def generateSubpeakFASTA(TFtoEnhancerDict, subpeaks, genomeDirectory,
projectName, projectFolder, constExtension):
'''
from a BED file of constituents
generate a FASTA for the consituients contained within the canidate supers
'''
subpeakDict = {}
subpeakBED = [['track name=' + projectName + ' color=204,0,204']]
subpeakTable = utils.parseTable(subpeaks, '\t')
subpeakLoci = [
utils.Locus(l[0], int(l[1]), int(l[2]), '.') for l in subpeakTable
]
subpeakCollection = utils.LocusCollection(subpeakLoci, 50)
for gene in TFtoEnhancerDict.keys():
subpeakDict[gene] = []
for region in TFtoEnhancerDict[gene]:
overlaps = subpeakCollection.getOverlap(region)
extendedOverlaps = [
utils.makeSearchLocus(x, constExtension, constExtension)
for x in overlaps
]
overlapCollectionTemp = utils.LocusCollection(extendedOverlaps, 50)
overlapCollection = overlapCollectionTemp.stitchCollection()
for overlap in overlapCollection.getLoci():
subpeakBED.append(
[overlap.chr(),
overlap.start(),
overlap.end()])
subpeakDict[gene].append(overlap)
bedfilename = projectFolder + projectName + '_subpeaks.bed'
utils.unParseTable(subpeakBED, bedfilename, '\t')
fasta = []
for gene in subpeakDict:
for subpeak in subpeakDict[gene]:
fastaTitle = gene + '|' + subpeak.chr() + '|' + str(
subpeak.start()) + '|' + str(subpeak.end())
fastaLine = utils.fetchSeq(genomeDirectory, subpeak.chr(),
int(subpeak.start() + 1),
int(subpeak.end() + 1))
fasta.append('>' + fastaTitle)
fasta.append(upper(fastaLine))
outname = projectFolder + projectName + '_SUBPEAKS.fa'
utils.unParseTable(fasta, outname, '')
def generateSubpeakFASTA(TFandSuperDict, subpeaks, genomeDirectory, projectName, projectFolder, motifExtension):
'''
takes as input a BED file of constituents
outputs a FASTA file of merged extended super-enhancer consituents and associated formated name
'''
print 'MAKE FASTA'
subpeakDict = {}
subpeakBED = [['track name=' + projectName + ' color=204,0,204']]
subpeakTable = utils.parseTable(subpeaks, '\t')
subpeakLoci = [utils.Locus(l[0], int(l[1]), int(l[2]), '.') for l in subpeakTable]
subpeakCollection = utils.LocusCollection(subpeakLoci, 50)
for gene in TFandSuperDict.keys():
subpeakDict[gene] = []
for region in TFandSuperDict[gene]:
overlaps = subpeakCollection.getOverlap(region)
extendedOverlaps = [utils.makeSearchLocus(x, motifExtension, motifExtension) for x in overlaps]
overlapCollectionTemp = utils.LocusCollection(extendedOverlaps, 50)
overlapCollection = overlapCollectionTemp.stitchCollection()
for overlap in overlapCollection.getLoci():
subpeakBED.append([overlap.chr(), overlap.start(), overlap.end()])
subpeakDict[gene].append(overlap)
bedfilename = projectFolder + projectName + '_subpeaks.bed'
utils.unParseTable(subpeakBED, bedfilename, '\t')
fasta = []
for gene in subpeakDict:
for subpeak in subpeakDict[gene]:
fastaTitle = gene + '|' + subpeak.chr() + '|' + str(subpeak.start()) + '|' + str(subpeak.end())
fastaLine = utils.fetchSeq(genomeDirectory, subpeak.chr(), int(subpeak.start()+1), int(subpeak.end()+1))
fasta.append('>' + fastaTitle)
fasta.append(upper(fastaLine))
# Output the fasta file of extended SE constituents
outname = projectFolder + projectName + '_SUBPEAKS.fa'
utils.unParseTable(fasta, outname, '')
def generateSubpeakFASTA(gene_to_enhancer_dict, subpeaks, genome, projectName, projectFolder, constExtension):
'''
from a BED file of constituents
generate a FASTA for the consituients contained within the canidate supers
'''
genomeDirectory = genome.directory()
subpeakDict = {}
subpeakBED = [['track name=' + projectName + ' color=204,0,204']]
subpeakTable = utils.parseTable(subpeaks, '\t')
subpeakLoci = [utils.Locus(l[0], int(l[1]), int(l[2]), '.') for l in subpeakTable]
subpeakCollection = utils.LocusCollection(subpeakLoci, 50)
for gene in gene_to_enhancer_dict.keys():
subpeakDict[gene] = []
for region in gene_to_enhancer_dict[gene]:
overlaps = subpeakCollection.getOverlap(region)
extendedOverlaps = [utils.makeSearchLocus(x, constExtension, constExtension) for x in overlaps]
overlapCollectionTemp = utils.LocusCollection(extendedOverlaps, 50)
overlapCollection = overlapCollectionTemp.stitchCollection()
for overlap in overlapCollection.getLoci():
subpeakBED.append([overlap.chr(), overlap.start(), overlap.end()])
subpeakDict[gene].append(overlap)
fasta = []
for gene in subpeakDict:
for subpeak in subpeakDict[gene]:
fastaTitle = gene + '|' + subpeak.chr() + '|' + str(subpeak.start()) + '|' + str(subpeak.end())
fastaLine = utils.fetchSeq(genomeDirectory, subpeak.chr(), int(subpeak.start()+1),
int(subpeak.end()+1))
fasta.append('>' + fastaTitle)
fasta.append(string.upper(fastaLine))
return subpeakBED,fasta
def mapGFFLineToAnnot(gffLine,
outFolder,
nBins,
geneDict,
txCollection,
sense='both',
header=''):
'''
for every line produces a file with all of the rectangles to draw
'''
if len(header) == 0:
gffString = '%s_%s_%s_%s' % (gffLine[0], gffLine[6], gffLine[3],
gffLine[4])
else:
gffString = header
diagramTable = [[0, 0, 0, 0]]
nameTable = [['', 0, 0]]
gffLocus = utils.Locus(gffLine[0], int(gffLine[3]), int(gffLine[4]),
gffLine[6], gffLine[1])
scaleFactor = float(nBins) / gffLocus.len()
# plotting buffer for diagrams
plotBuffer = int(gffLocus.len() / float(nBins) * 20)
overlapLoci = txCollection.getOverlap(gffLocus, sense='both')
geneList = [locus.ID() for locus in overlapLoci]
if gffLine[6] == '-':
refPoint = int(gffLine[4])
else:
refPoint = int(gffLine[3])
offsetCollection = utils.LocusCollection([], 500)
for geneID in geneList:
gene = geneDict[geneID]
print(gene.commonName())
if len(gene.commonName()) > 1:
name = gene.commonName()
else:
name = geneID
offset = 4 * len(offsetCollection.getOverlap(gene.txLocus()))
offsetCollection.append(
utils.makeSearchLocus(gene.txLocus(), plotBuffer, plotBuffer))
# write the name of the gene down
if gene.sense() == '+':
geneStart = gene.txLocus().start()
else:
geneStart = gene.txLocus().end()
geneStart = abs(geneStart - refPoint) * scaleFactor
nameTable.append([name, geneStart, -2 - offset])
# draw a line across the entire txLocus
[start, stop] = [
abs(x - refPoint) * scaleFactor for x in gene.txLocus().coords()
]
diagramTable.append([start, -0.01 - offset, stop, 0.01 - offset])
# now draw thin boxes for all txExons
if len(gene.txExons()) > 0:
for txExon in gene.txExons():
[start, stop] = [
abs(x - refPoint) * scaleFactor for x in txExon.coords()
]
diagramTable.append([start, -0.5 - offset, stop, 0.5 - offset])
# now draw fatty boxes for the coding exons if any
if len(gene.cdExons()) > 0:
