def calculatePromoterActivity(annotationFile,
bamFile,
projectName,
projectFolder,
refseqToNameDict,
background=False):
'''
calculates the level of acetylation at each TF promoter
'''
print 'GENERATING AN ACTIVITY TABLE USING CHIP DATA'
annotTable = utils.parseTable(annotationFile, '\t')
output = []
counter = 0
bam = utils.Bam(bamFile)
if background:
background = utils.Bam(background)
startDict = utils.makeStartDict(annotationFile)
tssLoci = []
for gene in startDict:
tssLoci.append(utils.makeTSSLocus(gene, startDict, 2500, 2500))
tssCollection = utils.LocusCollection(tssLoci, 50)
gff = utils.locusCollectionToGFF(tssCollection)
outputname = projectFolder + projectName + '_TSS.gff'
utils.unParseTable(gff, outputname, '\t')
mappingCmd = 'bamliquidator_batch'
mappingCmd += ' -r ' + outputname
mappingCmd += ' -o ' + projectFolder + 'bamliquidator'
mappingCmd += ' -m -e 200 '
mappingCmd += bamFile
subprocess.call(mappingCmd, shell=True)
print mappingCmd
def calculatePromoterActivity(annotationFile, bamFile, projectName, projectFolder, refseqToNameDict):
'''
calculates the level of H3K27ac at each promoter from a H3K27ac bam file
'''
print 'IDENTIFY EXPRESSED GENES'
annotTable = utils.parseTable(annotationFile, '\t')
output = []
counter = 0
bam = utils.Bam(bamFile)
startDict = utils.makeStartDict(annotationFile)
tssLoci = []
for gene in startDict:
tssLoci.append(utils.makeTSSLocus(gene,startDict,1000,1000))
tssCollection = utils.LocusCollection(tssLoci,50)
gff = utils.locusCollectionToGFF(tssCollection)
outputname = projectFolder + projectName + '_TSS.gff'
utils.unParseTable(gff, outputname, '\t')
# run bamToGFF.py to quantify signal at each TSS +/- 1kb
mappingCmd = 'python ./bamToGFF.py'
mappingCmd += ' -r '
mappingCmd += ' -d '
mappingCmd += ' -o ' + projectFolder + 'matrix.gff'
mappingCmd += ' -m 1 -f 0 -e 200 '
mappingCmd += ' -i ' + projectFolder + projectName + '_TSS.gff'
mappingCmd += ' -b ' + bamFile
call(mappingCmd, shell=True)
print mappingCmd
def make_probe_to_gene_dict(annotFile, array_1_path, array_2_path):
'''
keyed by probe ID w/ gene as value
'''
#see if it already exists
pickle_path = '%soberthuer_outcome/probe_dict.pkl' % (projectFolder)
if utils.checkOutput(pickle_path, 0, 0):
print('loading previously made probe dict at %s' % (pickle_path))
probe_gene_dict = pickle.load(open(pickle_path, "rb"))
return probe_gene_dict
#we want to intersect refseq common names w/ the array
startDict = utils.makeStartDict(annotFile)
ref_name_list = utils.uniquify(
[startDict[refID]['name'] for refID in startDict.keys()])
probe_gene_dict = {}
array_1 = utils.parseTable(array_1_path, '\t')
array_2 = utils.parseTable(array_2_path, '\t')
ticker = 0
for line in array_1 + array_2:
if len(line) < 5:
continue
ticker += 1
probe_id = line[4]
name = line[-1]
# print(probe_id)
# print(name)
# if ticker== 10:
# sys.exit()
# print(line)
if ref_name_list.count(name) > 0:
probe_gene_dict[probe_id] = name
pickle.dump(probe_gene_dict, open(pickle_path, 'wb'))
return probe_gene_dict
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 make_shep_on_mycn_landscape(shep_on_dataFile):
'''
finds mycn peaks in shep21 that are conserved in nb and segregates them into promoter or enhancer
'''
dataDict = pipeline_dfci.loadDataTable(shep_on_dataFile)
print('LOADING SHEP ON MYCN SITES')
#load all of the shep_on sites
# shep_on_gff_path = '%smeta_rose/SHEP_ON_MYC/gff/HG19_SHEP_ON_MYC_ALL_-0_+0.gff' % (projectFolder)
# shep_on_gff = utils.parseTable(shep_on_gff_path,'\t')
shep_on_bed_path = '%sSHEP_6HR_MYCN_peaks.bed' % (macsEnrichedFolder)
shep_on_bed = utils.parseTable(shep_on_bed_path,'\t')
shep_on_gff = utils.bedToGFF(shep_on_bed)
#now get the conserved NB MYCN regions
nb_conserved_mycn_gff_file = '%sHG19_NB_MYCN_CONSERVED_-0_+0.gff' % (gffFolder)
nb_conserved_mycn_collection = utils.gffToLocusCollection(nb_conserved_mycn_gff_file)
print('LOADING SHEP ACTIVE ENHANCERS')
#make a collection of enhancers
shep_enhancer_file = '%smeta_rose/SHEP_ON_H3K27AC/SHEP_ON_H3K27AC_AllEnhancers.table.txt' % (projectFolder)
shep_enhancer_collection = utils.makeSECollection(shep_enhancer_file,'SHEP_H3K27AC')
#now get the active promoters
print('LOADING SHEP ACTIVE PROMOTERS')
startDict = utils.makeStartDict(annotFile)
shep_transcribed_file = '%sHG19_SHEP_ON_H3K27AC_ACTIVE.txt' % (geneListFolder)
shep_transcribed_table = utils.parseTable(shep_transcribed_file,'\t')
transcribedList = [line[1] for line in shep_transcribed_table]
tssLoci = []
for refID in transcribedList:
tssLoci.append(utils.makeTSSLocus(refID,startDict,1000,1000))
shep_tss_collection = utils.LocusCollection(tssLoci,50)
#now initialize the 6 gffs we will need
shep_mycn_gff = []
shep_mycn_gff_5kb = []
shep_mycn_gff_1kb = []
shep_mycn_promoter_gff = []
shep_mycn_promoter_gff_1kb = []
shep_mycn_promoter_gff_5kb = []
shep_mycn_enhancer_gff = []
shep_mycn_enhancer_gff_1kb = []
shep_mycn_enhancer_gff_5kb = []
#and their respective file names
shep_mycn_gff_file = '%sHG19_SHEP_MYCN_CONSERVED_-0_+0.gff' % (gffFolder)
shep_mycn_gff_5kb_file = '%sHG19_SHEP_MYCN_CONSERVED_-5kb_+5kb.gff' % (gffFolder)
shep_mycn_gff_1kb_file = '%sHG19_SHEP_MYCN_CONSERVED_-1kb_+1kb.gff' % (gffFolder)
shep_mycn_promoter_gff_file = '%sHG19_SHEP_MYCN_CONSERVED_PROMOTER_-0_+0.gff' % (gffFolder)
shep_mycn_promoter_gff_5kb_file = '%sHG19_SHEP_MYCN_CONSERVED_PROMOTER_-5kb_+5kb.gff' % (gffFolder)
shep_mycn_promoter_gff_1kb_file = '%sHG19_SHEP_MYCN_CONSERVED_PROMOTER_-1kb_+1kb.gff' % (gffFolder)
shep_mycn_enhancer_gff_file = '%sHG19_SHEP_MYCN_CONSERVED_ENHANCER_-0_+0.gff' % (gffFolder)
shep_mycn_enhancer_gff_5kb_file = '%sHG19_SHEP_MYCN_CONSERVED_ENHANCER_-5kb_+5kb.gff' % (gffFolder)
shep_mycn_enhancer_gff_1kb_file = '%sHG19_SHEP_MYCN_CONSERVED_ENHANCER_-1kb_+1kb.gff' % (gffFolder)
print('ITERATING THROUGH SHEP MYCN PEAKS')
ticker = 0
enhancer = 0
promoter = 0
other = 0
for line in shep_on_gff:
if ticker % 1000 == 0:
print ticker
ticker+=1
peakID = '%s_%s' % ('SHEP_MYCN',str(ticker))
lineLocus = utils.Locus(line[0],line[3],line[4],'.',peakID)
if nb_conserved_mycn_collection.getOverlap(lineLocus):
gffLine = [line[0],peakID,peakID,line[3],line[4],'','.','',peakID]
peakCenter = (int(line[3]) + int(line[4]))/2
gffLine_5kb = [line[0],peakID,peakID,peakCenter - 5000,peakCenter + 5000,'','.','',peakID]
#the 1kb is not a center +/- but a flank
gffLine_1kb = [line[0],peakID,peakID,int(line[3]) - 1000,int(line[4]) + 1000,'','.','',peakID]
shep_mycn_gff.append(gffLine)
shep_mycn_gff_5kb.append(gffLine_5kb)
shep_mycn_gff_1kb.append(gffLine_1kb)
#tss overlap should take precedence over enhancer overlap
if shep_tss_collection.getOverlap(lineLocus,'both'):
shep_mycn_promoter_gff.append(gffLine)
shep_mycn_promoter_gff_5kb.append(gffLine_5kb)
shep_mycn_promoter_gff_1kb.append(gffLine_1kb)
promoter+=1
#now check for enhancer overlap
elif shep_enhancer_collection.getOverlap(lineLocus,'both'):
shep_mycn_enhancer_gff.append(gffLine)
shep_mycn_enhancer_gff_5kb.append(gffLine_5kb)
shep_mycn_enhancer_gff_1kb.append(gffLine_1kb)
enhancer+=1
else:
other+=1
print('Of %s shep on mycn peaks' % (len(shep_on_gff)))
print('%s are promoter' % (promoter))
print('%s are enhancer' % (enhancer))
print('%s are other' % (other))
#now write out the gffs
utils.unParseTable(shep_mycn_gff,shep_mycn_gff_file,'\t')
utils.unParseTable(shep_mycn_gff_5kb,shep_mycn_gff_5kb_file,'\t')
utils.unParseTable(shep_mycn_gff_1kb,shep_mycn_gff_1kb_file,'\t')
utils.unParseTable(shep_mycn_promoter_gff,shep_mycn_promoter_gff_file,'\t')
utils.unParseTable(shep_mycn_promoter_gff_5kb,shep_mycn_promoter_gff_5kb_file,'\t')
utils.unParseTable(shep_mycn_promoter_gff_1kb,shep_mycn_promoter_gff_1kb_file,'\t')
utils.unParseTable(shep_mycn_enhancer_gff,shep_mycn_enhancer_gff_file,'\t')
utils.unParseTable(shep_mycn_enhancer_gff_5kb,shep_mycn_enhancer_gff_5kb_file,'\t')
utils.unParseTable(shep_mycn_enhancer_gff_1kb,shep_mycn_enhancer_gff_1kb_file,'\t')
def main():
'''
main run call
'''
debug = False
from optparse import OptionParser
usage = "usage: %prog [options] -g [GENOME] -i [INPUT_REGION_GFF] -r [RANKBY_BAM_FILE] -o [OUTPUT_FOLDER] [OPTIONAL_FLAGS]"
parser = OptionParser(usage=usage)
# required flags
parser.add_option(
"-i",
"--i",
dest="input",
nargs=1,
default=None,
help=
"Enter a comma separated list of .gff or .bed file of binding sites used to make enhancers"
)
parser.add_option("-r",
"--rankby",
dest="rankby",
nargs=1,
default=None,
help="Enter a comma separated list of bams to rank by")
parser.add_option("-o",
"--out",
dest="out",
nargs=1,
default=None,
help="Enter an output folder")
parser.add_option("-g",
"--genome",
dest="genome",
nargs=1,
default=None,
help="Enter the genome build (MM9,MM8,HG18,HG19)")
# optional flags
parser.add_option(
"-n",
"--name",
dest="name",
nargs=1,
default=None,
help="Provide a name for the analysis otherwise ROSE will guess")
parser.add_option(
"-c",
"--control",
dest="control",
nargs=1,
default=None,
help=
"Enter a comma separated list of control bams. Can either provide a single control bam for all rankby bams, or provide a control bam for each individual bam"
)
parser.add_option(
"-s",
"--stitch",
dest="stitch",
nargs=1,
default='',
help=
"Enter a max linking distance for stitching. Default will determine optimal stitching parameter"
)
parser.add_option(
"-t",
"--tss",
dest="tss",
nargs=1,
default=0,
help="Enter a distance from TSS to exclude. 0 = no TSS exclusion")
parser.add_option(
"--mask",
dest="mask",
nargs=1,
default=None,
help=
"Mask a set of regions from analysis. Provide a .bed or .gff of masking regions"
)
# RETRIEVING FLAGS
(options, args) = parser.parse_args()
if not options.input or not options.rankby or not options.out or not options.genome:
print('hi there')
parser.print_help()
exit()
# making the out folder if it doesn't exist
outFolder = utils.formatFolder(options.out, True)
# figuring out folder schema
gffFolder = utils.formatFolder(outFolder + 'gff/', True)
mappedFolder = utils.formatFolder(outFolder + 'mappedGFF/', True)
# GETTING INPUT FILE(s)
inputList = [
inputFile for inputFile in options.input.split(',')
if len(inputFile) > 1
]
#converting all input files into GFFs and moving into the GFF folder
inputGFFList = []
for inputFile in inputList:
if inputFile.split('.')[-1] == 'bed':
# CONVERTING A BED TO GFF
inputGFFName = inputFile.split('/')[-1][
0:-4] #strips the last 4 characters i.e. '.bed'
inputGFFFile = '%s%s.gff' % (gffFolder, inputGFFName)
utils.bedToGFF(inputFile, inputGFFFile)
elif options.input.split('.')[-1] == 'gff':
# COPY THE INPUT GFF TO THE GFF FOLDER
os.system('cp %s %s' % (inputFile, gffFolder))
inputGFFFile = '%s%s' % (gffFolder, inputFile.split('/')[-1])
else:
print(
'WARNING: INPUT FILE DOES NOT END IN .gff or .bed. ASSUMING .gff FILE FORMAT'
)
# COPY THE INPUT GFF TO THE GFF FOLDER
os.system('cp %s %s' % (inputFile, gffFolder))
inputGFFFile = '%s%s' % (gffFolder, inputFile.split('/')[-1])
inputGFFList.append(inputGFFFile)
# GETTING THE LIST OF BAMFILES TO PROCESS
#either same number of bams for rankby and control
#or only 1 control #or none!
