def twilio():
body = request.form['Body'].split(' ')
resp = twiml.Response()
myText = "unknown command. Please enter: <buy/sell TICKER #OFSHARES> to make a trade, or enter <status> to see the current portfolio value."
if len(body) != 3:
if body[0].lower() == "status":
myText = str(utils.get_portfolio_val(g))
resp.message(myText)
return str(resp)
order, sym, value = body
myText = "Nice! You put in an order to " + order.lower() + " " + value + " shares of " + sym.upper()
resp.message(myText)
utils.order(order, sym, value, g)
return str(resp)
def promptio():
myJson = request.get_json()
myText = "enter <@stock_00017 status> to check portfolio value. enter <@stock_00017 buy/sell TICKER #OFSHARES> to make a trade."
body = myJson['message'].split(" ")
if body[0].lower() == "status":
myText = str(utils.get_portfolio_val(g))
if len(body) == 3:
order, sym, value = body
utils.order(order, sym, value, g)
myText = "Nice! You put in an order to " + order.lower() + " " + value + " shares of " + sym.upper()
dat = jsonify(sendmms=False, showauthurl=False, authstate=None,
text=myText, speech=myText, status="OK", webhookreply=None,
images=[{"imageurl": None, "alttext": None}]) # insert json responses here
resp = make_response(dat, 200, {"Content-Type": "application/json"})
return resp
def twilio():
body = request.form['Body'].split(' ')
resp = twiml.Response()
myText = "unknown command. Please enter: <buy/sell TICKER #OFSHARES> to make a trade, or enter <status> to see the current portfolio value."
if len(body) != 3:
if body[0].lower() == "status":
myText = str(utils.get_portfolio_val(g))
resp.message(myText)
return str(resp)
order, sym, value = body
myText = "Nice! You put in an order to " + order.lower(
) + " " + value + " shares of " + sym.upper()
resp.message(myText)
utils.order(order, sym, value, g)
return str(resp)
def visit_character(self, node, children) -> Any:
ident = children[0]['top']
char = children[0]['nodes'][ident]['label']
if char == '" "':
char = ' '
return order(char)
def buy():
order = request.form['order']
sym = request.form['sym']
val = request.form['val']
if utils.valid_order(order, sym, val):
r = utils.order(order, sym, val, g)
return str(r)
return "not valid"
def buy():
order = request.form['order']
sym = request.form['sym']
val = request.form['val']
if utils.valid_order(order, sym, val):
r = utils.order(order, sym, val, g)
return str(r)
return "not valid"
def solve(G, s):
"""
Args:
G: networkx.Graph
s: stress_budget
Returns:
D: Dictionary mapping for student to breakout room r e.g. {0:2, 1:0, 2:1, 3:2}
k: Number of breakout rooms
"""
n = G.order()
happiness = {}
stress = {}
for i in range(n):
happiness[i] = {}
stress[i] = {}
for i in range(n):
for j in range(i + 1, n):
happiness[i][j] = G.get_edge_data(i, j)['happiness']
stress[i][j] = G.get_edge_data(i, j)['stress']
print("Input Size:", n)
if n <= 10: #brute force approach
start_time = time.perf_counter()
bf_arr, bf_val = bruteforce.bruteforce(happiness, stress,
len(list(happiness.keys())), s)
bf_val = round(bf_val, 3)
end_time = time.perf_counter()
print("Brute Force Approach Time:", end_time - start_time)
#ILP
start_time = time.perf_counter()
answer, rooms, best_k = lp.lp_solve(happiness, stress, s, n)
end_time = time.perf_counter()
times.append(end_time - start_time)
print("Gurobi Approach Time: ", end_time - start_time)
print("Gurobi Answer: ", answer)
#print("Gurobi Rooms (raw): ", rooms, best_k)
print("Gurobi Rooms:", order(rooms)[0])
if n <= 10:
#assert bf_val == answer, "Incorrect computation"
print("Brutef Rooms:", bf_arr)
print("Brutef Answer:", bf_val)
return rooms, order(rooms)[1]
def mergeCollections(nameDict, analysisName, output='', superOnly=True):
'''
merges them collections
'''
allLoci = []
namesList = nameDict.keys()
for name in namesList:
seCollection = makeSECollection(nameDict[name]['enhancerFile'], name,
superOnly)
if superOnly:
print "DATASET: %s HAS %s SUPERENHANCERS" % (name,
len(seCollection))
else:
print "DATASET: %s HAS %s ENHANCERS" % (name, len(seCollection))
allLoci += seCollection.getLoci()
print len(allLoci)
mergedCollection = utils.LocusCollection(allLoci, 50)
#stitch the collection together
stitchedCollection = mergedCollection.stitchCollection()
stitchedLoci = stitchedCollection.getLoci()
print "IDENTIFIED %s CONSENSUS ENHANCER REGIONS" % (len(stitchedLoci))
#sort by size and provide a unique ID
sizeList = [locus.len() for locus in stitchedLoci]
sizeOrder = utils.order(sizeList, decreasing=True)
orderedLoci = [stitchedLoci[i] for i in sizeOrder]
for i in range(len(orderedLoci)):
orderedLoci[i]._ID = 'merged_%s_%s' % (analysisName, str(i + 1))
mergedGFF = []
for locus in orderedLoci:
newLine = [
locus.chr(),
locus.ID(), '',
locus.start(),
locus.end(), '',
locus.sense(), '',
locus.ID()
]
mergedGFF.append(newLine)
if len(output) == 0:
return mergedGFF
else:
print "writing merged gff to %s" % (output)
utils.unParseTable(mergedGFF, output, '\t')
return output
def mergeCollections(nameDict,analysisName,output='',superOnly=True):
'''
merges them collections
'''
allLoci = []
namesList = nameDict.keys()
for name in namesList:
seCollection =makeSECollection(nameDict[name]['enhancerFile'],name,superOnly)
if superOnly:
print "DATASET: %s HAS %s SUPERENHANCERS" % (name,len(seCollection))
else:
print "DATASET: %s HAS %s ENHANCERS" % (name,len(seCollection))
allLoci += seCollection.getLoci()
print len(allLoci)
mergedCollection = utils.LocusCollection(allLoci,50)
#stitch the collection together
stitchedCollection = mergedCollection.stitchCollection()
stitchedLoci = stitchedCollection.getLoci()
print "IDENTIFIED %s CONSENSUS ENHANCER REGIONS" % (len(stitchedLoci))
#sort by size and provide a unique ID
sizeList = [locus.len() for locus in stitchedLoci]
sizeOrder = utils.order(sizeList,decreasing=True)
orderedLoci = [stitchedLoci[i] for i in sizeOrder]
for i in range(len(orderedLoci)):
orderedLoci[i]._ID = 'merged_%s_%s' % (analysisName,str(i+1))
mergedGFF = []
for locus in orderedLoci:
newLine = [locus.chr(),locus.ID(),'',locus.start(),locus.end(),'',locus.sense(),'',locus.ID()]
mergedGFF.append(newLine)
if len(output) == 0:
return mergedGFF
else:
print "writing merged gff to %s" % (output)
utils.unParseTable(mergedGFF,output,'\t')
return output
def promptio():
myJson = request.get_json()
myText = "enter <@stock_00017 status> to check portfolio value. enter <@stock_00017 buy/sell TICKER #OFSHARES> to make a trade."
body = myJson['message'].split(" ")
if body[0].lower() == "status":
myText = str(utils.get_portfolio_val(g))
if len(body) == 3:
order, sym, value = body
utils.order(order, sym, value, g)
myText = "Nice! You put in an order to " + order.lower(
) + " " + value + " shares of " + sym.upper()
dat = jsonify(sendmms=False,
showauthurl=False,
authstate=None,
text=myText,
speech=myText,
status="OK",
webhookreply=None,
images=[{
"imageurl": None,
"alttext": None
}]) # insert json responses here
resp = make_response(dat, 200, {"Content-Type": "application/json"})
return resp
def __init__(self, conf_file=None, environment=None):
self.log = utils._setupLogging()
self.containers = {}
if environment:
self.load(environment)
else:
if not conf_file.startswith('/'):
conf_file = os.path.join(os.path.dirname(sys.argv[0]),
conf_file)
data = open(conf_file, 'r')
self.config = yaml.load(data)
# On load order containers into the proper startup sequence
self.start_order = utils.order(self.config['containers'])
def __init__(self, conf_file=None, environment=None):
self.log = utils.setupLogging()
self.containers = {}
self.templates = {}
self.state = 'live'
if environment:
self.load(environment)
else:
# If we didn't get an absolute path to a file, look for it in the current directory.
if not conf_file.startswith('/'):
conf_file = os.path.join(os.path.dirname(sys.argv[0]), conf_file)
data = open(conf_file, 'r')
self.config = yaml.load(data)
# On load, order templates into the proper startup sequence
self.start_order = utils.order(self.config['templates'])
def search():
body = request.get_json()
query_subs: List[Subreddit] = None
try:
query_subs = solr.validate_query(body["subreddits"])
except Exception as e:
traceback.print_exc()
return jsonify({"message": str(e)}), 400
query_words, query_words_lmtzd = utils.analyze_queries(query_subs)
doc_subs: List[Subreddit] = None
doc_subs = solr.get_docs(query_words)
docs_lmtzd = utils.analyze_documents(doc_subs)
result = utils.order(query_words_lmtzd, docs_lmtzd, doc_subs)
return jsonify({"result": list(result)})
def makeRigerTable(foldTableFile, output=''):
'''
blah
'''
#need a table of this format
rigerTable = [[
'Construct', 'GeneSymbol', 'NormalizedScore', 'Construct Rank',
'HairpinWeight'
]]
#set weight to 1 for now
foldTable = utils.parseTable(foldTableFile, '\t')
constructOrder = utils.order([float(line[2]) for line in foldTable[1:]],
decreasing=True)
#make geneCountDict
print("making gene count dictionary")
geneCountDict = defaultdict(int)
for line in foldTable[1:]:
geneCountDict[line[1]] += 1
print("iterating through constructs")
constructRank = 1
for i in constructOrder:
rowIndex = i + 1 # accounts for the header
geneName = foldTable[rowIndex][1]
if geneCountDict[geneName] == 1:
print(
"Gene %s only has one guide RNA. Excluding from FRIGER analysis"
% (geneName))
continue
newLine = foldTable[rowIndex][0:3] + [constructRank, 1]
rigerTable.append(newLine)
constructRank += 1
if len(output) == 0:
output = string.replace(foldTableFile, '_log2Ratio.txt', '_friger.txt')
utils.unParseTable(rigerTable, output, '\t')
return output
def load(self, filename='envrionment.yml'):
self.log.info('Loading environment from: %s', filename)
with open(filename, 'r') as input_file:
self.config = yaml.load(input_file)
self.state = self.config['state']
for tmpl in self.config['templates']:
# TODO fix hardcoded service name and version
self.templates[tmpl] = template.Template(tmpl, self.config['templates'][tmpl], 'service', '0.1')
self.containers[tmpl] = {}
self.start_order = utils.order(self.config['templates'])
for container in self.config['containers']:
tmpl = self.config['containers'][container]['template']
self.containers[tmpl][container] = Container(container, self.config['containers'][container],
self.config['templates'][tmpl]['config'])
def make_T_top_regions(signal_table_path, top=1000):
'''
makes a gff and fasta of the top N T regions based off the signal table
'''
signal_table = utils.parseTable(signal_table_path, '\t')
signal_dict = defaultdict(float)
for line in signal_table[1:]:
signal = (max(float(line[2]) - float(line[3]), 0) +
max(float(line[4]) - float(line[5]), 0)) / 2
signal_dict[line[1]] = signal
signal_vector = [signal_dict[line[1]] for line in signal_table[1:]]
signal_order = utils.order(signal_vector, decreasing=True)
t_top_gff_path = '%sCH22_T_UNION_TOP_%s_-0_+0.gff' % (gffFolder, str(top))
print(t_top_gff_path)
t_top_gff = []
for i in range(top):
signal_row = signal_order[i] + 1
line = signal_table[signal_row]
region_id = line[1]
chrom = region_id.split('(')[0]
coords = region_id.split(':')[-1].split('-')
gff_line = [
chrom, region_id, '', coords[0], coords[1], '', '.', '', region_id
]
t_top_gff.append(gff_line)
utils.unParseTable(t_top_gff, t_top_gff_path, '\t')
t_top_fasta = utils.gffToFasta('HG19', genomeDirectory, t_top_gff)
t_top_fasta_path = '%sHG19_CH22_T_UNION_TOP_%s_-0_+0.fasta' % (fastaFolder,
top)
utils.unParseTable(t_top_fasta, t_top_fasta_path, '')
return t_top_fasta_path
def makeRigerTable(foldTableFile,output=''):
'''
blah
'''
#need a table of this format
rigerTable = [['Construct','GeneSymbol','NormalizedScore','Construct Rank','HairpinWeight']]
#set weight to 1 for now
foldTable = utils.parseTable(foldTableFile,'\t')
