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 makePeakTable(paramDict,
splitGFFPath,
averageTablePath,
startDict,
geneList,
genomeDirectory,
tads_path=''):
'''
makes the final peak table with ebox info
'''
peakTable = [[
'REGION_ID', 'CHROM', 'START', 'STOP', 'LENGTH', 'TSS', 'CPG',
'CPG_FRACTION', 'GC_FREQ', 'SIGNAL', 'CANON_EBOX_COUNT',
'NON_CANON_EBOX_COUNT', 'TOTAL_EBOX_COUNT', 'OVERLAPPING_GENES',
'PROXIMAL_GENES'
]]
print('LOADING PEAK REGIONS')
peakGFF = utils.parseTable(splitGFFPath, '\t')
print('LOADING BINDING DATA')
signalTable = utils.parseTable(averageTablePath, '\t')
print('LOADING CPGS ISLANDS')
cpgBed = utils.parseTable(paramDict['cpgPath'], '\t')
cpgLoci = []
for line in cpgBed:
cpgLoci.append(utils.Locus(line[0], line[1], line[2], '.', line[-1]))
cpgCollection = utils.LocusCollection(cpgLoci, 50)
print("MAKING TSS COLLECTIONS")
if len(geneList) == 0:
geneList = startDict.keys()
tss_1kb_loci = []
tss_50kb_loci = []
for refID in geneList:
tss_1kb_loci.append(utils.makeTSSLocus(refID, startDict, 1000, 1000))
tss_50kb_loci.append(utils.makeTSSLocus(refID, startDict, 50000,
50000))
#make a 1kb flanking and 50kb flanking collection
tss_1kb_collection = utils.LocusCollection(tss_1kb_loci, 50)
tss_50kb_collection = utils.LocusCollection(tss_50kb_loci, 50)
if len(tads_path) > 0:
print('LOADING TADS FROM %s' % (tads_path))
tad_collection = utils.importBoundRegion(tads_path, 'tad')
use_tads = True
#building a tad dict keyed by tad ID w/ genes in that tad provided
tad_dict = defaultdict(list)
for tss_locus in tss_1kb_loci:
overlapping_tads = tad_collection.getOverlap(tss_locus, 'both')
for tad_locus in overlapping_tads:
tad_dict[tad_locus.ID()].append(tss_locus.ID())
else:
use_tads = False
print('CLASSIFYING PEAKS')
ticker = 0
no_tad_count = 0
for i in range(len(peakGFF)):
if ticker % 1000 == 0:
print(ticker)
ticker += 1
#getting the particulars of the region
gffLine = peakGFF[i]
peakID = gffLine[1]
chrom = gffLine[0]
start = int(gffLine[3])
stop = int(gffLine[4])
lineLocus = utils.Locus(chrom, start, stop, '.', peakID)
#getting the mapped signal
signalLine = signalTable[(i + 1)]
signalVector = [float(x) for x in signalLine[2:]]
#setting up the new line
newLine = [peakID, chrom, start, stop, lineLocus.len()]
#get the tss status from the gff itself (we are able to do this nicely from the split gff code earlier
newLine.append(gffLine[7])
#check cpg status
if cpgCollection.getOverlap(lineLocus, 'both'):
newLine.append(1)
else:
newLine.append(0)
#now do fractional cpgOverlap
overlappingCpGLoci = cpgCollection.getOverlap(lineLocus, 'both')
overlappingBases = 0
for locus in overlappingCpGLoci:
cpgStart = max(locus.start(), lineLocus.start())
cpgEnd = min(locus.end(), lineLocus.end())
overlappingBases += (cpgEnd - cpgStart)
overlapFraction = float(overlappingBases) / lineLocus.len()
newLine.append(round(overlapFraction, 2))
#now get the seq
lineSeq = string.upper(
utils.fetchSeq(genomeDirectory, chrom, start, stop, True))
if len(lineSeq) == 0:
print('UH OH')
print(lineSeq)
print(gffLine)
print(i)
print(chrom)
print(start)
print(stop)
sys.exit()
gcFreq = float(lineSeq.count('GC') +
lineSeq.count('CG')) / len(lineSeq)
newLine.append(gcFreq)
#this is where we add the ChIP-Seq signal
newLine += signalVector
eboxMatchList = re.findall('CA..TG', lineSeq)