for cdExon in gene.cdExons():
[start, stop] = [
abs(x - refPoint) * scaleFactor for x in cdExon.coords()
]
diagramTable.append([start, -1 - offset, stop, 1 - offset])
utils.unParseTable(diagramTable,
outFolder + gffString + '_diagramTemp.txt', '\t')
utils.unParseTable(nameTable, outFolder + gffString + '_nameTemp.txt',
'\t')
def findCanidateTFs(annotationFile, enhancerLoci, expressedNM, expressionDictNM,
bamFile, TFlist, refseqToNameDict, projectFolder, projectName, promoter):
'''
Assign each Super-Enhancer to the closest active TSS to its center
Return a dictionary keyed by TF that points to a list of loci
'''
print 'FINDING CANIDATE TFs'
enhancerAssignment = []
TFtoEnhancerDict = defaultdict(list)
startDict = utils.makeStartDict(annotationFile)
tssLoci = []
for gene in expressedNM:
tssLoci.append(utils.makeTSSLocus(gene,startDict,1000,1000))
tssCollection = utils.LocusCollection(tssLoci,50)
# Loop through enhancers
for enhancer in enhancerLoci:
# If the enhancer overlaps a TSS, save it
overlappingLoci = tssCollection.getOverlap(enhancer, 'both')
overlappingGenes =[]
for overlapLocus in overlappingLoci:
overlappingGenes.append(overlapLocus.ID())
# Find all gene TSS within 100 kb
proximalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancer,100000,100000),'both')
proximalGenes =[]
for proxLocus in proximalLoci:
proximalGenes.append(proxLocus.ID())
# If no genes are within 100 kb, find the closest active gene
closestGene = ''
if len(overlappingGenes) == 0 and len(proximalGenes) == 0:
distalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancer,1000000,1000000),'both')
distalGenes =[]
for distalLocus in distalLoci:
distalGenes.append(distalLocus.ID())
enhancerCenter = (int(enhancer.start()) + int(enhancer.end())) / 2
distList = [abs(enhancerCenter - startDict[geneID]['start'][0])
for geneID in distalGenes]
if distList:
closestGene = distalGenes[distList.index(min(distList))]
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
# If a TSS overlaps an enhancer, assign them together
if overlappingGenes:
for gene in overlappingGenes:
if gene in TFlist:
TFtoEnhancerDict[gene].append(enhancer)
enhancerAssignment.append([gene, enhancer.chr(), enhancer.start(), enhancer.end(), enhancer.ID()])
# Otherwise, assign the enhancer to the most active gene in 100 kb
elif not overlappingGenes and proximalGenes:
highestGene = ''
highestActivity = 0
for gene in proximalGenes:
if expressionDictNM[gene] > highestActivity:
highestActivity = expressionDictNM[gene]
highestGene = gene
if highestGene in TFlist:
TFtoEnhancerDict[gene].append(enhancer)
enhancerAssignment.append([gene, enhancer.chr(), enhancer.start(), enhancer.end(), enhancer.ID()])
elif not overlappingGenes and not proximalGenes and closestGene:
if closestGene in TFlist:
gene = closestGene
TFtoEnhancerDict[gene].append(enhancer)
enhancerAssignment.append([gene, enhancer.chr(), enhancer.start(), enhancer.end(), enhancer.ID()])
# Add promoter is it's not contained in the super
if promoter:
for gene in TFtoEnhancerDict.keys():
promoter = utils.Locus(startDict[gene]['chr'], int(startDict[gene]['start'][0]) - 2000,
int(startDict[gene]['start'][0]) + 2000, startDict[gene]['sense'])
overlapBool = False
for enhancer in TFtoEnhancerDict[gene]:
if promoter.overlaps(enhancer):
overlapBool = True
if not overlapBool:
TFtoEnhancerDict[gene].append(promoter)
seAssignmentFile = projectFolder + projectName + '_ENHANCER_ASSIGNMENT.txt'
utils.unParseTable(enhancerAssignment, seAssignmentFile, '\t')
return TFtoEnhancerDict
def mapEnhancerToGene(annotFile,enhancerFile,transcribedFile='',uniqueGenes=True,searchWindow =50000,noFormatTable = False):
'''
maps genes to enhancers. if uniqueGenes, reduces to gene name only. Otherwise, gives for each refseq
'''
startDict = utils.makeStartDict(annotFile)
enhancerTable = utils.parseTable(enhancerFile,'\t')
#internal parameter for debugging
byRefseq = False
if len(transcribedFile) > 0:
transcribedTable = utils.parseTable(transcribedFile,'\t')
transcribedGenes = [line[1] for line in transcribedTable]
else:
transcribedGenes = startDict.keys()
print('MAKING TRANSCRIPT COLLECTION')
transcribedCollection = utils.makeTranscriptCollection(annotFile,0,0,500,transcribedGenes)
print('MAKING TSS COLLECTION')
tssLoci = []
for geneID in transcribedGenes:
tssLoci.append(utils.makeTSSLocus(geneID,startDict,0,0))
#this turns the tssLoci list into a LocusCollection
#50 is the internal parameter for LocusCollection and doesn't really matter
tssCollection = utils.LocusCollection(tssLoci,50)
geneDict = {'overlapping':defaultdict(list),'proximal':defaultdict(list)}
#dictionaries to hold ranks and superstatus of gene nearby enhancers
rankDict = defaultdict(list)
superDict= defaultdict(list)
#list of all genes that appear in this analysis
overallGeneList = []
if noFormatTable:
#set up the output tables
#first by enhancer
enhancerToGeneTable = [enhancerTable[0]+['OVERLAP_GENES','PROXIMAL_GENES','CLOSEST_GENE']]
else:
#set up the output tables
#first by enhancer
enhancerToGeneTable = [enhancerTable[0][0:9]+['OVERLAP_GENES','PROXIMAL_GENES','CLOSEST_GENE'] + enhancerTable[5][-2:]]
#next by gene
geneToEnhancerTable = [['GENE_NAME','REFSEQ_ID','PROXIMAL_ENHANCERS']]
#next make the gene to enhancer table
geneToEnhancerTable = [['GENE_NAME','REFSEQ_ID','PROXIMAL_ENHANCERS','ENHANCER_RANKS','IS_SUPER']]
for line in enhancerTable:
if line[0][0] =='#' or line[0][0] == 'R':
continue
enhancerString = '%s:%s-%s' % (line[1],line[2],line[3])
enhancerLocus = utils.Locus(line[1],line[2],line[3],'.',line[0])
#overlapping genes are transcribed genes whose transcript is directly in the stitchedLocus
overlappingLoci = transcribedCollection.getOverlap(enhancerLocus,'both')
overlappingGenes =[]
for overlapLocus in overlappingLoci:
overlappingGenes.append(overlapLocus.ID())
#proximalGenes are transcribed genes where the tss is within 50kb of the boundary of the stitched loci
proximalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancerLocus,searchWindow,searchWindow),'both')
proximalGenes =[]
for proxLocus in proximalLoci:
proximalGenes.append(proxLocus.ID())
distalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancerLocus,1000000,1000000),'both')
distalGenes =[]
for proxLocus in distalLoci:
distalGenes.append(proxLocus.ID())
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
distalGenes = utils.uniquify(distalGenes)
allEnhancerGenes = overlappingGenes + proximalGenes + distalGenes
#these checks make sure each gene list is unique.
