import compareData as compare
import bioLibCG as cg
exonList = compare.tccFileToList('allExons.tcc', 0)
print cg.getTccListTotalLength(exonList)
nonOverlap = compare.collapseOverlaps(exonList)
print cg.getTccListTotalLength(nonOverlap)
o = open('mouseExons.tcc', 'w')
for tcc in nonOverlap:
o.write(tcc + '\n')
o.close()
import compareData as compare
tccList = compare.tccFileToList('snos.tcc', 0)
collapsed = compare.collapseOverlaps(tccList)
for tcc in collapsed:
print tcc
def intronNoisy(cName = None):
mConf = c.cgConfig('Main.conf')
conf = c.getConfig(cName)
#init
cHairs = getHairpins.getHairpins(conf.conf['resultsIntrons']) #CID: HAIRPIN
organism = conf.conf['organism']
exonList = compare.tccFileToList('%sExons.tcc' % organism, 0)
slide = 1000
#make prediction overlap hitmap
predMap = {}
predList = []
for CID in cHairs:
hPin = cHairs[CID]
predList.append(hPin)
#collapse Overlaps
print ' collapsing predictions'
predList = compare.collapseOverlaps(predList)
print ' collapsing exons'
exonList = compare.collapseOverlaps(exonList)
#collect levels for each hairpin region
cidLevels = {}
for CID in cHairs:
print CID
hPin = cHairs[CID]
chrom = ss(hPin, ':')[0]
strand = ss(hPin, ':')[1]
start = int(ss(hPin, ':')[2])
end = int(ss(hPin, ':')[3])
scanStart = start - slide
scanEnd = end + slide
scanRange = []
scanRange.append('%s:%s:%s:%s' % (chrom, strand, scanStart, start))
scanRange.append('%s:%s:%s:%s' % (chrom, strand, end, scanEnd))
print scanRange
scanRange = compare.subtractTwoTccLists(scanRange, predList)
scanRange = compare.subtractTwoTccLists(scanRange, exonList)
levels = []
print ' Retrieving Expression levels:', cg.getTccListTotalLength(scanRange)
levels = []
hPinLevels = stepVectorScan.scanVectorsHist(scanRange, cName)
for hPin in hPinLevels:
levels.extend(hPinLevels[hPin])
cidLevels[CID] = levels
#output levels to file
#find longest
longest = 0
for CID in cidLevels:
length = len(cidLevels[CID])
if length > longest:
longest = length
sortedKeys = cidLevels.keys()
sortedKeys.sort()
newLines = []
for j in range(0, longest): #how many lines are there
newLine = []
for CID in sortedKeys:
if len(cidLevels[CID]) > j:# add it
newLine.append(str(cidLevels[CID][j]))
else:
newLine.append('NA')
newLines.append('\t'.join(newLine) + '\n')
outFileN = conf.conf['intronNoiseData']
outFile = open(outFileN, 'w')
outFile.write('\t'.join(sortedKeys) + '\n')
outFile.writelines(newLines)
outFile.close()
import compareData as compare
tccList = compare.tccFileToList('snos.tcc', 0)
collapsed = compare.collapseOverlaps(tccList)
for tcc in collapsed:
print tcc
def exonNoisy(cName = None):
#init
mConf = c.cgConfig('Main.conf')
conf = c.getConfig(cName)
cHairs = getHairpins.getHairpins(conf.conf['resultsExons']) #CID: HAIRPIN
organism = conf.conf['organism']
geneSetFolder = mConf.conf['geneSets%s' % organism]
#make prediction overlap hitmap
print 'Making prediction list'
predList = []
for CID in cHairs:
hPin = cHairs[CID]
predList.append(hPin)
if compare.checkIfOverlaps(predList):
predList = compare.collapseOverlaps(predList)
#make genes for Ensemble/make list of tccs for exons.
print 'Creating gene set'
ensGenes = cgGenes.createGeneSetFromFile(geneSetFolder + '/ensemblAllExons.tsv')
print ' loaded # genes:', len(ensGenes.set)
#collect levels for each haipin region
print '[Checking all levels]'
cidLevels = {}
for CID in cHairs:
print CID
hPin = cHairs[CID]
#for each hairpin, --> find overlapping transcripts in same gene
overlappingGenes = ensGenes.geneOverlaps([hPin])
if len(overlappingGenes) > 0:
gIDs = [gene.id for gene in overlappingGenes]
allTccs = ensGenes.getTccsFromGIDs(gIDs)
if compare.checkIfOverlaps:
print ' Overlaps...collapsing'
allTccs = compare.collapseOverlaps(allTccs)
else:
print 'NO GENE OVERLAPS!!!!!', CID, hPin
#filter out my predictions.