if len(eboxMatchList) == 0:
newLine += [0] * 3
else:
totalCount = len(eboxMatchList)
canonCount = eboxMatchList.count('CACGTG')
otherCount = totalCount - canonCount
newLine += [canonCount, otherCount, totalCount]
#now find the overlapping and proximal genes
#here each overlapping gene the tss 1kb locus overlaps the peak
if use_tads:
tad_loci = tad_collection.getOverlap(lineLocus, 'both')
tad_id_list = [tad_locus.ID() for tad_locus in tad_loci]
tad_genes = []
for tad_id in tad_id_list:
tad_genes += tad_dict[tad_id]
if len(tad_genes) == 0:
#print('no tad for this region')
#print(gffLine)
no_tad_count += 1
else:
tad_genes = []
if len(tad_genes) > 0:
overlappingGenes = [
startDict[locus.ID()]['name']
for locus in tss_1kb_collection.getOverlap(lineLocus, 'both')
if tad_genes.count(locus.ID()) > 0
]
proximalGenes = [
startDict[locus.ID()]['name']
for locus in tss_50kb_collection.getOverlap(lineLocus, 'both')
if tad_genes.count(locus.ID()) > 0
]
# print('linked peak to tad genes')
# print([startDict[x]['name'] for x in tad_genes])
# print(tad_id_list)
# print(gffLine)
# print(overlappingGenes)
# print(proximalGenes)
else:
overlappingGenes = [
startDict[locus.ID()]['name']
for locus in tss_1kb_collection.getOverlap(lineLocus, 'both')
]
proximalGenes = [
startDict[locus.ID()]['name']
for locus in tss_50kb_collection.getOverlap(lineLocus, 'both')
]
overlappingGenes = utils.uniquify(overlappingGenes)
#here the tss 50kb locus overlaps the peak
#overlap takes priority over proximal
proximalGenes = [
gene for gene in proximalGenes if overlappingGenes.count(gene) == 0
]
proximalGenes = utils.uniquify(proximalGenes)
overlappingString = string.join(overlappingGenes, ',')
proximalString = string.join(proximalGenes, ',')
newLine += [overlappingString, proximalString]
peakTable.append(newLine)
print('Out of %s regions, %s were assigned to at least 1 tad' %
(len(peakTable), no_tad_count))
return peakTable
def makeEboxBeds(gff_path, name='', overwrite=False):
'''
makes an ebox bed for the corresponding gff
'''
if len(name) == 0:
gff_name = gff_path.split('/')[-1].split('.')[0]
else:
gff_name = name
#set output
canon_path = '%s%s_CANON_EBOX.bed' % (bedFolder, gff_name)
non_path = '%s%s_NONCANON_EBOX.bed' % (bedFolder, gff_name)
#check to see if already done
if not overwrite:
if utils.checkOutput(canon_path, 0.1, 0.1) and utils.checkOutput(
non_path, 0.1, 0.1):
print('Found bed output at %s and %s' % (canon_path, non_path))
return canon_path, non_path
#for each region spit out canonical and non canonical eboxes
canonBed = []
nonBed = []
#open up the repeat filtered gff
region_gff = utils.parseTable(gff_path, '\t')
ticker = 0
for line in region_gff:
if ticker % 100 == 0:
print(ticker)
ticker += 1
chrom = line[0]
start = int(line[3])
end = int(line[4])
seq = string.upper(
utils.fetchSeq(genomeDirectory, chrom, start, end, True))
#get the canonical starts
canonStarts = [
start + match.start() for match in re.finditer('CACGTG', seq)
]
#get the non canonical starts
nonStarts = [
start + match.start() for match in re.finditer('CA..TG', seq)
]
#filter out the canonicals
nonStarts = [x for x in nonStarts if canonStarts.count(x) == 0]
#now fill out the bed
if len(canonStarts) > 0:
for ebox_start in canonStarts:
canonBed.append([chrom, ebox_start, (ebox_start + 6), '.'])