#bamlist should be all rankby bams followed by control bams
bamFileList = []
if options.control:
controlBamList = [
bam for bam in options.control.split(',') if len(bam) > 0
]
rankbyBamList = [
bam for bam in options.rankby.split(',') if len(bam) > 0
]
if len(controlBamList) == len(rankbyBamList):
#case where an equal number of backgrounds are given
bamFileList = rankbyBamList + controlBamList
elif len(controlBamList) == 1:
#case where a universal background is applied
bamFileList = rankbyBamList + controlBamList * len(rankbyBamList)
else:
print(
'ERROR: EITHER PROVIDE A SINGLE CONTROL BAM FOR ALL SAMPLES, OR ONE CONTROL BAM FOR EACH SAMPLE'
)
sys.exit()
else:
bamFileList = [
bam for bam in options.rankby.split(',') if len(bam) > 0
]
# Stitch parameter
if options.stitch == '':
stitchWindow = ''
else:
stitchWindow = int(options.stitch)
# tss options
tssWindow = int(options.tss)
if tssWindow != 0:
removeTSS = True
else:
removeTSS = False
# GETTING THE GENOME
genome = string.upper(options.genome)
print('USING %s AS THE GENOME' % (genome))
# GETTING THE CORRECT ANNOT FILE
genomeDict = {
'HG18': '%s/annotation/hg18_refseq.ucsc' % (pipeline_dir),
'MM9': '%s/annotation/mm9_refseq.ucsc' % (pipeline_dir),
'HG19': '%s/annotation/hg19_refseq.ucsc' % (pipeline_dir),
'MM8': '%s/annotation/mm8_refseq.ucsc' % (pipeline_dir),
'MM10': '%s/annotation/mm10_refseq.ucsc' % (pipeline_dir),
'RN4': '%s/annotation/rn4_refseq.ucsc' % (pipeline_dir),
}
try:
annotFile = genomeDict[genome.upper()]
except KeyError:
print('ERROR: UNSUPPORTED GENOMES TYPE %s' % (genome))
sys.exit()
#FINDING THE ANALYSIS NAME
if options.name:
inputName = options.name
else:
inputName = inputGFFList[0].split('/')[-1].split('.')[0]
print('USING %s AS THE ANALYSIS NAME' % (inputName))
print('FORMATTING INPUT REGIONS')
# MAKING THE RAW INPUT FILE FROM THE INPUT GFFs
#use a simpler unique region naming system
if len(inputGFFList) == 1:
inputGFF = utils.parseTable(inputGFFList[0], '\t')
else:
inputLoci = []
for gffFile in inputGFFList:
print('\tprocessing %s' % (gffFile))
gff = utils.parseTable(gffFile, '\t')
gffCollection = utils.gffToLocusCollection(gff, 50)
inputLoci += gffCollection.getLoci()
inputCollection = utils.LocusCollection(inputLoci, 50)
inputCollection = inputCollection.stitchCollection(
) # stitches to produce unique regions
inputGFF = utils.locusCollectionToGFF(inputCollection)
formattedGFF = []
#now number things appropriately
for i, line in enumerate(inputGFF):
#use the coordinates to make a new id inputname_chr_sense_start_stop
chrom = line[0]
coords = [int(line[3]), int(line[4])]
sense = line[6]
lineID = '%s_%s' % (inputName, str(i + 1)) #1 indexing
newLine = [
chrom, lineID, lineID,
min(coords),
max(coords), '', sense, '', lineID
]
formattedGFF.append(newLine)
#name of the master input gff file
masterGFFFile = '%s%s_%s_ALL_-0_+0.gff' % (gffFolder, string.upper(genome),
inputName)
utils.unParseTable(formattedGFF, masterGFFFile, '\t')
print('USING %s AS THE INPUT GFF' % (masterGFFFile))
# MAKING THE START DICT
print('MAKING START DICT')
startDict = utils.makeStartDict(annotFile)
#GET CHROMS FOUND IN THE BAMS
print('GETTING CHROMS IN BAMFILES')
bamChromList = getBamChromList(bamFileList)
print("USING THE FOLLOWING CHROMS")
print(bamChromList)
#LOADING IN THE GFF AND FILTERING BY CHROM
print('LOADING AND FILTERING THE GFF')
inputGFF = filterGFF(masterGFFFile, bamChromList)
# LOADING IN THE BOUND REGION REFERENCE COLLECTION
print('LOADING IN GFF REGIONS')
referenceCollection = utils.gffToLocusCollection(inputGFF)
print('CHECKING REFERENCE COLLECTION:')
checkRefCollection(referenceCollection)
# MASKING REFERENCE COLLECTION
# see if there's a mask
if options.mask:
maskFile = options.mask
# if it's a bed file
if maskFile.split('.')[-1].upper() == 'BED':
maskGFF = utils.bedToGFF(maskFile)
elif maskFile.split('.')[-1].upper() == 'GFF':
maskGFF = utils.parseTable(maskFile, '\t')
else:
print("MASK MUST BE A .gff or .bed FILE")
sys.exit()
maskCollection = utils.gffToLocusCollection(maskGFF)
# now mask the reference loci
referenceLoci = referenceCollection.getLoci()
filteredLoci = [
locus for locus in referenceLoci
if len(maskCollection.getOverlap(locus, 'both')) == 0
]
print("FILTERED OUT %s LOCI THAT WERE MASKED IN %s" %
(len(referenceLoci) - len(filteredLoci), maskFile))
referenceCollection = utils.LocusCollection(filteredLoci, 50)
# NOW STITCH REGIONS
print('STITCHING REGIONS TOGETHER')
stitchedCollection, debugOutput, stitchWindow = regionStitching(
referenceCollection, inputName, outFolder, stitchWindow, tssWindow,
annotFile, removeTSS)
# NOW MAKE A STITCHED COLLECTION GFF
print('MAKING GFF FROM STITCHED COLLECTION')
stitchedGFF = utils.locusCollectionToGFF(stitchedCollection)
print(stitchWindow)
print(type(stitchWindow))
if not removeTSS:
stitchedGFFFile = '%s%s_%sKB_STITCHED.gff' % (gffFolder, inputName,
str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED' % (inputName,
str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED.debug' % (gffFolder, inputName,
str(stitchWindow / 1000))
else:
stitchedGFFFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.gff' % (
gffFolder, inputName, str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED_TSS_DISTAL' % (
inputName, str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.debug' % (
gffFolder, inputName, str(stitchWindow / 1000))
# WRITING DEBUG OUTPUT TO DISK
if debug:
print('WRITING DEBUG OUTPUT TO DISK AS %s' % (debugOutFile))
utils.unParseTable(debugOutput, debugOutFile, '\t')
# WRITE THE GFF TO DISK
print('WRITING STITCHED GFF TO DISK AS %s' % (stitchedGFFFile))
utils.unParseTable(stitchedGFF, stitchedGFFFile, '\t')
# SETTING UP THE OVERALL OUTPUT FILE
outputFile1 = outFolder + stitchedGFFName + '_ENHANCER_REGION_MAP.txt'
print('OUTPUT WILL BE WRITTEN TO %s' % (outputFile1))
