def splitRegions(inputGFF, tssCollection):
#if even a single coordinate is shared with the +/-1kb
splitGFF = []
debugCount = 0
for line in inputGFF:
chrom = line[0]
regionID = line[1]
lineLocus = utils.Locus(line[0], line[3], line[4], '.')
overlappingLoci = tssCollection.getOverlap(lineLocus)
if len(overlappingLoci) > 0: #case where a tss Overlap
#identify the parts of the line locus that are contained
localTSSCollection = utils.LocusCollection(overlappingLoci, 50)
overlappingCoords = lineLocus.coords()
for tssLocus in overlappingLoci:
overlappingCoords += tssLocus.coords()
overlappingCoords = utils.uniquify(overlappingCoords)
overlappingCoords.sort()
#you need to hack and slash add 1 to the last coordinate of the overlappingCoords
overlappingCoords[-1] += 1
i = 0
regionTicker = 1
while i < (len(overlappingCoords) - 1):
start = int(overlappingCoords[i])
stop = int(overlappingCoords[(i + 1)]) - 1
if (stop - start) < 50: #this eliminates really tiny regions
i += 1
continue
splitLocus = utils.Locus(chrom, start + 1, stop, '.')
if lineLocus.overlaps(splitLocus): #has to be a mycn site
newID = '%s_%s' % (regionID, regionTicker)
tssStatus = 0
if localTSSCollection.getOverlap(splitLocus):
tssStatus = 1
splitGFFLine = [
chrom, newID, newID, start, stop, '', '.', tssStatus,
newID
]
splitGFF.append(splitGFFLine)
regionTicker += 1
i += 1
else:
line[7] = 0
splitGFF.append(line)
return splitGFF
def merge_regions():
'''
merges ha peaks to identify all overlapping peaks
filters out anything overlapping the HA controls
'''
hk_dox_ha_1 = utils.importBoundRegion(
'%sHK_DOX_HA_1_peaks.bed' % (macsEnrichedFolder), 'HK_DOX_HA_1')
hk_dox_ha_2 = utils.importBoundRegion(
'%sHK_DOX_HA_2_peaks.bed' % (macsEnrichedFolder), 'HK_DOX_HA_2')
hk_dox_loci = hk_dox_ha_1.getLoci() + hk_dox_ha_2.getLoci()
#control datasets
hk_ctl_ha_1 = utils.importBoundRegion(
'%sHK_CTL_HA_1_peaks.bed' % (macsEnrichedFolder), 'HK_CTL_HA_1')
hk_ctl_ha_2 = utils.importBoundRegion(
'%sHK_CTL_HA_2_peaks.bed' % (macsEnrichedFolder), 'HK_CTL_HA_2')
hk_ctl_loci = hk_ctl_ha_1.getLoci() + hk_ctl_ha_2.getLoci()
hk_ctl_lc = utils.LocusCollection(hk_ctl_loci)
print(len(hk_dox_loci))
stitched_lc = utils.LocusCollection(hk_dox_loci).stitchCollection()
print(len(stitched_lc))
filtered_loci = []
for locus in stitched_lc.getLoci():
if len(hk_dox_ha_1.getOverlap(locus)) > 0 and len(
hk_dox_ha_2.getOverlap(locus)) > 0:
if len(hk_ctl_lc.getOverlap(locus)) == 0:
filtered_loci.append(locus)
print(len(filtered_loci))
filtered_lc = utils.LocusCollection(filtered_loci)
gff_path = '%sHG19_IRF2_HA_MERGED_FILTERED_CONSERVED_0_+0.gff' % (
gffFolder)
filtered_gff = utils.locusCollectionToGFF(filtered_lc)
utils.unParseTable(filtered_gff, gff_path, '\t')
def stitchValleys(valleyList):
'''
takes a list of valley loci
returns a stitched list of valleys to extract seq from
'''
valleyCollection = utils.LocusCollection(valleyList,1)
stitchedValleyCollection = valleyCollection.stitchCollection()
loci = []
regions = []
for valley in stitchedValleyCollection.getLoci():
if [valley.chr(), valley.start(), valley.end()] not in regions:
loci.append(valley)
regions.append([valley.chr(), valley.start(), valley.end()])
return loci
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 makeSECollection(enhancerFile,name,top=0):
'''
returns a locus collection from a super table
top gives the number of rows
'''
enhancerTable = utils.parseTable(enhancerFile,'\t')
superLoci = []
ticker = 0
for line in enhancerTable:
if line[0][0] == '#' or line[0][0] == 'R':
continue
else:
ticker+=1
superLoci.append(utils.Locus(line[1],line[2],line[3],'.',name+'_'+line[0]))
if ticker == top:
break
return utils.LocusCollection(superLoci,50)
def makeSECollection(enhancerFile, name, superOnly=True):
'''
returns a locus collection from a super table
top gives the number of rows
'''
enhancerTable = utils.parseTable(enhancerFile, '\t')
enhancerLoci = []
for line in enhancerTable:
if line[0][0] == '#' or line[0][0] == 'R':
continue
else:
if superOnly and int(line[-1]) == 0:
break
enhancerLoci.append(
utils.Locus(line[1], line[2], line[3], '.',
name + '_' + line[0]))
return utils.LocusCollection(enhancerLoci, 50)
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 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 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 collapseFimo(fimo_output, gene_to_enhancer_dict, candidate_tf_list,
output_folder, analysis_name, motifConvertFile):
'''
collapses motifs from fimo
for each source node (TF) and each target node (gene enhancer regions), collapse motif instances
then spit out a ginormous set of beds and a single crazy collapsed bed
'''
#first build up the motif name conversion database
motifDatabase = utils.parseTable(motifConvertFile, '\t')
motifDatabaseDict = defaultdict(list)
# The reverse of the other dict, from motif name to gene name
# a motif can go to multiple genes
for line in motifDatabase:
motifDatabaseDict[line[0]].append(line[1])
#make the folder to store motif beds
utils.formatFolder('%smotif_beds/' % (output_folder), True)
edgeDict = {}
#first layer are source nodes
for tf in candidate_tf_list:
edgeDict[tf] = defaultdict(
list
) #next layer are target nodes which are derived from the fimo output
fimoTable = utils.parseTable(fimo_output, '\t')
print(fimo_output)
#fimo sometimes puts the region in either the first or second column
fimo_line = fimoTable[1]
if fimo_line[1].count('|') > 0:
region_index = 1
else:
region_index = 2
print('USING COLUMN %s OF FIMO OUTPUT FOR REGION' % (region_index))
for line in fimoTable[1:]:
source_tfs = motifDatabaseDict[line[0]] #motifId
for source in source_tfs:
if candidate_tf_list.count(source) == 0:
continue
region = line[region_index].split('|')
target = region[0]
if region_index == 2:
target_locus = utils.Locus(region[1],
int(region[2]) + int(line[3]),
int(region[2]) + int(line[4]), '.')
else:
target_locus = utils.Locus(region[1],
int(region[2]) + int(line[2]),
int(region[2]) + int(line[3]), '.')