if len(nonStarts) > 0:
for ebox_start in nonStarts:
nonBed.append([chrom, ebox_start, (ebox_start + 6), '.'])
print('FOUND %s CANONICAL EBOXES' % (len(canonBed)))
print('FOUND %s NON CANONICAL EBOXES' % (len(nonBed)))
utils.unParseTable(canonBed, canon_path, '\t')
utils.unParseTable(nonBed, non_path, '\t')
return canon_path, non_path
se_id = line[0]
se_namelist.append(se_id)
subpeak_dict[se_id] = []
se_locus = utils.Locus(line[1], line[2], line[3], '.')
overlaps = subpeak_collection.getOverlap(se_locus)
for overlap in overlaps:
subpeak_dict[se_id].append(overlap)
subpeak = overlap
fastaTitle = se_id + '|' + 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, '')
# call FIMO and find the motifs within each enhancer
motifConvertFile = '/ark/home/af661/src/coreTFnetwork/annotations/MotifDictionary.txt'
motifDatabaseFile = '/ark/home/af661/src/coreTFnetwork/annotations/VertebratePWMs.txt'
motifDatabase = utils.parseTable(motifConvertFile, '\t')
motifDatabaseDict = {}
def rank_eboxes(nb_all_chip_dataFile,mycn_gff_path,macsFolder,genomeDirectory,window = 100):
'''
uses the conserved MYCN sites and ranks eboxes within them
by average background subtracted signal
searches 100bp (window variable) from mycn summits
'''
window = int(window)
#bring in the conserved mycn region
print('making gff of nb mycn summits')
nb_mycn_gff = utils.parseTable(mycn_gff_path,'\t')
nb_mycn_collection = utils.gffToLocusCollection(nb_mycn_gff,50)
dataDict =pipeline_dfci.loadDataTable(nb_all_chip_dataFile)
names_list = [name for name in dataDict.keys() if name.count('MYCN') == 1]
names_list.sort()
summit_loci = []
#first makes a gff of all summits +/- 100bp for all nb mycn datasets
for name in names_list:
summit_bed_path = '%s%s/%s_summits.bed' % (macsFolder,name,name)
summit_bed = utils.parseTable(summit_bed_path,'\t')
for line in summit_bed:
summit_locus = utils.Locus(line[0],int(line[1])-window,int(line[2])+window,'.',line[3])
if len(nb_mycn_collection.getOverlap(summit_locus)) > 0:
summit_loci.append(summit_locus)
summit_collection =utils.LocusCollection(summit_loci,50)
summit_merged_collection = summit_collection.stitchCollection()
summit_gff = utils.locusCollectionToGFF(summit_merged_collection)
summit_gff_path = '%sHG19_NB_MYCN_SUMMITS_-%s_+%s.gff' % (gffFolder,window,window)
utils.unParseTable(summit_gff,summit_gff_path,'\t')
#this is borrowed from above and maps chip-seq signal to the gff
print('mapping to nb mycn summits and making signal dict')
gffList = [summit_gff_path]
summit_signal_path = pipeline_dfci.map_regions(nb_all_chip_dataFile,gffList)
mycnSignalTable = utils.parseTable(summit_signal_path,'\t')
#making a signal dictionary for MYCN binding
names_list = ['BE2C_MYCN','KELLY_MYCN','NGP_MYCN','SHEP21_0HR_MYCN_NOSPIKE']
background_list = [dataDict[name]['background'] for name in names_list]
header = mycnSignalTable[0]
chip_columns = [header.index(name) for name in names_list]
background_columns = [header.index(background_name) for background_name in background_list]
mycn_sig_dict = {}
for line in mycnSignalTable[1:]:
line_sig = []
for i in range(len(names_list)):
line_sig.append(float(line[chip_columns[i]]) - float(line[background_columns[i]]))
region_id = line[1]
coords = [int(x) for x in line[1].split(':')[-1].split('-')]
line_length = coords[1]-coords[0]
mycn_sig_dict[region_id] = numpy.mean(line_sig)*line_length
#now for each region find the eboxes and then add up the signal
print('making ebox ranking')
ebox_list = ['CACGTG','CAGTTG','CAAGTG','CAGGTG','CAATTG','CAAATG','CATCTG','CAGCTG','CATGTG','CATATG']
eboxDict = {}
for ebox in ebox_list:
eboxDict[ebox] = []
ticker = 0
for line in summit_gff:
if ticker % 1000 == 0:
print(ticker)
ticker+=1
chrom = line[0]
sense = '.'