# MAPPING TO THE NON STITCHED (ORIGINAL GFF)
# MAPPING TO THE STITCHED GFF
# Try to use the bamliquidatior_path.py script on cluster, otherwise, failover to local (in path), otherwise fail.
bamFileListUnique = list(bamFileList)
bamFileListUnique = utils.uniquify(bamFileListUnique)
#prevent redundant mapping
print("MAPPING TO THE FOLLOWING BAMS:")
print(bamFileListUnique)
for bamFile in bamFileListUnique:
bamFileName = bamFile.split('/')[-1]
# MAPPING TO THE STITCHED GFF
mappedOut1Folder = '%s%s_%s_MAPPED' % (mappedFolder, stitchedGFFName,
bamFileName)
mappedOut1File = '%s%s_%s_MAPPED/matrix.txt' % (
mappedFolder, stitchedGFFName, bamFileName)
if utils.checkOutput(mappedOut1File, 0.2, 0.2):
print("FOUND %s MAPPING DATA FOR BAM: %s" %
(stitchedGFFFile, mappedOut1File))
else:
cmd1 = bamliquidator_path + " --sense . -e 200 --match_bamToGFF -r %s -o %s %s" % (
stitchedGFFFile, mappedOut1Folder, bamFile)
print(cmd1)
os.system(cmd1)
if utils.checkOutput(mappedOut1File, 0.2, 5):
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" %
(stitchedGFFFile, bamFileName))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" %
(stitchedGFFFile, bamFileName))
sys.exit()
print('BAM MAPPING COMPLETED NOW MAPPING DATA TO REGIONS')
# CALCULATE DENSITY BY REGION
# NEED TO FIX THIS FUNCTION TO ACCOUNT FOR DIFFERENT OUTPUTS OF LIQUIDATOR
mapCollection(stitchedCollection,
referenceCollection,
bamFileList,
mappedFolder,
outputFile1,
refName=stitchedGFFName)
print('FINDING AVERAGE SIGNAL AMONGST BAMS')
metaOutputFile = collapseRegionMap(outputFile1,
inputName + '_MERGED_SIGNAL',
controlBams=options.control)
#now try the merging
print('CALLING AND PLOTTING SUPER-ENHANCERS')
rankbyName = inputName + '_MERGED_SIGNAL'
controlName = 'NONE'
cmd = 'Rscript %sROSE2_callSuper.R %s %s %s %s' % (
pipeline_dir, outFolder, metaOutputFile, inputName, controlName)
print(cmd)
os.system(cmd)
# calling the gene mapper
print('CALLING GENE MAPPING')
superTableFile = "%s_SuperEnhancers.table.txt" % (inputName)
#for now don't use ranking bam to call top genes
cmd = "python %sROSE2_geneMapper.py -g %s -i %s%s -f" % (
pipeline_dir, genome, outFolder, superTableFile)
print(cmd)
os.system(cmd)
stretchTableFile = "%s_StretchEnhancers.table.txt" % (inputName)
cmd = "python %sROSE2_geneMapper.py -g %s -i %s%s -f" % (
pipeline_dir, genome, outFolder, stretchTableFile)
print(cmd)
os.system(cmd)
superStretchTableFile = "%s_SuperStretchEnhancers.table.txt" % (inputName)
cmd = "python %sROSE2_geneMapper.py -g %s -i %s%s -f" % (
pipeline_dir, genome, outFolder, superStretchTableFile)
os.system(cmd)
def main():
'''
main run call
'''
debug = False
from optparse import OptionParser
usage = "usage: %prog [options] -g [GENOME] -i [INPUT_REGION_GFF] -r [RANKBY_BAM_FILE] -o [OUTPUT_FOLDER] [OPTIONAL_FLAGS]"
parser = OptionParser(usage=usage)
# required flags
parser.add_option(
"-i",
"--i",
dest="input",
nargs=1,
default=None,
help="Enter a .gff or .bed file of binding sites used to make enhancers"
)
parser.add_option("-r",
"--rankby",
dest="rankby",
nargs=1,
default=None,
help="bamfile to rank enhancer by")
parser.add_option("-o",
"--out",
dest="out",
nargs=1,
default=None,
help="Enter an output folder")
parser.add_option("-g",
"--genome",
dest="genome",
nargs=1,
default=None,
help="Enter the genome build (MM9,MM8,HG18,HG19)")
# optional flags
parser.add_option(
"-b",
"--bams",
dest="bams",
nargs=1,
default=None,
help="Enter a comma separated list of additional bam files to map to")
parser.add_option("-c",
"--control",
dest="control",
nargs=1,
default=None,
help="bamfile to rank enhancer by")
parser.add_option(
"-s",
"--stitch",
dest="stitch",
nargs=1,
default='',
help=
"Enter a max linking distance for stitching. Default will determine optimal stitching parameter"
)
parser.add_option(
"-t",
"--tss",
dest="tss",
nargs=1,
default=0,
help="Enter a distance from TSS to exclude. 0 = no TSS exclusion")
parser.add_option(
"--mask",
dest="mask",
nargs=1,
default=None,
help=
"Mask a set of regions from analysis. Provide a .bed or .gff of masking regions"
)
# RETRIEVING FLAGS
(options, args) = parser.parse_args()
if not options.input or not options.rankby or not options.out or not options.genome:
print('hi there')
parser.print_help()
exit()
# making the out folder if it doesn't exist
outFolder = utils.formatFolder(options.out, True)
# figuring out folder schema
gffFolder = utils.formatFolder(outFolder + 'gff/', True)
mappedFolder = utils.formatFolder(outFolder + 'mappedGFF/', True)
# GETTING INPUT FILE
if options.input.split('.')[-1] == 'bed':
# CONVERTING A BED TO GFF
inputGFFName = options.input.split('/')[-1][0:-4]
inputGFFFile = '%s%s.gff' % (gffFolder, inputGFFName)
utils.bedToGFF(options.input, inputGFFFile)
elif options.input.split('.')[-1] == 'gff':
# COPY THE INPUT GFF TO THE GFF FOLDER
inputGFFFile = options.input
os.system('cp %s %s' % (inputGFFFile, gffFolder))
else:
print(
'WARNING: INPUT FILE DOES NOT END IN .gff or .bed. ASSUMING .gff FILE FORMAT'
)
# COPY THE INPUT GFF TO THE GFF FOLDER
inputGFFFile = options.input
os.system('cp %s %s' % (inputGFFFile, gffFolder))
# GETTING THE LIST OF BAMFILES TO PROCESS
if options.control:
bamFileList = [options.rankby, options.control]
else:
bamFileList = [options.rankby]
if options.bams:
bamFileList += options.bams.split(',')
#bamFileList = utils.uniquify(bamFileList) # makes sad when you have the same control bam over and over again
# optional args
# Stitch parameter
if options.stitch == '':
stitchWindow = ''
else:
stitchWindow = int(options.stitch)
# tss options
tssWindow = int(options.tss)
if tssWindow != 0:
removeTSS = True
else:
removeTSS = False
# GETTING THE BOUND REGION FILE USED TO DEFINE ENHANCERS
print('USING %s AS THE INPUT GFF' % (inputGFFFile))
inputName = inputGFFFile.split('/')[-1].split('.')[0]
# GETTING THE GENOME
genome = options.genome
print('USING %s AS THE GENOME' % genome)
# GETTING THE CORRECT ANNOT FILE
cwd = os.getcwd()
genomeDict = {
'HG18': '%s/annotation/hg18_refseq.ucsc' % (cwd),
'MM9': '%s/annotation/mm9_refseq.ucsc' % (cwd),
'HG19': '%s/annotation/hg19_refseq.ucsc' % (cwd),
'MM8': '%s/annotation/mm8_refseq.ucsc' % (cwd),
'MM10': '%s/annotation/mm10_refseq.ucsc' % (cwd),
'RN4': '%s/annotation/rn4_refseq.ucsc' % (cwd),
'RN6': '%s/annotation/rn6_refseq.ucsc' % (cwd),
}
annotFile = genomeDict[genome.upper()]
# MAKING THE START DICT
print('MAKING START DICT')
startDict = utils.makeStartDict(annotFile)
#GET CHROMS FOUND IN THE BAMS
print('GETTING CHROMS IN BAMFILES')
bamChromList = getBamChromList(bamFileList)
print("USING THE FOLLOWING CHROMS")
print(bamChromList)
#LOADING IN THE GFF AND FILTERING BY CHROM
print('LOADING AND FILTERING THE GFF')
inputGFF = filterGFF(inputGFFFile, bamChromList)
# LOADING IN THE BOUND REGION REFERENCE COLLECTION
print('LOADING IN GFF REGIONS')
referenceCollection = utils.gffToLocusCollection(inputGFF)
print('CHECKING REFERENCE COLLECTION:')
checkRefCollection(referenceCollection)
# MASKING REFERENCE COLLECTION
# see if there's a mask
if options.mask:
maskFile = options.mask
# if it's a bed file
if maskFile.split('.')[-1].upper() == 'BED':
maskGFF = utils.bedToGFF(maskFile)
elif maskFile.split('.')[-1].upper() == 'GFF':
maskGFF = utils.parseTable(maskFile, '\t')
else:
print("MASK MUST BE A .gff or .bed FILE")
sys.exit()
maskCollection = utils.gffToLocusCollection(maskGFF)
# now mask the reference loci
referenceLoci = referenceCollection.getLoci()
filteredLoci = [
locus for locus in referenceLoci
if len(maskCollection.getOverlap(locus, 'both')) == 0
]
print("FILTERED OUT %s LOCI THAT WERE MASKED IN %s" %
(len(referenceLoci) - len(filteredLoci), maskFile))
referenceCollection = utils.LocusCollection(filteredLoci, 50)
# NOW STITCH REGIONS
print('STITCHING REGIONS TOGETHER')
stitchedCollection, debugOutput, stitchWindow = regionStitching(
referenceCollection, inputName, outFolder, stitchWindow, tssWindow,
annotFile, removeTSS)
# NOW MAKE A STITCHED COLLECTION GFF
print('MAKING GFF FROM STITCHED COLLECTION')
stitchedGFF = utils.locusCollectionToGFF(stitchedCollection)
# making sure start/stop ordering are correct
for i in range(len(stitchedGFF)):
line = stitchedGFF[i]
start = int(line[3])
stop = int(line[4])
if start > stop:
line[3] = stop
line[4] = start
print(stitchWindow)
print(type(stitchWindow))
if not removeTSS:
stitchedGFFFile = '%s%s_%sKB_STITCHED.gff' % (gffFolder, inputName,
str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED' % (inputName,
str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED.debug' % (gffFolder, inputName,
str(stitchWindow / 1000))
else:
stitchedGFFFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.gff' % (
gffFolder, inputName, str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED_TSS_DISTAL' % (
inputName, str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.debug' % (
gffFolder, inputName, str(stitchWindow / 1000))
# WRITING DEBUG OUTPUT TO DISK
if debug:
print('WRITING DEBUG OUTPUT TO DISK AS %s' % (debugOutFile))
utils.unParseTable(debugOutput, debugOutFile, '\t')
# WRITE THE GFF TO DISK
print('WRITING STITCHED GFF TO DISK AS %s' % (stitchedGFFFile))
utils.unParseTable(stitchedGFF, stitchedGFFFile, '\t')
# SETTING UP THE OVERALL OUTPUT FILE
outputFile1 = outFolder + stitchedGFFName + '_ENHANCER_REGION_MAP.txt'
print('OUTPUT WILL BE WRITTEN TO %s' % (outputFile1))