#technically it is possible for a gene to be overlapping, but not proximal since the
#gene could be longer than the 50kb window, but we'll let that slide here
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
for refID in proximalGenes:
if distalGenes.count(refID) == 1:
distalGenes.remove(refID)
#Now find the closest gene
if len(allEnhancerGenes) == 0:
closestGene = ''
else:
#get enhancerCenter
enhancerCenter = (int(line[2]) + int(line[3]))/2
#get absolute distance to enhancer center
distList = [abs(enhancerCenter - startDict[geneID]['start'][0]) for geneID in allEnhancerGenes]
#get the ID and convert to name
closestGene = startDict[allEnhancerGenes[distList.index(min(distList))]]['name']
#NOW WRITE THE ROW FOR THE ENHANCER TABLE
if noFormatTable:
newEnhancerLine = list(line)
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]),','))
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]),','))
newEnhancerLine.append(closestGene)
else:
newEnhancerLine = line[0:9]
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]),','))
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]),','))
newEnhancerLine.append(closestGene)
newEnhancerLine += line[-2:]
enhancerToGeneTable.append(newEnhancerLine)
#Now grab all overlapping and proximal genes for the gene ordered table
overallGeneList +=overlappingGenes
for refID in overlappingGenes:
geneDict['overlapping'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
overallGeneList+=proximalGenes
for refID in proximalGenes:
geneDict['proximal'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
#End loop through
#Make table by gene
overallGeneList = utils.uniquify(overallGeneList)
#use enhancer rank to order
rankOrder = utils.order([min(rankDict[x]) for x in overallGeneList])
usedNames = []
for i in rankOrder:
refID = overallGeneList[i]
geneName = startDict[refID]['name']
if usedNames.count(geneName) > 0 and uniqueGenes == True:
continue
else:
usedNames.append(geneName)
proxEnhancers = geneDict['overlapping'][refID]+geneDict['proximal'][refID]
superStatus = max(superDict[refID])
enhancerRanks = join([str(x) for x in rankDict[refID]],',')
newLine = [geneName,refID,join(proxEnhancers,','),enhancerRanks,superStatus]
geneToEnhancerTable.append(newLine)
#resort enhancerToGeneTable
if noFormatTable:
return enhancerToGeneTable,geneToEnhancerTable
else:
enhancerOrder = utils.order([int(line[-2]) for line in enhancerToGeneTable[1:]])
sortedTable = [enhancerToGeneTable[0]]
for i in enhancerOrder:
sortedTable.append(enhancerToGeneTable[(i+1)])
return sortedTable,geneToEnhancerTable
def mapEnhancerToGeneTop(rankByBamFile, controlBamFile, genome, annotFile, enhancerFile, transcribedFile='', uniqueGenes=True, searchWindow=50000, noFormatTable=False):
'''
maps genes to enhancers. if uniqueGenes, reduces to gene name only. Otherwise, gives for each refseq
'''
startDict = utils.makeStartDict(annotFile)
enhancerName = enhancerFile.split('/')[-1].split('.')[0]
enhancerTable = utils.parseTable(enhancerFile, '\t')
# internal parameter for debugging
byRefseq = False
if len(transcribedFile) > 0:
transcribedTable = utils.parseTable(transcribedFile, '\t')
transcribedGenes = [line[1] for line in transcribedTable]
else:
transcribedGenes = startDict.keys()
print('MAKING TRANSCRIPT COLLECTION')
transcribedCollection = utils.makeTranscriptCollection(
annotFile, 0, 0, 500, transcribedGenes)
print('MAKING TSS COLLECTION')
tssLoci = []
for geneID in transcribedGenes:
tssLoci.append(utils.makeTSSLocus(geneID, startDict, 0, 0))
# this turns the tssLoci list into a LocusCollection
# 50 is the internal parameter for LocusCollection and doesn't really
# matter
tssCollection = utils.LocusCollection(tssLoci, 50)
geneDict = {'overlapping': defaultdict(
list), 'proximal': defaultdict(list)}
# dictionaries to hold ranks and superstatus of gene nearby enhancers
rankDict = defaultdict(list)
superDict = defaultdict(list)
# list of all genes that appear in this analysis
overallGeneList = []
# find the damn header
for line in enhancerTable:
if line[0][0] == '#':
continue
else:
header = line
break
if noFormatTable:
# set up the output tables
# first by enhancer
enhancerToGeneTable = [
header + ['OVERLAP_GENES', 'PROXIMAL_GENES', 'CLOSEST_GENE']]
else:
# set up the output tables
# first by enhancer
enhancerToGeneTable = [
header[0:9] + ['OVERLAP_GENES', 'PROXIMAL_GENES', 'CLOSEST_GENE'] + header[-2:]]
# next by gene
geneToEnhancerTable = [
['GENE_NAME', 'REFSEQ_ID', 'PROXIMAL_ENHANCERS']]
# next make the gene to enhancer table
geneToEnhancerTable = [
['GENE_NAME', 'REFSEQ_ID', 'PROXIMAL_ENHANCERS', 'ENHANCER_RANKS', 'IS_SUPER', 'ENHANCER_SIGNAL']]
for line in enhancerTable:
if line[0][0] == '#' or line[0][0] == 'R':
continue
enhancerString = '%s:%s-%s' % (line[1], line[2], line[3])
enhancerLocus = utils.Locus(line[1], line[2], line[3], '.', line[0])
# overlapping genes are transcribed genes whose transcript is directly
# in the stitchedLocus
overlappingLoci = transcribedCollection.getOverlap(
enhancerLocus, 'both')
overlappingGenes = []
for overlapLocus in overlappingLoci:
overlappingGenes.append(overlapLocus.ID())
# proximalGenes are transcribed genes where the tss is within 50kb of
# the boundary of the stitched loci
proximalLoci = tssCollection.getOverlap(
utils.makeSearchLocus(enhancerLocus, searchWindow, searchWindow), 'both')
proximalGenes = []
for proxLocus in proximalLoci:
proximalGenes.append(proxLocus.ID())
distalLoci = tssCollection.getOverlap(
utils.makeSearchLocus(enhancerLocus, 1000000, 1000000), 'both')
distalGenes = []
for proxLocus in distalLoci:
distalGenes.append(proxLocus.ID())
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
distalGenes = utils.uniquify(distalGenes)