start = int(line[3])
end = int(line[4])
region_id = '%s(%s):%s-%s' % (line[0],line[6],line[3],line[4])
signal = mycn_sig_dict[region_id]
sequenceLine = utils.fetchSeq(genomeDirectory,chrom,start,end,True)
motifVector = []
matches = re.finditer('CA..TG',str.upper(sequenceLine))
if matches:
for match in matches:
motifVector.append(match.group())
#count only 1 of each motif type per line
#motifVector = utils.uniquify(motifVector)
for motif in motifVector:
if ebox_list.count(motif) > 0:
eboxDict[motif].append(signal)
else:
eboxDict[utils.revComp(motif)].append(signal)
eboxTable =[]
eboxTableOrdered =[['EBOX','OCCURENCES','AVG_HEIGHT']]
for ebox in eboxDict.keys():
newLine = [ebox,len(eboxDict[ebox]),numpy.mean(eboxDict[ebox])]
eboxTable.append(newLine)
occurenceOrder = utils.order([line[2] for line in eboxTable],decreasing=True)
for x in occurenceOrder:
eboxTableOrdered.append(eboxTable[x])
print(eboxTableOrdered)
ebox_outfile = '%sHG19_NB_MYCN_CONSERVED_SUMMITS_-%s_+%s_EBOX_RANK.txt' % (tableFolder,window,window)
utils.unParseTable(eboxTableOrdered,ebox_outfile,'\t')
return ebox_outfile
def make_mycn_stats_table(nb_all_chip_dataFile,outFile):
'''
making a table of conserved mycn peaks w/ some additional stats
mycn and h3k27ac signal is avg. background normalized across 4 samples
active tss defined as the union of all H3K27ac occupied promoters in NB
active enhancers defined as the union of all H3K27ac sites outside of promoters
'''
dataDict = pipeline_dfci.loadDataTable(nb_all_chip_dataFile)
print('SETTING UP OUTPUT TABLE')
outTable = [['PEAK_ID','CHROM','START','STOP','LENGTH','ACTIVE_TSS_OVERLAP','ENHANCER_OVERLAP','CPG_ISLAND_OVERLAP','CPG_ISLAND_FRACTION','GC_FREQ','MYCN_RANK','AVG_MYCN_SIGNAL','AVG_H3K27AC_SIGNAL','CANON_EBOX_COUNT','NONCANON_EBOX_COUNT','TOTAL_EBOX_COUNT','CANON_EXP','NON_CANON_EXP','GABPA_COUNT','GABPA_EXP','GATA_COUNT','GATA_EXP']]
dinuc = nmers(2,['A','T','G','C'])
#input files
mycnSignalFile = '%sHG19_NB_MYCN_CONSERVED_-0_+0_NB_ALL_SIGNAL.txt' % (signalFolder)
h3k27acSignalFile = '%sHG19_NB_MYCN_CONSERVED_-500_+500_NB_ALL_SIGNAL.txt' % (signalFolder)
mycnRankFile = '%smeta_rose/NB_MYCN/NB_MYCN_0KB_STITCHED_ENHANCER_REGION_RANK_CONSERVED.txt' % (projectFolder)
activeGeneFile = '%sHG19_NB_H3K27AC_ACTIVE_UNION.txt' % (geneListFolder)
#note, this is the ucsc hg19 cpg islands extended file
#to download and format run ./beds/download_cpg.sh
cpgFile = '%sbeds/hg19_cpg_islands.bed' % (projectFolder)
enhancerFile = '%smeta_rose/NB_H3K27AC/NB_H3K27AC_AllEnhancers.table.txt' % (projectFolder)
print('LOADING MYCN BINDING DATA')
mycnSignalTable = utils.parseTable(mycnSignalFile,'\t')
#making a signal dictionary for MYCN binding
names_list = ['BE2C_MYCN','KELLY_MYCN','NGP_MYCN','SHEP21_0HR_MYCN_NOSPIKE']
background_list = [dataDict[name]['background'] for name in names_list]
header = mycnSignalTable[0]
chip_columns = [header.index(name) for name in names_list]