# MAPPING TO THE NON STITCHED (ORIGINAL GFF)
# MAPPING TO THE STITCHED GFF
# Try to use the bamliquidatior_path.py script on cluster, otherwise, failover to local (in path), otherwise fail.
bamliquidator_path = 'bamliquidator_batch.py'
bamFileListUnique = list(bamFileList)
bamFileListUnique = utils.uniquify(bamFileListUnique)
#prevent redundant mapping
print("MAPPING TO THE FOLLOWING BAMS:")
print(bamFileListUnique)
for bamFile in bamFileListUnique:
bamFileName = bamFile.split('/')[-1]
# MAPPING TO THE STITCHED GFF
mappedOut1Folder = '%s%s_%s_MAPPED' % (mappedFolder, stitchedGFFName,
bamFileName)
mappedOut1File = '%s%s_%s_MAPPED/matrix.txt' % (
mappedFolder, stitchedGFFName, bamFileName)
if utils.checkOutput(mappedOut1File, 0.2, 0.2):
print("FOUND %s MAPPING DATA FOR BAM: %s" %
(stitchedGFFFile, mappedOut1File))
else:
cmd1 = bamliquidator_path + " --sense . -e 200 --match_bamToGFF -r %s -o %s %s" % (
stitchedGFFFile, mappedOut1Folder, bamFile)
print(cmd1)
os.system(cmd1)
if utils.checkOutput(mappedOut1File, 0.2, 5):
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" %
(stitchedGFFFile, bamFileName))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" %
(stitchedGFFFile, bamFileName))
sys.exit()
print('BAM MAPPING COMPLETED NOW MAPPING DATA TO REGIONS')
# CALCULATE DENSITY BY REGION
# NEED TO FIX THIS FUNCTION TO ACCOUNT FOR DIFFERENT OUTPUTS OF LIQUIDATOR
mapCollection(stitchedCollection,
referenceCollection,
bamFileList,
mappedFolder,
outputFile1,
refName=stitchedGFFName)
print('CALLING AND PLOTTING SUPER-ENHANCERS')
if options.control:
rankbyName = options.rankby.split('/')[-1]
controlName = options.control.split('/')[-1]
cmd = 'R --no-save %s %s %s %s < ROSE2_callSuper.R' % (
outFolder, outputFile1, inputName, controlName)
else:
rankbyName = options.rankby.split('/')[-1]
controlName = 'NONE'
cmd = 'R --no-save %s %s %s %s < ROSE2_callSuper.R' % (
outFolder, outputFile1, inputName, controlName)
print(cmd)
os.system(cmd)
# calling the gene mapper
time.sleep(20)
superTableFile = "%s_SuperEnhancers.table.txt" % (inputName)
if options.control:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -c %s -i %s%s &" % (
genome, options.rankby, options.control, outFolder, superTableFile)
else:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -i %s%s &" % (
genome, options.rankby, outFolder, superTableFile)
os.system(cmd)
stretchTableFile = "%s_StretchEnhancers.table.txt" % (inputName)
if options.control:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -c %s -i %s%s &" % (
genome, options.rankby, options.control, outFolder,
stretchTableFile)
else:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -i %s%s &" % (
genome, options.rankby, outFolder, stretchTableFile)
os.system(cmd)
superStretchTableFile = "%s_SuperStretchEnhancers.table.txt" % (inputName)
if options.control:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -c %s -i %s%s &" % (
genome, options.rankby, options.control, outFolder,
superStretchTableFile)
else:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -i %s%s &" % (
genome, options.rankby, outFolder, superStretchTableFile)
os.system(cmd)
def main():
'''
main run call
'''
debug = False
from optparse import OptionParser
usage = "usage: %prog [options] -g [GENOME] -i [INPUT_REGION_GFF] -r [RANKBY_BAM_FILE] -o [OUTPUT_FOLDER] [OPTIONAL_FLAGS]"
parser = OptionParser(usage=usage)
# required flags
parser.add_option("-i", "--i", dest="input", nargs=1, default=None,
help="Enter a comma separated list of .gff or .bed file of binding sites used to make enhancers")
parser.add_option("-r", "--rankby", dest="rankby", nargs=1, default=None,
help="Enter a comma separated list of bams to rank by")
parser.add_option("-o", "--out", dest="out", nargs=1, default=None,
help="Enter an output folder")
parser.add_option("-g", "--genome", dest="genome", nargs=1, default=None,
help="Enter the genome build (MM9,MM8,HG18,HG19)")
# optional flags
parser.add_option("-n", "--name", dest="name", nargs=1, default=None,
help="Provide a name for the analysis otherwise ROSE will guess")
parser.add_option("-c", "--control", dest="control", nargs=1, default=None,
help="Enter a comma separated list of control bams. Can either provide a single control bam for all rankby bams, or provide a control bam for each individual bam")
parser.add_option("-s", "--stitch", dest="stitch", nargs=1, default='',
help="Enter a max linking distance for stitching. Default will determine optimal stitching parameter")
parser.add_option("-t", "--tss", dest="tss", nargs=1, default=0,
help="Enter a distance from TSS to exclude. 0 = no TSS exclusion")
parser.add_option("--mask", dest="mask", nargs=1, default=None,
help="Mask a set of regions from analysis. Provide a .bed or .gff of masking regions")
# RETRIEVING FLAGS
(options, args) = parser.parse_args()
if not options.input or not options.rankby or not options.out or not options.genome:
print('hi there')
parser.print_help()
exit()
# making the out folder if it doesn't exist
outFolder = utils.formatFolder(options.out, True)
# figuring out folder schema
gffFolder = utils.formatFolder(outFolder + 'gff/', True)
mappedFolder = utils.formatFolder(outFolder + 'mappedGFF/', True)
# GETTING INPUT FILE(s)
inputList = [inputFile for inputFile in options.input.split(',') if len(inputFile) > 1]
#converting all input files into GFFs and moving into the GFF folder
inputGFFList = []
for inputFile in inputList:
if inputFile.split('.')[-1] == 'bed':
# CONVERTING A BED TO GFF
inputGFFName = inputFile.split('/')[-1][0:-4] #strips the last 4 characters i.e. '.bed'
inputGFFFile = '%s%s.gff' % (gffFolder, inputGFFName)
utils.bedToGFF(inputFile, inputGFFFile)
elif options.input.split('.')[-1] == 'gff':
# COPY THE INPUT GFF TO THE GFF FOLDER
os.system('cp %s %s' % (inputFile, gffFolder))
inputGFFFile = '%s%s' % (gffFolder,inputFile.split('/')[-1])
else:
print('WARNING: INPUT FILE DOES NOT END IN .gff or .bed. ASSUMING .gff FILE FORMAT')
# COPY THE INPUT GFF TO THE GFF FOLDER
os.system('cp %s %s' % (inputFile, gffFolder))
inputGFFFile = '%s%s' % (gffFolder,inputFile.split('/')[-1])
inputGFFList.append(inputGFFFile)
# GETTING THE LIST OF BAMFILES TO PROCESS
#either same number of bams for rankby and control
#or only 1 control #or none!
#bamlist should be all rankby bams followed by control bams
bamFileList = []
if options.control:
controlBamList = [bam for bam in options.control.split(',') if len(bam) >0]
rankbyBamList = [bam for bam in options.rankby.split(',') if len(bam) >0]
if len(controlBamList) == len(rankbyBamList):
#case where an equal number of backgrounds are given
bamFileList = rankbyBamList + controlBamList
elif len(controlBamList) == 1:
#case where a universal background is applied
bamFileList = rankbyBamList + controlBamList*len(rankbyBamList)
else:
print('ERROR: EITHER PROVIDE A SINGLE CONTROL BAM FOR ALL SAMPLES, OR ONE CONTROL BAM FOR EACH SAMPLE')
sys.exit()
else:
bamFileList = [bam for bam in options.rankby.split(',') if len(bam) > 0]
# Stitch parameter
if options.stitch == '':
stitchWindow = ''
else:
stitchWindow = int(options.stitch)
# tss options
tssWindow = int(options.tss)
if tssWindow != 0:
removeTSS = True
else:
removeTSS = False
# GETTING THE GENOME
genome = string.upper(options.genome)
print('USING %s AS THE GENOME' % (genome))
# GETTING THE CORRECT ANNOT FILE
genomeDict = {
'HG18': '%s/annotation/hg18_refseq.ucsc' % (pipeline_dir),
'MM9': '%s/annotation/mm9_refseq.ucsc' % (pipeline_dir),
'HG19': '%s/annotation/hg19_refseq.ucsc' % (pipeline_dir),
'MM8': '%s/annotation/mm8_refseq.ucsc' % (pipeline_dir),
'MM10': '%s/annotation/mm10_refseq.ucsc' % (pipeline_dir),
'RN4': '%s/annotation/rn4_refseq.ucsc' % (pipeline_dir),
}
try:
annotFile = genomeDict[genome.upper()]
except KeyError:
print('ERROR: UNSUPPORTED GENOMES TYPE %s' % (genome))
sys.exit()
#FINDING THE ANALYSIS NAME
if options.name:
inputName = options.name
else:
inputName = inputGFFList[0].split('/')[-1].split('.')[0]
print('USING %s AS THE ANALYSIS NAME' % (inputName))
print('FORMATTING INPUT REGIONS')
# MAKING THE RAW INPUT FILE FROM THE INPUT GFFs
#use a simpler unique region naming system
if len(inputGFFList) == 1:
inputGFF = utils.parseTable(inputGFFList[0],'\t')
else:
inputLoci = []
for gffFile in inputGFFList:
print('\tprocessing %s' % (gffFile))
gff = utils.parseTable(gffFile,'\t')
gffCollection = utils.gffToLocusCollection(gff,50)
inputLoci += gffCollection.getLoci()
inputCollection = utils.LocusCollection(inputLoci,50)
inputCollection = inputCollection.stitchCollection() # stitches to produce unique regions
inputGFF = utils.locusCollectionToGFF(inputCollection)
formattedGFF = []
#now number things appropriately
for i,line in enumerate(inputGFF):
#use the coordinates to make a new id inputname_chr_sense_start_stop
chrom = line[0]
coords = [int(line[3]) ,int(line[4])]
sense = line[6]
lineID = '%s_%s' % (inputName,str(i+1)) #1 indexing
newLine = [chrom,lineID,lineID,min(coords),max(coords),'',sense,'',lineID]
formattedGFF.append(newLine)
#name of the master input gff file
masterGFFFile = '%s%s_%s_ALL_-0_+0.gff' % (gffFolder,string.upper(genome),inputName)
utils.unParseTable(formattedGFF,masterGFFFile,'\t')
print('USING %s AS THE INPUT GFF' % (masterGFFFile))
# MAKING THE START DICT
print('MAKING START DICT')
startDict = utils.makeStartDict(annotFile)
#GET CHROMS FOUND IN THE BAMS
print('GETTING CHROMS IN BAMFILES')
bamChromList = getBamChromList(bamFileList)
print("USING THE FOLLOWING CHROMS")
print(bamChromList)
#LOADING IN THE GFF AND FILTERING BY CHROM
print('LOADING AND FILTERING THE GFF')
inputGFF = filterGFF(masterGFFFile,bamChromList)
# LOADING IN THE BOUND REGION REFERENCE COLLECTION
print('LOADING IN GFF REGIONS')
referenceCollection = utils.gffToLocusCollection(inputGFF)
print('CHECKING REFERENCE COLLECTION:')
checkRefCollection(referenceCollection)
# MASKING REFERENCE COLLECTION
# see if there's a mask
if options.mask:
maskFile = options.mask
# if it's a bed file
if maskFile.split('.')[-1].upper() == 'BED':
maskGFF = utils.bedToGFF(maskFile)
elif maskFile.split('.')[-1].upper() == 'GFF':
maskGFF = utils.parseTable(maskFile, '\t')
else:
print("MASK MUST BE A .gff or .bed FILE")
sys.exit()
maskCollection = utils.gffToLocusCollection(maskGFF)
# now mask the reference loci
referenceLoci = referenceCollection.getLoci()
filteredLoci = [locus for locus in referenceLoci if len(maskCollection.getOverlap(locus, 'both')) == 0]
print("FILTERED OUT %s LOCI THAT WERE MASKED IN %s" % (len(referenceLoci) - len(filteredLoci), maskFile))
referenceCollection = utils.LocusCollection(filteredLoci, 50)
# NOW STITCH REGIONS
print('STITCHING REGIONS TOGETHER')
stitchedCollection, debugOutput, stitchWindow = regionStitching(referenceCollection, inputName, outFolder, stitchWindow, tssWindow, annotFile, removeTSS)
# NOW MAKE A STITCHED COLLECTION GFF
print('MAKING GFF FROM STITCHED COLLECTION')
stitchedGFF = utils.locusCollectionToGFF(stitchedCollection)
print(stitchWindow)
print(type(stitchWindow))
if not removeTSS:
stitchedGFFFile = '%s%s_%sKB_STITCHED.gff' % (gffFolder, inputName, str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED' % (inputName, str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED.debug' % (gffFolder, inputName, str(stitchWindow / 1000))
else:
stitchedGFFFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.gff' % (gffFolder, inputName, str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED_TSS_DISTAL' % (inputName, str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.debug' % (gffFolder, inputName, str(stitchWindow / 1000))
# WRITING DEBUG OUTPUT TO DISK
if debug:
print('WRITING DEBUG OUTPUT TO DISK AS %s' % (debugOutFile))
utils.unParseTable(debugOutput, debugOutFile, '\t')
# WRITE THE GFF TO DISK
print('WRITING STITCHED GFF TO DISK AS %s' % (stitchedGFFFile))
utils.unParseTable(stitchedGFF, stitchedGFFFile, '\t')
# SETTING UP THE OVERALL OUTPUT FILE
outputFile1 = outFolder + stitchedGFFName + '_ENHANCER_REGION_MAP.txt'
print('OUTPUT WILL BE WRITTEN TO %s' % (outputFile1))