#what's missing here is the enhancer id of the target locus
try:
edgeDict[source][target].append(target_locus)
except KeyError:
print('this motif is not in the network')
print(line)
sys.exit()
#now we actually want to collapse this down in a meaningful way
#overlapping motifs count as a single binding site. This way a TF with tons of motifs
#that finds the same site over and over again doesn't get over counted
all_bed = []
all_bed_path = '%s%s_all_motifs.bed' % (output_folder, analysis_name)
for tf in candidate_tf_list:
print(tf)
target_nodes = edgeDict[tf].keys()
bed_header = [
'track name = "%s" description="%s motifs in %s"' %
(tf, tf, analysis_name)
]
all_bed.append(bed_header)
target_bed = [bed_header]
target_bed_path = '%smotif_beds/%s_motifs.bed' % (output_folder, tf)
for target in target_nodes:
edgeCollection = utils.LocusCollection(edgeDict[tf][target], 50)
edgeCollection = edgeCollection.stitchCollection()
edgeLoci = edgeCollection.getLoci()
edgeDict[tf][target] = edgeLoci
for locus in edgeLoci:
bed_line = [
locus.chr(),
locus.start(),
locus.end(), target, '', '+'
]
target_bed.append(bed_line)
all_bed.append(bed_line)
utils.unParseTable(target_bed, target_bed_path, '\t')
#now the loci are all stitched up
utils.unParseTable(all_bed, all_bed_path, '\t')
return edgeDict
def main():
"""
main run function
"""
#usage = "usage: %prog [options] -g [GENOME] -b [SORTED BAMFILE(S)] -i [INPUTFILE] -o [OUTPUTFOLDER]"
parser = argparse.ArgumentParser(usage='%(prog)s [options]')
# required flags
parser.add_argument(
"-b",
"--bam",
dest="bam",
nargs='*',
help="Enter a comma separated list of .bam files to be processed.",
required=True)
parser.add_argument(
"-i",
"--input",
dest="input",
type=str,
help="Enter .gff or genomic region e.g. chr1:+:1-1000.",
required=True)
parser.add_argument(
"-g",
"--genome",
dest="genome",
type=str,
help="specify a genome, HG18,HG19,MM8,MM9,MM10 are currently supported",
required=True)
# output flag
parser.add_argument("-o",
"--output",
dest="output",
type=str,
help="Enter the output folder.",
required=True)
# additional options
parser.add_argument(
"--stretch-input",
dest="stretch_input",
default=None,
type=int,
help=
"Stretch the input regions to a minimum length in bp, e.g. 10000 (for 10kb)"
)
parser.add_argument(
"-c",
"--color",
dest="color",
default=None,
help=
"Enter a colon separated list of colors e.g. 255,0,0:255,125,0, default samples the rainbow"
)
parser.add_argument(
"-s",
"--sense",
dest="sense",
default='both',
help="Map to '+','-' or 'both' strands. Default maps to both.")
parser.add_argument("-e",
"--extension",
dest="extension",
default=200,
help="Extends reads by n bp. Default value is 200bp")
parser.add_argument(
"-r",
"--rpm",
dest="rpm",
action='store_true',
default=False,
help="Normalizes density to reads per million (rpm) Default is False")
parser.add_argument(
"-y",
"--yScale",
dest="yScale",
default="relative",
help=
"Choose either relative or uniform y axis scaling. options = 'relative,uniform' Default is relative scaling"
)
parser.add_argument(
"-n",
"--names",
dest="names",
default=None,
help="Enter a comma separated list of names for your bams")
parser.add_argument(
"-p",
"--plot",
dest="plot",
default="MULTIPLE",
help=
"Choose either all lines on a single plot or multiple plots. options = 'SINGLE,MULTIPLE,MERGE'"
)
parser.add_argument(
"-t",
"--title",
dest="title",
default='',
help=
"Specify a title for the output plot(s), default will be the coordinate region"
)
# DEBUG OPTION TO SAVE TEMP FILES
parser.add_argument(
"--scale",
dest="scale",
default='',
help=
"Enter a comma separated list of scaling factors for your bams. Default is none"
)
parser.add_argument(
"--save-temp",
dest="save",
action='store_true',
default=False,
help="If flagged will save temporary files made by bamPlot")
parser.add_argument("--bed",
dest="bed",
help="Add a space-delimited list of bed files to plot")
parser.add_argument(
"--multi-page",
dest="multi",
action='store_true',
default=False,
help="If flagged will create a new pdf for each region")
args = parser.parse_args()
print(args)
if args.bam and args.input and args.genome and args.output:
# Support a legacy mode where a ',' delimited multiple files
bamFileList = args.bam
if len(args.bam) == 1:
bamFileList = args.bam[0].split(',')
# Make sure these are actually files & readable (!)
for filename in bamFileList:
assert (os.access(filename, os.R_OK))
# bringing in any beds
if args.bed:
bedFileList = args.bed
if type(bedFileList) == str:
bedFileList = args.bed.split(',')
print(bedFileList)
bedCollection = makeBedCollection(bedFileList)
else:
bedCollection = utils.LocusCollection([], 50)
# Load the input for graphing. One of:
# - A .gff
# - A .bed
# - a specific input region (e.g. chr10:.:93150000-93180000)
valid_sense_options = {'+', '-', '.'}
if os.access(args.input, os.R_OK):
if args.input.endswith('.bed'):
# Uniquely graph every input of this bed
parsed_input_bed = utils.parseTable(args.input, '\t')
gffName = os.path.basename(args.input) # Graph title
gff = None
try:
if parsed_input_bed[0][5] in valid_sense_options:
# This .bed might have a sense parameter
gff = [[
e[0], '', args.input, e[1], e[2], '', e[5], '', ''
] for e in parsed_input_bed]
except IndexError:
pass
if gff is None:
print(
"Your bed doesn't have a valid senese parameter. Defaulting to both strands, '.'"
)
# We only take chr/start/stop and ignore everything else.
gff = [[e[0], '', args.input, e[1], e[2], '', '.', '', '']
for e in parsed_input_bed]
else:
# Default to .gff, since that's the original behavior
gff = utils.parseTable(args.input, '\t')
gffName = args.input.split('/')[-1].split('.')[0]
else:
# means a coordinate line has been given e.g. chr1:+:1-100
chromLine = args.input.split(':')
try:
chrom = chromLine[0]
sense = chromLine[1]
except IndexError:
print(
'Invalid input line or inaccessible file. Try: chr1:.:1-5000'
)
exit()
assert (sense in valid_sense_options)
[start, end] = chromLine[2].split('-')
if chrom[0:3] != 'chr':
print('ERROR: UNRECOGNIZED GFF OR CHROMOSOME LINE INPUT')
exit()
gffLine = [chrom, '', args.input, start, end, '', sense, '', '']
gffName = "%s_%s_%s_%s" % (chrom, sense, start, end)
gff = [gffLine]
# Consider stretching the regions to a fixed minimum size
if args.stretch_input:
print('Stretching inputs to a minimum of: %d bp' %
(args.stretch_input))
minLength = args.stretch_input
stretchGff = []
for e in gff:
difference = int(e[4]) - int(e[3])
if difference < minLength:
pad = int((minLength - difference) / 2)
stretchGff.append([
e[0], e[1], e[2],
int(e[3]) - pad,
int(e[4]) + pad, e[5], e[6], e[7], e[8]
])
else:
stretchGff.append(e)
gff = stretchGff
# Sanity test the gff object
assert (all([e[6] in valid_sense_options
for e in gff])) # All strands are sane
#assert(all([int(e[3]) < int(e[4]) for e in gff])) # All start/stops are ordered
# bring in the genome
genome = args.genome.upper()
if ['HG18', 'HG19', 'HG19_RIBO', 'MM9', 'MM10',
'RN4'].count(genome) == 0:
print(
'ERROR: UNSUPPORTED GENOME TYPE %s. USE HG19,HG18, RN4, MM9, or MM10'
% (genome))
parser.print_help()
exit()
# bring in the rest of the options
# output
rootFolder = args.output
if rootFolder[-1] != '/':
rootFolder += '/'
try:
os.listdir(rootFolder)
except OSError:
print('ERROR: UNABLE TO FIND OUTPUT DIRECTORY %s' % (rootFolder))
exit()
# Get analysis title
if len(args.title) == 0:
title = gffName
else:
title = args.title
# make a temp folder
tempFolder = rootFolder + title + '/'
print("CREATING TEMP FOLDER %s" % (tempFolder))
pipeline_dfci.formatFolder(tempFolder, create=True)
# colors
if args.color:
colorList = args.color.split(':')
colorList = [x.split(',') for x in colorList]
if len(colorList) < len(bamFileList):
print(
'WARNING: FEWER COLORS THAN BAMS SPECIFIED. COLORS WILL BE RECYCLED'
)
# recycling the color list
colorList += colorList * (len(bamFileList) / len(colorList))
colorList = colorList[0:len(bamFileList)]
else:
# cycles through the colors of the rainbow
colorList = tasteTheRainbow(len(bamFileList))
# sense
sense = args.sense
extension = int(args.extension)
rpm = args.rpm
scale = args.scale
yScale = args.yScale.upper()
# names
if args.names:
names = args.names.split(',')
if len(names) != len(bamFileList):
print(
'ERROR: NUMBER OF NAMES AND NUMBER OF BAMS DO NOT CORRESPOND'
)
parser.print_help()
exit()
else:
names = [x.split('/')[-1] for x in bamFileList]
# plot style
plotStyle = args.plot.upper()
if ['SINGLE', 'MULTIPLE', 'MERGE'].count(plotStyle) == 0:
print('ERROR: PLOT STYLE %s NOT AN OPTION' % (plotStyle))
parser.print_help()
exit()
