def recurseSubtract(sList, otherList):
'''When a tcc is subtracted, it's subtraction must be subtracted
with all the other tcc's in otherlist. The best way to do this is
to break it up in a recursive function'''
#print 'here recurse'
totalOverlap = False
subtractList = []
#print 'Lists (keep, other):', sList, otherList
for tccK in sList: #
overlap = False
#print 'known check', tccK
for tccO in otherList:
#print ' other check', tccO
if bioLibCG.tccOverlap(tccK, tccO):
totalOverlap = True
overlap = True
for sTcc in subtractTwoTcc(tccK, tccO):
#print ' adding', sTcc, 'to list'
if sTcc not in subtractList: subtractList.append(sTcc)
#print ' list', subtractList
break
if not overlap:
#print 'tccK did not overlap anything, adding to list'
subtractList.append(tccK)
#print subtractList
if totalOverlap:
rList = recurseSubtract(subtractList, otherList)
return rList #once all other recursions have ended return the final list
else:
return subtractList #no overlaps, return all the way to top...
def recurseSubtract(sList, otherList): '''When a tcc is subtracted, it's subtraction must be subtracted with all the other tcc's in otherlist. The best way to do this is to break it up in a recursive function''' #print 'here recurse' totalOverlap = False subtractList = [] #print 'Lists (keep, other):', sList, otherList for tccK in sList: # overlap = False #print 'known check', tccK for tccO in otherList: #print ' other check', tccO if bioLibCG.tccOverlap(tccK, tccO): totalOverlap = True overlap = True for sTcc in subtractTwoTcc(tccK, tccO): #print ' adding', sTcc, 'to list' if sTcc not in subtractList: subtractList.append(sTcc) #print ' list', subtractList break if not overlap: #print 'tccK did not overlap anything, adding to list' subtractList.append(tccK) #print subtractList if totalOverlap: rList = recurseSubtract(subtractList, otherList) return rList #once all other recursions have ended return the final list else: return subtractList #no overlaps, return all the way to top...
def makeConnectionsDict(tccList, complexity=None):
'''A connections dictionary gives each coordinates connections to other coordinates in the list
tcc : [tcc1, tcc2, etc]'''
#make sure complexity is set
if complexity == None:
complexity = len(tccList)
#make the index
(tccIndex, indexMin, indexStep) = returnTccIndex(tccList, complexity)
#compare vs sequences
connectionsDict = {
} # format is coord: [tcc, tcc, etc]. It means X coord is connected to other coords
for coord in tccList:
#Add coord to connections dict --> every coord is a key
connectionsDict[coord] = []
numCheckLow = int(coord.strip().split(':')[2])
numCheckHigh = int(coord.strip().split(':')[3])
indexCheckLow = getStepIndex(numCheckLow, indexMin, indexStep)
indexCheckHigh = getStepIndex(numCheckHigh, indexMin, indexStep)
indexChecks = range(indexCheckLow, indexCheckHigh + 1, indexStep)
for indexCheck in indexChecks:
if indexCheck in tccIndex:
for indexCoord in tccIndex[indexCheck]:
if indexCoord == coord:
pass
elif bioLibCG.tccOverlap(coord, indexCoord):
#print 'overlapped',indexCoord,coord
if indexCoord not in connectionsDict[coord]:
connectionsDict[coord].append(indexCoord)
#break #wouldn't breaking here prevent finding all overlaps?
return connectionsDict
def makeConnectionsDict(tccList, complexity = None):
'''A connections dictionary gives each coordinates connections to other coordinates in the list
tcc : [tcc1, tcc2, etc]'''
#make sure complexity is set
if complexity == None:
complexity = len(tccList)
#make the index
(tccIndex, indexMin, indexStep) = returnTccIndex(tccList, complexity)
#compare vs sequences
connectionsDict = {} # format is coord: [tcc, tcc, etc]. It means X coord is connected to other coords
for coord in tccList:
#Add coord to connections dict --> every coord is a key
connectionsDict[coord] = []
numCheckLow = int(coord.strip().split(':')[2])
numCheckHigh = int(coord.strip().split(':')[3])
indexCheckLow = getStepIndex(numCheckLow, indexMin, indexStep)
indexCheckHigh = getStepIndex(numCheckHigh, indexMin, indexStep)
indexChecks = range(indexCheckLow, indexCheckHigh + 1, indexStep)
for indexCheck in indexChecks:
if indexCheck in tccIndex:
for indexCoord in tccIndex[indexCheck]:
if indexCoord == coord:
pass
elif bioLibCG.tccOverlap(coord, indexCoord):
#print 'overlapped',indexCoord,coord
if indexCoord not in connectionsDict[coord]:
connectionsDict[coord].append(indexCoord)
#break #wouldn't breaking here prevent finding all overlaps?
