragmentDetails2(currFragment)
event.setGenome1Name(strain.name)
event.setGenome2Name(strain.name)
event.setTechnique('Self Global Alignment (Operon)')
event.setAncestralOperonGeneSequence(
copy.deepcopy(
op1)) #Set the ancestral operon sequence
score, event = performGlobalAlignment(
op1, op2, event) #Perform the global alignment
event.setScore(score)
#Compute whether this comparison is interesting
threshold = max(len(op1), len(op2))
threshold = threshold // 3
numOperonDifferences = computeOperonDifferences(
op1, op2)
if numOperonDifferences <= threshold and score < bestScore:
bestScore = score
bestEvent = event
#Make sure an origin or a terminus doesn't get mapped with a singleton gene
elif len(
unmarkedFragment.sequence
) == 1 and unmarkedFragment.description != 'Origin' and unmarkedFragment.description != 'Terminus':
for j in range(0, len(coverageTracker)):
if i != j and coverageTracker[j] == True:
filteredList = iter(
filter(lambda x: x.fragmentIndex == j, fragments)
) #Get the fragment we need based on the index
currFragment = next(filteredList, None)
op1 = unmarkedFragment.sequence
op2 = currFragment.sequence
event = Event(0)
event.setFragmentDetails1(unmarkedFragment)
event.setFragmentDetails2(currFragment)
event.setGenome1Name(strain.name)
event.setGenome2Name(strain.name)
event.setTechnique('Self Global Alignment (Singleton)')
event.setAncestralOperonGeneSequence(
copy.deepcopy(
op1)) #Set the ancestral operon sequence
if op1[0] in op2 and abs(
i - j
) < minDistance: #Checks if the singleton gene is located in the operon and if the distance is smaller
minDistance = abs(i - j)
event.setScore(0)
bestEvent = event
if bestEvent != None: #A match was found meaning the operon is a duplicate therefor do not add it into the ancestor
globals.trackingId += 1
bestEvent.trackingEventId = globals.trackingId
coverageTracker[i] = True
duplicationEvents.append(bestEvent)
duplicationDetails = ''
position = bestEvent.fragmentDetails1.startPositionInGenome
op = copy.deepcopy(bestEvent.fragmentDetails1.sequence)
if bestEvent.fragmentDetails1.isNegativeOrientation == True: #Reverses the genes if the operon was originally negative to ensure the correct position is computed
op.reverse()
for gene in op:
duplicationDetails += gene + ' ' + str(position) + ', '
position += 1
duplicationDetails = duplicationDetails[0:(
len(duplicationDetails) - 2)]
duplicationDetails += ';'
#Increment the duplicate counter with size of operon since the operon is a duplication
strain = addDuplicationEventsToStrain(
strain, [len(bestEvent.fragmentDetails1.sequence)],
duplicationDetails)
print('\n&&&&&& Self Global Alignment &&&&&')
print(bestEvent.toString())
print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n')
else: #No match was found therefore it must have been lost in the sibling therefore we include it in the ancestor
coverageTracker[i] = True
globals.trackingId += 1
event = Event(globals.trackingId)
event.setScore(-1)
event.setFragmentDetails1(unmarkedFragment)
event.setFragmentDetails2(unmarkedFragment)
event.setGenome1Name(strain.name)
event.setGenome2Name(strain.name)
event.setTechnique('Self Global Alignment (No match!)')