# now run!
summaryTableFileName = makeBamPlotTables(gff, genome, bamFileList,
colorList, nBins, sense,
extension, rpm, tempFolder,
names, title, bedCollection,
scale)
print("%s is the summary table" % (summaryTableFileName))
#running the R command to plot
multi = args.multi
outFile = "%s%s_plots.pdf" % (rootFolder, title)
rCmd = callRPlot(summaryTableFileName, outFile, yScale, plotStyle,
multi)
# open a bash file
bashFileName = "%s%s_Rcmd.sh" % (tempFolder, title)
bashFile = open(bashFileName, 'w')
bashFile.write('#!/usr/bin/bash\n')
bashFile.write(rCmd)
bashFile.close()
print("Wrote R command to %s" % (bashFileName))
os.system("bash %s" % (bashFileName))
# delete temp files
if not args.save:
if utils.checkOutput(outFile, 1, 10):
# This is super dangerous (!). Add some sanity checks.
assert (" " not in tempFolder)
assert (tempFolder is not "/")
removeCommand = "rm -rf %s" % (tempFolder)
print(removeCommand)
os.system(removeCommand)
else:
print("ERROR: NO OUTPUT FILE %s DETECTED" % (outFile))
else:
parser.print_help()
sys.exit()
def mapCollection(stitchedCollection, referenceCollection, bamFileList,
mappedFolder, output, refName):
'''
makes a table of factor density in a stitched locus and ranks table by number of loci stitched together
'''
print('FORMATTING TABLE')
loci = stitchedCollection.getLoci()
locusTable = [[
'REGION_ID', 'CHROM', 'START', 'STOP', 'NUM_LOCI', 'CONSTITUENT_SIZE'
]]
lociLenList = []
# strip out any that are in chrY
for locus in list(loci):
if locus.chr() == 'chrY':
loci.remove(locus)
for locus in loci:
# numLociList.append(int(stitchLocus.ID().split('_')[1]))
lociLenList.append(locus.len())
# numOrder = order(numLociList,decreasing=True)
lenOrder = utils.order(lociLenList, decreasing=True)
ticker = 0
for i in lenOrder:
ticker += 1
if ticker % 1000 == 0:
print(ticker)
locus = loci[i]
# First get the size of the enriched regions within the stitched locus
refEnrichSize = 0
refOverlappingLoci = referenceCollection.getOverlap(locus, 'both')
for refLocus in refOverlappingLoci:
refEnrichSize += refLocus.len()
try:
stitchCount = int(locus.ID().split('_')[0])
except ValueError:
stitchCount = 1
coords = [int(x) for x in locus.coords()]
locusTable.append([
locus.ID(),
locus.chr(),
min(coords),
max(coords), stitchCount, refEnrichSize
])
print('GETTING MAPPED DATA')
print("USING A BAMFILE LIST:")
print(bamFileList)
for bamFile in bamFileList:
bamFileName = bamFile.split('/')[-1]
print('GETTING MAPPING DATA FOR %s' % bamFile)
# assumes standard convention for naming enriched region gffs
# opening up the mapped GFF
print('OPENING %s%s_%s_MAPPED/matrix.txt' %
(mappedFolder, refName, bamFileName))
mappedGFF = utils.parseTable(
'%s%s_%s_MAPPED/matrix.txt' % (mappedFolder, refName, bamFileName),
'\t')
signalDict = defaultdict(float)
print('MAKING SIGNAL DICT FOR %s' % (bamFile))
mappedLoci = []
for line in mappedGFF[1:]:
chrom = line[1].split('(')[0]
start = int(line[1].split(':')[-1].split('-')[0])
end = int(line[1].split(':')[-1].split('-')[1])
mappedLoci.append(utils.Locus(chrom, start, end, '.', line[0]))
try:
signalDict[line[0]] = float(line[2]) * (abs(end - start))
except ValueError:
print('WARNING NO SIGNAL FOR LINE:')
print(line)
continue
mappedCollection = utils.LocusCollection(mappedLoci, 500)
locusTable[0].append(bamFileName)
for i in range(1, len(locusTable)):
signal = 0.0
line = locusTable[i]
lineLocus = utils.Locus(line[1], line[2], line[3], '.')
overlappingRegions = mappedCollection.getOverlap(lineLocus,
sense='both')
for region in overlappingRegions:
signal += signalDict[region.ID()]
locusTable[i].append(signal)
utils.unParseTable(locusTable, output, '\t')
import os, sys
sys.path.append('/storage/cylin/bin/pipeline/')
import utils
motif_bed_dir = '/storage/cylin/grail/projects/rasmc_all/beds/srf_motif_analysis/'
motif_beds = os.listdir(motif_bed_dir)
allLoci = []
for bed in motif_beds:
TF_name = bed.split('_')[0]
collection = utils.importBoundRegion('%s%s' % (motif_bed_dir, bed),
TF_name)
allLoci += collection.getLoci()
giant_collection = utils.LocusCollection(allLoci, 50)
stitched_collection = giant_collection.stitchCollection(stitchWindow=50)
new_bed = utils.locusCollectionToBed(stitched_collection)
utils.unParseTable(
new_bed, '%s50_bp_stitched_srf_motif_analysis_bed.bed' % (motif_bed_dir),
'\t')
def main():
projectFolder = '/storage/goodell/home/jmreyes/projects/amish_ayala/'
#gather up DMR tables
#ayala MUT vs WT
mutWT_hypo = utils.parseTable(projectFolder + 'bed/hypoDMRsWT.vs.Mut.bed',
'\t')
mutWT_hyper = utils.parseTable(
projectFolder + 'bed/hyperDMRsWT.vs.Mut.bed', '\t')
mutWT_control = utils.parseTable(
projectFolder + 'bed/Control_nonDMRsWT.vs.Mut.bed', '\t')
#ley all
tbrs_all = utils.parseTable(projectFolder + 'bed/TBRS_DMRs.bed', '\t')
aml_all = utils.parseTable(projectFolder + 'bed/AML_DMRs.bed', '\t')
tbrs_hypo = []
tbrs_hyper = []
aml_hypo = []
aml_hyper = []
tbrs_all_loci = []
aml_all_loci = []
for line in tbrs_all:
chrom = 'chr' + line[0]
start = line[1]
end = line[2]
if 'hypo' in line:
tbrs_all_loci.append(
utils.Locus(
chrom, start, end, '.', 'tbrs_all_hypo_' + str(chrom) +
':' + str(start) + '-' + str(end)))
elif 'hyper' in line:
tbrs_all_loci.append(
utils.Locus(
chrom, start, end, '.', 'tbrs_all_hyper_' + str(chrom) +
':' + str(start) + '-' + str(end)))
for line in aml_all:
chrom = 'chr' + line[0]
start = line[1]
end = line[2]
if 'hypo' in line:
aml_all_loci.append(
utils.Locus(
chrom, start, end, '.', 'aml_all_hypo_' + str(chrom) +
':' + str(start) + '-' + str(end)))
elif 'hyper' in line:
aml_all_loci.append(
utils.Locus(
chrom, start, end, '.', 'aml_all_hyper_' + str(chrom) +
':' + str(start) + '-' + str(end)))
mutWT_hypo_loci = []
for line in mutWT_hypo:
chrom = line[0]
start = line[1]
end = line[2]
sense = '.'
locusID = 'hypo_' + str(chrom) + ':' + str(start) + '-' + str(end)
new_line = utils.Locus(chrom, start, end, '.', locusID)
mutWT_hypo_loci.append(new_line)
mutWT_hyper_loci = []
for line in mutWT_hyper:
chrom = line[0]
start = line[1]
end = line[2]
sense = '.'
