def findInsertions(bwFile, bedData, interval, x):
if interval =='start':
sL = int(bedData[x][1])-options.l
sR = int(bedData[x][1])+options.r
elif interval == 'end':
sL = int(bedData[x][2])-options.l
sR = int(bedData[x][2])+options.r
else:
sL = int(bedData[x][1])-options.l
sR = int(bedData[x][2])+options.r
# get signal data
f = open(bwFile, "rb")
bigwig_class = BigWigFile(f)
try: signal = bigwig_class.get_as_array(bedData[x][0],sL,sR)
except OverflowError: signal = np.array([np.nan]*(sR-sL))
f.close()
if signal is not None:
if np.sum(np.isfinite(signal)) > 0:
out = np.nanmean(signal)
else: out = 0
else: out = 0
out = signal
return out
def get_mean_phastcons(bedtool, phastcons_location, sample_size=1000):
"""
Get means phastcons scores for all intervals in a bed tool
bedtool - bedtool to extract data from
phastcons_location - location of phastcons file
"""
with open(phastcons_location) as bw_file:
bw = BigWigFile(bw_file)
data = []
for bedline in bedtool.random_subset(min(len(bedtool), sample_size)):
conservation_values = bw.get_as_array(bedline.chrom, bedline.start,
bedline.stop)
try:
if len(conservation_values) > 0:
mean_phastcons = np.mean(conservation_values)
else:
mean_phastcons = 0
data.append(mean_phastcons)
except TypeError:
pass
return data
def getsignal(inputfile, outputfile, pcut, pspan):
# p=BwIO(pcut)
# chrom_len = {}
# for i in p.chromosomeTree['nodes']:
# chrom_len[i['key']] = i['chromSize']
pcutbw = BigWigFile(open(pcut, 'rb'))
inf = open(inputfile)
testll = inf.readline().split()
ml = int(testll[2]) - int(testll[1])
inf.seek(0)
outf = open(outputfile, 'w')
for line in inf:
ll = line.split()
# if not chrom_len.has_key(ll[0]):
# continue
cut = list(
pcutbw.summarize(ll[0],
int(ll[1]) + ml / 2 - pspan,
int(ll[1]) + ml / 2 + pspan, 2 * pspan).sum_data)
TC = sum(cut)
C = sum(cut[(pspan - ml / 2):(pspan - ml / 2 + ml)])
L = sum(cut[(pspan - ml / 2 - ml):(pspan - ml / 2)])
R = sum(cut[(pspan - ml / 2 + ml):(pspan - ml / 2 + 2 * ml)])
FOS = -1 * ((C + 1) / (R + 1) + (C + 1) / (L + 1))
newll = ll + [TC, FOS]
outf.write("\t".join(map(str, newll)) + "\n")
outf.close()
def Readbw(bwfile,chrm,start,end,n):
bwHandle=BigWigFile(open(bwfile, 'rb'))
summary = bwHandle.summarize(chrm,int(start),int(end),(int(end)-int(start))/n)
count = map(sudocount,summary.valid_count)
sum = summary.sum_data
scores = list(sum/count)
return scores
def getChromatinDataSeries(bigwigFile, libraryTable, sgInfoTable, tssTable, colname = '', naValue = 0): bwindex = BigWigFile(open(bigwigFile)) chromDict = tssTable['chromosome'].to_dict() chromatinScores = [] for name, sgInfo in sgInfoTable.iterrows(): geneTup = (sgInfo['gene_name'],','.join(sgInfo['transcript_list'])) if geneTup not in chromDict: #negative controls chromatinScores.append(np.nan) continue if sgInfo['strand'] == '+': sgRange = sgInfo['pam coordinate'] + sgInfo['length'] else: sgRange = sgInfo['pam coordinate'] - sgInfo['length'] chrom = chromDict[geneTup] chromatinArray = bwindex.get_as_array(chrom, min(sgInfo['pam coordinate'], sgRange), max(sgInfo['pam coordinate'], sgRange)) if chromatinArray is not None and len(chromatinArray) > 0: chromatinScores.append(np.nanmean(chromatinArray)) else: #often chrY when using K562 data.. # print name # print chrom, min(sgInfo['pam coordinate'], sgRange), max(sgInfo['pam coordinate'], sgRange) chromatinScores.append(np.nan) chromatinSeries = pd.Series(chromatinScores, index=libraryTable.index, name = colname) return chromatinSeries.fillna(naValue)
def createMappabilityList(fragmentsMap, bwfile, fragmentCount, options):
# keep record which fragment has decent mappability
mappable = np.zeros((fragmentCount, ), dtype=np.float)
# lazy load
from bx.intervals.io import GenomicIntervalReader
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile(open(bwfile))
for fragmentId in fragmentsMap.keys():
(chrom, start, end) = fragmentsMap[fragmentId]
if (options.vverbose):
print >> sys.stdout, "- process %s %d-%d " % (chrom, start, end)
try:
mappable[fragmentId] = bw.query(chrom, start, end, 1)[0]["mean"]
if (np.isnan(mappable[fragmentId])):
mappable[fragmentId] = 0
except:
mappable[fragmentId] = 0.