constructOrder = utils.order([float(line[2]) for line in foldTable[1:]],decreasing=True)
#make geneCountDict
print("making gene count dictionary")
geneCountDict= defaultdict(int)
for line in foldTable[1:]:
geneCountDict[line[1]] +=1
print("iterating through constructs")
constructRank = 1
for i in constructOrder:
rowIndex = i+1 # accounts for the header
geneName = foldTable[rowIndex][1]
if geneCountDict[geneName] == 1:
print("Gene %s only has one guide RNA. Excluding from FRIGER analysis" % (geneName))
continue
newLine = foldTable[rowIndex][0:3] + [constructRank,1]
rigerTable.append(newLine)
constructRank += 1
if len(output) == 0:
output = string.replace(foldTableFile,'_log2Ratio.txt','_friger.txt')
utils.unParseTable(rigerTable,output,'\t')
return output
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 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')
def visit_character_set(self, node, children) -> Any:
negated = children[0] == '^'
if negated:
children = children[1:]
classes, values = set(), set()
if children[0] in ['-', ']']:
values.add(ord(children[0]))
children = children[1:]
for child in children:
if isinstance(child, dict):
ident = child['top']
label = child['nodes'][ident]['label']
try:
values.update(order(label))
except TypeError:
classes.add(label)
else:
values.update(child)
graph = add_node(
f"{'^' if negated else ''}charset",
font=Font.ITALIC,
shape=Shape.TRAPEZIUM,
color=NEGATED if negated else None,
)
source = graph['top']
for class_ in sorted(classes):
child = add_node(class_, shape=Shape.BOX, style=Style.FILLED)
graph = merge(graph, child)
graph = add_edge(source, child['top'], graph)
for group, symbol in [
(BUT_SPACE, '\\S'),
(BUT_DIGIT, '\\D'),
(BUT_WORD, '\\W'),
(WORD, '\\w'),
(DIGIT, '\\d'),
(SPACE, '\\s'),
]:
if group in values:
child = add_node(symbol, shape=Shape.BOX, style=Style.FILLED)
graph = merge(graph, child)
graph = add_edge(source, child['top'], graph)
values -= group
start, last = None, None
for value in sorted(values):
if last is None:
start, last = value, value
elif value == last + 1:
last = value
else:
label = normal(
start
) if last == start else f"{normal(start)}-{normal(last)}"
child = add_node(label, shape=Shape.BOX)
graph = merge(graph, child)
graph = add_edge(source, child['top'], graph)
start, last = None, None
if last is not None:
label = normal(
start) if last == start else f"{normal(start)}-{normal(last)}"
child = add_node(label, shape=Shape.BOX)
graph = merge(graph, child)
graph = add_edge(source, child['top'], graph)
return graph
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')
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 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 mapEnhancerToGeneTop(rankByBamFile, controlBamFile, genome, annotFile, enhancerFile, transcribedFile='', uniqueGenes=True, searchWindow=50000, noFormatTable=False):
'''
maps genes to enhancers. if uniqueGenes, reduces to gene name only. Otherwise, gives for each refseq
'''
startDict = utils.makeStartDict(annotFile)
enhancerName = enhancerFile.split('/')[-1].split('.')[0]
enhancerTable = utils.parseTable(enhancerFile, '\t')
# internal parameter for debugging
byRefseq = False
if len(transcribedFile) > 0:
transcribedTable = utils.parseTable(transcribedFile, '\t')
transcribedGenes = [line[1] for line in transcribedTable]
else:
transcribedGenes = startDict.keys()
print('MAKING TRANSCRIPT COLLECTION')
transcribedCollection = utils.makeTranscriptCollection(
annotFile, 0, 0, 500, transcribedGenes)
print('MAKING TSS COLLECTION')
tssLoci = []
for geneID in transcribedGenes:
tssLoci.append(utils.makeTSSLocus(geneID, startDict, 0, 0))
# this turns the tssLoci list into a LocusCollection
# 50 is the internal parameter for LocusCollection and doesn't really
# matter
tssCollection = utils.LocusCollection(tssLoci, 50)
geneDict = {'overlapping': defaultdict(
list), 'proximal': defaultdict(list)}
# dictionaries to hold ranks and superstatus of gene nearby enhancers
rankDict = defaultdict(list)
superDict = defaultdict(list)
# list of all genes that appear in this analysis
overallGeneList = []