locusID = 'hyper_' + str(chrom) + ':' + str(start) + '-' + str(end)
new_line = utils.Locus(chrom, start, end, '.', locusID)
mutWT_hyper_loci.append(new_line)
print len(mutWT_hyper_loci)
print len(mutWT_hypo_loci)
mutWT_all_loci = mutWT_hyper_loci + mutWT_hypo_loci
mutWT_hypo_LC = utils.LocusCollection(mutWT_hypo_loci)
tbrs_all_LC = utils.LocusCollection(tbrs_all_loci)
aml_all_LC = utils.LocusCollection(aml_all_loci)
tbrs_all_overlap = []
aml_all_overlap = []
for locus in mutWT_hypo_LC.getLoci():
tbrs_overlap = tbrs_all_LC.getOverlap(locus, 'both')
if len(tbrs_overlap) > 0:
for overlapLocus in tbrs_overlap:
overlapChrom = overlapLocus.chr()
overlapStart = overlapLocus.start()
overlapEnd = overlapLocus.end()
tbrs_all_overlap.append([
locus.ID(), overlapChrom, overlapStart, overlapEnd,
overlapLocus.ID()
])
aml_overlap = aml_all_LC.getOverlap(locus, 'both')
if len(aml_overlap) > 0:
for overlapLocus in aml_overlap:
overlapChrom = overlapLocus.chr()
overlapStart = overlapLocus.start()
overlapEnd = overlapLocus.end()
aml_all_overlap.append([
locus.ID(), overlapChrom, overlapStart, overlapEnd,
overlapLocus.ID()
])
utils.unParseTable(tbrs_all_overlap,
projectFolder + 'tables/DMRsvsTBRS_all_overlaps.txt',
'\t')
utils.unParseTable(aml_all_overlap,
projectFolder + 'tables/DMRsvsAML_all_overlaps.txt',
'\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)
#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 rank_eboxes(nb_all_chip_dataFile,mycn_gff_path,macsFolder,genomeDirectory,window = 100):
'''
uses the conserved MYCN sites and ranks eboxes within them
by average background subtracted signal
searches 100bp (window variable) from mycn summits
'''
window = int(window)
#bring in the conserved mycn region
print('making gff of nb mycn summits')
nb_mycn_gff = utils.parseTable(mycn_gff_path,'\t')
nb_mycn_collection = utils.gffToLocusCollection(nb_mycn_gff,50)
dataDict =pipeline_dfci.loadDataTable(nb_all_chip_dataFile)
names_list = [name for name in dataDict.keys() if name.count('MYCN') == 1]
names_list.sort()
summit_loci = []
#first makes a gff of all summits +/- 100bp for all nb mycn datasets
for name in names_list:
summit_bed_path = '%s%s/%s_summits.bed' % (macsFolder,name,name)
summit_bed = utils.parseTable(summit_bed_path,'\t')
for line in summit_bed:
summit_locus = utils.Locus(line[0],int(line[1])-window,int(line[2])+window,'.',line[3])
if len(nb_mycn_collection.getOverlap(summit_locus)) > 0:
summit_loci.append(summit_locus)
summit_collection =utils.LocusCollection(summit_loci,50)
summit_merged_collection = summit_collection.stitchCollection()
summit_gff = utils.locusCollectionToGFF(summit_merged_collection)
summit_gff_path = '%sHG19_NB_MYCN_SUMMITS_-%s_+%s.gff' % (gffFolder,window,window)
utils.unParseTable(summit_gff,summit_gff_path,'\t')
#this is borrowed from above and maps chip-seq signal to the gff
print('mapping to nb mycn summits and making signal dict')
gffList = [summit_gff_path]
summit_signal_path = pipeline_dfci.map_regions(nb_all_chip_dataFile,gffList)
mycnSignalTable = utils.parseTable(summit_signal_path,'\t')
#making a signal dictionary for MYCN binding
names_list = ['BE2C_MYCN','KELLY_MYCN','NGP_MYCN','SHEP21_0HR_MYCN_NOSPIKE']
background_list = [dataDict[name]['background'] for name in names_list]
header = mycnSignalTable[0]
chip_columns = [header.index(name) for name in names_list]
background_columns = [header.index(background_name) for background_name in background_list]
mycn_sig_dict = {}
for line in mycnSignalTable[1:]:
line_sig = []
for i in range(len(names_list)):
line_sig.append(float(line[chip_columns[i]]) - float(line[background_columns[i]]))
region_id = line[1]
coords = [int(x) for x in line[1].split(':')[-1].split('-')]
line_length = coords[1]-coords[0]
mycn_sig_dict[region_id] = numpy.mean(line_sig)*line_length
#now for each region find the eboxes and then add up the signal
print('making ebox ranking')
ebox_list = ['CACGTG','CAGTTG','CAAGTG','CAGGTG','CAATTG','CAAATG','CATCTG','CAGCTG','CATGTG','CATATG']
eboxDict = {}
for ebox in ebox_list:
eboxDict[ebox] = []
ticker = 0
for line in summit_gff:
if ticker % 1000 == 0:
print(ticker)
ticker+=1
chrom = line[0]
sense = '.'
start = int(line[3])
end = int(line[4])
region_id = '%s(%s):%s-%s' % (line[0],line[6],line[3],line[4])
signal = mycn_sig_dict[region_id]
sequenceLine = utils.fetchSeq(genomeDirectory,chrom,start,end,True)
motifVector = []
matches = re.finditer('CA..TG',str.upper(sequenceLine))
if matches:
for match in matches:
motifVector.append(match.group())
#count only 1 of each motif type per line
#motifVector = utils.uniquify(motifVector)
for motif in motifVector:
if ebox_list.count(motif) > 0:
eboxDict[motif].append(signal)
else:
eboxDict[utils.revComp(motif)].append(signal)
eboxTable =[]
eboxTableOrdered =[['EBOX','OCCURENCES','AVG_HEIGHT']]
for ebox in eboxDict.keys():
newLine = [ebox,len(eboxDict[ebox]),numpy.mean(eboxDict[ebox])]
eboxTable.append(newLine)
occurenceOrder = utils.order([line[2] for line in eboxTable],decreasing=True)
for x in occurenceOrder:
eboxTableOrdered.append(eboxTable[x])
print(eboxTableOrdered)
ebox_outfile = '%sHG19_NB_MYCN_CONSERVED_SUMMITS_-%s_+%s_EBOX_RANK.txt' % (tableFolder,window,window)
utils.unParseTable(eboxTableOrdered,ebox_outfile,'\t')
return ebox_outfile
def make_mycn_stats_table(nb_all_chip_dataFile,outFile):
'''
making a table of conserved mycn peaks w/ some additional stats
mycn and h3k27ac signal is avg. background normalized across 4 samples
active tss defined as the union of all H3K27ac occupied promoters in NB
active enhancers defined as the union of all H3K27ac sites outside of promoters
'''
dataDict = pipeline_dfci.loadDataTable(nb_all_chip_dataFile)
print('SETTING UP OUTPUT TABLE')
outTable = [['PEAK_ID','CHROM','START','STOP','LENGTH','ACTIVE_TSS_OVERLAP','ENHANCER_OVERLAP','CPG_ISLAND_OVERLAP','CPG_ISLAND_FRACTION','GC_FREQ','MYCN_RANK','AVG_MYCN_SIGNAL','AVG_H3K27AC_SIGNAL','CANON_EBOX_COUNT','NONCANON_EBOX_COUNT','TOTAL_EBOX_COUNT','CANON_EXP','NON_CANON_EXP','GABPA_COUNT','GABPA_EXP','GATA_COUNT','GATA_EXP']]
dinuc = nmers(2,['A','T','G','C'])
#input files
mycnSignalFile = '%sHG19_NB_MYCN_CONSERVED_-0_+0_NB_ALL_SIGNAL.txt' % (signalFolder)
h3k27acSignalFile = '%sHG19_NB_MYCN_CONSERVED_-500_+500_NB_ALL_SIGNAL.txt' % (signalFolder)
mycnRankFile = '%smeta_rose/NB_MYCN/NB_MYCN_0KB_STITCHED_ENHANCER_REGION_RANK_CONSERVED.txt' % (projectFolder)
activeGeneFile = '%sHG19_NB_H3K27AC_ACTIVE_UNION.txt' % (geneListFolder)
#note, this is the ucsc hg19 cpg islands extended file
#to download and format run ./beds/download_cpg.sh
cpgFile = '%sbeds/hg19_cpg_islands.bed' % (projectFolder)
enhancerFile = '%smeta_rose/NB_H3K27AC/NB_H3K27AC_AllEnhancers.table.txt' % (projectFolder)
print('LOADING MYCN BINDING DATA')
mycnSignalTable = utils.parseTable(mycnSignalFile,'\t')
#making a signal dictionary for MYCN binding
names_list = ['BE2C_MYCN','KELLY_MYCN','NGP_MYCN','SHEP21_0HR_MYCN_NOSPIKE']