print ' Filtering out predictions'
checkList = compare.subtractTwoTccLists(allTccs, predList)
#Get Expression level for gene.
print ' Retrieving Expression levels:', cg.getTccListTotalLength(checkList)
levels = []
hPinLevels = stepVectorScan.scanVectorsHist(checkList, cName)
for hPin in hPinLevels:
levels.extend(hPinLevels[hPin])
cidLevels[CID] = levels
#output levels to file
print 'Outputting to file'
#find longest
longest = 0
for CID in cidLevels:
length = len(cidLevels[CID])
if length > longest:
longest = length
sortedKeys = cidLevels.keys()
sortedKeys.sort()
#print sortedKeys
newLines = []
for j in range(0, longest): #how many lines are there
newLine = []
for CID in sortedKeys:
if len(cidLevels[CID]) > j:# add it
newLine.append(str(cidLevels[CID][j]))
else:
newLine.append('NA')
newLines.append('\t'.join(newLine) + '\n')
outFileN = conf.conf['exonNoiseData']
outFile = open(outFileN, 'w')
outFile.write('\t'.join(sortedKeys) + '\n')
outFile.writelines(newLines)
outFile.close()
def intronNoisy(cName=None):
mConf = c.cgConfig('Main.conf')
conf = c.getConfig(cName)
#init
cHairs = getHairpins.getHairpins(
conf.conf['resultsIntrons']) #CID: HAIRPIN
organism = conf.conf['organism']
exonList = compare.tccFileToList('%sExons.tcc' % organism, 0)
slide = 1000
#make prediction overlap hitmap
predMap = {}
predList = []
for CID in cHairs:
hPin = cHairs[CID]
predList.append(hPin)
#collapse Overlaps
print ' collapsing predictions'
predList = compare.collapseOverlaps(predList)
print ' collapsing exons'
exonList = compare.collapseOverlaps(exonList)
#collect levels for each hairpin region
cidLevels = {}
for CID in cHairs:
print CID
hPin = cHairs[CID]
chrom = ss(hPin, ':')[0]
strand = ss(hPin, ':')[1]
start = int(ss(hPin, ':')[2])
end = int(ss(hPin, ':')[3])
scanStart = start - slide
scanEnd = end + slide
scanRange = []
scanRange.append('%s:%s:%s:%s' % (chrom, strand, scanStart, start))
scanRange.append('%s:%s:%s:%s' % (chrom, strand, end, scanEnd))
print scanRange
scanRange = compare.subtractTwoTccLists(scanRange, predList)
scanRange = compare.subtractTwoTccLists(scanRange, exonList)
levels = []
print ' Retrieving Expression levels:', cg.getTccListTotalLength(
scanRange)
levels = []
hPinLevels = stepVectorScan.scanVectorsHist(scanRange, cName)
for hPin in hPinLevels:
levels.extend(hPinLevels[hPin])
cidLevels[CID] = levels
#output levels to file
#find longest
longest = 0
for CID in cidLevels:
length = len(cidLevels[CID])
if length > longest:
longest = length
sortedKeys = cidLevels.keys()
sortedKeys.sort()
newLines = []
for j in range(0, longest): #how many lines are there
newLine = []
for CID in sortedKeys:
if len(cidLevels[CID]) > j: # add it
newLine.append(str(cidLevels[CID][j]))
else:
newLine.append('NA')
newLines.append('\t'.join(newLine) + '\n')
outFileN = conf.conf['intronNoiseData']
outFile = open(outFileN, 'w')
outFile.write('\t'.join(sortedKeys) + '\n')
outFile.writelines(newLines)
outFile.close()
import compareData as compare
import bioLibCG as cg
exonList = compare.tccFileToList('allExons.tcc', 0)
print cg.getTccListTotalLength(exonList)
nonOverlap = compare.collapseOverlaps(exonList)
print cg.getTccListTotalLength(nonOverlap)
o = open('mouseExons.tcc', 'w')
for tcc in nonOverlap:
o.write(tcc + '\n')
o.close()