# problem with invalid values
if (options.vverbose):
print >> sys.stderr, "Problem with bw file at %s %d-%d" % (
chrom, start, end)
print traceback.format_exc()
return mappable
def main():
p = optparse.OptionParser(__doc__)
p.add_option('-A', '--absolute', action='store_true',dest='A',\
default=False, help='absolute threshold')
p.add_option('-s','--standard_background', action='store_true',\
dest='stdbg')
p.add_option('-D', '--debug', action='store_true', dest='debug')
options, args = p.parse_args()
debug_c = 0
BEDFILE = open(args[0], 'rU')
BW = BigWigFile(file=open(args[1]))
BEDout = open(args[2], 'w')
for line in BEDFILE:
print(line)
line = line.strip().split('\t')
x = BW.query(line[0], int(line[1]), int(line[2]),1)
line.append(str(round(x[0]['mean'], 5)))
BEDout.write("\t".join(line)+"\n")
"""
for i in x:
print i['mean']
"""
if options.debug:
debug_c +=1
if debug_c >= 10:
break
if __name__ == '__main__':
main()
def summary(bwfile,bedfile,topnumber,out):
total_result = []
p=BwIO(bwfile)
chrom_len = {}
for i in p.chromosomeTree['nodes']:
chrom_len[i['key']] = i['chromSize']
bwHandle=BigWigFile(open(bwfile, 'rb'))
inf = open(bedfile)
t = time.time()
for line in inf:
ll = line.split()
ll[3]="-"
if chrom_len.has_key(ll[0]):
summary = bwHandle.summarize(ll[0],int(ll[1]),int(ll[2]),1)
if summary.valid_count == 0:
mean_value = 0
else:
mean_value = (summary.sum_data/summary.valid_count)[0]
total_result.append(ll+[mean_value])
inf.close()
total_result.sort(reverse=True,key=lambda x:x[-1])
outf = open(out,'w')
print "scaning 1st ",time.time()-t
t=time.time()
for i in range(topnumber):
ll = total_result[i]
summary = bwHandle.summarize(ll[0],int(ll[1]),int(ll[2]),(int(ll[2])-int(ll[1])))
additional_value = ",".join(map(str,list(summary.sum_data)))
result = map(str,(ll+[additional_value]))
outf.write("\t".join(result)+"\n")
outf.close()
print "scaning 2nd ",time.time()-t
def main():
usage="%prog [options]"
parser = OptionParser(usage,version="%prog " + __version__)
parser.add_option("-i","--bwfile1",action="store",type="string",dest="BigWig_File1",help="BigWig files")
parser.add_option("-j","--bwfile2",action="store",type="string",dest="BigWig_File2",help="BigWig files")
parser.add_option("-a","--action",action="store",type="string",dest="action",help='After pairwise align two bigwig files, perform the follow actions (Only select one keyword):"Add" = add signals. "Average" = average signals. "Division"= divide bigwig2 from bigwig1. Add 1 to both bigwig. "Max" = pick the signal that is larger. "Min" = pick the signal that is smaller. "Product" = multiply signals. "Subtract" = subtract signals in 2nd bigwig file from the corresponiding ones in the 1st bigwig file. "geometricMean" = take the geometric mean of signals.')
parser.add_option("-o","--output",action="store",type="string",dest="output_wig",help="Output wig file")
parser.add_option("-s","--chromSize",action="store",type="string",dest="chromSize",help="Chromosome size file. Tab or space separated text file with 2 columns: first column is chromosome name, second column is size of the chromosome.")
parser.add_option("-c","--chunk",action="store",type="int",dest="chunk_size",default=100000,help="Chromosome chunk size. Each chomosome will be cut into samll chunks of this size. Decrease chunk size will save more RAM. default=%default (bp)")
(options,args)=parser.parse_args()
if not (options.BigWig_File1 and options.BigWig_File2 and options.output_wig and options.chromSize):
parser.print_help()
sys.exit(0)
OUT=open(options.output_wig,'w')
bw1 = BigWigFile( file=open(options.BigWig_File1) )
bw2 = BigWigFile( file=open(options.BigWig_File2) )
chrom_sizes = load_chromsize(options.chromSize)
for chr_name, chr_size in chrom_sizes.items(): #iterate each chrom
print >>sys.stderr, "Processing " + chr_name + " ..."