# find the damn header
for line in enhancerTable:
if line[0][0] == '#':
continue
else:
header = line
break
if noFormatTable:
# set up the output tables
# first by enhancer
enhancerToGeneTable = [
header + ['OVERLAP_GENES', 'PROXIMAL_GENES', 'CLOSEST_GENE']]
else:
# set up the output tables
# first by enhancer
enhancerToGeneTable = [
header[0:9] + ['OVERLAP_GENES', 'PROXIMAL_GENES', 'CLOSEST_GENE'] + header[-2:]]
# next by gene
geneToEnhancerTable = [
['GENE_NAME', 'REFSEQ_ID', 'PROXIMAL_ENHANCERS']]
# next make the gene to enhancer table
geneToEnhancerTable = [
['GENE_NAME', 'REFSEQ_ID', 'PROXIMAL_ENHANCERS', 'ENHANCER_RANKS', 'IS_SUPER', 'ENHANCER_SIGNAL']]
for line in enhancerTable:
if line[0][0] == '#' or line[0][0] == 'R':
continue
enhancerString = '%s:%s-%s' % (line[1], line[2], line[3])
enhancerLocus = utils.Locus(line[1], line[2], line[3], '.', line[0])
# overlapping genes are transcribed genes whose transcript is directly
# in the stitchedLocus
overlappingLoci = transcribedCollection.getOverlap(
enhancerLocus, 'both')
overlappingGenes = []
for overlapLocus in overlappingLoci:
overlappingGenes.append(overlapLocus.ID())
# proximalGenes are transcribed genes where the tss is within 50kb of
# the boundary of the stitched loci
proximalLoci = tssCollection.getOverlap(
utils.makeSearchLocus(enhancerLocus, searchWindow, searchWindow), 'both')
proximalGenes = []
for proxLocus in proximalLoci:
proximalGenes.append(proxLocus.ID())
distalLoci = tssCollection.getOverlap(
utils.makeSearchLocus(enhancerLocus, 1000000, 1000000), 'both')
distalGenes = []
for proxLocus in distalLoci:
distalGenes.append(proxLocus.ID())
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
distalGenes = utils.uniquify(distalGenes)
allEnhancerGenes = overlappingGenes + proximalGenes + distalGenes
# these checks make sure each gene list is unique.
# technically it is possible for a gene to be overlapping, but not proximal since the
# gene could be longer than the 50kb window, but we'll let that slide
# here
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
for refID in proximalGenes:
if distalGenes.count(refID) == 1:
distalGenes.remove(refID)
# Now find the closest gene
if len(allEnhancerGenes) == 0:
closestGene = ''
else:
# get enhancerCenter
enhancerCenter = (int(line[2]) + int(line[3])) / 2
# get absolute distance to enhancer center
distList = [abs(enhancerCenter - startDict[geneID]['start'][0])
for geneID in allEnhancerGenes]
# get the ID and convert to name
closestGene = startDict[
allEnhancerGenes[distList.index(min(distList))]]['name']
# NOW WRITE THE ROW FOR THE ENHANCER TABLE
if noFormatTable:
newEnhancerLine = list(line)
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]), ','))
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]), ','))
newEnhancerLine.append(closestGene)
else:
newEnhancerLine = line[0:9]
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]), ','))
newEnhancerLine.append(
join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]), ','))
newEnhancerLine.append(closestGene)
newEnhancerLine += line[-2:]
enhancerToGeneTable.append(newEnhancerLine)
# Now grab all overlapping and proximal genes for the gene ordered
# table
overallGeneList += overlappingGenes
for refID in overlappingGenes:
geneDict['overlapping'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
overallGeneList += proximalGenes
for refID in proximalGenes:
geneDict['proximal'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
# End loop through
# Make table by gene
print('MAKING ENHANCER ASSOCIATED GENE TSS COLLECTION')
overallGeneList = utils.uniquify(overallGeneList)
enhancerGeneCollection = utils.makeTranscriptCollection(
annotFile, 5000, 5000, 500, overallGeneList)
enhancerGeneGFF = utils.locusCollectionToGFF(enhancerGeneCollection)
# dump the gff to file
enhancerFolder = utils.getParentFolder(enhancerFile)
gffRootName = "%s_TSS_ENHANCER_GENES_-5000_+5000" % (genome)
enhancerGeneGFFFile = "%s%s_%s.gff" % (enhancerFolder, enhancerName,gffRootName)
utils.unParseTable(enhancerGeneGFF, enhancerGeneGFFFile, '\t')