event.setAncestralOperonGeneSequence(
copy.deepcopy(unmarkedFragment.sequence))
lossEvents.append(event)
deletionDetails = ''
position = event.fragmentDetails1.startPositionInGenome
op = copy.deepcopy(event.fragmentDetails1.sequence)
if event.fragmentDetails1.isNegativeOrientation == True: #Reverses the genes if the operon was originally negative to ensure the correct position is computed
op.reverse()
for gene in op:
deletionDetails += gene + ' ' + str(position) + ', '
position += 1
deletionDetails = deletionDetails[0:(len(deletionDetails) - 2)]
deletionDetails += ';'
#Increment the loss counter with the size of the operon since the operon is a loss
sibling = addDeletionEventsToStrain(
sibling, [len(event.fragmentDetails1.sequence)],
deletionDetails)
print('\n&&&&&& Self Global Alignment &&&&&')
print(event.toString())
print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n')
return duplicationEvents, lossEvents, coverageTracker, strain, sibling
def findOrthologsBySelfGlobalAlignment(strain, coverageTracker, sibling):
if globals.printToConsole:
print('Performing self-global alignment on strain: %s' % (strain.name))
lossEvents = []
duplicationEvents = []
fragments = strain.genomeFragments
for i in range(0, len(coverageTracker)):
if coverageTracker[i] == False: #Operon has not been marked
bestScore = -1000 #Make the best score some large numer
bestEvent = None #Initialize the event
minDistance = 1000 #Used to track the minimum distance from singleton to operon that has an identical gene
bestJ = -999
filteredList = iter(
filter(
lambda x: x.fragmentIndex == i,
fragments)) #Get the fragment we need based on the index
unmarkedFragment = next(filteredList, None)
if len(unmarkedFragment.sequence) > 1: #We're processing an operon
for j in range(0, len(coverageTracker)):
filteredList = iter(
filter(lambda x: x.fragmentIndex == j, fragments)
) #Get the fragment we need based on the index
currFragment = next(filteredList, None)
if i != j and currFragment.isDuplicate == False and len(
currFragment.sequence) > 1:
op1 = unmarkedFragment.sequence
op2 = currFragment.sequence
event = Event(0)
event.setFragmentDetails1(unmarkedFragment)
event.setFragmentDetails2(currFragment)
event.setGenome1Name(strain.name)
event.setGenome2Name(strain.name)
event.setTechnique('Self Global Alignment (Operon)')
event.setAncestralOperonGeneSequence(
copy.deepcopy(
op1)) #Set the ancestral operon sequence
score, event = performGlobalAlignment(
op1, op2, event) #Perform the global alignment
event.setScore(score)
#Compute whether this comparison is interesting
#threshold = max(len(op1), len(op2))
#threshold = threshold//3
#numOperonDifferences = computeOperonDifferences(op1, op2)
#if numOperonDifferences <= threshold and score < bestScore:
if (score > 0 and score > bestScore) or (
score == bestScore
and abs(i - j) < minDistance):
bestScore = score
bestEvent = event
minDistance = abs(i - j)
bestJ = j
#Make sure an origin or a terminus doesn't get mapped with a singleton gene
elif len(
unmarkedFragment.sequence
) == 1 and unmarkedFragment.description != 'Origin' and unmarkedFragment.description != 'Terminus':
for j in range(0, len(coverageTracker)):
filteredList = iter(
filter(lambda x: x.fragmentIndex == j, fragments)
) #Get the fragment we need based on the index
currFragment = next(filteredList, None)
if i != j and currFragment.isDuplicate == False:
op1 = unmarkedFragment.sequence
op2 = currFragment.sequence
event = Event(0)
event.setFragmentDetails1(unmarkedFragment)
event.setFragmentDetails2(currFragment)
event.setGenome1Name(strain.name)
event.setGenome2Name(strain.name)
event.setTechnique('Self Global Alignment (Singleton)')
event.setAncestralOperonGeneSequence(
copy.deepcopy(
op1)) #Set the ancestral operon sequence
if op1[0] in op2 and abs(
i - j
) < minDistance: #Checks if the singleton gene is located in the operon and if the distance is smaller
minDistance = abs(i - j)
event.setScore(0)
bestEvent = event
bestJ = j
if bestEvent != None: #A match was found meaning the operon is a duplicate therefor do not add it into the ancestor
#Handle special case where two unmarked operons are selected as the best matches
cycleDuplication = False
if coverageTracker[i] == False and coverageTracker[
bestJ] == False:
bestEvent.fragmentDetails2.isDuplicate = True
coverageTracker[bestJ] = True
cycleDuplication = True
bestEvent.setScore(-1)
bestEvent.setAncestralOperonGeneSequence(
copy.deepcopy(bestEvent.fragmentDetails2.sequence)
) #insert source as ancestral operon
bestEvent.setTechnique(
'Self Global Alignment (Cyclic Duplication!)')