OUT.write('variableStep chrom='+chr_name+'\n')
for interval in BED.tillingBed(chrName = chr_name,chrSize = chr_size,stepSize = options.chunk_size):
coord = interval[1]
bw_signal1 = bw1.get_as_array(chr_name,interval[1],interval[2])
bw_signal2 = bw2.get_as_array(chr_name,interval[1],interval[2])
if all_nan(bw_signal1) and all_nan(bw_signal2):
continue
bw_signal1 = replace_nan( bw_signal1 )
bw_signal2 = replace_nan( bw_signal2 )
call_back = getattr(twoList,options.action)
for v in call_back(bw_signal1,bw_signal2):
coord +=1
if v != 0: print >>OUT, "%d\t%.2f" % (coord,v)
def get_mean_phastcons(bedtool, phastcons_location):
"""
Get means phastcons scores for all intervals in a bed tool
bedtool - bedtool to extract data from
phastcons_location - location of phastcons file
"""
f = open(phastcons_location, 'r')
bw = BigWigFile(file=f)
#if bedtool
data = np.ndarray(len(bedtool))
for i, bedline in enumerate(bedtool):
conservation_values = bw.get_as_array(bedline.chrom, bedline.start, bedline.stop)
if len(conservation_values) > 0:
mean_phastcons = np.mean(conservation_values)
else:
mean_phastcons = 0
data[i] = mean_phastcons
return data
def createMappabilityList(fragmentsMap, bwfile, fragmentCount, options):
# keep record which fragment has decent mappability
mappable = np.zeros((fragmentCount,), dtype=np.float)
# lazy load
from bx.intervals.io import GenomicIntervalReader
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile( open( bwfile ) )
for fragmentId in fragmentsMap.keys():
(chrom, start, end) = fragmentsMap[fragmentId]
if (options.vverbose):
print >> sys.stdout, "- process %s %d-%d " % (chrom, start, end)
try:
mappable[fragmentId] = bw.query(chrom, start, end, 1)[0]["mean"]
if (np.isnan(mappable[fragmentId])):
mappable[fragmentId] = 0
except:
mappable[fragmentId] = 0.
# problem with invalid values
if (options.vverbose):
print >> sys.stderr, "Problem with bw file at %s %d-%d" % (chrom, start, end)
print traceback.format_exc()
return mappable
def get_mean_phastcons(bedtool, phastcons_location, sample_size = 1000):
"""
Get means phastcons scores for all intervals in a bed tool
bedtool - bedtool to extract data from
phastcons_location - location of phastcons file
"""
with open(phastcons_location) as bw_file:
bw = BigWigFile(bw_file)
data = []
for bedline in bedtool.random_subset(min(len(bedtool), sample_size)):
conservation_values = bw.get_as_array(bedline.chrom, bedline.start, bedline.stop)
try:
if len(conservation_values) > 0:
mean_phastcons = np.mean(conservation_values)
else:
mean_phastcons = 0
data.append(mean_phastcons)
except TypeError:
pass
return data
def count_cut_nmers(fp, w_minus, lflank, rflank, single_nmer_cutoff, sequence,
offset):
"""
count the number of cuts associated with each nmer in sequence covered by X.
offset is the position of the cut to be associated with each nmer.
if offset = 0 the first base of the tag is lined up with the nmer start
"""
# w_plus_H=BigWigFile(open(w_plus, 'rb'))
w_minus_H = BigWigFile(open(w_minus, 'rb'))
genome = twobitreader.TwoBitFile(sequence)
# keep count of the number of occurrences of each n-mer
seq_nmer_dict = {}
cut_nmer_dict = {}
for line in fp.readlines():
ll = line.split()
chrm = ll[0]
start = int(ll[1])
end = int(ll[2])
# pseq = genome[chrm][(start-lflank+offset):(end+rflank+offset)].upper()
nseq = genome[chrm][(start - lflank - offset):(end + rflank -
offset)].upper()