background_list = [dataDict[name]['background'] for name in names_list]
header = mycnSignalTable[0]
chip_columns = [header.index(name) for name in names_list]
background_columns = [header.index(background_name) for background_name in background_list]
mycn_sig_dict = {}
#this only works if the first column are unique identifiers
if len(mycnSignalTable) != len(utils.uniquify([line[0] for line in mycnSignalTable])):
print('Error: Column 1 of must contain unique identifiers.' % (mycnSignalFile))
sys.exit()
for line in mycnSignalTable[1:]:
line_sig = []
for i in range(len(names_list)):
line_sig.append(float(line[chip_columns[i]]) - float(line[background_columns[i]]))
mycn_sig_dict[line[0]] = numpy.mean(line_sig)
print('LOADING MYCN RANK DATA')
mycnRankTable = utils.parseTable(mycnRankFile,'\t')
print('LOADING H3K27AC BINDING DATA')
h3k27acSignalTable = utils.parseTable(h3k27acSignalFile,'\t')
#making a signal dictionary for background subtracted H3K27ac binding
names_list = ['BE2C_H3K27AC','KELLY_H3K27AC','NGP_H3K27AC','SHEP21_0HR_H3K27AC_NOSPIKE']
background_list = [dataDict[name]['background'] for name in names_list]
header = h3k27acSignalTable[0]
chip_columns = [header.index(name) for name in names_list]
background_columns = [header.index(background_name) for background_name in background_list]
h3k27ac_sig_dict = {}
#this only works if the first column are unique identifiers
if len(h3k27acSignalTable) != len(utils.uniquify([line[0] for line in h3k27acSignalTable])):
print('Error: Column 1 of must contain unique identifiers.' % (h3k27acSignalFile))
sys.exit()
for line in h3k27acSignalTable[1:]:
line_sig = []
for i in range(len(names_list)):
line_sig.append(float(line[chip_columns[i]]) - float(line[background_columns[i]]))
h3k27ac_sig_dict[line[0]] = numpy.mean(line_sig)
#making the cpg collection
print('LOADING CPGS ISLANDS')
cpgBed = utils.parseTable(cpgFile,'\t')
cpgLoci = []
for line in cpgBed:
cpgLoci.append(utils.Locus(line[0],line[1],line[2],'.',line[-1]))
cpgCollection = utils.LocusCollection(cpgLoci,50)
#next make the tss collection of active promoters
print('LOADING ACTIVE PROMOTERS')
startDict = utils.makeStartDict(annotFile)
activeTable = utils.parseTable(activeGeneFile,'\t')
tss_1kb_loci = []
for line in activeTable:
tss_1kb_loci.append(utils.makeTSSLocus(line[1],startDict,1000,1000))
tss_1kb_collection = utils.LocusCollection(tss_1kb_loci,50)
#enhancer file
print("LOADING ACTIVE ENHANCERS")
enhancerTable = utils.parseTable(enhancerFile,'\t')
print('STARTING WITH THE FOLLOWING NUMBER OF ENHANCERS IN NB')
print(len(enhancerTable) - 6)
enhancerLoci = []
for line in enhancerTable:
if line[0][0] != '#' and line[0][0] != 'R':
try:
lineLocus = utils.Locus(line[1],int(line[2]),int(line[3]),'.',line[0])
enhancerLoci.append(lineLocus)
except IndexError:
print(line)
sys.exit()
enhancerCollection = utils.LocusCollection(enhancerLoci,50)
print('CLASSIFYING MYCN PEAKS')
ticker = 0
for i in range(1,len(mycnSignalTable)):
if ticker%100 == 0:
print(ticker)
ticker +=1
line = mycnSignalTable[i]
mycn_signal = round(mycn_sig_dict[line[0]],4)
h3k27ac_signal = round(h3k27ac_sig_dict[line[0]],4)
peakID = line[0]
locusString = line[1]
chrom = locusString.split('(')[0]
[start,stop] = [int(x) for x in line[1].split(':')[-1].split('-')]
lineLocus = utils.Locus(chrom,start,stop,'.',peakID)
tssOverlap = 0
if tss_1kb_collection.getOverlap(lineLocus,'both'):
tssOverlap = 1
enhancerOverlap = 0
if enhancerCollection.getOverlap(lineLocus,'both') and tssOverlap == 0:
enhancerOverlap = 1
cpgIslandOverlap = 0
if cpgCollection.getOverlap(lineLocus,'both'):
cpgIslandOverlap = 1
#now do fractional cpgOverlap
overlappingCpGLoci = cpgCollection.getOverlap(lineLocus,'both')
overlappingBases = 0
for locus in overlappingCpGLoci:
cpgStart = max(locus.start(),lineLocus.start())
cpgEnd = min(locus.end(),lineLocus.end())
overlappingBases += (cpgEnd-cpgStart)
overlapFraction = round(float(overlappingBases)/lineLocus.len(),2)
#now get the seq
lineSeq = string.upper(utils.fetchSeq(genomeDirectory,chrom,start,stop,True))
gcFreq = round(float(lineSeq.count('GC') + lineSeq.count('CG'))/len(lineSeq),2)
dinuc_dict = {}
for nmer in dinuc:
dinuc_dict[nmer] = float(lineSeq.count('GC'))/len(lineSeq)
mycnRankLine = mycnRankTable[i]
mycnRank = numpy.mean([float(x) for x in mycnRankLine[6:]])
canonMatchList = re.findall('CACGTG',lineSeq)
canon_count = len(canonMatchList)
eboxMatchList = re.findall('CA..TG',lineSeq)
ebox_count = len(eboxMatchList)
non_canon_count = ebox_count-canon_count
#get the expected values
canon_exp = dinuc_dict['CA']*dinuc_dict['CG']*dinuc_dict['TG']*(len(lineSeq) - 5)
canon_exp = round(canon_exp,2)
notCG = 1- dinuc_dict['CG']
non_exp = dinuc_dict['CA']*notCG*dinuc_dict['TG']*(len(lineSeq) - 5)
non_exp = round(non_exp,2)
#for gata and GABPA
gabpaMatchList = re.findall('CGGAAG',lineSeq) + re.findall('CTTCCG',lineSeq)
gabpa_count = len(gabpaMatchList)
gabpa_exp_f = dinuc_dict['CG'] * dinuc_dict['GA'] * dinuc_dict['AG']*(len(lineSeq) - 5)
gabpa_exp_r = dinuc_dict['CT'] * dinuc_dict['TC'] * dinuc_dict['CG']*(len(lineSeq) - 5)
gabpa_exp = round(gabpa_exp_f,2) + round(gabpa_exp_r,2)
gataMatchList = re.findall('GATAA',lineSeq) + re.findall('TTATC',lineSeq)
gata_count = len(gataMatchList)
an_freq = 1 - dinuc_dict['AA'] - dinuc_dict['AT'] - dinuc_dict['AG'] -dinuc_dict['AC']
cn_freq = 1 - dinuc_dict['CA'] - dinuc_dict['CT'] - dinuc_dict['CG'] -dinuc_dict['CC']
gata_exp_f = dinuc_dict['GA'] * dinuc_dict['TA'] * an_freq*(len(lineSeq) - 5)
gata_exp_r = dinuc_dict['TT'] * dinuc_dict['AT'] * cn_freq*(len(lineSeq) - 5)
gata_exp = round(gata_exp_f,2) + round(gata_exp_r,2)
newLine = [peakID,chrom,start,stop,lineLocus.len(),tssOverlap,enhancerOverlap,cpgIslandOverlap,overlapFraction,gcFreq,mycnRank,mycn_signal,h3k27ac_signal,canon_count,non_canon_count,ebox_count,canon_exp,non_exp,gabpa_count,gabpa_exp,gata_count,gata_exp]
outTable.append(newLine)
utils.unParseTable(outTable,outFile,'\t')
return outFile
def main():
print('main analysis for MYCN project')
print('changing directory to project folder')
os.chdir(projectFolder)
print('\n\n')
print(
'#======================================================================'
)
print(
'#======================I, LOADING DATA ANNOTATION======================'
)
print(
'#======================================================================'
)
print('\n\n')
#This section sanity checks each data table and makes sure both bam and .bai files are accessible
#for ChIP-Seq
#these are the datasets we will use
pipeline_dfci.summary(shep21_dataFile)
print('\n\n')
print(
'#======================================================================'
)
print(
'#================II. RUNNING DIFFERENTIAL ROSE ANALYSIS================'
)
print(
'#======================================================================'
)
print('\n\n')
#use the dynamic rose tools to first map twist1 binding sites
#and then quantify
name1 = 'SHEP21_0HR_TWIST'