allEnhancerGenes = overlappingGenes + proximalGenes + distalGenes
# these checks make sure each gene list is unique.
# technically it is possible for a gene to be overlapping, but not proximal since the
# gene could be longer than the 50kb window, but we'll let that slide
# here
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
for refID in proximalGenes:
if distalGenes.count(refID) == 1:
distalGenes.remove(refID)
# Now find the closest gene
if len(allEnhancerGenes) == 0:
closestGene = ''
else:
# get enhancerCenter
enhancerCenter = (int(line[2]) + int(line[3])) / 2
# get absolute distance to enhancer center
distList = [abs(enhancerCenter - startDict[geneID]['start'][0])
for geneID in allEnhancerGenes]
# get the ID and convert to name
closestGene = startDict[
allEnhancerGenes[distList.index(min(distList))]]['name']
# NOW WRITE THE ROW FOR THE ENHANCER TABLE
if noFormatTable:
newEnhancerLine = list(line)
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]), ','))
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]), ','))
newEnhancerLine.append(closestGene)
else:
newEnhancerLine = line[0:9]
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]), ','))
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]), ','))
newEnhancerLine.append(closestGene)
newEnhancerLine += line[-2:]
enhancerToGeneTable.append(newEnhancerLine)
# Now grab all overlapping and proximal genes for the gene ordered
# table
overallGeneList += overlappingGenes
for refID in overlappingGenes:
geneDict['overlapping'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
overallGeneList += proximalGenes
for refID in proximalGenes:
geneDict['proximal'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
# End loop through
# Make table by gene
print('MAKING ENHANCER ASSOCIATED GENE TSS COLLECTION')
overallGeneList = utils.uniquify(overallGeneList)
enhancerGeneCollection = utils.makeTranscriptCollection(
annotFile, 5000, 5000, 500, overallGeneList)
enhancerGeneGFF = utils.locusCollectionToGFF(enhancerGeneCollection)
# dump the gff to file
enhancerFolder = utils.getParentFolder(enhancerFile)
gffRootName = "%s_TSS_ENHANCER_GENES_-5000_+5000" % (genome)
enhancerGeneGFFFile = "%s%s_%s.gff" % (enhancerFolder, enhancerName,gffRootName)
utils.unParseTable(enhancerGeneGFF, enhancerGeneGFFFile, '\t')
# now we need to run bamToGFF
# Try to use the bamliquidatior_path.py script on cluster, otherwise, failover to local (in path), otherwise fail.
bamliquidator_path = '/ark/home/jdm/pipeline/bamliquidator_batch.py'
if not os.path.isfile(bamliquidator_path):
bamliquidator_path = 'bamliquidator_batch.py'
if not os.path.isfile(bamliquidator_path):
raise ValueError('bamliquidator_batch.py not found in path')
print('MAPPING SIGNAL AT ENHANCER ASSOCIATED GENE TSS')
# map density at genes in the +/- 5kb tss region
# first on the rankBy bam
bamName = rankByBamFile.split('/')[-1]
mappedRankByFolder = "%s%s_%s_%s/" % (enhancerFolder, enhancerName,gffRootName, bamName)
mappedRankByFile = "%s%s_%s_%s/matrix.gff" % (enhancerFolder,enhancerName, gffRootName, bamName)
cmd = 'python ' + bamliquidator_path + ' --sense . -e 200 --match_bamToGFF -r %s -o %s %s' % (enhancerGeneGFFFile, mappedRankByFolder,rankByBamFile)
print("Mapping rankby bam %s" % (rankByBamFile))
print(cmd)
outputRank = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
outputRank = outputRank.communicate()
if len(outputRank[0]) > 0: # test if mapping worked correctly
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (enhancerGeneGFFFile, rankByBamFile))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (enhancerGeneGFFFile, rankByBamFile))
sys.exit()
# next on the control bam if it exists
if len(controlBamFile) > 0:
controlName = controlBamFile.split('/')[-1]
mappedControlFolder = "%s%s_%s_%s/" % (
enhancerFolder, enhancerName,gffRootName, controlName)
mappedControlFile = "%s%s_%s_%s/matrix.gff" % (
enhancerFolder, enhancerName,gffRootName, controlName)
cmd = 'python ' + bamliquidator_path + ' --sense . -e 200 --match_bamToGFF -r %s -o %s %s' % (enhancerGeneGFFFile, mappedControlFolder,controlBamFile)
print("Mapping control bam %s" % (controlBamFile))
print(cmd)
outputControl = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
outputControl = outputControl.communicate()
if len(outputControl[0]) > 0: # test if mapping worked correctly
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (enhancerGeneGFFFile, controlBamFile))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (enhancerGeneGFFFile, controlBamFile))
sys.exit()
# now get the appropriate output files
if len(controlBamFile) > 0:
print("CHECKING FOR MAPPED OUTPUT AT %s AND %s" %
(mappedRankByFile, mappedControlFile))
if utils.checkOutput(mappedRankByFile, 1, 1) and utils.checkOutput(mappedControlFile, 1, 1):
print('MAKING ENHANCER ASSOCIATED GENE TSS SIGNAL DICTIONARIES')
signalDict = makeSignalDict(mappedRankByFile, mappedControlFile)
else:
print("NO MAPPING OUTPUT DETECTED")
sys.exit()
else:
print("CHECKING FOR MAPPED OUTPUT AT %s" % (mappedRankByFile))
if utils.checkOutput(mappedRankByFile, 1, 30):
print('MAKING ENHANCER ASSOCIATED GENE TSS SIGNAL DICTIONARIES')
signalDict = makeSignalDict(mappedRankByFile)
else:
print("NO MAPPING OUTPUT DETECTED")
sys.exit()
# use enhancer rank to order
rankOrder = utils.order([min(rankDict[x]) for x in overallGeneList])