# cp = list(w_plus_H.summarize(ll[0],start,end,end-start).sum_data)
cn = list(w_minus_H.summarize(ll[0], start, end, end - start).sum_data)
#each = (len(ll)-5)/2
#cp = (map(float,ll[5:(5+each)]))
#cn = (map(float,ll[(5+each):(5+each*2)]))
for k in range(len(cn)):
# p_cut = cp[k]
n_cut = cn[k]
# p_seq = pseq[k:(k+lflank+rflank)]
n_seq = nseq[(k + 1):(k + lflank + rflank + 1)]
# rev_n_seq = rev(n_seq)
# if 'N' not in p_seq and p_cut <= single_nmer_cutoff :
# try:
# cut_nmer_dict[ p_seq ] += p_cut
# except:
# cut_nmer_dict[ p_seq ] = p_cut
# try:
# seq_nmer_dict[ p_seq ] += 1
# except:
# seq_nmer_dict[ p_seq ] = 1
if 'N' not in n_seq and n_cut <= single_nmer_cutoff:
rev_n_seq = rev(n_seq)
try:
cut_nmer_dict[rev_n_seq] += n_cut
except:
cut_nmer_dict[rev_n_seq] = n_cut
try:
seq_nmer_dict[rev_n_seq] += 1
except:
seq_nmer_dict[rev_n_seq] = 1
return seq_nmer_dict, cut_nmer_dict
def refine_with_summit(_soft,_mark,_tissue):
_temp_peak = [i.rstrip().split('\t') for i in open("/Data/adam/dnase/top_bed/{0}.{1}.{2}.bed"\
.format(_soft,_mark,_tissue))]
_temp_bw = open("/Data/adam/dnase/bigwig/{0}.{1}.rep0.bw".format(_mark,_tissue))
_temp_enrich = open("/Data/adam/dnase/enrich_bed/{0}.{1}.{2}.bed".format(_soft,_mark,_tissue),'w')
_bw = BigWigFile(file=_temp_bw)
for line in _temp_peak:
vals = _bw.get(line[0],int(line[1]),int(line[2]))
vals =tuple(vals)
if len(vals)>0:
maxs = 0
for _key in vals:
if float(_key[2])>maxs:
maxs = float(_key[2])
summit = _key[:2]
summit_p=int((float(summit[0])+float(summit[1]))/2)
if summit_p-1000>0:
print >> _temp_enrich, "{0}\t{1}\t{2}".format(line[0],str(summit_p-1000),str(summit_p+999))
else:
print >> _temp_enrich, "{0}\t{1}\t{2}".format(line[0],1,2000)
_temp_enrich.close()
sh('sort -k 1,1 -k 2g,2g /Data/adam/dnase/enrich_bed/{0}.{1}.{2}.bed| bedtools merge -i stdin\
>/Data/adam/dnase/enrich_merge_bed/{0}.{1}.{2}.bed'.format(_soft,_mark,_tissue))
sh('bash ../get_enrich.sh /Data/adam/dnase/enrich_merge_bed/{0}.{1}.{2}.bed {1} {2} {3}'\
.format(_soft,_mark,_tissue,_soft))
def get_signal(inputfile, output, bwfiles, bwfolder, extend):
signalbw = bwfiles.strip().strip(',').split(',')
if not bwfolder:
bwfolder = "./"
if not bwfolder.endswith('/'):
bwfolder += '/'
bwHs = []
for sb in signalbw:
if sb.startswith('/'):
bwHs.append(BigWigFile(open(sb, 'rb')))
else:
bwHs.append(BigWigFile(open(bwfolder + sb, 'rb')))
inf = open(inputfile)
outf = open(output, 'w')
for line in inf:
ll = line.split()
if "_" in ll[0]:
continue
#center = (int(ll[1]) + int(ll[2]))/2
#S = max(0,center - extend)
#E = center + extend
#C = (int(ll[1]) + int(ll[2]) ) /2
#S = C - extend
#E = C + extend
S = int(ll[1])
E = int(ll[2])
for bwHandle in bwHs:
try:
signal1 = (bwHandle.summarize(ll[0], max(0, S - extend), S,
20))
signal2 = (bwHandle.summarize(ll[0], S, E, 20))
signal3 = (bwHandle.summarize(ll[0], E, E + extend, 20))
binlen1 = extend * 1.0 / 20
binlen2 = (E - S) * 1.0 / 20
binlen3 = extend * 1.0 / 20
if type(signal1.sum_data) == None or type(
signal2.sum_data) == None or type(
signal3.sum_data) == None:
addsig = [0] * 60
else:
addsig1 = signal1.sum_data / binlen1 #float(signal.sum_data/signal.valid_count)
addsig2 = signal2.sum_data / binlen2
addsig3 = signal3.sum_data / binlen3
addsig = list(addsig1) + list(addsig2) + list(addsig3)
except:
#print 'c2',line
addsig = [0] * 60 #'nan'
# ll.extend(list(signal.sum_data/signal.valid_count))
if len(ll) >= 6 and ll[5] == "-":
ll.extend(addsig[::-1])
else:
ll.extend(addsig)
outf.write("\t".join(map(str, ll)) + "\n")