return connectionsDict
def compareTwoTcc(tccListOne, tccListTwo, order = 0, complexity = None, amount = False):
'''Checks overlaps between two TCC lists
Indexed --> Runs quicker.
Complexity is roughly # of bins --> defaults to length of shortest list
Returns list with overlapping sequences from BOTH lists --> think shared space of vinn diagram
Takes longer than returning single list --> see other function
ORDER DECIDES WHICH LIST IS TO BE INDEX --> NON INDEXED LIST IS RETURNED
0 = shortest is indexed, 1 = first list is returned, 2 = second list is returned
amount refers to if you want to return the amount of overlap for each transcript [coord, amount]'''
#decide which list is to be indexed
if order == 0: #shortest list is indexed
if len(tccListOne) < len(tccListTwo):
indexList = tccListOne
otherList = tccListTwo
else:
indexList = tccListTwo
otherList = tccListOne
elif order == 1:
indexList = tccListTwo
otherList = tccListOne
elif order == 2:
indexList = tccListOne
otherList = tccListTwo
#make sure complexity is set, should set max complexity here...
if complexity == None:
complexity = len(indexList)
#make the index
(tccIndex, indexMin, indexStep) = returnTccIndex(indexList, complexity)
#print tccIndex, complexity
#compare vs sequences
reducedList = []
for coord in otherList:
#print coord
numCheckLow = int(coord.strip().split(':')[2])
numCheckHigh = int(coord.strip().split(':')[3])
indexCheckLow = getStepIndex(numCheckLow, indexMin, indexStep)
indexCheckHigh = getStepIndex(numCheckHigh, indexMin, indexStep)
indexChecks = range(indexCheckLow, indexCheckHigh + 1, indexStep)
#print ' ',numCheckLow, indexCheckLow
#print ' ',numCheckHigh, indexCheckHigh
for indexCheck in indexChecks:
if coord in reducedList: break #Already overlapped from another indexCheck...
if indexCheck in tccIndex:
#print 'Got Here'
for indexCoord in tccIndex[indexCheck]:
#print 'Checking Overlap', indexCoord, coord
overlap = bioLibCG.tccOverlap(coord, indexCoord, True)
if overlap:#if there is any overlap...
#print 'overlapped',indexCoord,coord
if amount:
reducedList.append([coord, overlap])
else:
reducedList.append(coord)
break #already found this coords overlap --> don't check other indexCoords
return reducedList
def compareTwoTcc(tccListOne, tccListTwo, order=0, complexity=None):
'''Checks overlaps between two TCC lists
Indexed --> Runs quicker.
Complexity is roughly # of bins --> defaults to length of shortest list
Returns list with overlapping sequences from BOTH lists --> think shared space of vinn diagram
Takes longer than returning single list --> see other function
ORDER DECIDES WHICH LIST IS TO BE INDEX --> NON INDEXED LIST IS RETURNED
0 = shortest is indexed, 1 = first list is returned, 2 = second list is returned'''
#decide which list is to be indexed
if order == 0: #shortest list is indexed
if len(tccListOne) < len(tccListTwo):
indexList = tccListOne
otherList = tccListTwo
else:
indexList = tccListTwo
otherList = tccListOne
elif order == 1:
indexList = tccListTwo
otherList = tccListOne
elif order == 2:
indexList = tccListOne
otherList = tccListTwo
#make sure complexity is set, should set max complexity here...