name2 = 'SHEP21_24HR_B_TWIST'
analysis_name = 'SHEP21_TWIST1'
rank_gff_path = wrapDRose(shep21_dataFile, name1, name2, analysis_name)
print('\n\n')
print(
'#======================================================================'
)
print(
'#=================III. MAPPING MYCN DATA TO RANK GFF==================='
)
print(
'#======================================================================'
)
print('\n\n')
#for shep21 nospike
gffList = [rank_gff_path]
dataDict = pipeline_dfci.loadDataTable(shep21_dataFile)
names_list = [
name for name in dataDict.keys()
if name.count('MYCN') == 1 or name.count('INPUT') == 1
or name.count('TWIST') == 1 and name.count('rep2') == 0
]
print(names_list)
#map_regions(shep21_dataFile,gffList,names_list)
gffList = ['%smacsEnriched/SHEP21_0HR_TWIST_peaks.bed' % (projectFolder)]
#map_regions(shep21_dataFile,gffList,names_list)
#make a gff of twist and mycn sites at 0hr
twist_collection = utils.importBoundRegion(
'%smacsEnriched/SHEP21_0HR_TWIST_peaks.bed' % (projectFolder),
'SHEP21_0HR_TWIST')
mycn_collection = utils.importBoundRegion(
'%smacsEnriched/SHEP21_0HR_MYCN_NOSPIKE_peaks.bed' % (projectFolder),
'SHEP21_0HR_MYCN_NOSPIKE')
all_loci = twist_collection.getLoci() + mycn_collection.getLoci()
all_collection = utils.LocusCollection(all_loci, 50)
stitched_collection = all_collection.stitchCollection()
stitched_loci = stitched_collection.getLoci()
overlap_loci = []
for locus in stitched_loci:
if len(twist_collection.getOverlap(locus, 'both')) > 0 and len(
mycn_collection.getOverlap(locus, 'both')) > 0:
overlap_loci.append(locus)
overlap_collection = utils.LocusCollection(overlap_loci, 50)
overlap_gff = utils.locusCollectionToGFF(overlap_collection)
overlap_gff_path = '%sHG19_SHEP21_0HR_TWIST_MYCN_INTERSECTION_-0_+0.gff' % (
gffFolder)
utils.unParseTable(overlap_gff, overlap_gff_path, '\t')
gffList = [overlap_gff_path]
map_regions(shep21_dataFile, gffList, names_list)
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)
# From networks already constructed from CRC2.py
node_file = '/crusader/projects/cll/final/network/lines/zinba/' + projectName + '/' + projectName + '_NODELIST.txt'
node_table = utils.parseTable(node_file, '\t')
nodelist = [x[0] for x in node_table]
print nodelist
super_enhancer_file = '/crusader/projects/cll/final/rose/' + projectName + '_H3K27ac/' + projectName + '_H3K27ac_peaks_SuperEnhancers.table.txt'
se_table = utils.parseTable(super_enhancer_file, '\t')
subpeak_file = '/crusader/projects/cll/final/zinba/lines/MEC1_ATAC/MEC1_ATAC.peaks.bed'
subpeak_table = utils.parseTable(subpeak_file, '\t')
subpeak_loci = []
for line in subpeak_table:
subpeak_loci.append(utils.Locus(line[0], line[1], line[2], '.'))
subpeak_collection = utils.LocusCollection(subpeak_loci, 100)
subpeak_dict = {} # key is enhancer ID, points to a list of loci
# assign subpeak Loci to each super enhancer
fasta = []
se_namelist = []
for line in se_table[6:]:
se_id = line[0]
se_namelist.append(se_id)
subpeak_dict[se_id] = []
se_locus = utils.Locus(line[1], line[2], line[3], '.')
overlaps = subpeak_collection.getOverlap(se_locus)
for overlap in overlaps:
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 makePeakTable(paramDict,
splitGFFPath,
averageTablePath,
startDict,
geneList,
genomeDirectory,
tads_path=''):
'''
makes the final peak table with ebox info
'''
peakTable = [[
'REGION_ID', 'CHROM', 'START', 'STOP', 'LENGTH', 'TSS', 'CPG',
'CPG_FRACTION', 'GC_FREQ', 'SIGNAL', 'CANON_EBOX_COUNT',
'NON_CANON_EBOX_COUNT', 'TOTAL_EBOX_COUNT', 'OVERLAPPING_GENES',
'PROXIMAL_GENES'
]]
print('LOADING PEAK REGIONS')
peakGFF = utils.parseTable(splitGFFPath, '\t')
print('LOADING BINDING DATA')
signalTable = utils.parseTable(averageTablePath, '\t')
print('LOADING CPGS ISLANDS')
cpgBed = utils.parseTable(paramDict['cpgPath'], '\t')
cpgLoci = []
for line in cpgBed:
cpgLoci.append(utils.Locus(line[0], line[1], line[2], '.', line[-1]))
cpgCollection = utils.LocusCollection(cpgLoci, 50)
print("MAKING TSS COLLECTIONS")
if len(geneList) == 0:
geneList = startDict.keys()
tss_1kb_loci = []
tss_50kb_loci = []
for refID in geneList:
tss_1kb_loci.append(utils.makeTSSLocus(refID, startDict, 1000, 1000))
tss_50kb_loci.append(utils.makeTSSLocus(refID, startDict, 50000,
50000))
#make a 1kb flanking and 50kb flanking collection
tss_1kb_collection = utils.LocusCollection(tss_1kb_loci, 50)
tss_50kb_collection = utils.LocusCollection(tss_50kb_loci, 50)
if len(tads_path) > 0:
print('LOADING TADS FROM %s' % (tads_path))
tad_collection = utils.importBoundRegion(tads_path, 'tad')
use_tads = True
#building a tad dict keyed by tad ID w/ genes in that tad provided
tad_dict = defaultdict(list)
for tss_locus in tss_1kb_loci:
overlapping_tads = tad_collection.getOverlap(tss_locus, 'both')
for tad_locus in overlapping_tads:
tad_dict[tad_locus.ID()].append(tss_locus.ID())
else:
use_tads = False
print('CLASSIFYING PEAKS')
ticker = 0
no_tad_count = 0
for i in range(len(peakGFF)):
if ticker % 1000 == 0:
print(ticker)
ticker += 1
#getting the particulars of the region
gffLine = peakGFF[i]
peakID = gffLine[1]
chrom = gffLine[0]
start = int(gffLine[3])
stop = int(gffLine[4])
lineLocus = utils.Locus(chrom, start, stop, '.', peakID)
#getting the mapped signal
signalLine = signalTable[(i + 1)]
signalVector = [float(x) for x in signalLine[2:]]
#setting up the new line
newLine = [peakID, chrom, start, stop, lineLocus.len()]
#get the tss status from the gff itself (we are able to do this nicely from the split gff code earlier
newLine.append(gffLine[7])
#check cpg status
if cpgCollection.getOverlap(lineLocus, 'both'):
newLine.append(1)
else:
newLine.append(0)
#now do fractional cpgOverlap
overlappingCpGLoci = cpgCollection.getOverlap(lineLocus, 'both')
overlappingBases = 0
for locus in overlappingCpGLoci:
cpgStart = max(locus.start(), lineLocus.start())
cpgEnd = min(locus.end(), lineLocus.end())
overlappingBases += (cpgEnd - cpgStart)
overlapFraction = float(overlappingBases) / lineLocus.len()
newLine.append(round(overlapFraction, 2))
#now get the seq
lineSeq = string.upper(
utils.fetchSeq(genomeDirectory, chrom, start, stop, True))
if len(lineSeq) == 0:
print('UH OH')
print(lineSeq)
print(gffLine)
print(i)
print(chrom)
print(start)
print(stop)
sys.exit()
gcFreq = float(lineSeq.count('GC') +
lineSeq.count('CG')) / len(lineSeq)
newLine.append(gcFreq)
#this is where we add the ChIP-Seq signal
newLine += signalVector
eboxMatchList = re.findall('CA..TG', lineSeq)
if len(eboxMatchList) == 0:
newLine += [0] * 3
else:
totalCount = len(eboxMatchList)
canonCount = eboxMatchList.count('CACGTG')
otherCount = totalCount - canonCount
newLine += [canonCount, otherCount, totalCount]
#now find the overlapping and proximal genes
#here each overlapping gene the tss 1kb locus overlaps the peak
if use_tads:
tad_loci = tad_collection.getOverlap(lineLocus, 'both')
tad_id_list = [tad_locus.ID() for tad_locus in tad_loci]
tad_genes = []
for tad_id in tad_id_list:
tad_genes += tad_dict[tad_id]
if len(tad_genes) == 0:
#print('no tad for this region')
#print(gffLine)
no_tad_count += 1
else:
tad_genes = []
if len(tad_genes) > 0:
overlappingGenes = [
startDict[locus.ID()]['name']
for locus in tss_1kb_collection.getOverlap(lineLocus, 'both')
if tad_genes.count(locus.ID()) > 0
]
proximalGenes = [
startDict[locus.ID()]['name']
for locus in tss_50kb_collection.getOverlap(lineLocus, 'both')
if tad_genes.count(locus.ID()) > 0
]
# print('linked peak to tad genes')
# print([startDict[x]['name'] for x in tad_genes])
# print(tad_id_list)
# print(gffLine)
# print(overlappingGenes)
# print(proximalGenes)
else:
overlappingGenes = [
startDict[locus.ID()]['name']
for locus in tss_1kb_collection.getOverlap(lineLocus, 'both')
]
proximalGenes = [
startDict[locus.ID()]['name']
for locus in tss_50kb_collection.getOverlap(lineLocus, 'both')
]
overlappingGenes = utils.uniquify(overlappingGenes)
#here the tss 50kb locus overlaps the peak
#overlap takes priority over proximal
proximalGenes = [
gene for gene in proximalGenes if overlappingGenes.count(gene) == 0
]
proximalGenes = utils.uniquify(proximalGenes)
overlappingString = string.join(overlappingGenes, ',')
proximalString = string.join(proximalGenes, ',')
newLine += [overlappingString, proximalString]
peakTable.append(newLine)
print('Out of %s regions, %s were assigned to at least 1 tad' %
(len(peakTable), no_tad_count))
return peakTable
def 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():
print('main analysis for MYCN project')
print('changing directory to project folder')
os.chdir(projectFolder)
print('\n\n')
print(
'#======================================================================'
)
print(
'#======================I, LOADING DATA ANNOTATION======================'
)
print(
'#======================================================================'
)
print('\n\n')
#This section sanity checks each data table and makes sure both bam and .bai files are accessible
#for ChIP-Seq
pipeline_dfci.summary(mouse_dataFile)
print('\n\n')
print(
'#======================================================================'
)
print(
'#==========================II. CALLING MACS============================'
)
print(
'#======================================================================'
)
print('\n\n')
#running peak finding using macs 1.4.2 on all chip datasets
#this usually takes ~2-3 hours on a reasonably fast machine
#a 3 hour time out on this entire operation is set
#if peak calling takes longer than 3 hours, simply run the script again after completion
#run_macs(mouse_dataFile)
print('\n\n')
print(
'#======================================================================'
)
print(
'#=================II. DEFINING ACTIVE GENES IN MOUSE==================='
)
print(
'#======================================================================'
)
print('\n\n')
#here we will identify active promoters in various contexts as those with
#an H3K27AC peak in the +/- 1kb tss region
#UCSC refseq annotations are used for all genes
#make_active_gene_lists(mouse_dataFile)
print('\n\n')
print(
'#======================================================================'
)
print(
'#==================III. CALLING ROSE TO MAP ENHANCERS=================='
)
print(
'#======================================================================'
)
print('\n\n')
# #for SCG_H3K27AC
# analysisName = 'SCG_H3K27AC'
# namesList = ['SCG_H3K27Ac']
# bashFileName,region_map_path,namesList=define_enhancer_landscape(mouse_dataFile,analysisName,namesList)
# #for CG_H3K27AC
# analysisName = 'CG_H3K27AC'
# namesList = ['CG_H3K27Ac']
# bashFileName,region_map_path,namesList=define_enhancer_landscape(mouse_dataFile,analysisName,namesList)
# #for GANGLIA_H3K27AC
# analysisName = 'GANGLIA_H3K27AC'
# namesList = ['CG_H3K27Ac','SCG_H3K27Ac']
# bashFileName,region_map_path,namesList=define_enhancer_landscape(mouse_dataFile,analysisName,namesList)
# #for THMYCN
# analysisName = 'THMYCN_H3K27AC'
# namesList = ['THMYCN_139076_H3K27Ac','THMYCN_139423_H3K27Ac','THMYCN1_H3K27Ac']
# bashFileName,region_map_path,namesList=define_enhancer_landscape(mouse_dataFile,analysisName,namesList)
print('\n\n')
print(
'#======================================================================'
)
print(
'#=================IV. LIFTING OVER NB CONSERVED REGIONS================'
)
print(
'#======================================================================'
)
print('\n\n')
# #liftover a pair of gffs
# #first convert to bed
# nb_promoter_gff_path = '%sgff/HG19_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000.gff' % (hg19_projectFolder)
# nb_enhancer_gff_path = '%sgff/HG19_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000.gff' % (hg19_projectFolder)
# nb_promoter_bed_path ='%sbeds/HG19_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000.bed' % (hg19_projectFolder)
# nb_enhancer_bed_path ='%sbeds/HG19_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000.bed' % (hg19_projectFolder)
# nb_promoter_gff = utils.parseTable(nb_promoter_gff_path,'\t')
# nb_enhancer_gff = utils.parseTable(nb_enhancer_gff_path,'\t')
# utils.gffToBed(nb_promoter_gff,nb_promoter_bed_path)
# utils.gffToBed(nb_enhancer_gff,nb_enhancer_bed_path)
# print('converted NB conserved gffs to beds at %s and %s' % (nb_promoter_bed_path,nb_enhancer_bed_path))
# #note, now you have to liftover manually to create beds
# mm9_promoter_bed_path = '%sMM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000.bed' % (bedFolder)
# mm9_enhancer_bed_path = '%sMM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000.bed' % (bedFolder)
# mm9_promoter_gff_path = '%sMM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000.gff' % (gffFolder)
# mm9_enhancer_gff_path = '%sMM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000.gff' % (gffFolder)
# utils.bedToGFF(mm9_promoter_bed_path,mm9_promoter_gff_path)
# utils.bedToGFF(mm9_enhancer_bed_path,mm9_enhancer_gff_path)
# print('writing mm9 nb mycn sites to %s and %s' % (mm9_promoter_gff_path,mm9_enhancer_gff_path))
print('\n\n')
print(
'#======================================================================'
)
print(
'#======================V. MAPPING ENRICHED TO GFFS====================='
)
print(
'#======================================================================'
)
print('\n\n')