inf.close()
outf.close()
def getsignal(inputfile,outputfile,BGmatrix,pcut,ncut,Ipcut,Incut,pspan,tspan,gen,left,right,fetch_length=100):
# p=BwIO(pcut)
# chrom_len = {}
# for i in p.chromosomeTree['nodes']:
# chrom_len[i['key']] = i['chromSize']
genome = twobitreader.TwoBitFile(gen)
pcutbw = BigWigFile(open(pcut, 'rb'))
ncutbw = BigWigFile(open(ncut, 'rb'))
Ipcutbw = BigWigFile(open(Ipcut, 'rb'))
Incutbw = BigWigFile(open(Incut, 'rb'))
inf = open(inputfile)
testll = inf.readline().split()
ml = int(testll[2]) - int(testll[1])
pspan = pspan - ml/2
inf.seek(0)
pBG,nBG = readBG(BGmatrix)
outf = open(outputfile,'w')
for line in inf:
ll = line.split()
chrom = ll[0]
start = int(ll[1])
end = int(ll[2])
strand = ll[5]
seq = genome[chrom][(start-pspan-left):(end + pspan+right)]
pout = make_cut(pcutbw,ll,pspan,fetch_length)
nout = make_cut(ncutbw,ll,pspan,fetch_length)
Ipout = make_cut(Ipcutbw,ll,pspan,fetch_length)
Inout = make_cut(Incutbw,ll,pspan,fetch_length)
if strand == "-":
pout,nout = nout,pout
Ipout,Inout = Inout,Ipout
if pout == 'NA':
continue
if 'N' in seq.upper():
continue
#print 1
pseq = seq[:-1]
nseq = seq[1:]
p=[]
n=[]
for k in range(len(pseq) +1 - left-right):
p.append(pBG[pseq[k:k+left+right].upper()])
n.append(nBG[nseq[k:k+left+right].upper()])
if strand != '-':
pbglist = p
nbglist = n
else:
pbglist = n[::-1]
nbglist = p[::-1]
TC,FOS = makeTCFOS(pcutbw,ncutbw,ll,tspan,ml)
newll = ll + [TC,FOS] + pout + nout + Ipout + Inout + pbglist + nbglist
outf.write("\t".join(map(str,newll))+"\n")
outf.close()
inf.close()
def Main():
global args
args = ParseArg()
bw = BigWigFile(open(args.bigwig))
CheckFolderExist(args.output)
fout = WriteToFile(args.output + '/' + args.name + '.bed')
wout = WriteToFile(args.output + '/' + args.name + '.wig')
genome = LoadGenome(args.genome)
if args.smooth:
logging("Options: turn on smooth mode")
for chrom in SortGenome(genome):
chrom_size = genome[chrom]
logging("Process: %s\t%d" % (chrom, chrom_size))
array = bw.get_as_array(chrom, 0, chrom_size)
invalid = np.isnan(array)
array[invalid] = 0
agg_array = []
start = 0
stop = args.window
for nn in range(int(math.ceil(len(array) / float(args.window)))):
if stop >= len(array):
stop = len(array)
agg_array.append(np.mean(array[start:stop]))
break
agg_array.append(np.mean(array[start:stop]))
start += args.window
stop += args.window
agg_array = np.array(agg_array)
if args.smooth:
smooth_array = Smooth(agg_array)
else:
smooth_array = agg_array
print >> wout, "variableStep chrom=%s span=%d" % (chrom, args.window)
for nn, value in enumerate(smooth_array):
if nn == 0:
print >> fout, "%s\t0\t%d\t%.6f" % (chrom,
(nn + 1) * args.window,
float(value))
print >> wout, "%d\t%.6f" % (nn + 1, value)
elif nn == len(smooth_array) - 1:
print >> fout, "%s\t%d\t%d\t%.6f" % (chrom, nn * args.window,
chrom_size, float(value))
print >> wout, "variableStep chrom=%s span=%d" % (
chrom, chrom_size - ((nn) * args.window))
print >> wout, "%d\t%.6f" % (nn * args.window + 1,
float(value))
else:
print >> fout, "%s\t%d\t%d\t%.6f" % (chrom, nn * args.window,
(nn + 1) * args.window,
float(value))
print >> wout, "%d\t%.6f" % (nn * args.window + 1,
float(value))
fout.flush()
wout.flush()
wig2bw = "wigToBigWig -clip %s %s %s" % (args.output + '/' + args.name +
'.wig', args.genome, args.output +
'/' + args.name + '.bw')
os.system(wig2bw)
logging("Finish: TSA_smooth DONE!!!")
def summarize(self, interval, bins=None, method='summarize',
function='mean'):