if complexity == None:
complexity = len(indexList)
#make the index
(tccIndex, indexMin, indexStep) = returnTccIndex(indexList, complexity)
#print tccIndex, complexity
#compare vs sequences
reducedList = []
for coord in otherList:
#print coord
numCheckLow = int(coord.strip().split(':')[2])
numCheckHigh = int(coord.strip().split(':')[3])
indexCheckLow = getStepIndex(numCheckLow, indexMin, indexStep)
indexCheckHigh = getStepIndex(numCheckHigh, indexMin, indexStep)
indexChecks = range(indexCheckLow, indexCheckHigh + 1, indexStep)
#print ' ',numCheckLow, indexCheckLow
#print ' ',numCheckHigh, indexCheckHigh
for indexCheck in indexChecks:
if coord in reducedList:
break #Already overlapped from another indexCheck...
if indexCheck in tccIndex:
#print 'Got Here'
for indexCoord in tccIndex[indexCheck]:
#print 'Checking Overlap', indexCoord, coord
if bioLibCG.tccOverlap(coord, indexCoord):
#print 'overlapped',indexCoord,coord
reducedList.append(coord)
break #already found this coords overlap --> don't check other indexCoords
return reducedList
def getOverlappingElements(self, tcc):
'''Given region, Which element (INTRON, EXON, 5UTR, 3UTR)'''
overlappingElements = []
try:
for utrSegment in self.utr5:
utr5Tcc = bioLibCG.makeTcc(self.chromosome, self.strand,
utrSegment[0], utrSegment[1])
if bioLibCG.tccOverlap(utr5Tcc, tcc):
overlappingElements.append([utrSegment, '5UTR'])
except IndexError:
pass
for exon in self.exonList:
exonTcc = bioLibCG.makeTcc(self.chromosome, self.strand, exon[0],
exon[1])
#print '@ ', exonTcc, tcc, 'EXON'
if bioLibCG.tccOverlap(exonTcc, tcc):
overlappingElements.append([exon, 'EXON'])
for intron in self.intronList:
intronTcc = bioLibCG.makeTcc(self.chromosome, self.strand,
intron[0], intron[1])
#print '@ ', intronTcc, tcc, 'INTRON'
if bioLibCG.tccOverlap(intronTcc, tcc):
overlappingElements.append([intron, 'INTRON'])
try:
for utrSegment in self.utr3:
utr3Tcc = bioLibCG.makeTcc(self.chromosome, self.strand,
utrSegment[0], utrSegment[1])
if bioLibCG.tccOverlap(utr3Tcc, tcc):
overlappingElements.append([utrSegment, '3UTR'])
except IndexError:
pass
#!!!Eventually add a way to find if overlapping EXON_UTR as well
if 'EXON' in overlappingElements and 'INTRON' in overlappingElements:
overlappingElements.append('EXON_INTRON')
overlappingElements.remove('EXON')
overlappingElements.remove('INTRON')
return overlappingElements
def getOverlappingElements(self, tcc):
"""Given region, Which element (INTRON, EXON, 5UTR, 3UTR)"""
overlappingElements = []
try:
for utrSegment in self.utr5:
utr5Tcc = bioLibCG.makeTcc(self.chromosome, self.strand, utrSegment[0], utrSegment[1])
if bioLibCG.tccOverlap(utr5Tcc, tcc):
overlappingElements.append([utrSegment, "5UTR"])
except IndexError:
pass
for exon in self.exonList:
exonTcc = bioLibCG.makeTcc(self.chromosome, self.strand, exon[0], exon[1])
# print '@ ', exonTcc, tcc, 'EXON'
if bioLibCG.tccOverlap(exonTcc, tcc):
overlappingElements.append([exon, "EXON"])
for intron in self.intronList:
intronTcc = bioLibCG.makeTcc(self.chromosome, self.strand, intron[0], intron[1])
# print '@ ', intronTcc, tcc, 'INTRON'
if bioLibCG.tccOverlap(intronTcc, tcc):
overlappingElements.append([intron, "INTRON"])
try:
for utrSegment in self.utr3:
utr3Tcc = bioLibCG.makeTcc(self.chromosome, self.strand, utrSegment[0], utrSegment[1])
if bioLibCG.tccOverlap(utr3Tcc, tcc):
overlappingElements.append([utrSegment, "3UTR"])
except IndexError:
pass
#!!!Eventually add a way to find if overlapping EXON_UTR as well
if "EXON" in overlappingElements and "INTRON" in overlappingElements:
overlappingElements.append("EXON_INTRON")
overlappingElements.remove("EXON")
overlappingElements.remove("INTRON")
return overlappingElements
def updateDensity(cName=None):
#Create hitmap for blocks, cValdict for block
conf = cgConfig.getConfig(cName)
blockFileName = conf.conf[
'hitsPerFrame'] # created in defineCluster script folder
blockFile = open(blockFileName, 'r')
blocksList = []
cValBlockDict = {}
for line in blockFile:
blocksList.append(line.strip().split('\t')[0])
cValBlockDict[line.strip().split('\t')[0]] = int(
line.strip().split('\t')[1])
blockFile.close()
blockHitmap = bioLibCG.createHitMap(blocksList)
#Now append the cVal for each predicted line:
predictedFileName = conf.conf['results']
predictedFile = open(predictedFileName, 'r')
newFileList = []
counter = 0
for line in predictedFile:
counter = counter + 1
#print counter
cVal = 0
#what blocks does this prediction overlap?