# MAPPING TO THE NON STITCHED (ORIGINAL GFF)
# MAPPING TO THE STITCHED GFF
# Try to use the bamliquidatior_path.py script on cluster, otherwise, failover to local (in path), otherwise fail.
bamFileListUnique = list(bamFileList)
bamFileListUnique = utils.uniquify(bamFileListUnique)
#prevent redundant mapping
print("MAPPING TO THE FOLLOWING BAMS:")
print(bamFileListUnique)
for bamFile in bamFileListUnique:
bamFileName = bamFile.split('/')[-1]
# MAPPING TO THE STITCHED GFF
mappedOut1Folder = '%s%s_%s_MAPPED' % (mappedFolder, stitchedGFFName, bamFileName)
mappedOut1File = '%s%s_%s_MAPPED/matrix.txt' % (mappedFolder, stitchedGFFName, bamFileName)
if utils.checkOutput(mappedOut1File, 0.2, 0.2):
print("FOUND %s MAPPING DATA FOR BAM: %s" % (stitchedGFFFile, mappedOut1File))
else:
cmd1 = bamliquidator_path + " --sense . -e 200 --match_bamToGFF -r %s -o %s %s" % (stitchedGFFFile, mappedOut1Folder, bamFile)
print(cmd1)
os.system(cmd1)
if utils.checkOutput(mappedOut1File,0.2,5):
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (stitchedGFFFile, bamFileName))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (stitchedGFFFile, bamFileName))
sys.exit()
print('BAM MAPPING COMPLETED NOW MAPPING DATA TO REGIONS')
# CALCULATE DENSITY BY REGION
# NEED TO FIX THIS FUNCTION TO ACCOUNT FOR DIFFERENT OUTPUTS OF LIQUIDATOR
mapCollection(stitchedCollection, referenceCollection, bamFileList, mappedFolder, outputFile1, refName=stitchedGFFName)
print('FINDING AVERAGE SIGNAL AMONGST BAMS')
metaOutputFile = collapseRegionMap(outputFile1,inputName + '_MERGED_SIGNAL',controlBams=options.control)
#now try the merging
print('CALLING AND PLOTTING SUPER-ENHANCERS')
rankbyName = inputName + '_MERGED_SIGNAL'
controlName = 'NONE'
cmd = 'Rscript %sROSE2_callSuper.R %s %s %s %s' % (pipeline_dir,outFolder, metaOutputFile, inputName, controlName)
print(cmd)
os.system(cmd)
# calling the gene mapper
print('CALLING GENE MAPPING')
superTableFile = "%s_SuperEnhancers.table.txt" % (inputName)
#for now don't use ranking bam to call top genes
cmd = "python %sROSE2_geneMapper.py -g %s -i %s%s -f" % (pipeline_dir,genome, outFolder, superTableFile)
print(cmd)
os.system(cmd)
stretchTableFile = "%s_StretchEnhancers.table.txt" % (inputName)
cmd = "python %sROSE2_geneMapper.py -g %s -i %s%s -f" % (pipeline_dir,genome, outFolder, stretchTableFile)
print(cmd)
os.system(cmd)
superStretchTableFile = "%s_SuperStretchEnhancers.table.txt" % (inputName)
cmd = "python %sROSE2_geneMapper.py -g %s -i %s%s -f" % (pipeline_dir,genome, outFolder, superStretchTableFile)
os.system(cmd)
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 binPeakTable(peak_table_path,activity_path,binSize = 1000000,output = ''):
'''
calculates the promoter/enahncer AUC signal
across bins
sets the output to the same path unless otherwise specified
'''
if len(output) == 0:
output = string.replace(peak_table,'.txt','bin_table.txt')
binSize = int(binSize)
stepSize = binSize/2
activityTable = utils.parseTable(activity_path,'\t')
startDict = utils.makeStartDict(annotFile)
tssLoci = []
print('making tss collection for active genes')
for line in activityTable:
tssLoci.append(utils.makeTSSLocus(line[1],startDict,0,0))
tssCollection = utils.LocusCollection(tssLoci,50)
promoterDict = {}
enhancerDict = {}
tssDict = {}
#hard wired for hg19
chrom_path = '/ark/home/cl512/pipeline/annotation/hg19.chrom.sizes'
chrom_table = utils.parseTable(chrom_path,'\t')
chromDict = {}
for line in chrom_table:
chromDict[line[0]] = int(line[1])
chromList = ['chr'+str(i) for i in range(1,23)] + ['chrX','chrY'] #set the hg19 chroms
#need to seed the dict
for chrom in chromList:
promoterDict[chrom] = defaultdict(float)
enhancerDict[chrom] = defaultdict(float)
tssDict[chrom] =defaultdict(int) # dict to count active promoters
#now as we iterate through the peak table
peak_table = utils.parseTable(peak_table_path,'\t')
print('filling in enhancer dict')
for line in peak_table[1:]:
chrom = line[1]
signal = float(line[9])*int(line[4])
#for approximation use the center coordinate to assign bin
#every region should be in 2 bins
center = (int(line[2]) + int(line[3]))/2
first_bin = center/stepSize
if center % stepSize < stepSize:
second_bin = first_bin - 1
else:
second_bin = first_bin + 1
if int(line[5]) == 1:
promoterDict[chrom][first_bin] +=signal
promoterDict[chrom][second_bin] +=signal
else:
enhancerDict[chrom][first_bin] +=signal
enhancerDict[chrom][second_bin] +=signal
#now load up the new peak table
outTable = [['BIN','CHROM','START','STOP','TSS_COUNT','PROMOTER','ENHANCER']]
print('making out table')
for chrom in chromList:
print(chrom)
chromLength = chromDict[chrom]
for i in range(chromLength/stepSize):
bin_start = i*stepSize + 1
bin_stop = i*stepSize + binSize
bin_locus = utils.Locus(chrom,bin_start,bin_stop,'.')
overlapTSSCount = len(tssCollection.getOverlap(bin_locus,'both'))
bin_id = '%s_%s' % (chrom,str(i+1))
promoterSignal = promoterDict[chrom][i]
enhancerSignal = enhancerDict[chrom][i]
newLine = [bin_id,chrom,bin_start,bin_stop,overlapTSSCount,promoterSignal,enhancerSignal]
outTable.append(newLine)
utils.unParseTable(outTable,output,'\t')
return outTable
#
#####
import sys
sys.path.append('/ark/home/af661/src/utils/')
import utils
from collections import defaultdict
from string import upper
import numpy as np
from math import log
# Annotation file for hg19
annotationFile = '/ark/home/cl512/pipeline/annotation/hg19_refseq.ucsc'
startDict = utils.makeStartDict(annotationFile)
print 'making TSS loci'
tssLoci = []
counter = 0
for gene in startDict:
counter += 1
if counter % 1000 == 0:
print counter
tssLoci.append(utils.makeTSSLocus(gene, startDict, 100000,
100000)) # proximal = within 100kb
tssCollection = utils.LocusCollection(tssLoci, 200)
print 'converting gene names'
refseqToNameDict = {}
annotTable = utils.parseTable(annotationFile, '\t')
def make_shep21_mycn_landscape(nb_all_chip_dataFile):
'''
finds mycn peaks in shep21 that are conserved in nb and segregates them into promoter or enhancer
'''
#first get the shep21 regions
print('LOADING SHEP21 MYCN SITES')
dataDict = pipeline_dfci.loadDataTable(nb_all_chip_dataFile)
shep21_0hr_mycn_enriched_file = '%s%s' % (
macsEnrichedFolder,
dataDict['SHEP21_0HR_MYCN_NOSPIKE']['enrichedMacs'])
shep21_0hr_mycn_bed = utils.parseTable(shep21_0hr_mycn_enriched_file, '\t')
#now get the conserved NB MYCN regions
nb_conserved_mycn_gff_file = '%sHG19_NB_MYCN_CONSERVED_-0_+0.gff' % (
gffFolder)
nb_conserved_mycn_collection = utils.gffToLocusCollection(
nb_conserved_mycn_gff_file)
print('LOADING SHEP21 ACTIVE ENHANCERS')
#make a collection of enhancers
shep21_enhancer_file = '%senhancer_rose/SHEP21_0HR_H3K27AC_NOSPIKE_ROSE/SHEP21_0HR_H3K27AC_NOSPIKE_peaks_AllEnhancers.table.txt' % (
projectFolder)
shep21_enhancer_collection = utils.makeSECollection(
shep21_enhancer_file, 'SHEP21_0HR_H3K27AC_NOSPIKE')
#now get the active promoters
print('LOADING SHEP21 ACTIVE PROMOTERS')
startDict = utils.makeStartDict(annotFile)
shep21_transcribed_file = '%sHG19_SHEP21_H3K27AC_TRANSCRIBED.txt' % (
geneListFolder)
shep21_transcribed_table = utils.parseTable(shep21_transcribed_file, '\t')
transcribedList = [line[1] for line in shep21_transcribed_table]
tssLoci = []
for refID in transcribedList:
tssLoci.append(utils.makeTSSLocus(refID, startDict, 1000, 1000))
shep21_tss_collection = utils.LocusCollection(tssLoci, 50)
#now initialize the 6 gffs we will need
shep21_mycn_conserved_gff = []
shep21_mycn_conserved_gff_5kb = []
shep21_mycn_conserved_gff_1kb = []
shep21_mycn_conserved_promoter_gff = []
shep21_mycn_conserved_promoter_gff_1kb = []
shep21_mycn_conserved_promoter_gff_5kb = []
shep21_mycn_conserved_enhancer_gff = []
shep21_mycn_conserved_enhancer_gff_1kb = []
shep21_mycn_conserved_enhancer_gff_5kb = []
#and their respective file names
shep21_mycn_conserved_gff_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_-0_+0.gff' % (
gffFolder)
shep21_mycn_conserved_gff_5kb_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_-5kb_+5kb.gff' % (
gffFolder)
shep21_mycn_conserved_gff_1kb_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_-1kb_+1kb.gff' % (
gffFolder)
shep21_mycn_conserved_promoter_gff_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_PROMOTER_-0_+0.gff' % (
gffFolder)
shep21_mycn_conserved_promoter_gff_5kb_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_PROMOTER_-5kb_+5kb.gff' % (
gffFolder)
shep21_mycn_conserved_promoter_gff_1kb_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_PROMOTER_-1kb_+1kb.gff' % (
gffFolder)
shep21_mycn_conserved_enhancer_gff_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_ENHANCER_-0_+0.gff' % (
gffFolder)
shep21_mycn_conserved_enhancer_gff_5kb_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_ENHANCER_-5kb_+5kb.gff' % (
gffFolder)
shep21_mycn_conserved_enhancer_gff_1kb_file = '%sHG19_SHEP21_0HR_MYCN_NOSPIKE_CONSERVED_ENHANCER_-1kb_+1kb.gff' % (
gffFolder)
print('ITERATING THROUGH SHEP21 MYCN PEAKS')
ticker = 0
for line in shep21_0hr_mycn_bed:
if ticker % 1000 == 0:
print ticker
ticker += 1
peakID = '%s_%s' % ('SHEP21_0HR_MYCN_NOSPIKE', str(ticker))
lineLocus = utils.Locus(line[0], line[1], line[2], '.', peakID)