background_columns = [header.index(background_name) for background_name in background_list]
mycn_sig_dict = {}
#this only works if the first column are unique identifiers
if len(mycnSignalTable) != len(utils.uniquify([line[0] for line in mycnSignalTable])):
print('Error: Column 1 of must contain unique identifiers.' % (mycnSignalFile))
sys.exit()
for line in mycnSignalTable[1:]:
line_sig = []
for i in range(len(names_list)):
line_sig.append(float(line[chip_columns[i]]) - float(line[background_columns[i]]))
mycn_sig_dict[line[0]] = numpy.mean(line_sig)
print('LOADING MYCN RANK DATA')
mycnRankTable = utils.parseTable(mycnRankFile,'\t')
print('LOADING H3K27AC BINDING DATA')
h3k27acSignalTable = utils.parseTable(h3k27acSignalFile,'\t')
#making a signal dictionary for background subtracted H3K27ac binding
names_list = ['BE2C_H3K27AC','KELLY_H3K27AC','NGP_H3K27AC','SHEP21_0HR_H3K27AC_NOSPIKE']
background_list = [dataDict[name]['background'] for name in names_list]
header = h3k27acSignalTable[0]
chip_columns = [header.index(name) for name in names_list]
background_columns = [header.index(background_name) for background_name in background_list]
h3k27ac_sig_dict = {}
#this only works if the first column are unique identifiers
if len(h3k27acSignalTable) != len(utils.uniquify([line[0] for line in h3k27acSignalTable])):
print('Error: Column 1 of must contain unique identifiers.' % (h3k27acSignalFile))
sys.exit()
for line in h3k27acSignalTable[1:]:
line_sig = []
for i in range(len(names_list)):
line_sig.append(float(line[chip_columns[i]]) - float(line[background_columns[i]]))
h3k27ac_sig_dict[line[0]] = numpy.mean(line_sig)
#making the cpg collection
print('LOADING CPGS ISLANDS')
cpgBed = utils.parseTable(cpgFile,'\t')
cpgLoci = []
for line in cpgBed:
cpgLoci.append(utils.Locus(line[0],line[1],line[2],'.',line[-1]))
cpgCollection = utils.LocusCollection(cpgLoci,50)
#next make the tss collection of active promoters
print('LOADING ACTIVE PROMOTERS')
startDict = utils.makeStartDict(annotFile)
activeTable = utils.parseTable(activeGeneFile,'\t')
tss_1kb_loci = []
for line in activeTable:
tss_1kb_loci.append(utils.makeTSSLocus(line[1],startDict,1000,1000))
tss_1kb_collection = utils.LocusCollection(tss_1kb_loci,50)
#enhancer file
print("LOADING ACTIVE ENHANCERS")
enhancerTable = utils.parseTable(enhancerFile,'\t')
print('STARTING WITH THE FOLLOWING NUMBER OF ENHANCERS IN NB')
print(len(enhancerTable) - 6)
enhancerLoci = []
for line in enhancerTable:
if line[0][0] != '#' and line[0][0] != 'R':
try:
lineLocus = utils.Locus(line[1],int(line[2]),int(line[3]),'.',line[0])
enhancerLoci.append(lineLocus)
except IndexError:
print(line)
sys.exit()
enhancerCollection = utils.LocusCollection(enhancerLoci,50)
print('CLASSIFYING MYCN PEAKS')
ticker = 0
for i in range(1,len(mycnSignalTable)):
if ticker%100 == 0:
print(ticker)
ticker +=1
line = mycnSignalTable[i]