tccPrediction = line.strip().split('\t')[1] #This should be mature?
coordsPrediction = bioLibCG.stripTripleColon(tccPrediction)
for i in range(int(coordsPrediction['start']),
int(coordsPrediction['end'])):
if i in blockHitmap:
for block in blockHitmap[i]:
if bioLibCG.tccOverlap(tccPrediction, block):
if cValBlockDict[block] > cVal:
cVal = cValBlockDict[block]
newLine = line.strip().split('\t')
newLine[5] = str(cVal)
newLine = '\t'.join(newLine) + '\n'
newFileList.append(newLine)
predictedFile.close()
newFileName = conf.conf['results']
newFile = open(newFileName, 'w')
for line in newFileList:
newFile.write(line)
newFile.close()
def getIndividualOverlaps(tccListOne, tccListTwo, order, complexity=None):
'''Checks overlaps between two TCC lists
Indexed --> Runs quicker.
Complexity is roughly # of bins --> defaults to length of shortest list
Returns list with overlapping sequences from BOTH lists --> think shared space of vinn diagram
Takes longer than returning single list --> see other function
0 = shortest is indexed, 1 = first list is returned, 2 = second list is returned'''
#decide which list is to be indexed
if order == 1:
indexList = tccListTwo
otherList = tccListOne
elif order == 2:
indexList = tccListOne
otherList = tccListTwo
#make sure complexity is set, should set max complexity here...
if complexity == None:
complexity = len(indexList)
#make the index
(tccIndex, indexMin, indexStep) = returnTccIndex(indexList, complexity)
#print tccIndex, complexity
#compare vs sequences
individualOverlaps = {} # tcc : [tcc, tcc]
for coord in otherList:
individualOverlaps[coord] = []
#print coord
numCheckLow = int(coord.strip().split(':')[2])
numCheckHigh = int(coord.strip().split(':')[3])
indexCheckLow = getStepIndex(numCheckLow, indexMin, indexStep)
indexCheckHigh = getStepIndex(numCheckHigh, indexMin, indexStep)
indexChecks = range(indexCheckLow, indexCheckHigh + 1, indexStep)
#print ' ',numCheckLow, indexCheckLow
#print ' ',numCheckHigh, indexCheckHigh
for indexCheck in indexChecks:
if indexCheck in tccIndex:
for indexCoord in tccIndex[indexCheck]:
overlap = bioLibCG.tccOverlap(coord, indexCoord, True)
if overlap: #if there is any overlap...
if indexCoord not in individualOverlaps[coord]:
individualOverlaps[coord].append(indexCoord)
return individualOverlaps
def getIndividualOverlaps(tccListOne, tccListTwo, order, complexity = None):
'''Checks overlaps between two TCC lists
Indexed --> Runs quicker.