# We may be dividing by zero in some cases, which raises a warning in
# NumPy based on the IEEE 754 standard (see
# http://docs.scipy.org/doc/numpy/reference/generated/
# numpy.seterr.html)
#
# That's OK -- we're expecting that to happen sometimes. So temporarily
# disable this error reporting for the duration of this method.
orig = np.geterr()['invalid']
np.seterr(invalid='ignore')
if (bins is None) or (method == 'get_as_array'):
bw = BigWigFile(open(self.fn))
s = bw.get_as_array(
interval.chrom,
interval.start,
interval.stop,)
if s is None:
s = np.zeros((interval.stop - interval.start,))
else:
s[np.isnan(s)] = 0
elif method == 'ucsc_summarize':
if function in ['mean', 'min', 'max', 'std', 'coverage']:
return self.ucsc_summarize(interval, bins, function=function)
else:
raise ValueError('function "%s" not supported by UCSC\'s'
'bigWigSummary')
else:
bw = BigWigFile(open(self.fn))
s = bw.summarize(
interval.chrom,
interval.start,
interval.stop, bins)
if s is None:
s = np.zeros((bins,))
else:
if function == 'sum':
s = s.sum_data
if function == 'mean':
s = s.sum_data / s.valid_count
s[np.isnan(s)] = 0
if function == 'min':
s = s.min_val
s[np.isinf(s)] = 0
if function == 'max':
s = s.max_val
s[np.isinf(s)] = 0
if function == 'std':
s = (s.sum_squares / s.valid_count)
s[np.isnan(s)] = 0
# Reset NumPy error reporting
np.seterr(divide=orig)
return s
def wig_reader(infile, chrom_sizes=None, informat='wiggle', bin_size=2000):
'''infile: either a wiggle or bigwig format file
chromsize: chrom_name: size, only needed is format is bigwig
format: either 'wiggle' or 'bigwig'
return: chrom, position (0-based), value
'''
if informat.upper() == 'WIGGLE':
point_num = 1
count = 0
for chrom, start, end, strand, score in bx.wiggle.IntervalReader(
infile):
yield (chrom, start, end, score)
"""
count += 1
if count ==1:
chrom = fields[0]
up_bound = fields[1]+1
score = fields[2]
continue
if (fields[0] == chrom) and (fields[1] +1 == up_bound + 1) and (fields[2] == score):
point_num += 1
up_bound = fields[1]+1
continue
else:
yield((chrom, up_bound - point_num, up_bound, score))
chrom = fields[0]
score = fields[2]
up_bound = fields[1]+1
point_num = 1
"""
elif informat.upper() == 'BIGWIG':
bw_obj = BigWigFile(file=open(infile))
for chr_name, chr_size in list(chrom_sizes.items()):
for chrom, st, end in BED.tillingBed(chrName=chr_name,
chrSize=chr_size,
stepSize=bin_size):
sig_list = bw_obj.get_as_array(chrom, st, end)
if sig_list is None:
continue
sig_list = numpy.nan_to_num(sig_list)
if numpy.sum(sig_list) == 0:
continue
low_bound = st
point_num = 1
score = sig_list[0]
for value in (sig_list[1:]):
if value == score:
point_num += 1
else:
yield ((chrom, low_bound, low_bound + point_num,
score))
score = value
low_bound = low_bound + point_num
point_num = 1
else:
raise Exception("Unknown format. Must be 'wiggle' or 'bigwig'")
def get_signal(inputfile, output, bwfiles, extend, N, bwfolder):
signalbw = bwfiles.strip().strip(',').split(',')
if not bwfolder:
bwfolder = "./"
if not bwfolder.endswith('/') and not bwfolder != "":
bwfolder += '/'
bwHs = []
for sb in signalbw:
if sb.startswith('/') or startswith("./") or startswith("../"):
bwHs.append(BigWigFile(open(sb, 'rb')))
else:
bwHs.append(BigWigFile(open(bwfolder + sb, 'rb')))
inf = open(inputfile)
outf = open(output, 'w')
for line in inf:
ll = line.split()
if "_" in ll[0]:
continue
if len(ll) >= 6 and ll[5] == "-":
start = int(ll[2])
strand_flap = 1
else:
start = int(ll[1])
strand_flap = 0
S = max(0, start - extend)
E = start + extend
# S = int(ll[1])
# E = int(ll[2])
outdata = ll
for bwHandle in bwHs:
try:
signal = (bwHandle.summarize(ll[0], S, E, N))
binlen = (E - S) * 1.0 / N
if type(signal.sum_data) == None:
print 'c1', line
addsig = ["na"] * N
else:
addsig = list(
signal.sum_data * 1.0 /
(binlen)) #float(signal.sum_data/signal.valid_count)
except:
print 'c2', line
addsig = ["na"] * N #'nan'
# ll.extend(list(signal.sum_data/signal.valid_count))
if strand_flap == 1:
ll.extend(addsig[::-1])
else:
ll.extend(addsig)
# ll.extend(list(signal.sum_data/signal.valid_count))
outf.write("\t".join(map(str, ll)) + "\n")
inf.close()
outf.close()
def bigwig_to_wav(args):