usedNames = []
# make a new dict to hold TSS signal by max per geneName
geneNameSigDict = defaultdict(list)
print('MAKING GENE TABLE')
for i in rankOrder:
refID = overallGeneList[i]
geneName = startDict[refID]['name']
if usedNames.count(geneName) > 0 and uniqueGenes == True:
continue
else:
usedNames.append(geneName)
proxEnhancers = geneDict['overlapping'][
refID] + geneDict['proximal'][refID]
superStatus = max(superDict[refID])
enhancerRanks = join([str(x) for x in rankDict[refID]], ',')
enhancerSignal = signalDict[refID]
geneNameSigDict[geneName].append(enhancerSignal)
newLine = [geneName, refID, join(
proxEnhancers, ','), enhancerRanks, superStatus, enhancerSignal]
geneToEnhancerTable.append(newLine)
#utils.unParseTable(geneToEnhancerTable,'/grail/projects/newRose/geneMapper/foo.txt','\t')
print('MAKING ENHANCER TO TOP GENE TABLE')
if noFormatTable:
enhancerToTopGeneTable = [
enhancerToGeneTable[0] + ['TOP_GENE', 'TSS_SIGNAL']]
else:
enhancerToTopGeneTable = [enhancerToGeneTable[0][0:12] + [
'TOP_GENE', 'TSS_SIGNAL'] + enhancerToGeneTable[0][-2:]]
for line in enhancerToGeneTable[1:]:
geneList = []
if noFormatTable:
geneList += line[-3].split(',')
geneList += line[-2].split(',')
else:
geneList += line[10].split(',')
geneList += line[11].split(',')
geneList = utils.uniquify([x for x in geneList if len(x) > 0])
if len(geneList) > 0:
try:
sigVector = [max(geneNameSigDict[x]) for x in geneList]
maxIndex = sigVector.index(max(sigVector))
maxGene = geneList[maxIndex]
maxSig = sigVector[maxIndex]
if maxSig == 0.0:
maxGene = 'NONE'
maxSig = 'NONE'
except ValueError:
if len(geneList) == 1:
maxGene = geneList[0]
maxSig = 'NONE'
else:
maxGene = 'NONE'
maxSig = 'NONE'
else:
maxGene = 'NONE'
maxSig = 'NONE'
if noFormatTable:
newLine = line + [maxGene, maxSig]
else:
newLine = line[0:12] + [maxGene, maxSig] + line[-2:]
enhancerToTopGeneTable.append(newLine)
# resort enhancerToGeneTable
if noFormatTable:
return enhancerToGeneTable, enhancerToTopGeneTable, geneToEnhancerTable
else:
enhancerOrder = utils.order([int(line[-2])
for line in enhancerToGeneTable[1:]])
sortedTable = [enhancerToGeneTable[0]]
sortedTopGeneTable = [enhancerToTopGeneTable[0]]
for i in enhancerOrder:
sortedTable.append(enhancerToGeneTable[(i + 1)])
sortedTopGeneTable.append(enhancerToTopGeneTable[(i + 1)])
return sortedTable, sortedTopGeneTable, geneToEnhancerTable
def mapEnhancerToGene(annotFile,enhancerFile,transcribedFile='',uniqueGenes=True,searchWindow =50000,noFormatTable = False):
'''
maps genes to enhancers. if uniqueGenes, reduces to gene name only. Otherwise, gives for each refseq
'''
startDict = utils.makeStartDict(annotFile)
enhancerTable = utils.parseTable(enhancerFile,'\t')
#internal parameter for debugging
byRefseq = False
if len(transcribedFile) > 0:
transcribedTable = utils.parseTable(transcribedFile,'\t')
transcribedGenes = [line[1] for line in transcribedTable]
else:
transcribedGenes = startDict.keys()
print('MAKING TRANSCRIPT COLLECTION')
transcribedCollection = utils.makeTranscriptCollection(annotFile,0,0,500,transcribedGenes)
print('MAKING TSS COLLECTION')
tssLoci = []
for geneID in transcribedGenes:
tssLoci.append(utils.makeTSSLocus(geneID,startDict,0,0))
#this turns the tssLoci list into a LocusCollection
#50 is the internal parameter for LocusCollection and doesn't really matter
tssCollection = utils.LocusCollection(tssLoci,50)
geneDict = {'overlapping':defaultdict(list),'proximal':defaultdict(list)}
#dictionaries to hold ranks and superstatus of gene nearby enhancers
rankDict = defaultdict(list)
superDict= defaultdict(list)
#list of all genes that appear in this analysis
overallGeneList = []
if noFormatTable:
#set up the output tables
#first by enhancer
enhancerToGeneTable = [enhancerTable[0]+['OVERLAP_GENES','PROXIMAL_GENES','CLOSEST_GENE']]
else:
#set up the output tables
#first by enhancer
enhancerToGeneTable = [enhancerTable[0][0:9]+['OVERLAP_GENES','PROXIMAL_GENES','CLOSEST_GENE'] + enhancerTable[5][-2:]]
#next by gene
geneToEnhancerTable = [['GENE_NAME','REFSEQ_ID','PROXIMAL_ENHANCERS']]
#next make the gene to enhancer table
geneToEnhancerTable = [['GENE_NAME','REFSEQ_ID','PROXIMAL_ENHANCERS','ENHANCER_RANKS','IS_SUPER']]
for line in enhancerTable:
if line[0][0] =='#' or line[0][0] == 'R':
continue
enhancerString = '%s:%s-%s' % (line[1],line[2],line[3])
enhancerLocus = utils.Locus(line[1],line[2],line[3],'.',line[0])
#overlapping genes are transcribed genes whose transcript is directly in the stitchedLocus
overlappingLoci = transcribedCollection.getOverlap(enhancerLocus,'both')
overlappingGenes =[]
for overlapLocus in overlappingLoci:
overlappingGenes.append(overlapLocus.ID())
#proximalGenes are transcribed genes where the tss is within 50kb of the boundary of the stitched loci
proximalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancerLocus,searchWindow,searchWindow),'both')
proximalGenes =[]
for proxLocus in proximalLoci:
proximalGenes.append(proxLocus.ID())
distalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancerLocus,1000000,1000000),'both')
distalGenes =[]
for proxLocus in distalLoci:
distalGenes.append(proxLocus.ID())
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
distalGenes = utils.uniquify(distalGenes)
allEnhancerGenes = overlappingGenes + proximalGenes + distalGenes
#these checks make sure each gene list is unique.