if nb_conserved_mycn_collection.getOverlap(lineLocus):
gffLine = [
line[0], peakID, peakID, line[1], line[2], '', '.', '', peakID
]
peakCenter = (int(line[1]) + int(line[2])) / 2
gffLine_5kb = [
line[0], peakID, peakID, peakCenter - 5000, peakCenter + 5000,
'', '.', '', peakID
]
#the 1kb is not a center +/- but a flank
gffLine_1kb = [
line[0], peakID, peakID,
int(line[1]) - 1000,
int(line[2]) + 1000, '', '.', '', peakID
]
shep21_mycn_conserved_gff.append(gffLine)
shep21_mycn_conserved_gff_5kb.append(gffLine_5kb)
shep21_mycn_conserved_gff_1kb.append(gffLine_1kb)
#tss overlap should take precedence over enhancer overlap
if shep21_tss_collection.getOverlap(lineLocus, 'both'):
shep21_mycn_conserved_promoter_gff.append(gffLine)
shep21_mycn_conserved_promoter_gff_5kb.append(gffLine_5kb)
shep21_mycn_conserved_promoter_gff_1kb.append(gffLine_1kb)
#now check for enhancer overlap
elif shep21_enhancer_collection.getOverlap(lineLocus, 'both'):
shep21_mycn_conserved_enhancer_gff.append(gffLine)
shep21_mycn_conserved_enhancer_gff_5kb.append(gffLine_5kb)
shep21_mycn_conserved_enhancer_gff_1kb.append(gffLine_1kb)
#now write out the gffs
utils.unParseTable(shep21_mycn_conserved_gff,
shep21_mycn_conserved_gff_file, '\t')
utils.unParseTable(shep21_mycn_conserved_gff_5kb,
shep21_mycn_conserved_gff_5kb_file, '\t')
utils.unParseTable(shep21_mycn_conserved_gff_1kb,
shep21_mycn_conserved_gff_1kb_file, '\t')
utils.unParseTable(shep21_mycn_conserved_promoter_gff,
shep21_mycn_conserved_promoter_gff_file, '\t')
utils.unParseTable(shep21_mycn_conserved_promoter_gff_5kb,
shep21_mycn_conserved_promoter_gff_5kb_file, '\t')
utils.unParseTable(shep21_mycn_conserved_promoter_gff_1kb,
shep21_mycn_conserved_promoter_gff_1kb_file, '\t')
utils.unParseTable(shep21_mycn_conserved_enhancer_gff,
shep21_mycn_conserved_enhancer_gff_file, '\t')
utils.unParseTable(shep21_mycn_conserved_enhancer_gff_5kb,
shep21_mycn_conserved_enhancer_gff_5kb_file, '\t')
utils.unParseTable(shep21_mycn_conserved_enhancer_gff_1kb,
shep21_mycn_conserved_enhancer_gff_1kb_file, '\t')
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 findCanidateTFs(annotationFile, superLoci, expressedNM, TFlist, refseqToNameDict, projectFolder, projectName):
'''
find all TFs within 1Mb of the super-enhancer center that are considered expressed
return a dictionary keyed by TF that points to a list of super-enhancer loci
'''
print 'FINDING CANIDATE TFs'
startDict = utils.makeStartDict(annotationFile)
# Find the location of the TSS of all transcripts (NMid) considered expressed
tssLoci = []
for geneID in expressedNM:
tssLoci.append(utils.makeTSSLocus(geneID,startDict,0,0))
tssCollection = utils.LocusCollection(tssLoci,50)
# Assign all transcripts (NMid) that are TFs to a super-enhancer if it is the closest gene
seAssignment = []
seAssignmentGene = []
TFandSuperDict = {}
for superEnh in superLoci:
seCenter = (superEnh.start() + superEnh.end()) / 2
# Find all transcripts whose TSS occur within 1Mb of the SE center
searchLocus = utils.Locus(superEnh.chr(), superEnh.start()-1000000, superEnh.end()+1000000, '.')
allEnhancerLoci = tssCollection.getOverlap(searchLocus)
allEnhancerGenes = [locus.ID() for locus in allEnhancerLoci]
# Find the transcript that is closest to the center
if allEnhancerGenes:
distList = [abs(seCenter - startDict[geneID]['start'][0]) for geneID in allEnhancerGenes]
closestGene = allEnhancerGenes[distList.index(min(distList))]
else:
closestGene = ''
seAssignment.append([superEnh.chr(), superEnh.start(), superEnh.end(), closestGene])
# Select the transcript if it is a TF, and allow for a TF to have multiple SEs
if closestGene in TFlist and closestGene not in TFandSuperDict.keys():
TFandSuperDict[closestGene] = [superEnh]
elif closestGene in TFlist and closestGene in TFandSuperDict.keys():
TFandSuperDict[closestGene].append(superEnh)
# Convert the selected TF NMids to gene names
if closestGene != '':
geneName = refseqToNameDict[closestGene]
seAssignmentGene.append([superEnh.chr(), superEnh.start(), superEnh.end(), geneName])
# Output the list of SE-assigned transcripts (NMids)
seAssignmentFile = projectFolder + projectName + '_SE_ASSIGNMENT_TRANSCRIPT.txt'
utils.unParseTable(seAssignment, seAssignmentFile, '\t')
# Output the list of SE-assigned genes
seAssignmentGeneFile = projectFolder + projectName + '_SE_ASSIGNMENT_GENE.txt'
utils.unParseTable(seAssignmentGene, seAssignmentGeneFile, '\t')
print 'Number of canidate TFs:', len(TFandSuperDict)
return TFandSuperDict
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 regionStitching(referenceCollection, name, outFolder, stitchWindow, tssWindow, annotFile, removeTSS=True):
print('PERFORMING REGION STITCHING')
# first have to turn bound region file into a locus collection
# need to make sure this names correctly... each region should have a unique name
#referenceCollection
debugOutput = []
# filter out all bound regions that overlap the TSS of an ACTIVE GENE
if removeTSS:
print('REMOVING TSS FROM REGIONS USING AN EXCLUSION WINDOW OF %sBP' % (tssWindow))
# first make a locus collection of TSS
startDict = utils.makeStartDict(annotFile)
# now makeTSS loci for active genes
removeTicker = 0
# this loop makes a locus centered around +/- tssWindow of transcribed genes
# then adds it to the list tssLoci
tssLoci = []
for geneID in startDict.keys():
tssLoci.append(utils.makeTSSLocus(geneID, startDict, tssWindow, tssWindow))
# 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)
# gives all the loci in referenceCollection
boundLoci = referenceCollection.getLoci()
# this loop will check if each bound region is contained by the TSS exclusion zone
# this will drop out a lot of the promoter only regions that are tiny
# typical exclusion window is around 2kb
for locus in boundLoci:
if len(tssCollection.getContainers(locus, 'both')) > 0:
# if true, the bound locus overlaps an active gene
referenceCollection.remove(locus)
debugOutput.append([locus.__str__(), locus.ID(), 'CONTAINED'])
removeTicker += 1
print('REMOVED %s LOCI BECAUSE THEY WERE CONTAINED BY A TSS' % (removeTicker))
# referenceCollection is now all enriched region loci that don't overlap an active TSS
if stitchWindow == '':
print('DETERMINING OPTIMUM STITCHING PARAMTER')
optCollection = copy.deepcopy(referenceCollection)
stitchWindow = optimizeStitching(optCollection, name, outFolder, stepSize=500)
print('USING A STITCHING PARAMETER OF %s' % stitchWindow)
stitchedCollection = referenceCollection.stitchCollection(stitchWindow, 'both')
if removeTSS:
# now replace any stitched region that overlap 2 distinct genes
# with the original loci that were there
fixedLoci = []
tssLoci = []
for geneID in startDict.keys():
tssLoci.append(utils.makeTSSLocus(geneID, startDict, 50, 50))
# 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)
removeTicker = 0
originalTicker = 0
for stitchedLocus in stitchedCollection.getLoci():
overlappingTSSLoci = tssCollection.getOverlap(stitchedLocus, 'both')
tssNames = [startDict[tssLocus.ID()]['name'] for tssLocus in overlappingTSSLoci]
tssNames = utils.uniquify(tssNames)
if len(tssNames) > 2:
# stitchedCollection.remove(stitchedLocus)
originalLoci = referenceCollection.getOverlap(stitchedLocus, 'both')
originalTicker += len(originalLoci)
fixedLoci += originalLoci
debugOutput.append([stitchedLocus.__str__(), stitchedLocus.ID(), 'MULTIPLE_TSS'])
removeTicker += 1
else:
fixedLoci.append(stitchedLocus)
print('REMOVED %s STITCHED LOCI BECAUSE THEY OVERLAPPED MULTIPLE TSSs' % (removeTicker))
print('ADDED BACK %s ORIGINAL LOCI' % (originalTicker))
fixedCollection = utils.LocusCollection(fixedLoci, 50)
return fixedCollection, debugOutput, stitchWindow
else:
return stitchedCollection, debugOutput, stitchWindow
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
def loadAnnotFile(genome,window,geneList=[],skip_cache=False):
"""
load in the annotation and create a startDict and tss collection for a set of refseq IDs a given genome
20170213, add by Quanhu Sheng
return validGenes
"""
genomeDict = {
'HG18': 'annotation/hg18_refseq.ucsc',
'MM9': 'annotation/mm9_refseq.ucsc',
'MM10': 'annotation/mm10_refseq.ucsc',
'HG19': 'annotation/hg19_refseq.ucsc',
'HG19_RIBO': 'annotation/hg19_refseq.ucsc',
'RN4': 'annotation/rn4_refseq.ucsc',
'RN6': 'annotation/rn6_refseq.ucsc',
}
annotFile = whereAmI + '/' + genomeDict[string.upper(genome)]
if not skip_cache:
# Try loading from a cache, if the crc32 matches
annotPathHash = zlib.crc32(annotFile) & 0xFFFFFFFF # hash the entire location of this script
annotFileHash = zlib.crc32(open(annotFile, "rb").read()) & 0xFFFFFFFF
cache_file_name = "%s.%s.%s.cache" % (genome, annotPathHash, annotFileHash)
cache_file_path = '%s/%s' % (tempfile.gettempdir(), cache_file_name)
if os.path.isfile(cache_file_path):
# Cache exists! Load it!
try:
print('\tLoading genome data from cache.')
with open(cache_file_path, 'rb') as cache_fh:
cached_data = cPickle.load(cache_fh)
print('\tCache loaded.')
return cached_data
except (IOError, cPickle.UnpicklingError):
# Pickle corrupt? Let's get rid of it.
print('\tWARNING: Cache corrupt or unreadable. Ignoring.')
else:
print('\tNo cache exists: Loading annotation (slow).')