lossEvents.append(bestEvent)
globals.trackingId += 1
coverageTracker[i] = True
bestEvent.trackingEventId = globals.trackingId
if len(bestEvent.fragmentDetails1.sequence) > 1 and len(
bestEvent.fragmentDetails2.sequence) > 1:
handleDuplicateDetails(bestEvent, strain, sibling,
cycleDuplication)
else:
#Singleton was mapped to an operon
if len(bestEvent.fragmentDetails1.sequence) == 1:
gene = bestEvent.fragmentDetails1.sequence[0]
position = bestEvent.fragmentDetails1.startPositionInGenome
else:
gene = bestEvent.fragmentDetails2.sequence[0]
position = bestEvent.fragmentDetails2.startPositionInGenome
tempString = gene + ' ' + str(position) + ';'
strain = addDuplicationEventsToStrain(
strain, [1], tempString)
duplicationEvents.append(bestEvent)
#This is now being handled with the function handleDuplicateDetails
#duplicationDetails = ''
#position = bestEvent.fragmentDetails1.startPositionInGenome
#op = copy.deepcopy(bestEvent.fragmentDetails1.sequence)
#if bestEvent.fragmentDetails1.isNegativeOrientation == True: #Reverses the genes if the operon was originally negative to ensure the correct position is computed
#op.reverse()
#for gene in op:
#duplicationDetails += gene + ' ' + str(position) + ', '
#position += 1
#duplicationDetails = duplicationDetails[0:(len(duplicationDetails) - 2)]
#duplicationDetails += ';'
#Increment the duplicate counter with size of operon since the operon is a duplication
#strain = addDuplicationEventsToStrain(strain, [len(bestEvent.fragmentDetails1.sequence)], duplicationDetails)
if globals.printToConsole:
print('\n&&&&&& Self Global Alignment &&&&&')
print(bestEvent.toString())
print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n')
else: #No match was found therefore it must have been lost in the sibling therefore we include it in the ancestor
coverageTracker[i] = True
globals.trackingId += 1
event = Event(globals.trackingId)
event.setScore(-1)
event.setFragmentDetails1(unmarkedFragment)
event.setFragmentDetails2(unmarkedFragment)
event.setGenome1Name(strain.name)
event.setGenome2Name(strain.name)
event.setTechnique('Self Global Alignment (No match!)')
event.setAncestralOperonGeneSequence(
copy.deepcopy(unmarkedFragment.sequence))
lossEvents.append(event)
position = event.fragmentDetails1.startPositionInGenome
op = copy.deepcopy(event.fragmentDetails1.sequence)
tempString = ''
for n in range(0, len(op)):
gene = op[n]
if event.fragmentDetails1.isNegativeOrientation == False:
tempString += gene + ' ' + str(n + position) + ', '
else:
tempString = gene + ' ' + str(position + len(op) - n -
1) + ', ' + tempString
tempString = tempString[0:(
len(tempString) - 2)] #Remove the last comma and space
tempString += ';'
#Increment the loss counter with the size of the operon since the operon is a loss
sibling = addDeletionEventsToStrain(
sibling, [len(event.fragmentDetails1.sequence)],
tempString)
if globals.printToConsole:
print('\n&&&&&& Self Global Alignment &&&&&')
print(event.toString())
print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n')
return duplicationEvents, lossEvents, coverageTracker, strain, sibling
def findOrthologsBySelfGlobalAlignment(strain, coverageTracker, targetStrain):
print('Performing self-global alignment on strain: %s' %
(strain.getName()))
lossEvents = []
duplicationEvents = []
sequence = strain.getSequence()
for x in range(0, len(coverageTracker)):
#Check if marked
if coverageTracker[x] == False:
bestScore = 1000 #Make the best score some large numer
bestEvent = None #Initialize the event
minDistance = 1000 #Used to track the minimum distance from singleton to operon that has an identical gene
if len(sequence[x].split(',')) > 1: #This is an operon
for y in range(0, len(sequence)):
#make sure we're not comparing the same operons and that the second operon is NOT a singleton
if x != y and len(sequence[y].split(
',')) > 1 and coverageTracker[y] == True:
op1 = sequence[x]