#technically it is possible for a gene to be overlapping, but not proximal since the
#gene could be longer than the 50kb window, but we'll let that slide here
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
for refID in proximalGenes:
if distalGenes.count(refID) == 1:
distalGenes.remove(refID)
#Now find the closest gene
if len(allEnhancerGenes) == 0:
closestGene = ''
else:
#get enhancerCenter
enhancerCenter = (int(line[2]) + int(line[3]))/2
#get absolute distance to enhancer center
distList = [abs(enhancerCenter - startDict[geneID]['start'][0]) for geneID in allEnhancerGenes]
#get the ID and convert to name
closestGene = startDict[allEnhancerGenes[distList.index(min(distList))]]['name']
#NOW WRITE THE ROW FOR THE ENHANCER TABLE
if noFormatTable:
newEnhancerLine = list(line)
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]),','))
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]),','))
newEnhancerLine.append(closestGene)
else:
newEnhancerLine = line[0:9]
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]),','))
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]),','))
newEnhancerLine.append(closestGene)
newEnhancerLine += line[-2:]
enhancerToGeneTable.append(newEnhancerLine)
#Now grab all overlapping and proximal genes for the gene ordered table
overallGeneList +=overlappingGenes
for refID in overlappingGenes:
geneDict['overlapping'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
overallGeneList+=proximalGenes
for refID in proximalGenes:
geneDict['proximal'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
#End loop through
#Make table by gene
overallGeneList = utils.uniquify(overallGeneList)
#use enhancer rank to order
rankOrder = utils.order([min(rankDict[x]) for x in overallGeneList])
usedNames = []
for i in rankOrder:
refID = overallGeneList[i]
geneName = startDict[refID]['name']
if usedNames.count(geneName) > 0 and uniqueGenes == True:
continue
else:
usedNames.append(geneName)
proxEnhancers = geneDict['overlapping'][refID]+geneDict['proximal'][refID]
superStatus = max(superDict[refID])
enhancerRanks = join([str(x) for x in rankDict[refID]],',')
newLine = [geneName,refID,join(proxEnhancers,','),enhancerRanks,superStatus]
geneToEnhancerTable.append(newLine)
#resort enhancerToGeneTable
if noFormatTable:
return enhancerToGeneTable,geneToEnhancerTable
else:
enhancerOrder = utils.order([int(line[-2]) for line in enhancerToGeneTable[1:]])
sortedTable = [enhancerToGeneTable[0]]
for i in enhancerOrder:
sortedTable.append(enhancerToGeneTable[(i+1)])
return sortedTable,geneToEnhancerTable
def mapEnhancerToGeneTop(rankByBamFile, controlBamFile, genome, annotFile, enhancerFile, transcribedFile='', uniqueGenes=True, searchWindow=50000, noFormatTable=False):
'''
maps genes to enhancers. if uniqueGenes, reduces to gene name only. Otherwise, gives for each refseq
'''
startDict = utils.makeStartDict(annotFile)
enhancerName = enhancerFile.split('/')[-1].split('.')[0]
enhancerTable = utils.parseTable(enhancerFile, '\t')
# internal parameter for debugging
byRefseq = False
if len(transcribedFile) > 0:
transcribedTable = utils.parseTable(transcribedFile, '\t')
transcribedGenes = [line[1] for line in transcribedTable]
else:
transcribedGenes = startDict.keys()
print('MAKING TRANSCRIPT COLLECTION')
transcribedCollection = utils.makeTranscriptCollection(
annotFile, 0, 0, 500, transcribedGenes)
print('MAKING TSS COLLECTION')
tssLoci = []
for geneID in transcribedGenes:
tssLoci.append(utils.makeTSSLocus(geneID, startDict, 0, 0))
# this turns the tssLoci list into a LocusCollection
# 50 is the internal parameter for LocusCollection and doesn't really
# matter
tssCollection = utils.LocusCollection(tssLoci, 50)
geneDict = {'overlapping': defaultdict(
list), 'proximal': defaultdict(list)}
# dictionaries to hold ranks and superstatus of gene nearby enhancers
rankDict = defaultdict(list)
superDict = defaultdict(list)
# list of all genes that appear in this analysis
overallGeneList = []
# find the damn header
for line in enhancerTable:
if line[0][0] == '#':
continue
else:
header = line
break
if noFormatTable:
# set up the output tables
# first by enhancer
enhancerToGeneTable = [
header + ['OVERLAP_GENES', 'PROXIMAL_GENES', 'CLOSEST_GENE']]
else:
# set up the output tables
# first by enhancer
enhancerToGeneTable = [
header[0:9] + ['OVERLAP_GENES', 'PROXIMAL_GENES', 'CLOSEST_GENE'] + header[-2:]]
# next by gene
geneToEnhancerTable = [
['GENE_NAME', 'REFSEQ_ID', 'PROXIMAL_ENHANCERS']]
# next make the gene to enhancer table
geneToEnhancerTable = [
['GENE_NAME', 'REFSEQ_ID', 'PROXIMAL_ENHANCERS', 'ENHANCER_RANKS', 'IS_SUPER', 'ENHANCER_SIGNAL']]
for line in enhancerTable:
if line[0][0] == '#' or line[0][0] == 'R':
continue
enhancerString = '%s:%s-%s' % (line[1], line[2], line[3])
enhancerLocus = utils.Locus(line[1], line[2], line[3], '.', line[0])
# overlapping genes are transcribed genes whose transcript is directly
# in the stitchedLocus
overlappingLoci = transcribedCollection.getOverlap(
enhancerLocus, 'both')
overlappingGenes = []
for overlapLocus in overlappingLoci:
overlappingGenes.append(overlapLocus.ID())
# proximalGenes are transcribed genes where the tss is within 50kb of
# the boundary of the stitched loci
proximalLoci = tssCollection.getOverlap(
utils.makeSearchLocus(enhancerLocus, searchWindow, searchWindow), 'both')
proximalGenes = []
for proxLocus in proximalLoci:
proximalGenes.append(proxLocus.ID())
distalLoci = tssCollection.getOverlap(
utils.makeSearchLocus(enhancerLocus, 1000000, 1000000), 'both')
distalGenes = []
for proxLocus in distalLoci:
distalGenes.append(proxLocus.ID())
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
distalGenes = utils.uniquify(distalGenes)
allEnhancerGenes = overlappingGenes + proximalGenes + distalGenes
# these checks make sure each gene list is unique.
# technically it is possible for a gene to be overlapping, but not proximal since the
# gene could be longer than the 50kb window, but we'll let that slide
# here
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
for refID in proximalGenes:
if distalGenes.count(refID) == 1:
distalGenes.remove(refID)
# Now find the closest gene
if len(allEnhancerGenes) == 0:
closestGene = ''
else:
# get enhancerCenter
enhancerCenter = (int(line[2]) + int(line[3])) / 2
# get absolute distance to enhancer center
distList = [abs(enhancerCenter - startDict[geneID]['start'][0])
for geneID in allEnhancerGenes]
# get the ID and convert to name
closestGene = startDict[
allEnhancerGenes[distList.index(min(distList))]]['name']
# NOW WRITE THE ROW FOR THE ENHANCER TABLE
if noFormatTable:
newEnhancerLine = list(line)
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]), ','))
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]), ','))
newEnhancerLine.append(closestGene)
else:
newEnhancerLine = line[0:9]
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]), ','))
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]), ','))
newEnhancerLine.append(closestGene)
newEnhancerLine += line[-2:]
enhancerToGeneTable.append(newEnhancerLine)
# Now grab all overlapping and proximal genes for the gene ordered
# table
overallGeneList += overlappingGenes
for refID in overlappingGenes:
geneDict['overlapping'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
overallGeneList += proximalGenes
for refID in proximalGenes:
geneDict['proximal'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
# End loop through
# Make table by gene
print('MAKING ENHANCER ASSOCIATED GENE TSS COLLECTION')
overallGeneList = utils.uniquify(overallGeneList)
#get the chromLists from the various bams here
cmd = 'samtools idxstats %s' % (rankByBamFile)
idxStats = subprocess.Popen(cmd,stdout=subprocess.PIPE,shell=True)
idxStats= idxStats.communicate()
bamChromList = [line.split('\t')[0] for line in idxStats[0].split('\n')[0:-2]]
if len(controlBamFile) > 0:
cmd = 'samtools idxstats %s' % (controlBamFile)
idxStats = subprocess.Popen(cmd,stdout=subprocess.PIPE,shell=True)
idxStats= idxStats.communicate()
bamChromListControl = [line.split('\t')[0] for line in idxStats[0].split('\n')[0:-2]]
bamChromList = [chrom for chrom in bamChromList if bamChromListControl.count(chrom) != 0]
#now make sure no genes have a bad chrom
overallGeneList = [gene for gene in overallGeneList if bamChromList.count(startDict[gene]['chr']) != 0]
#now make an enhancer collection of all transcripts
enhancerGeneCollection = utils.makeTranscriptCollection(
annotFile, 5000, 5000, 500, overallGeneList)
enhancerGeneGFF = utils.locusCollectionToGFF(enhancerGeneCollection)
# dump the gff to file
enhancerFolder = utils.getParentFolder(enhancerFile)
gffRootName = "%s_TSS_ENHANCER_GENES_-5000_+5000" % (genome)
enhancerGeneGFFFile = "%s%s_%s.gff" % (enhancerFolder, enhancerName,gffRootName)
utils.unParseTable(enhancerGeneGFF, enhancerGeneGFFFile, '\t')