# now we need to run bamToGFF
# Try to use the bamliquidatior_path.py script on cluster, otherwise, failover to local (in path), otherwise fail.
bamliquidator_path = '/ark/home/jdm/pipeline/bamliquidator_batch.py'
if not os.path.isfile(bamliquidator_path):
bamliquidator_path = 'bamliquidator_batch.py'
if not os.path.isfile(bamliquidator_path):
raise ValueError('bamliquidator_batch.py not found in path')
print('MAPPING SIGNAL AT ENHANCER ASSOCIATED GENE TSS')
# map density at genes in the +/- 5kb tss region
# first on the rankBy bam
bamName = rankByBamFile.split('/')[-1]
mappedRankByFolder = "%s%s_%s_%s/" % (enhancerFolder, enhancerName,gffRootName, bamName)
mappedRankByFile = "%s%s_%s_%s/matrix.gff" % (enhancerFolder,enhancerName, gffRootName, bamName)
cmd = 'python ' + bamliquidator_path + ' --sense . -e 200 --match_bamToGFF -r %s -o %s %s' % (enhancerGeneGFFFile, mappedRankByFolder,rankByBamFile)
print("Mapping rankby bam %s" % (rankByBamFile))
print(cmd)
outputRank = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
outputRank = outputRank.communicate()
if len(outputRank[0]) > 0: # test if mapping worked correctly
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (enhancerGeneGFFFile, rankByBamFile))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (enhancerGeneGFFFile, rankByBamFile))
sys.exit()
# next on the control bam if it exists
if len(controlBamFile) > 0:
controlName = controlBamFile.split('/')[-1]
mappedControlFolder = "%s%s_%s_%s/" % (
enhancerFolder, enhancerName,gffRootName, controlName)
mappedControlFile = "%s%s_%s_%s/matrix.gff" % (
enhancerFolder, enhancerName,gffRootName, controlName)
cmd = 'python ' + bamliquidator_path + ' --sense . -e 200 --match_bamToGFF -r %s -o %s %s' % (enhancerGeneGFFFile, mappedControlFolder,controlBamFile)
print("Mapping control bam %s" % (controlBamFile))
print(cmd)
outputControl = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
outputControl = outputControl.communicate()
if len(outputControl[0]) > 0: # test if mapping worked correctly
print("SUCCESSFULLY MAPPED TO %s FROM BAM: %s" % (enhancerGeneGFFFile, controlBamFile))
else:
print("ERROR: FAILED TO MAP %s FROM BAM: %s" % (enhancerGeneGFFFile, controlBamFile))
sys.exit()
# now get the appropriate output files
if len(controlBamFile) > 0:
print("CHECKING FOR MAPPED OUTPUT AT %s AND %s" %
(mappedRankByFile, mappedControlFile))
if utils.checkOutput(mappedRankByFile, 1, 1) and utils.checkOutput(mappedControlFile, 1, 1):
print('MAKING ENHANCER ASSOCIATED GENE TSS SIGNAL DICTIONARIES')
signalDict = makeSignalDict(mappedRankByFile, mappedControlFile)
else:
print("NO MAPPING OUTPUT DETECTED")
sys.exit()
else:
print("CHECKING FOR MAPPED OUTPUT AT %s" % (mappedRankByFile))
if utils.checkOutput(mappedRankByFile, 1, 30):
print('MAKING ENHANCER ASSOCIATED GENE TSS SIGNAL DICTIONARIES')
signalDict = makeSignalDict(mappedRankByFile)
else:
print("NO MAPPING OUTPUT DETECTED")
sys.exit()
# use enhancer rank to order
rankOrder = utils.order([min(rankDict[x]) for x in overallGeneList])
usedNames = []
# make a new dict to hold TSS signal by max per geneName
geneNameSigDict = defaultdict(list)
print('MAKING GENE TABLE')
for i in rankOrder:
refID = overallGeneList[i]
geneName = startDict[refID]['name']
if usedNames.count(geneName) > 0 and uniqueGenes == True:
continue
else:
usedNames.append(geneName)
proxEnhancers = geneDict['overlapping'][
refID] + geneDict['proximal'][refID]
superStatus = max(superDict[refID])
enhancerRanks = join([str(x) for x in rankDict[refID]], ',')
enhancerSignal = signalDict[refID]
geneNameSigDict[geneName].append(enhancerSignal)
newLine = [geneName, refID, join(
proxEnhancers, ','), enhancerRanks, superStatus, enhancerSignal]