# setName = 'THMYCN'
# gffList = [mm9_promoter_gff_path,mm9_enhancer_gff_path]
# cellTypeList = ['THMYCN1','THMYCN2','THMYCN','CG','SCG']
# mapList = ['CG_H3K27Ac',
# 'SCG_H3K27Ac',
# 'THMYCN1_H3K27Ac',
# 'THMYCN_139423_H3K27Ac',
# 'THMYCN_139076_H3K27Ac',
# ]
# #pipeline_dfci.mapEnrichedToGFF(mouse_dataFile,setName,gffList,cellTypeList,macsEnrichedFolder,mappedEnrichedFolder,macs=True,namesList=mapList,useBackground=True)
# #summarize info for venn diagrams for each
# promoter_mapped_path = '%sMM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000/MM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000_THMYCN.txt' % (mappedEnrichedFolder)
# promoter_venn_path = '%sMM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000_VENN.txt' % (tableFolder)
# summarizeVenn(promoter_mapped_path,group_list = ['CG','THMYCN'],output=promoter_venn_path)
# enhancer_mapped_path = '%sMM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000/MM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000_THMYCN.txt' % (mappedEnrichedFolder)
# enhancer_venn_path = '%sMM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000_VENN.txt' % (tableFolder)
# summarizeVenn(enhancer_mapped_path,group_list = ['CG','THMYCN'],output=enhancer_venn_path)
print('\n\n')
print(
'#======================================================================'
)
print(
'#=====================VI. MAKING MYCN REGIONS GFF======================'
)
print(
'#======================================================================'
)
print('\n\n')
dataDict = pipeline_dfci.loadDataTable(mouse_dataFile)
names_list = [
'THMYCN2_MYCN',
'THMYCN_139076_MYCN',
'THMYCN_139423_MYCN',
]
mycn_loci = []
for name in names_list:
mycn_collection = utils.importBoundRegion(
'%s%s' % (macsEnrichedFolder, dataDict[name]['enrichedMacs']),
name)
mycn_loci += mycn_collection.getLoci()
mycn_collection = utils.LocusCollection(mycn_loci, 50)
mycn_collection.stitchCollection()
mycn_gff = utils.locusCollectionToGFF(mycn_collection)
mycn_gff_path = '%sMM9_THMYCN_MYCN_-0_+0.gff' % (gffFolder)
utils.unParseTable(mycn_gff, mycn_gff_path, '\t')
#make collections
promoter_collection = utils.gffToLocusCollection(
'%sMM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000.gff' % (gffFolder))
enhancer_collection = utils.gffToLocusCollection(
'%sMM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000.gff' % (gffFolder))
#make the overlap table
overlap_table = [['PROMOTER', 'ENHANCER', 'NONE']]
promoter_count = 0
enhancer_count = 0
none_count = 0
for line in mycn_gff:
locus = utils.Locus(line[0],
int(line[3]) - 10000,
int(line[4]) + 10000, '.')
if enhancer_collection.getOverlap(locus, 'both'):
enhancer_count += 1
continue
if promoter_collection.getOverlap(locus, 'both'):
promoter_count += 1
else:
none_count += 1
overlap_table.append([promoter_count, enhancer_count, none_count])
overlap_table_path = '%sMM9_THMYCN_OVERLAP.txt' % (tableFolder)
utils.unParseTable(overlap_table, overlap_table_path, '\t')
print('\n\n')
print(
'#======================================================================'
)
print(
'#=====================VI. MAPPING GFFS FOR HEATMAP====================='
)
print(
'#======================================================================'
)
print('\n\n')
#map_for_heatmap(mouse_dataFile)
print('\n\n')
print(
'#======================================================================'
)
print(
'#=====================VII. AVERAGING MAPPED SIGNAL====================='
)
print(
'#======================================================================'
)
print('\n\n')
# set_list = ['GANGLIA_H3K27AC','THMYCN_H3K27AC','THMYCN_MYCN']
# set_names = [
# ['CG_H3K27Ac','SCG_H3K27Ac'],
# ['THMYCN1_H3K27Ac','THMYCN_139423_H3K27Ac','THMYCN_139076_H3K27Ac'],
# ['THMYCN2_MYCN','THMYCN_139076_MYCN','THMYCN_139423_MYCN']
# ]
# for i in range(len(set_list)):
# setName = set_list[i]
# names_list =set_names[i]
# print(setName)
# print(names_list)
# #for promoters
# mapped_list = ['%sMM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000/MM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000_%s.gff' % (mappedFolder,name) for name in names_list]
# output_path = '%sMM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000/MM9_NB_MYCN_CONSERVED_PROMOTER_-5000_+5000_%s.gff' % (mappedFolder,setName)
# print(output_path)
# averagingMappedSignal(mapped_list,output_path,setName)
# #for enhancers
# mapped_list = ['%sMM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000/MM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000_%s.gff' % (mappedFolder,name) for name in names_list]
# output_path = '%sMM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000/MM9_NB_MYCN_CONSERVED_ENHANCER_-5000_+5000_%s.gff' % (mappedFolder,setName)
# print(output_path)
# averagingMappedSignal(mapped_list,output_path,setName)
print('\n\n')
print(
'#======================================================================'
)
print(
'#=====================VIII. MAKING HEATMAPS/METAS======================'
)
print(
'#======================================================================'
)
print('\n\n')
def mapGFFLineToAnnot(gffLine,
outFolder,
nBins,
geneDict,
txCollection,
sense='both',
header=''):
'''
for every line produces a file with all of the rectangles to draw
'''
if len(header) == 0:
gffString = '%s_%s_%s_%s' % (gffLine[0], gffLine[6], gffLine[3],
gffLine[4])
else:
gffString = header
diagramTable = [[0, 0, 0, 0]]
nameTable = [['', 0, 0]]
gffLocus = utils.Locus(gffLine[0], int(gffLine[3]), int(gffLine[4]),
gffLine[6], gffLine[1])
scaleFactor = float(nBins) / gffLocus.len()
# plotting buffer for diagrams
plotBuffer = int(gffLocus.len() / float(nBins) * 20)
overlapLoci = txCollection.getOverlap(gffLocus, sense='both')
geneList = [locus.ID() for locus in overlapLoci]
if gffLine[6] == '-':
refPoint = int(gffLine[4])
else:
refPoint = int(gffLine[3])
offsetCollection = utils.LocusCollection([], 500)
for geneID in geneList:
gene = geneDict[geneID]
print(gene.commonName())
if len(gene.commonName()) > 1:
name = gene.commonName()
else:
name = geneID
offset = 4 * len(offsetCollection.getOverlap(gene.txLocus()))
offsetCollection.append(
utils.makeSearchLocus(gene.txLocus(), plotBuffer, plotBuffer))
# write the name of the gene down
if gene.sense() == '+':
geneStart = gene.txLocus().start()
else:
geneStart = gene.txLocus().end()
geneStart = abs(geneStart - refPoint) * scaleFactor
nameTable.append([name, geneStart, -2 - offset])
# draw a line across the entire txLocus
[start, stop] = [
abs(x - refPoint) * scaleFactor for x in gene.txLocus().coords()
]
diagramTable.append([start, -0.01 - offset, stop, 0.01 - offset])
# now draw thin boxes for all txExons
if len(gene.txExons()) > 0:
for txExon in gene.txExons():
[start, stop] = [
abs(x - refPoint) * scaleFactor for x in txExon.coords()
]
diagramTable.append([start, -0.5 - offset, stop, 0.5 - offset])
# now draw fatty boxes for the coding exons if any
if len(gene.cdExons()) > 0:
for cdExon in gene.cdExons():
[start, stop] = [
abs(x - refPoint) * scaleFactor for x in cdExon.coords()
]
diagramTable.append([start, -1 - offset, stop, 1 - offset])
utils.unParseTable(diagramTable,
outFolder + gffString + '_diagramTemp.txt', '\t')
utils.unParseTable(nameTable, outFolder + gffString + '_nameTemp.txt',
'\t')
def findCanidateTFs(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 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 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 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 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