mycn_signal = round(mycn_sig_dict[line[0]],4)
h3k27ac_signal = round(h3k27ac_sig_dict[line[0]],4)
peakID = line[0]
locusString = line[1]
chrom = locusString.split('(')[0]
[start,stop] = [int(x) for x in line[1].split(':')[-1].split('-')]
lineLocus = utils.Locus(chrom,start,stop,'.',peakID)
tssOverlap = 0
if tss_1kb_collection.getOverlap(lineLocus,'both'):
tssOverlap = 1
enhancerOverlap = 0
if enhancerCollection.getOverlap(lineLocus,'both') and tssOverlap == 0:
enhancerOverlap = 1
cpgIslandOverlap = 0
if cpgCollection.getOverlap(lineLocus,'both'):
cpgIslandOverlap = 1
#now do fractional cpgOverlap
overlappingCpGLoci = cpgCollection.getOverlap(lineLocus,'both')
overlappingBases = 0
for locus in overlappingCpGLoci:
cpgStart = max(locus.start(),lineLocus.start())
cpgEnd = min(locus.end(),lineLocus.end())
overlappingBases += (cpgEnd-cpgStart)
overlapFraction = round(float(overlappingBases)/lineLocus.len(),2)
#now get the seq
lineSeq = string.upper(utils.fetchSeq(genomeDirectory,chrom,start,stop,True))
gcFreq = round(float(lineSeq.count('GC') + lineSeq.count('CG'))/len(lineSeq),2)
dinuc_dict = {}
for nmer in dinuc:
dinuc_dict[nmer] = float(lineSeq.count('GC'))/len(lineSeq)
mycnRankLine = mycnRankTable[i]
mycnRank = numpy.mean([float(x) for x in mycnRankLine[6:]])
canonMatchList = re.findall('CACGTG',lineSeq)
canon_count = len(canonMatchList)
eboxMatchList = re.findall('CA..TG',lineSeq)
ebox_count = len(eboxMatchList)
non_canon_count = ebox_count-canon_count
#get the expected values
canon_exp = dinuc_dict['CA']*dinuc_dict['CG']*dinuc_dict['TG']*(len(lineSeq) - 5)
canon_exp = round(canon_exp,2)
notCG = 1- dinuc_dict['CG']
non_exp = dinuc_dict['CA']*notCG*dinuc_dict['TG']*(len(lineSeq) - 5)
non_exp = round(non_exp,2)
#for gata and GABPA
gabpaMatchList = re.findall('CGGAAG',lineSeq) + re.findall('CTTCCG',lineSeq)
gabpa_count = len(gabpaMatchList)
gabpa_exp_f = dinuc_dict['CG'] * dinuc_dict['GA'] * dinuc_dict['AG']*(len(lineSeq) - 5)
gabpa_exp_r = dinuc_dict['CT'] * dinuc_dict['TC'] * dinuc_dict['CG']*(len(lineSeq) - 5)
gabpa_exp = round(gabpa_exp_f,2) + round(gabpa_exp_r,2)
gataMatchList = re.findall('GATAA',lineSeq) + re.findall('TTATC',lineSeq)
gata_count = len(gataMatchList)
an_freq = 1 - dinuc_dict['AA'] - dinuc_dict['AT'] - dinuc_dict['AG'] -dinuc_dict['AC']
cn_freq = 1 - dinuc_dict['CA'] - dinuc_dict['CT'] - dinuc_dict['CG'] -dinuc_dict['CC']
gata_exp_f = dinuc_dict['GA'] * dinuc_dict['TA'] * an_freq*(len(lineSeq) - 5)
gata_exp_r = dinuc_dict['TT'] * dinuc_dict['AT'] * cn_freq*(len(lineSeq) - 5)
gata_exp = round(gata_exp_f,2) + round(gata_exp_r,2)
newLine = [peakID,chrom,start,stop,lineLocus.len(),tssOverlap,enhancerOverlap,cpgIslandOverlap,overlapFraction,gcFreq,mycnRank,mycn_signal,h3k27ac_signal,canon_count,non_canon_count,ebox_count,canon_exp,non_exp,gabpa_count,gabpa_exp,gata_count,gata_exp]
outTable.append(newLine)
utils.unParseTable(outTable,outFile,'\t')
return outFile