# We're still here, so either caching was disabled, or the cache doesn't exist
startDict = utils.makeStartDict(annotFile, geneList)
tssLoci =[]
validGenes = []
for gene in geneList:
if gene in startDict:
tssLoci.append(utils.makeTSSLocus(gene,startDict,window,window))
validGenes.append(gene)
else:
print('\tWARNING: gene %s not in annotation database. Ignoring.' % gene)
tssCollection = utils.LocusCollection(tssLoci,50)
if not skip_cache:
print('Writing cache for the first time.')
with open(cache_file_path, 'wb') as cache_fh:
cPickle.dump((startDict, tssCollection), cache_fh, cPickle.HIGHEST_PROTOCOL)
return startDict, tssCollection, validGenes
def main():
'''
main run call
'''
debug = False
from optparse import OptionParser
usage = "usage: %prog [options] -g [GENOME] -i [INPUT_REGION_GFF] -r [RANKBY_BAM_FILE] -o [OUTPUT_FOLDER] [OPTIONAL_FLAGS]"
parser = OptionParser(usage=usage)
# required flags
parser.add_option("-i", "--i", dest="input", nargs=1, default=None,
help="Enter a .gff or .bed file of binding sites used to make enhancers")
parser.add_option("-r", "--rankby", dest="rankby", nargs=1, default=None,
help="bamfile to rank enhancer by")
parser.add_option("-o", "--out", dest="out", nargs=1, default=None,
help="Enter an output folder")
parser.add_option("-g", "--genome", dest="genome", nargs=1, default=None,
help="Enter the genome build (MM9,MM8,HG18,HG19)")
# optional flags
parser.add_option("-b", "--bams", dest="bams", nargs=1, default=None,
help="Enter a comma separated list of additional bam files to map to")
parser.add_option("-c", "--control", dest="control", nargs=1, default=None,
help="bamfile to rank enhancer by")
parser.add_option("-s", "--stitch", dest="stitch", nargs=1, default='',
help="Enter a max linking distance for stitching. Default will determine optimal stitching parameter")
parser.add_option("-t", "--tss", dest="tss", nargs=1, default=0,
help="Enter a distance from TSS to exclude. 0 = no TSS exclusion")
parser.add_option("--mask", dest="mask", nargs=1, default=None,
help="Mask a set of regions from analysis. Provide a .bed or .gff of masking regions")
# RETRIEVING FLAGS
(options, args) = parser.parse_args()
if not options.input or not options.rankby or not options.out or not options.genome:
print('hi there')
parser.print_help()
exit()
# making the out folder if it doesn't exist
outFolder = utils.formatFolder(options.out, True)
# figuring out folder schema
gffFolder = utils.formatFolder(outFolder + 'gff/', True)
mappedFolder = utils.formatFolder(outFolder + 'mappedGFF/', True)
# GETTING INPUT FILE
if options.input.split('.')[-1] == 'bed':
# CONVERTING A BED TO GFF
inputGFFName = options.input.split('/')[-1][0:-4]
inputGFFFile = '%s%s.gff' % (gffFolder, inputGFFName)
utils.bedToGFF(options.input, inputGFFFile)
elif options.input.split('.')[-1] == 'gff':
# COPY THE INPUT GFF TO THE GFF FOLDER
inputGFFFile = options.input
os.system('cp %s %s' % (inputGFFFile, gffFolder))
else:
print('WARNING: INPUT FILE DOES NOT END IN .gff or .bed. ASSUMING .gff FILE FORMAT')
# COPY THE INPUT GFF TO THE GFF FOLDER
inputGFFFile = options.input
os.system('cp %s %s' % (inputGFFFile, gffFolder))
# GETTING THE LIST OF BAMFILES TO PROCESS
if options.control:
bamFileList = [options.rankby, options.control]
else:
bamFileList = [options.rankby]
if options.bams:
bamFileList += options.bams.split(',')
bamFileList = utils.uniquify(bamFileList)
# optional args
# Stitch parameter
if options.stitch == '':
stitchWindow = ''
else:
stitchWindow = int(options.stitch)
# tss options
tssWindow = int(options.tss)
if tssWindow != 0:
removeTSS = True
else:
removeTSS = False
# GETTING THE BOUND REGION FILE USED TO DEFINE ENHANCERS
print('USING %s AS THE INPUT GFF' % (inputGFFFile))
inputName = inputGFFFile.split('/')[-1].split('.')[0]
# GETTING THE GENOME
genome = options.genome
print('USING %s AS THE GENOME' % genome)
# GETTING THE CORRECT ANNOT FILE
cwd = os.getcwd()
genomeDict = {
'HG18': '%s/annotation/hg18_refseq.ucsc' % (cwd),
'MM9': '%s/annotation/mm9_refseq.ucsc' % (cwd),
'HG19': '%s/annotation/hg19_refseq.ucsc' % (cwd),
'MM8': '%s/annotation/mm8_refseq.ucsc' % (cwd),
'MM10': '%s/annotation/mm10_refseq.ucsc' % (cwd),
}
annotFile = genomeDict[genome.upper()]
# MAKING THE START DICT
print('MAKING START DICT')
startDict = utils.makeStartDict(annotFile)
# LOADING IN THE BOUND REGION REFERENCE COLLECTION
print('LOADING IN GFF REGIONS')
referenceCollection = utils.gffToLocusCollection(inputGFFFile)
# MASKING REFERENCE COLLECTION
# see if there's a mask
if options.mask:
maskFile = options.mask
# if it's a bed file
if maskFile.split('.')[-1].upper() == 'BED':
maskGFF = utils.bedToGFF(maskFile)
elif maskFile.split('.')[-1].upper() == 'GFF':
maskGFF = utils.parseTable(maskFile, '\t')
else:
print("MASK MUST BE A .gff or .bed FILE")
sys.exit()
maskCollection = utils.gffToLocusCollection(maskGFF)
# now mask the reference loci
referenceLoci = referenceCollection.getLoci()
filteredLoci = [locus for locus in referenceLoci if len(maskCollection.getOverlap(locus, 'both')) == 0]
print("FILTERED OUT %s LOCI THAT WERE MASKED IN %s" % (len(referenceLoci) - len(filteredLoci), maskFile))
referenceCollection = utils.LocusCollection(filteredLoci, 50)
# NOW STITCH REGIONS
print('STITCHING REGIONS TOGETHER')
stitchedCollection, debugOutput, stitchWindow = regionStitching(inputGFFFile, inputName, outFolder, stitchWindow, tssWindow, annotFile, removeTSS)
# NOW MAKE A STITCHED COLLECTION GFF
print('MAKING GFF FROM STITCHED COLLECTION')
stitchedGFF = utils.locusCollectionToGFF(stitchedCollection)
# making sure start/stop ordering are correct
for i in range(len(stitchedGFF)):
line = stitchedGFF[i]
start = int(line[3])
stop = int(line[4])
if start > stop:
line[3] = stop
line[4] = start
print(stitchWindow)
print(type(stitchWindow))
if not removeTSS:
stitchedGFFFile = '%s%s_%sKB_STITCHED.gff' % (gffFolder, inputName, str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED' % (inputName, str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED.debug' % (gffFolder, inputName, str(stitchWindow / 1000))
else:
stitchedGFFFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.gff' % (gffFolder, inputName, str(stitchWindow / 1000))
stitchedGFFName = '%s_%sKB_STITCHED_TSS_DISTAL' % (inputName, str(stitchWindow / 1000))
debugOutFile = '%s%s_%sKB_STITCHED_TSS_DISTAL.debug' % (gffFolder, inputName, str(stitchWindow / 1000))
# WRITING DEBUG OUTPUT TO DISK
if debug:
print('WRITING DEBUG OUTPUT TO DISK AS %s' % (debugOutFile))
utils.unParseTable(debugOutput, debugOutFile, '\t')
# WRITE THE GFF TO DISK
print('WRITING STITCHED GFF TO DISK AS %s' % (stitchedGFFFile))
utils.unParseTable(stitchedGFF, stitchedGFFFile, '\t')
# SETTING UP THE OVERALL OUTPUT FILE
outputFile1 = outFolder + stitchedGFFName + '_ENHANCER_REGION_MAP.txt'
print('OUTPUT WILL BE WRITTEN TO %s' % (outputFile1))
# MAPPING TO THE NON STITCHED (ORIGINAL GFF)
# MAPPING TO THE STITCHED GFF
# 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')
for bamFile in bamFileList:
bamFileName = bamFile.split('/')[-1]
# MAPPING TO THE STITCHED GFF
mappedOut1Folder = '%s%s_%s_MAPPED' % (mappedFolder, stitchedGFFName, bamFileName)
mappedOut1File = '%s%s_%s_MAPPED/matrix.gff' % (mappedFolder, stitchedGFFName, bamFileName)
if utils.checkOutput(mappedOut1File, 0.2, 0.2):
print("FOUND %s MAPPING DATA FOR BAM: %s" % (stitchedGFFFile, mappedOut1File))
else:
cmd1 = "python " + bamliquidator_path + " --sense . -e 200 --match_bamToGFF -r %s -o %s %s" % (stitchedGFFFile, mappedOut1Folder, bamFile)
print(cmd1)
output1 = subprocess.Popen(cmd1, stdout=subprocess.PIPE, shell=True)
output1 = output1.communicate()
if len(output1[0]) > 0: # test if mapping worked correctly
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (stitchedGFFFile, bamFileName))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (stitchedGFFFile, bamFileName))
sys.exit()
# MAPPING TO THE ORIGINAL GFF
mappedOut2Folder = '%s%s_%s_MAPPED' % (mappedFolder, inputName, bamFileName)
mappedOut2File = '%s%s_%s_MAPPED/matrix.gff' % (mappedFolder, inputName, bamFileName)
if utils.checkOutput(mappedOut2File, 0.2, 0.2):
print("FOUND %s MAPPING DATA FOR BAM: %s" % (stitchedGFFFile, mappedOut2File))
else:
cmd2 = "python " + bamliquidator_path + " --sense . -e 200 --match_bamToGFF -r %s -o %s %s" % (inputGFFFile, mappedOut2Folder, bamFile)
print(cmd2)
output2 = subprocess.Popen(cmd2, stdout=subprocess.PIPE, shell=True)
output2 = output2.communicate()
if len(output2[0]) > 0: # test if mapping worked correctly
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (inputGFFFile, bamFileName))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (inputGFFFile, bamFileName))
sys.exit()
print('BAM MAPPING COMPLETED NOW MAPPING DATA TO REGIONS')
# CALCULATE DENSITY BY REGION
# NEED TO FIX THIS FUNCTION TO ACCOUNT FOR DIFFERENT OUTPUTS OF LIQUIDATOR
mapCollection(stitchedCollection, referenceCollection, bamFileList, mappedFolder, outputFile1, refName=stitchedGFFName)
print('CALLING AND PLOTTING SUPER-ENHANCERS')
if options.control:
rankbyName = options.rankby.split('/')[-1]
controlName = options.control.split('/')[-1]
cmd = 'R --no-save %s %s %s %s < ROSE2_callSuper.R' % (outFolder, outputFile1, inputName, controlName)
else:
rankbyName = options.rankby.split('/')[-1]
controlName = 'NONE'
cmd = 'R --no-save %s %s %s %s < ROSE2_callSuper.R' % (outFolder, outputFile1, inputName, controlName)
print(cmd)
os.system(cmd)
# calling the gene mapper
time.sleep(20)
superTableFile = "%s_SuperEnhancers.table.txt" % (inputName)
if options.control:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -c %s -i %s%s" % (genome, options.rankby, options.control, outFolder, superTableFile)
else:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -i %s%s" % (genome, options.rankby, outFolder, superTableFile)
os.system(cmd)
stretchTableFile = "%s_StretchEnhancers.table.txt" % (inputName)
if options.control:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -c %s -i %s%s" % (genome, options.rankby, options.control, outFolder, stretchTableFile)
else:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -i %s%s" % (genome, options.rankby, outFolder, stretchTableFile)
os.system(cmd)
superStretchTableFile = "%s_SuperStretchEnhancers.table.txt" % (inputName)