Complexity is roughly # of bins --> defaults to length of shortest list
Returns list with overlapping sequences from BOTH lists --> think shared space of vinn diagram
Takes longer than returning single list --> see other function
0 = shortest is indexed, 1 = first list is returned, 2 = second list is returned'''
#decide which list is to be indexed
if order == 1:
indexList = tccListTwo
otherList = tccListOne
elif order == 2:
indexList = tccListOne
otherList = tccListTwo
#make sure complexity is set, should set max complexity here...
if complexity == None:
complexity = len(indexList)
#make the index
(tccIndex, indexMin, indexStep) = returnTccIndex(indexList, complexity)
#print tccIndex, complexity
#compare vs sequences
individualOverlaps = {} # tcc : [tcc, tcc]
for coord in otherList:
individualOverlaps[coord] = []
#print coord
numCheckLow = int(coord.strip().split(':')[2])
numCheckHigh = int(coord.strip().split(':')[3])
indexCheckLow = getStepIndex(numCheckLow, indexMin, indexStep)
indexCheckHigh = getStepIndex(numCheckHigh, indexMin, indexStep)
indexChecks = range(indexCheckLow, indexCheckHigh + 1, indexStep)
#print ' ',numCheckLow, indexCheckLow
#print ' ',numCheckHigh, indexCheckHigh
for indexCheck in indexChecks:
if indexCheck in tccIndex:
for indexCoord in tccIndex[indexCheck]:
overlap = bioLibCG.tccOverlap(coord, indexCoord, True)
if overlap:#if there is any overlap...
if indexCoord not in individualOverlaps[coord]:
individualOverlaps[coord].append(indexCoord)
return individualOverlaps
def updateDensity(cName=None):
# Create hitmap for blocks, cValdict for block
conf = cgConfig.getConfig(cName)
blockFileName = conf.conf["hitsPerFrame"] # created in defineCluster script folder
blockFile = open(blockFileName, "r")
blocksList = []
cValBlockDict = {}
for line in blockFile:
blocksList.append(line.strip().split("\t")[0])
cValBlockDict[line.strip().split("\t")[0]] = int(line.strip().split("\t")[1])
blockFile.close()
blockHitmap = bioLibCG.createHitMap(blocksList)
# Now append the cVal for each predicted line:
predictedFileName = conf.conf["results"]
predictedFile = open(predictedFileName, "r")
newFileList = []
counter = 0
for line in predictedFile:
counter = counter + 1
# print counter
cVal = 0
# what blocks does this prediction overlap?
tccPrediction = line.strip().split("\t")[1] # This should be mature?
coordsPrediction = bioLibCG.stripTripleColon(tccPrediction)
for i in range(int(coordsPrediction["start"]), int(coordsPrediction["end"])):
if i in blockHitmap:
for block in blockHitmap[i]:
if bioLibCG.tccOverlap(tccPrediction, block):
if cValBlockDict[block] > cVal:
cVal = cValBlockDict[block]
newLine = line.strip().split("\t")
newLine[5] = str(cVal)
newLine = "\t".join(newLine) + "\n"
newFileList.append(newLine)
predictedFile.close()
newFileName = conf.conf["results"]
newFile = open(newFileName, "w")
for line in newFileList:
newFile.write(line)
newFile.close()
def consolidatePeaksByTcc(dFN, outFN):
fOut = open(outFN, 'w')
f = open(dFN, 'r')
lastLine = ''
lastTcc = 'chr100:1:100:1000'
for line in f:
ls = line.strip().split('\t')
tcc = ls[1]
if bioLibCG.tccOverlap(tcc, lastTcc):
#skip line...
lastLine = line
lastTcc = tcc
else:
fOut.write(lastLine)
lastLine = line
lastTcc = tcc
fOut.close()
f.close()
def consolidatePeaksByTcc(dFN, outFN):
fOut = open(outFN, 'w')
f = open(dFN, 'r')
lastLine = ''
lastTcc = 'chr100:1:100:1000'
for line in f:
ls = line.strip().split('\t')
tcc = ls[1]
if bioLibCG.tccOverlap(tcc, lastTcc):
#skip line...
lastLine = line
lastTcc = tcc
else:
fOut.write(lastLine)
lastLine = line
lastTcc = tcc
fOut.close()
f.close()