import numpy as np
from bx.bbi.bigwig_file import BigWigFile
from scipy.signal import convolve
from scipy.stats import norm
from scipy.ndimage import zoom
logger.info('read input BigWigfile: ' + args.bigwig_file)
f_bigwig = open(args.bigwig_file, 'rb')
logger.info('read input BED file: ' + args.bed_file)
f_bed = open(args.bed_file, 'r')
bigwig = BigWigFile(f_bigwig)
smooth_filter = None
scale_factors = None
if args.smooth == 'boxcar':
smooth_filter = np.ones(args.window_size, dtype=np.float32)
elif args.smooth == 'gaussian':
smooth_filter = norm.pdf(
np.linspace(-3, 3, args.window_size * 3,
endpoint=True)).astype(np.float32)
if args.smooth != 'none':
scale_factors = convolve(np.ones(smooth_filter.shape[0]),
smooth_filter,
mode='same')
if not os.path.exists(args.output_dir):
logger.info('create output directory: ' + args.output_dir)
os.makedirs(args.output_dir)
for line in f_bed:
c = line.strip().split('\t')
chrom = c[0]
start = int(c[1])
end = int(c[2])
x = np.nan_to_num(bigwig.get_as_array(chrom, start, end))
# zoom the signals
x = zoom(x, args.zoom)
if args.smooth != 'none':
# smooth the raw signal with a moving window
x = convolve(x, smooth_filter, mode='same')
# scale the signal
filter_length = smooth_filter.shape[0]
x[:(filter_length / 2)] /= scale_factors[:(filter_length / 2)]
x[(-filter_length / 2):] /= scale_factors[(-filter_length / 2):]
if x.shape[0] > filter_length:
x[(filter_length / 2):(-filter_length /
2)] /= np.sum(smooth_filter)
wav_file = os.path.join(args.output_dir,
'%s:%d-%d.wav' % (chrom, start, end))
logger.info('create wav file: ' + wav_file)
modulate(x,
wav_file,
sample_rate=args.sample_rate,
n_channels=args.n_channels)
def refine_with_summit(_soft,_mark,_tissue,_reps):
_temp_peak = [i.rstrip().split('\t') for i in open("/Data/adam/dnase/sort_bed/{0}.{1}.{2}.bed"\
.format(_soft,_mark,_tissue))]
_temp_bw = open("/Data/adam/dnase/bigwig/{0}.{1}.{2}.bw".format(_mark,_tissue,))
_temp_enrich = open("/Data/adam/dnase/enrich_all_bed/{0}.{1}.{2}.bed".format(_soft,_mark,_tissue),'w')
_bw = BigWigFile(file=_temp_bw)
for line in _temp_peak:
vals = _bw.get(line[0],int(line[1]),int(line[2]))
vals =tuple(vals)
if len(vals)>0:
maxs = 0
for _key in vals:
if float(_key[2])>maxs:
maxs = float(_key[2])
summit = _key[:2]
summit_p=int((float(summit[0])+float(summit[1]))/2)
if summit_p-1000>0:
print >> _temp_enrich, "{0}\t{1}\t{2}".format(line[0],str(summit_p-1000),str(summit_p+999))
else:
print >> _temp_enrich, "{0}\t{1}\t{2}".format(line[0],1,2000)
_temp_enrich.close()
awk_args='{printf "%s\\t%s\\n", $0,NR}'
sh("sort -k 1,1 -k 2g,2g /Data/adam/dnase/enrich_all_bed/{0}.{1}.{2}.bed| bedtools merge -i stdin\
| awk '{3}'>/Data/adam/dnase/enrich_all_merge_bed/{0}.{1}.{2}.bed".format(_soft,_mark,_tissue,awk_args))
enhancer_dir ="/Data/adam/dnase/enhancer/tissue_enhancer/"
sh("bash /Data/adam/dnase/enhancer/roc_pr.sh {0}{1}_enhancer.txt {0}negative_enhancer.txt \
{2} {3} {1}".format(enhancer_dir, _tissue,_soft,_mark))
raw_pr = [i.rstrip().split('\t') for i in open("/Data/adam/dnase/roc_pr_value/{0}.{1}.{2}.bed"\
.format(_soft,_mark,_tissue))]
pr_refine = []
temp_positive = [0,0]
temp_negative = [0,0]
for _line in raw_pr:
if _line[3]==0 and _line[4]==0:
temp_positive = _line[1:3]
out_line = _line[:]
out_line[3:5] = temp_negative
precision = float(out_line[1])/(float(out_line[1])+float(out_line[3]))
out_line.append(str(precision))
pr_refine.append(out_line)
elif _line[1]==0 and _line[2]==0:
temp_negative = _line[3:5]
out_line = _line[:]
out_line[1:3] = temp_positive
precision = float(out_line[1])/(float(out_line[1])+float(out_line[3]))
out_line.append(str(precision))
pr_refine.append(out_line)
else:
print "error in {0}.{1}.{2}, {3}".format(_soft,_mark,_tissue,_line)
pr_refine2 = ['\t'.join(i) for i in pr_refine]
with open("/Data/adam/dnase/roc_pr_final/{0}.{1}.{2}.bed".format(_soft,_mark,_tissue),'w') as f:
for item in pr_refine2:
print >>f, item
def evaluateTC((signalFileName,chrom,start,end)): signalFile = open(signalFileName,"r") bw = BigWigFile(signalFile) mid = (int(start)+int(end))/2 p1 = max(mid - halfWindow,0) p2 = mid + halfWindow try: nCount = int(sum(correctBW(bw.get(chrom,p1,p2),p1,p2))) except Exception: nCount = 0 signalFile.close() return nCount