geneToEnhancerTable.append(newLine)
#utils.unParseTable(geneToEnhancerTable,'/grail/projects/newRose/geneMapper/foo.txt','\t')
print('MAKING ENHANCER TO TOP GENE TABLE')
if noFormatTable:
enhancerToTopGeneTable = [
enhancerToGeneTable[0] + ['TOP_GENE', 'TSS_SIGNAL']]
else:
enhancerToTopGeneTable = [enhancerToGeneTable[0][0:12] + [
'TOP_GENE', 'TSS_SIGNAL'] + enhancerToGeneTable[0][-2:]]
for line in enhancerToGeneTable[1:]:
geneList = []
if noFormatTable:
geneList += line[-3].split(',')
geneList += line[-2].split(',')
else:
geneList += line[10].split(',')
geneList += line[11].split(',')
geneList = utils.uniquify([x for x in geneList if len(x) > 0])
if len(geneList) > 0:
try:
sigVector = [max(geneNameSigDict[x]) for x in geneList]
maxIndex = sigVector.index(max(sigVector))
maxGene = geneList[maxIndex]
maxSig = sigVector[maxIndex]
if maxSig == 0.0:
maxGene = 'NONE'
maxSig = 'NONE'
except ValueError:
if len(geneList) == 1:
maxGene = geneList[0]
maxSig = 'NONE'
else:
maxGene = 'NONE'
maxSig = 'NONE'
else:
maxGene = 'NONE'
maxSig = 'NONE'
if noFormatTable:
newLine = line + [maxGene, maxSig]
else:
newLine = line[0:12] + [maxGene, maxSig] + line[-2:]
enhancerToTopGeneTable.append(newLine)
# resort enhancerToGeneTable
if noFormatTable:
return enhancerToGeneTable, enhancerToTopGeneTable, geneToEnhancerTable
else:
enhancerOrder = utils.order([int(line[-2])
for line in enhancerToGeneTable[1:]])
sortedTable = [enhancerToGeneTable[0]]
sortedTopGeneTable = [enhancerToTopGeneTable[0]]
for i in enhancerOrder:
sortedTable.append(enhancerToGeneTable[(i + 1)])
sortedTopGeneTable.append(enhancerToTopGeneTable[(i + 1)])
return sortedTable, sortedTopGeneTable, geneToEnhancerTable
def mapEnhancerToGene(annotFile,enhancerFile,transcribedFile='',uniqueGenes=True,searchWindow =50000,noFormatTable = False):
'''
maps genes to enhancers. if uniqueGenes, reduces to gene name only. Otherwise, gives for each refseq
'''
startDict = utils.makeStartDict(annotFile)
enhancerTable = utils.parseTable(enhancerFile,'\t')
#internal parameter for debugging
byRefseq = False
if len(transcribedFile) > 0:
transcribedTable = utils.parseTable(transcribedFile,'\t')
transcribedGenes = [line[1] for line in transcribedTable]
else:
transcribedGenes = startDict.keys()
print('MAKING TRANSCRIPT COLLECTION')
transcribedCollection = utils.makeTranscriptCollection(annotFile,0,0,500,transcribedGenes)
print('MAKING TSS COLLECTION')
tssLoci = []
for geneID in transcribedGenes:
tssLoci.append(utils.makeTSSLocus(geneID,startDict,0,0))
#this turns the tssLoci list into a LocusCollection
#50 is the internal parameter for LocusCollection and doesn't really matter
tssCollection = utils.LocusCollection(tssLoci,50)
geneDict = {'overlapping':defaultdict(list),'proximal':defaultdict(list)}
#dictionaries to hold ranks and superstatus of gene nearby enhancers
rankDict = defaultdict(list)
superDict= defaultdict(list)
#list of all genes that appear in this analysis
overallGeneList = []
if noFormatTable:
#set up the output tables
#first by enhancer
enhancerToGeneTable = [enhancerTable[0]+['OVERLAP_GENES','PROXIMAL_GENES','CLOSEST_GENE']]
else:
#set up the output tables
#first by enhancer
enhancerToGeneTable = [enhancerTable[0][0:9]+['OVERLAP_GENES','PROXIMAL_GENES','CLOSEST_GENE'] + enhancerTable[5][-2:]]
#next by gene
geneToEnhancerTable = [['GENE_NAME','REFSEQ_ID','PROXIMAL_ENHANCERS']]
#next make the gene to enhancer table
geneToEnhancerTable = [['GENE_NAME','REFSEQ_ID','PROXIMAL_ENHANCERS','ENHANCER_RANKS','IS_SUPER']]
for line in enhancerTable:
if line[0][0] =='#' or line[0][0] == 'R':
continue
enhancerString = '%s:%s-%s' % (line[1],line[2],line[3])
enhancerLocus = utils.Locus(line[1],line[2],line[3],'.',line[0])