# now we need to run bamToGFF
# Try to use the bamliquidatior_path.py script on cluster, otherwise, failover to local (in path), otherwise fail.
bamliquidator_path = 'bamliquidator_batch'
print('MAPPING SIGNAL AT ENHANCER ASSOCIATED GENE TSS')
# map density at genes in the +/- 5kb tss region
# first on the rankBy bam
bamName = rankByBamFile.split('/')[-1]
mappedRankByFolder = "%s%s_%s_%s/" % (enhancerFolder, enhancerName,gffRootName, bamName)
mappedRankByFile = "%s%s_%s_%s/matrix.txt" % (enhancerFolder,enhancerName, gffRootName, bamName)
cmd = bamliquidator_path + ' --sense . -e 200 --match_bamToGFF -r %s -o %s %s' % (enhancerGeneGFFFile, mappedRankByFolder,rankByBamFile)
print("Mapping rankby bam %s" % (rankByBamFile))
print(cmd)
os.system(cmd)
#check for completion
if utils.checkOutput(mappedRankByFile,0.2,5):
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (enhancerGeneGFFFile, rankByBamFile))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (enhancerGeneGFFFile, rankByBamFile))
sys.exit()
# next on the control bam if it exists
if len(controlBamFile) > 0:
controlName = controlBamFile.split('/')[-1]
mappedControlFolder = "%s%s_%s_%s/" % (
enhancerFolder, enhancerName,gffRootName, controlName)
mappedControlFile = "%s%s_%s_%s/matrix.txt" % (
enhancerFolder, enhancerName,gffRootName, controlName)
cmd = bamliquidator_path + ' --sense . -e 200 --match_bamToGFF -r %s -o %s %s' % (enhancerGeneGFFFile, mappedControlFolder,controlBamFile)
print("Mapping control bam %s" % (controlBamFile))
print(cmd)
os.system(cmd)
#check for completion
if utils.checkOutput(mappedControlFile,0.2,5):
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (enhancerGeneGFFFile, controlBamFile))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (enhancerGeneGFFFile, controlBamFile))
sys.exit()
# now get the appropriate output files
if len(controlBamFile) > 0:
print("CHECKING FOR MAPPED OUTPUT AT %s AND %s" %
(mappedRankByFile, mappedControlFile))
if utils.checkOutput(mappedRankByFile, 1, 1) and utils.checkOutput(mappedControlFile, 1, 1):
print('MAKING ENHANCER ASSOCIATED GENE TSS SIGNAL DICTIONARIES')
signalDict = makeSignalDict(mappedRankByFile, mappedControlFile)
else:
print("NO MAPPING OUTPUT DETECTED")
sys.exit()
else:
print("CHECKING FOR MAPPED OUTPUT AT %s" % (mappedRankByFile))
if utils.checkOutput(mappedRankByFile, 1, 30):
print('MAKING ENHANCER ASSOCIATED GENE TSS SIGNAL DICTIONARIES')
signalDict = makeSignalDict(mappedRankByFile)
else:
print("NO MAPPING OUTPUT DETECTED")
sys.exit()
# use enhancer rank to order
rankOrder = utils.order([min(rankDict[x]) for x in overallGeneList])
usedNames = []
# make a new dict to hold TSS signal by max per geneName
geneNameSigDict = defaultdict(list)
print('MAKING GENE TABLE')
for i in rankOrder:
refID = overallGeneList[i]
geneName = startDict[refID]['name']
if usedNames.count(geneName) > 0 and uniqueGenes == True:
continue
else:
usedNames.append(geneName)
proxEnhancers = geneDict['overlapping'][
refID] + geneDict['proximal'][refID]
superStatus = max(superDict[refID])
enhancerRanks = join([str(x) for x in rankDict[refID]], ',')
enhancerSignal = signalDict[refID]
geneNameSigDict[geneName].append(enhancerSignal)
newLine = [geneName, refID, join(
proxEnhancers, ','), enhancerRanks, superStatus, enhancerSignal]
geneToEnhancerTable.append(newLine)
#utils.unParseTable(geneToEnhancerTable,'/grail/projects/newRose/geneMapper/foo.txt','\t')
print('MAKING ENHANCER TO TOP GENE TABLE')
if noFormatTable:
enhancerToTopGeneTable = [
enhancerToGeneTable[0] + ['TOP_GENE', 'TSS_SIGNAL']]
else:
enhancerToTopGeneTable = [enhancerToGeneTable[0][0:12] + [
'TOP_GENE', 'TSS_SIGNAL'] + enhancerToGeneTable[0][-2:]]
for line in enhancerToGeneTable[1:]:
geneList = []
if noFormatTable:
geneList += line[-3].split(',')
geneList += line[-2].split(',')
else:
geneList += line[10].split(',')
geneList += line[11].split(',')
geneList = utils.uniquify([x for x in geneList if len(x) > 0])
if len(geneList) > 0:
try:
sigVector = [max(geneNameSigDict[x]) for x in geneList]
maxIndex = sigVector.index(max(sigVector))
maxGene = geneList[maxIndex]
maxSig = sigVector[maxIndex]
if maxSig == 0.0:
maxGene = 'NONE'
maxSig = 'NONE'
except ValueError:
if len(geneList) == 1:
maxGene = geneList[0]
maxSig = 'NONE'
else:
maxGene = 'NONE'
maxSig = 'NONE'
else:
maxGene = 'NONE'
maxSig = 'NONE'
if noFormatTable:
newLine = line + [maxGene, maxSig]
else:
newLine = line[0:12] + [maxGene, maxSig] + line[-2:]
enhancerToTopGeneTable.append(newLine)
# resort enhancerToGeneTable
if noFormatTable:
return enhancerToGeneTable, enhancerToTopGeneTable, geneToEnhancerTable
else:
enhancerOrder = utils.order([int(line[-2])
for line in enhancerToGeneTable[1:]])
sortedTable = [enhancerToGeneTable[0]]
sortedTopGeneTable = [enhancerToTopGeneTable[0]]
for i in enhancerOrder:
sortedTable.append(enhancerToGeneTable[(i + 1)])
sortedTopGeneTable.append(enhancerToTopGeneTable[(i + 1)])
return sortedTable, sortedTopGeneTable, geneToEnhancerTable
def findCanidateTFs(genome, enhancer_gff, expressedNM, expressionDictNM,
bamFile, TFlist, refseqToNameDict, projectFolder, projectName, promoter):
'''
Assign each Super-Enhancer to the closest active TSS to its center
Return a dictionary keyed by TF that points to a list of loci
'''
#loading in the enhancer gff regions
enhancer_collection = utils.gffToLocusCollection(enhancer_gff)
enhancer_loci = enhancer_collection.getLoci()
#loading in the genome and TF info
annot_file = genome.returnFeature('annot_file')
startDict = utils.makeStartDict(annot_file)
tf_table = utils.parseTable(genome.returnFeature('tf_file'),'\t')
refID_list = [line[0] for line in tf_table] #creates a list of all NM IDs for TFs
#make a collection of all TF TSSs
tssLoci = []
for refID in refID_list:
tssLoci.append(utils.makeTSSLocus(refID,startDict,0,0)) #this is a precise 1 coordinate TSS locus
tssCollection = utils.LocusCollection(tssLoci,50)
enhancerTable = [['ENHANCER_ID','CHROM','START','STOP','GENE_LIST']]
gene_to_enhancer_dict = defaultdict(list)
# Loop through enhancers
#all gene nnames stored by refID
for enhancer in enhancer_loci:
# If the enhancer overlaps a TSS, save it
overlapping_loci = tssCollection.getOverlap(enhancer, 'both')
overlapping_refIDs =[locus.ID() for locus in overlapping_loci]
# Find all gene TSS within 100 kb
proximal_loci = tssCollection.getOverlap(utils.makeSearchLocus(enhancer,100000,100000),'both')
proximal_refIDs =[locus.ID() for locus in proximal_loci]
# If no genes are within 100 kb, find the closest active gene within 1 million bp
closest_refID = []
if len(overlapping_refIDs) == 0 and len(proximal_refIDs) == 0:
distal_loci = tssCollection.getOverlap(utils.makeSearchLocus(enhancer,1000000,1000000),'both')
distal_refIDs =[locus.ID() for locus in distal_loci]
enhancerCenter = (int(enhancer.start()) + int(enhancer.end())) / 2
distance_list = [abs(enhancerCenter - startDict[geneID]['start'][0])
for geneID in distal_refIDs]
if len(distance_list) > 0:
closest_refID = [distalGenes[distance_list.index(min(distance_list))]]
#now we have all potential gene cases
all_refIDs = overlappingGenes + proximalGenes + closest_refID
#now we get all names and refIDs
all_refIDs = utils.uniquify([refID for refID in all_refIDs if len(refID) > 0 ])
all_names = utils.uniquify([startDict[refID]['name'] for refID in all_refIDs])
#first do enhancer level assignment
names_string = ','.join(all_names)
enhancer_table.append([enhancer.ID(),enhancer.chr(),enhancer.start(),enhancer.end(),names_string])
#now do gene level assignment
for refID in all_refIDs:
gene_to_enhancer_dict[refID].append(enhancer.ID())
#an enhancer can be assigned to multiple genes
#a promoter can only be assigned to 1 gene
#promoters don't have enhancerIDs so don't add them yet
#this should just be an enhancer level table
#followed by a gene level table
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
# If a TSS overlaps an enhancer, assign them together
if overlappingGenes:
for gene in overlappingGenes:
if gene in tf_list:
TFtoEnhancerDict[gene].append(enhancer)
enhancerAssignment.append([gene, enhancer.chr(), enhancer.start(), enhancer.end(), enhancer.ID()])
# Otherwise, assign the enhancer to the most active gene in 100 kb
elif not overlappingGenes and proximalGenes:
highestGene = ''
highestActivity = 0
for gene in proximalGenes:
if expressionDictNM[gene] > highestActivity:
highestActivity = expressionDictNM[gene]
highestGene = gene
if highestGene in TFlist:
TFtoEnhancerDict[gene].append(enhancer)
enhancerAssignment.append([gene, enhancer.chr(), enhancer.start(), enhancer.end(), enhancer.ID()])
elif not overlappingGenes and not proximalGenes and closestGene:
if closestGene in TFlist:
gene = closestGene
TFtoEnhancerDict[gene].append(enhancer)
enhancerAssignment.append([gene, enhancer.chr(), enhancer.start(), enhancer.end(), enhancer.ID()])
# Add promoter is it's not contained in the super
if promoter:
for gene in TFtoEnhancerDict.keys():
promoter = utils.Locus(startDict[gene]['chr'], int(startDict[gene]['start'][0]) - 2000,
int(startDict[gene]['start'][0]) + 2000, startDict[gene]['sense'])
overlapBool = False
for enhancer in TFtoEnhancerDict[gene]:
if promoter.overlaps(enhancer):
overlapBool = True
if not overlapBool:
TFtoEnhancerDict[gene].append(promoter)
seAssignmentFile = projectFolder + projectName + '_ENHANCER_ASSIGNMENT.txt'
utils.unParseTable(enhancerAssignment, seAssignmentFile, '\t')
return TFtoEnhancerDict
def mapGFFLineToAnnot(gffLine, outFolder, nBins, geneDict, txCollection, sense='both', header=''):
'''
for every line produces a file with all of the rectangles to draw
'''
if len(header) == 0:
gffString = '%s_%s_%s_%s' % (gffLine[0], gffLine[6], gffLine[3], gffLine[4])
else:
gffString = header
diagramTable = [[0, 0, 0, 0]]
nameTable = [['', 0, 0]]
gffLocus = utils.Locus(gffLine[0], int(gffLine[3]), int(gffLine[4]), gffLine[6], gffLine[1])
scaleFactor = float(nBins) / gffLocus.len()
# plotting buffer for diagrams
plotBuffer = int(gffLocus.len() / float(nBins) * 20)
overlapLoci = txCollection.getOverlap(gffLocus, sense='both')
geneList = [locus.ID() for locus in overlapLoci]
if gffLine[6] == '-':
refPoint = int(gffLine[4])
else:
refPoint = int(gffLine[3])
offsetCollection = utils.LocusCollection([], 500)
for geneID in geneList:
gene = geneDict[geneID]
print(gene.commonName())
if len(gene.commonName()) > 1:
name = gene.commonName()
else:
name = geneID
offset = 4 * len(offsetCollection.getOverlap(gene.txLocus()))
offsetCollection.append(utils.makeSearchLocus(gene.txLocus(), plotBuffer, plotBuffer))
# write the name of the gene down
if gene.sense() == '+':
geneStart = gene.txLocus().start()
else:
geneStart = gene.txLocus().end()
geneStart = abs(geneStart - refPoint) * scaleFactor
nameTable.append([name, geneStart, -2 - offset])
# draw a line across the entire txLocus
[start, stop] = [abs(x - refPoint) * scaleFactor for x in gene.txLocus().coords()]
diagramTable.append([start, -0.01 - offset, stop, 0.01 - offset])
# now draw thin boxes for all txExons
if len(gene.txExons()) > 0:
for txExon in gene.txExons():
[start, stop] = [abs(x - refPoint) * scaleFactor for x in txExon.coords()]
diagramTable.append([start, -0.5 - offset, stop, 0.5 - offset])
# now draw fatty boxes for the coding exons if any
if len(gene.cdExons()) > 0:
for cdExon in gene.cdExons():
[start, stop] = [abs(x - refPoint) * scaleFactor for x in cdExon.coords()]
diagramTable.append([start, -1 - offset, stop, 1 - offset])
utils.unParseTable(diagramTable, outFolder + gffString + '_diagramTemp.txt', '\t')
utils.unParseTable(nameTable, outFolder + gffString + '_nameTemp.txt', '\t')