op2 = sequence[y]
#Gene content differences ie which one has more genes, operons 1 and 2, and the number of unique genes between the operons being compared (ie does the operon have any unique genes)
geneContentDifferences, operon1, operon2, numUniqueGenes = formatAndComputeOperonDifferences(
op1, op2)
#Checks if either operons are in the - orientation
negativeOrientationOp1 = reverseSequence(op1)
negativeOrientationOp2 = reverseSequence(op2)
#Tracks whether we reversed the operons
operon1Reversed = False
operon2Reversed = False
#Create event for this comparison
event = Event(0)
event.setGenome1Operon(operon1)
event.setGenome2Operon(operon2)
event.setGenome1Name(strain.getName())
event.setGenome2Name(strain.getName())
event.isOriginallyNegativeOrientationOp1(
negativeOrientationOp1
) #This tracks the original orientation of op1
event.isOriginallyNegativeOrientationOp2(
negativeOrientationOp2
) #This tracks the original orientation of op2
event.setOperon1Index(x)
event.setOperon2Index(y)
event.setTechnique('Self Global Alignment (Operon)')
#If the orientation of the operons does not match, then flip the operon in the negative orientation to the positive orientation
if negativeOrientationOp1 != negativeOrientationOp2:
if negativeOrientationOp1:
operon1.reverse()
operon1Reversed = True
negativeOrientationOp1 = False
if negativeOrientationOp2:
operon2.reverse()
operon2Reversed = True
negativeOrientationOp2 = False
if negativeOrientationOp1 != negativeOrientationOp2:
print(
'Check code! These operons should be in the same orientation!'
)
#Track whether these operons were reversed
event.isReversedOp1(
operon1Reversed
) #This tracks whether op1 was reversed
event.isReversedOp2(
operon2Reversed
) #This tracks whether op2 was reversed
event.setAncestralOperonGeneSequence(
copy.deepcopy(
operon1)) #Set the ancestral operon sequence
score, event = performGlobalAlignment(
operon1, operon2,
event) #Perform the global alignment
event.setScore(score)
threshold = max(len(operon1), len(operon2))
threshold = threshold // 3
if geneContentDifferences <= threshold and score < bestScore:
bestScore = score
bestEvent = event
else: #This is a singleton gene
for y in range(0, len(sequence)):
if x != y and coverageTracker[
y] == True: #Make sure we're not comparing the same singleton genes
op1 = sequence[x]
op2 = sequence[y]
#Gene content differences ie which one has more genes, operons 1 and 2, and the number of unique genes between the operons being compared (ie does the operon have any unique genes)
geneContentDifferences, operon1, operon2, numUniqueGenes = formatAndComputeOperonDifferences(
op1, op2)
#Checks if either operons are in the - orientation
negativeOrientationOp1 = reverseSequence(op1)
negativeOrientationOp2 = reverseSequence(op2)
#Tracks whether we reversed the operons
operon1Reversed = False
operon2Reversed = False
#Create event for this comparison
event = Event(0)
event.setGenome1Operon(operon1)
event.setGenome2Operon(operon2)
event.setGenome1Name(strain.getName())
event.setGenome2Name(strain.getName())
event.isOriginallyNegativeOrientationOp1(
negativeOrientationOp1
) #This tracks the original orientation of op1
event.isOriginallyNegativeOrientationOp2(
negativeOrientationOp2
) #This tracks the original orientation of op2
event.setOperon1Index(x)
event.setOperon2Index(y)
event.setTechnique('Self Global Alignment (Singleton)')
#If the orientation of the operons does not match, then flip the operon in the negative orientation to the positive orientation
if negativeOrientationOp1 != negativeOrientationOp2:
if negativeOrientationOp1:
operon1.reverse()
operon1Reversed = True
negativeOrientationOp1 = False
if negativeOrientationOp2:
operon2.reverse()
operon2Reversed = True
negativeOrientationOp2 = False
if negativeOrientationOp1 != negativeOrientationOp2:
print(
'Check code! These operons should be in the same orientation!'