#overlapping genes are transcribed genes whose transcript is directly in the stitchedLocus
overlappingLoci = transcribedCollection.getOverlap(enhancerLocus,'both')
overlappingGenes =[]
for overlapLocus in overlappingLoci:
overlappingGenes.append(overlapLocus.ID())
#proximalGenes are transcribed genes where the tss is within 50kb of the boundary of the stitched loci
proximalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancerLocus,searchWindow,searchWindow),'both')
proximalGenes =[]
for proxLocus in proximalLoci:
proximalGenes.append(proxLocus.ID())
distalLoci = tssCollection.getOverlap(utils.makeSearchLocus(enhancerLocus,1000000,1000000),'both')
distalGenes =[]
for proxLocus in distalLoci:
distalGenes.append(proxLocus.ID())
overlappingGenes = utils.uniquify(overlappingGenes)
proximalGenes = utils.uniquify(proximalGenes)
distalGenes = utils.uniquify(distalGenes)
allEnhancerGenes = overlappingGenes + proximalGenes + distalGenes
#these checks make sure each gene list is unique.
#technically it is possible for a gene to be overlapping, but not proximal since the
#gene could be longer than the 50kb window, but we'll let that slide here
for refID in overlappingGenes:
if proximalGenes.count(refID) == 1:
proximalGenes.remove(refID)
for refID in proximalGenes:
if distalGenes.count(refID) == 1:
distalGenes.remove(refID)
#Now find the closest gene
if len(allEnhancerGenes) == 0:
closestGene = ''
else:
#get enhancerCenter
enhancerCenter = (int(line[2]) + int(line[3]))/2
#get absolute distance to enhancer center
distList = [abs(enhancerCenter - startDict[geneID]['start'][0]) for geneID in allEnhancerGenes]
#get the ID and convert to name
closestGene = startDict[allEnhancerGenes[distList.index(min(distList))]]['name']
#NOW WRITE THE ROW FOR THE ENHANCER TABLE
if noFormatTable:
newEnhancerLine = list(line)
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]),','))
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]),','))
newEnhancerLine.append(closestGene)
else:
newEnhancerLine = line[0:9]
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in overlappingGenes]),','))
newEnhancerLine.append(join(utils.uniquify([startDict[x]['name'] for x in proximalGenes]),','))
newEnhancerLine.append(closestGene)
newEnhancerLine += line[-2:]
enhancerToGeneTable.append(newEnhancerLine)
#Now grab all overlapping and proximal genes for the gene ordered table
overallGeneList +=overlappingGenes
for refID in overlappingGenes:
geneDict['overlapping'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
overallGeneList+=proximalGenes
for refID in proximalGenes:
geneDict['proximal'][refID].append(enhancerString)
rankDict[refID].append(int(line[-2]))
superDict[refID].append(int(line[-1]))
#End loop through
#Make table by gene
overallGeneList = utils.uniquify(overallGeneList)
#use enhancer rank to order
rankOrder = utils.order([min(rankDict[x]) for x in overallGeneList])
usedNames = []
for i in rankOrder:
refID = overallGeneList[i]
geneName = startDict[refID]['name']
if usedNames.count(geneName) > 0 and uniqueGenes == True:
continue
else:
usedNames.append(geneName)
proxEnhancers = geneDict['overlapping'][refID]+geneDict['proximal'][refID]
superStatus = max(superDict[refID])
enhancerRanks = join([str(x) for x in rankDict[refID]],',')
newLine = [geneName,refID,join(proxEnhancers,','),enhancerRanks,superStatus]
geneToEnhancerTable.append(newLine)
#resort enhancerToGeneTable
if noFormatTable:
return enhancerToGeneTable,geneToEnhancerTable
else:
enhancerOrder = utils.order([int(line[-2]) for line in enhancerToGeneTable[1:]])
sortedTable = [enhancerToGeneTable[0]]
for i in enhancerOrder:
sortedTable.append(enhancerToGeneTable[(i+1)])
return sortedTable,geneToEnhancerTable