if options.control:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -c %s -i %s%s" % (genome, options.rankby, options.control, outFolder, superStretchTableFile)
else:
cmd = "python ROSE2_geneMapper.py -g %s -r %s -i %s%s" % (genome, options.rankby, outFolder, superStretchTableFile)
os.system(cmd)
def regionStitching(referenceCollection,
name,
outFolder,
stitchWindow,
tssWindow,
annotFile,
removeTSS=True):
print('PERFORMING REGION STITCHING')
# first have to turn bound region file into a locus collection
# need to make sure this names correctly... each region should have a unique name
#referenceCollection
debugOutput = []
# filter out all bound regions that overlap the TSS of an ACTIVE GENE
if removeTSS:
print('REMOVING TSS FROM REGIONS USING AN EXCLUSION WINDOW OF %sBP' %
(tssWindow))
# first make a locus collection of TSS
startDict = utils.makeStartDict(annotFile)
# now makeTSS loci for active genes
removeTicker = 0
# this loop makes a locus centered around +/- tssWindow of transcribed genes
# then adds it to the list tssLoci
tssLoci = []
for geneID in startDict.keys():
tssLoci.append(
utils.makeTSSLocus(geneID, startDict, tssWindow, tssWindow))
# 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)
# gives all the loci in referenceCollection
boundLoci = referenceCollection.getLoci()
# this loop will check if each bound region is contained by the TSS exclusion zone
# this will drop out a lot of the promoter only regions that are tiny
# typical exclusion window is around 2kb
for locus in boundLoci:
if len(tssCollection.getContainers(locus, 'both')) > 0:
# if true, the bound locus overlaps an active gene
referenceCollection.remove(locus)
debugOutput.append([locus.__str__(), locus.ID(), 'CONTAINED'])
removeTicker += 1
print('REMOVED %s LOCI BECAUSE THEY WERE CONTAINED BY A TSS' %
(removeTicker))
# referenceCollection is now all enriched region loci that don't overlap an active TSS
if stitchWindow == '':
print('DETERMINING OPTIMUM STITCHING PARAMTER')
optCollection = copy.deepcopy(referenceCollection)
stitchWindow = optimizeStitching(optCollection,
name,
outFolder,
stepSize=500)
print('USING A STITCHING PARAMETER OF %s' % stitchWindow)
stitchedCollection = referenceCollection.stitchCollection(
stitchWindow, 'both')
if removeTSS:
# now replace any stitched region that overlap 2 distinct genes
# with the original loci that were there
fixedLoci = []
tssLoci = []
for geneID in startDict.keys():
tssLoci.append(utils.makeTSSLocus(geneID, startDict, 50, 50))
# 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)
removeTicker = 0
originalTicker = 0
for stitchedLocus in stitchedCollection.getLoci():
overlappingTSSLoci = tssCollection.getOverlap(
stitchedLocus, 'both')
tssNames = [
startDict[tssLocus.ID()]['name']
for tssLocus in overlappingTSSLoci
]
tssNames = utils.uniquify(tssNames)
if len(tssNames) > 2:
# stitchedCollection.remove(stitchedLocus)
originalLoci = referenceCollection.getOverlap(
stitchedLocus, 'both')
originalTicker += len(originalLoci)
fixedLoci += originalLoci
debugOutput.append([
stitchedLocus.__str__(),
stitchedLocus.ID(), 'MULTIPLE_TSS'
])
removeTicker += 1
else:
fixedLoci.append(stitchedLocus)
print(
'REMOVED %s STITCHED LOCI BECAUSE THEY OVERLAPPED MULTIPLE TSSs' %
(removeTicker))
print('ADDED BACK %s ORIGINAL LOCI' % (originalTicker))
fixedCollection = utils.LocusCollection(fixedLoci, 50)
return fixedCollection, debugOutput, stitchWindow
else:
return stitchedCollection, debugOutput, stitchWindow
#================================================================================
#===================================CLASSES======================================
#================================================================================
#user defined classes here
#================================================================================
#=================================FUNCTIONS======================================
#================================================================================
#write your specific functions here
annotFile = '/storage/goodell/home/jmreyes/pipeline/annotation/%s_refseq.ucsc' % (
genome)
startDict = utils.makeStartDict(annotFile)
startLoci = []
#for TR, -30, +300 and genebody +0
for gene in startDict.keys():
geneChrom = startDict[gene]['chr']
geneStart = startDict[gene]['start']
geneEnd = startDict[gene]['end']
geneSense = startDict[gene]['sense']
# newLocus = [geneChrom, gene, '', geneStart]
newLocus = utils.makeTSSLocus(gene, startDict, 0, 0)
startLoci.append([
newLocus.chr(),
def loadAnnotFile(genome, window, geneList=[], skip_cache=False):
"""
load in the annotation and create a startDict and tss collection for a set of refseq IDs a given genome
"""
genomeDict = {
'HG18': 'annotation/hg18_refseq.ucsc',
'MM9': 'annotation/mm9_refseq.ucsc',
'MM10': 'annotation/mm10_refseq.ucsc',
'HG19': 'annotation/hg19_refseq.ucsc',
'HG19_RIBO': 'annotation/hg19_refseq.ucsc',
'RN4': 'annotation/rn4_refseq.ucsc',
'RN6': 'annotation/rn6_refseq.ucsc',
'HG38': 'annotation/hg38_refseq.ucsc',
}
genomeDirectoryDict = {
'HG19':
'/storage/cylin/grail/genomes/Homo_sapiens/UCSC/hg19/Sequence/Chromosomes/',
'RN6':
'/storage/cylin/grail/genomes/Rattus_norvegicus/UCSC/rn6/Sequence/Chromosomes/',
'MM9':
'/storage/cylin/grail/genomes/Mus_musculus/UCSC/mm9/Sequence/Chromosomes/',
'MM10':
'/storage/cylin/grail/genomes/Mus_musculus/UCSC/mm10/Sequence/Chromosomes/',
'HG38':
'/storage/cylin/grail/genomes/Homo_sapiens/UCSC/hg38/Sequence/Chromosomes/',
}
mouse_convert_file = '%s/annotation/HMD_HumanPhenotype.rpt' % (whereAmI)
#making a dictionary for mouse to human conversion
mouse_convert_dict = defaultdict(str)
mouse_convert_table = utils.parseTable(mouse_convert_file, '\t')
for line in mouse_convert_table:
mouse_convert_dict[line[4]] = line[0]
genomeDirectory = genomeDirectoryDict[string.upper(genome)]
#making a chrom_dict that is a list of all chroms with sequence
chrom_list = utils.uniquify([
name.split('.')[0] for name in os.listdir(genomeDirectory)
if len(name) > 0
])
annotFile = whereAmI + '/' + genomeDict[string.upper(genome)]
if not skip_cache:
# Try loading from a cache, if the crc32 matches
annotPathHash = zlib.crc32(
annotFile) & 0xFFFFFFFF # hash the entire location of this script
annotFileHash = zlib.crc32(open(annotFile, "rb").read()) & 0xFFFFFFFF
cache_file_name = "%s.%s.%s.cache" % (genome, annotPathHash,
annotFileHash)
cache_file_path = '%s/%s' % (tempfile.gettempdir(), cache_file_name)
if os.path.isfile(cache_file_path):
# Cache exists! Load it!
try:
print('\tLoading genome data from cache.')
with open(cache_file_path, 'rb') as cache_fh:
cached_data = cPickle.load(cache_fh)
print('\tCache loaded.')
return cached_data
except (IOError, cPickle.UnpicklingError):
# Pickle corrupt? Let's get rid of it.
print('\tWARNING: Cache corrupt or unreadable. Ignoring.')
else:
print('\tNo cache exists: Loading annotation (slow).')
# We're still here, so either caching was disabled, or the cache doesn't exist
startDict = utils.makeStartDict(annotFile, geneList)
tssLoci = []
if geneList == []:
geneList = startDict.keys()
for gene in geneList:
tssLoci.append(utils.makeTSSLocus(gene, startDict, window, window))
tssCollection = utils.LocusCollection(tssLoci, 50)
if not skip_cache:
print('Writing cache for the first time.')
with open(cache_file_path, 'wb') as cache_fh:
cPickle.dump((startDict, tssCollection), cache_fh,
cPickle.HIGHEST_PROTOCOL)
return startDict, tssCollection, genomeDirectory, chrom_list, mouse_convert_dict
def main():
#get WGBS files
projectFolder = '/storage/goodell/projects/jmreyes/amish_ayala/'
wgbsList = [
'dc5_mutant_BS.txt', 'dc3_mutant_BS.txt', 'dc15_WT_BS.txt',
'dc16_WT_BS.txt'
]
#import out genes of interest
sigGenesFile = utils.parseTable(
projectFolder + 'tables/Amish_significant.txt', '\r')
sigTable = [x.split('\t') for x in sigGenesFile[0]]
sigGenes = [x[0] for x in sigTable]
#make start dict containing all TSS start sites
startDict = utils.makeStartDict(annotFile)
#converter form refseq to gene name
revDict = {}
for name in startDict.keys():
revDict[startDict[name]['name']] = name
#get out subset of genes
sigLoci = []
window = 500
for gene in sigGenes:
if gene in revDict.keys():
refSeq = revDict[gene]
geneChr = startDict[refSeq]['chr']
geneStart = startDict[refSeq]['start']
geneEnd = startDict[refSeq]['end']
geneSense = startDict[refSeq]['sense']
newLocus = [
geneChr, geneStart[0] - window, geneStart[0] + window,
geneSense, gene + ':' + refSeq
]
sigLoci.append(newLocus)
else:
refSeq = 'NA'
# print len(sigLoci)
# print sigLoci[1:5]
# utils.unParseTable(sigLoci, projectFolder+'bed/Amish_sigTSS_-500_+500.bed', '\t')
sortedBed = projectFolder + 'bed/Amish_sigTSS_-500_+500.sorted.bed'
binNumber = 200
ts = time.time()
timestamp = datetime.datetime.fromtimestamp(ts).strftime(
'%Y%m%d_%Hh%Mm%Ss')
dateFolder = projectFolder + 'scripts/' + datetime.datetime.fromtimestamp(
ts).strftime('%Y%m%d') + '/'
utils.formatFolder(dateFolder, True)
bedList = [sortedBed]
for wgbsCalls in wgbsList:
methylPlotBash = [['#!/usr/bin/bash']]
catBash = []
wgbsName = wgbsCalls.split('.')[0]
outDir = projectFolder + 'temp/' + wgbsName + '/'
outBed = projectFolder + 'temp/' + wgbsName + '/bed/'
utils.formatFolder(outDir, True)
utils.formatFolder(outBed, True)
ticker = 0
for bed in bedList:
bedName = bed.split('.bed')[0].split('/')[-1]
splitCmd = 'split -l 1000 %s %s' % (bed, outBed + bedName)
os.system(splitCmd)
bedSplitList = [x for x in os.listdir(outBed) if bedName in x]
catBedList = []
for bed in bedSplitList:
ticker += 1
outName = wgbsName + '_' + bed
if ticker % 10 == 0:
sepMark = '&'
else:
sepMark = '&'
methylCall = 'python /storage/goodell/home/jmreyes/xwing/methylPlot.py -i %s -b %s -o %s -n %s %s' % (
projectFolder + 'wgbs/' + wgbsCalls, outBed + bed,
outDir + outName, binNumber, sepMark)
methylPlotBash.append([methylCall])
catBedList.append(outDir + outName)
catBedListSort = sorted(catBedList)
catOut = projectFolder + 'mapped/' + wgbsName + '_' + bedName + '_' + timestamp + '_avgMethyl.txt'
catCmd = '#cat %s > %s' % (' '.join(catBedListSort), catOut)
catBash.append([catCmd])
rmCmd = ['#rm -rf %s' % (outDir)]
outputBash = methylPlotBash + catBash
utils.unParseTable(
outputBash,
dateFolder + wgbsName + '_TSS_mapping_' + timestamp + '.sh', '\t')