)
#Track whether these operons were reversed
event.isReversedOp1(
operon1Reversed
) #This tracks whether op1 was reversed
event.isReversedOp2(
operon2Reversed
) #This tracks whether op2 was reversed
event.setAncestralOperonGeneSequence(
copy.deepcopy(
operon1)) #Set the ancestral operon sequence
if operon1[0] in operon2 and abs(
x - y
) < minDistance: #Checks if the singleton gene is located in the operon and if the distance is smaller
minDistance = abs(x - y)
event.setScore(0)
bestEvent = event
#Take the event and append it to the duplicate event list
if bestEvent != None:
globals.trackingId += 1
bestEvent.trackingEventId = globals.trackingId
coverageTracker[x] = True
duplicationEvents.append(bestEvent)
#Increment the duplicate counter with size of operon since the operon is a duplication
#incrementDuplicateSizeCounters([len(event.genome1Operon)])
strain = addDuplicationEventsToStrain(
[len(event.genome1Operon)], strain)
print('\n&&&&&& Self Global Alignment &&&&&')
bestEvent.printEvent()
print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n')
else:
coverageTracker[x] = True
globals.trackingId += 1
event = Event(globals.trackingId)
event.setScore(-1)
event.setGenome2Operon([])
event.setGenome1Name(strain.getName())
event.setGenome2Name('None')
event.isOriginallyNegativeOrientationOp1(
reverseSequence(sequence[x]))
event.isOriginallyNegativeOrientationOp2(False)
event.setOperon1Index(x)
event.setOperon2Index(-1)
event.setTechnique('Self Global Alignment (No match!)')
event.isReversedOp1(False)
event.isReversedOp2(False)
#Set the ancestral operon sequence
ancestralGenes = []
operonGenes = sequence[x]
operonGenes = operonGenes.replace('-', '')
operonGenes = operonGenes.replace('[', '')
operonGenes = operonGenes.replace(']', '')
operonGenesList = operonGenes.split(',')
for gene in operonGenesList:
ancestralGenes.append(gene.strip())
event.setAncestralOperonGeneSequence(ancestralGenes)
event.setGenome1Operon(copy.deepcopy(ancestralGenes))
lossEvents.append(event)
#Increment the loss counter with the size of the operon since the operon is a loss
#incrementDeletionSizeCounters([len(event.genome1Operon)])
targetStrain = addDeletionEventsToStrain(
[len(event.genome1Operon)], targetStrain)
print('\n&&&&&& Self Global Alignment &&&&&')
event.printEvent()
print('&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\n')
return duplicationEvents, lossEvents, coverageTracker, targetStrain, strain
def reconstructOperonSequence(event, strain1, strain2):
ancestralOperon = copy.deepcopy(event.operon1Alignment)
if event.score == 0:
print('No differences detected between these two operons')
event.setAncestralOperonGeneSequence(ancestralOperon)
else:
print('Differences detected between these two operons!')
operon1Gaps = event.operon1Gaps
operon2Gaps = event.operon2Gaps
operon1GapIndexes = event.operon1GapIndexes
operon2GapIndexes = event.operon2GapIndexes
print('These are the extra genes for operon 1: %s' % (operon1Gaps))
print('These are the indexes for extra genes in operon 1: %s' %
(operon1GapIndexes))
print('These are the extra genes for operon 2: %s' % (operon2Gaps))
print('These are the indexes for extra genes in operon 2: %s' %
(operon2GapIndexes))
#Checks if these extra genes are duplicates by checking if they exist within the alignment and removes them if they do
operon1Gaps, duplicateSizesWithinAlignment1 = checkForMatchesInAlignment(
operon1Gaps, event.operon1Alignment)
operon2Gaps, duplicateSizesWithinAlignment2 = checkForMatchesInAlignment(
operon2Gaps, event.operon2Alignment)
#increment the duplicate counters
#incrementDuplicateSizeCounters(duplicateSizesWithinAlignment1)
#incrementDuplicateSizeCounters(duplicateSizesWithinAlignment2)
strain1 = addDuplicationEventsToStrain(duplicateSizesWithinAlignment1,
strain1)
strain2 = addDuplicationEventsToStrain(duplicateSizesWithinAlignment2,
strain2)
i = len(operon1Gaps)
j = len(operon2Gaps)
while (i > 0) or (j > 0):
#Select the gap with the biggest index b/c we will be performing the insertion rear to front of operon to avoid messing up the indexes of the other gaps
if i > 0 and j > 0 and operon1GapIndexes[
i - 1] > operon2GapIndexes[j - 1]:
#This means both queues have gaps however the index in queue 1 is bigger so we'll deal with that one first
#print('Gap being processed: %s' % (operon1Gaps[i]))
numUniqueFound, deletionSizes, duplicationSizes, updateUnaligned = findUniqueGenes(
operon1Gaps[i - 1], strain1.formattedSequence,
strain1.sequenceConversion[event.operon1Index])
strain1 = addDuplicationEventsToStrain(duplicationSizes,
strain1)
strain2 = addDeletionEventsToStrain(deletionSizes, strain2)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon1Gaps[i - 1]) > 0:
#Insert gap into operon
operon1Gaps[i - 1].reverse()
for gene in operon1Gaps[i - 1]:
ancestralOperon.insert(operon1GapIndexes[i - 1], gene)
i = i - 1
elif i > 0 and j > 0 and operon1GapIndexes[
i - 1] < operon2GapIndexes[j - 1]:
#This means both queues have gaps however the index in queue 2 is bigger so we'll insert that one first
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
numUniqueFound, deletionSizes, duplicationSizes, updateUnaligned = findUniqueGenes(
operon2Gaps[j - 1], strain2.formattedSequence,
strain2.sequenceConversion[event.operon2Index])
strain2 = addDuplicationEventsToStrain(duplicationSizes,
strain2)
strain1 = addDeletionEventsToStrain(deletionSizes, strain1)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon2Gaps[j - 1]) > 0:
#Insert gap into operon
operon2Gaps[j - 1].reverse()
for gene in operon2Gaps[j - 1]:
ancestralOperon.insert(operon2GapIndexes[j - 1], gene)
j = j - 1
elif i > 0:
#This means that queue 2 has no more gaps so we process the remaining gaps in queue 1
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
numUniqueFound, deletionSizes, duplicationSizes, updateUnaligned = findUniqueGenes(
operon1Gaps[i - 1], strain1.formattedSequence,
strain1.sequenceConversion[event.operon1Index])
strain1 = addDuplicationEventsToStrain(duplicationSizes,
strain1)
strain2 = addDeletionEventsToStrain(deletionSizes, strain2)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon1Gaps[i-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon1Gaps[i - 1]) > 0:
#Insert gap into operon
operon1Gaps[i - 1].reverse()
for gene in operon1Gaps[i - 1]:
ancestralOperon.insert(operon1GapIndexes[i - 1], gene)
i = i - 1
elif j > 0:
#This means that queue 1 has no more gaps to process so we deal with the remaining gaps in queue 2
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
numUniqueFound, deletionSizes, duplicationSizes, updateUnaligned = findUniqueGenes(
operon2Gaps[j - 1], strain2.formattedSequence,
strain2.sequenceConversion[event.operon2Index])
strain2 = addDuplicationEventsToStrain(duplicationSizes,
strain2)
strain1 = addDeletionEventsToStrain(deletionSizes, strain1)
#incrementDuplicateSizeCounters(duplicationSizes)
#incrementDeletionSizeCounters(deletionSizes)
#print('Gap being processed: %s' % (operon2Gaps[j-1]))
#print('Number of unique genes found: %s' %(numUniqueFound))
#print('Number of deletion genes found: %s' %(deletionSizes))
#print('Number of duplicate genes found: %s' %(duplicationSizes))
if len(operon2Gaps[j - 1]) > 0:
#Insert gap into operon
operon2Gaps[j - 1].reverse()
for gene in operon2Gaps[j - 1]:
ancestralOperon.insert(operon2GapIndexes[j - 1], gene)
j = j - 1
#Set ancestral operon
event.setAncestralOperonGeneSequence(ancestralOperon)
#print('This is the resulting ancestral operon: %s' % (ancestralOperon))
#print('\n\n')
#print('These are the extra genes remaining for operon 1: %s' %(operon1Gaps))
#print('These are the extra genes remaining for operon 2: %s' %(operon2Gaps))
#print('These are the duplicate sizes operon 1: %s' %(duplicateSizesWithinAlignment1))
#print('These are the duplicate sizes operon 2: %s\n\n' %(duplicateSizesWithinAlignment2))
return event, strain1, strain2