def get_signal(inputfile, output, bwfiles, bwfolder, extend, N):
signalbw = bwfiles.strip().strip(',').split(',')
if not bwfolder:
bwfolder = "./"
if not bwfolder.endswith('/'):
bwfolder += '/'
bwHs = []
for sb in signalbw:
if sb.startswith('/'):
bwHs.append(BigWigFile(open(sb, 'rb')))
else:
bwHs.append(BigWigFile(open(bwfolder + sb, 'rb')))
inf = open(inputfile)
outf = open(output, 'w')
for line in inf:
ll = line.split()
if "_" in ll[0]:
continue
#center = (int(ll[1]) + int(ll[2]))/2
#S = max(0,center - extend)
#E = center + extend
C = (int(ll[1]) + int(ll[2])) / 2
#if len(ll)>=6 and ll[5] == "-":
# C = int(ll[1])
#else:
# C = int(ll[2])
S = max(0, C - extend)
E = C + extend
for bwHandle in bwHs:
try:
signal = (bwHandle.summarize(ll[0], S, E, N))
binlen = extend * 2.0 / N
if type(
signal.sum_data
) == None: #or type(signal2.sum_data) == None or type(signal3.sum_data) == None:
addsig = [0] * N
else:
addsig_tmp = signal.sum_data / binlen #float(signal.sum_data/signal.valid_count)
addsig = list(addsig_tmp) #+ list(addsig2) + list(addsig3)
except:
#print 'c2',line
addsig = [0] * N #'nan'
# ll.extend(list(signal.sum_data/signal.valid_count))
if len(ll) >= 6 and ll[5] == "-":
ll.extend(addsig[::-1])
else:
ll.extend(addsig)
outf.write("\t".join(map(str, ll)) + "\n")
inf.close()
outf.close()
def get_signal(inputfile, output, bwfiles, bwfolder, extend):
signalbw = bwfiles.strip().strip(',').split(',')
if not bwfolder:
bwfolder = ""
if not bwfolder.endswith('/'):
bwfolder += '/'
bwHs = []
for sb in signalbw:
if sb.startswith('/'):
bwHs.append(BigWigFile(open(sb, 'rb')))
else:
bwHs.append(BigWigFile(open(bwfolder + sb, 'rb')))
inf = open(inputfile)
outf = open(output, 'w')
for line in inf:
ll = line.split()
if "_" in ll[0]:
continue
if len(ll) >= 6 and ll[5] == "-":
strand_flap = 1
else:
strand_flap = 0
# center = (int(ll[1]) + int(ll[2]))/2
# S = max(0,center - extend)
# E = center + extend
S = int(ll[1])
E = int(ll[2])
outdata = []
for bwHandle in bwHs:
try:
signal = (bwHandle.summarize(ll[0], S, E, (E - S)))
if signal:
thisdata_tmp = list(signal.sum_data)
# if strand_flap == 1:
# thisdata = map(round,thisdata_tmp,[4]*(E-S))[::-1]
# else:
thisdata = map(round, thisdata_tmp, [4] * (E - S))
else:
thisdata = ["NA"] * (E - S)
except:
thisdata = ["NA"] * (E - S)
outdata.append(thisdata)
# ll.extend(list(signal.sum_data/signal.valid_count))
for pos in range(len(outdata[0])):
newll = [ll[0], S + pos, S + pos + 1]
for dataorder in range(len(outdata)):
newll.append(outdata[dataorder][pos])
outf.write("\t".join(map(str, newll)) + "\n")
inf.close()
outf.close()
def big_wig_summary_worker((span, bw_list, region_bed_file_name, nb_proc,
verbose)):
results = list()
bw_label = [os.path.basename(p) for p in bw_list]
bw_label = [os.path.splitext(os.path.basename(p))[0] for p in bw_list]
if verbose:
sys.stderr.write("Processing: " + region_bed_file_name)
for big_wig, cpt in zip(bw_list, range(len(bw_list))):
bigwig = BigWigFile(open(big_wig, "r"))
if verbose:
sys.stderr.write("Computing coverage for file: " + big_wig + " [" +
str(multiprocessing.current_process()) + "], " +
str(span[1] - span[0]) + " chunks to process.\n")
bed_windows = pybedtools.BedTool(region_bed_file_name)
chr_cur = None
# Loop through bed lines (features object)
for i in bed_windows[slice(span[0], span[1])]:
if chr_cur == None:
chr_cur = i.chrom
else:
if i.chrom != chr_cur:
chr_cur = i.chrom
# Note: bigWig is zero-based/half open as bed.
bw_sum = bigwig.query(i.chrom, i.start, i.end, 1)
if bw_sum is not None:
bw_sum = bw_sum[0]['mean']
bw_sum = np.nan_to_num(bw_sum)
bw_sum = np.round(bw_sum, 2)
else:
bw_sum = 0.00
results.append(
(i.chrom + ":" + str(i.start), bw_label[cpt], float(bw_sum)))
if verbose:
sys.stderr.write("Computing coverage for file: " + big_wig + " [" +
str(multiprocessing.current_process()) +
"]. Job done.\n")
return results
def get_mappability(mappability_file,
vcf_file,
out_file,
region=None,
append_chr=True):
map_reader = BigWigFile(open(mappability_file, 'rb'))
vcf_reader = vcf.Reader(filename=vcf_file)
if region is not None:
chrom, beg, end = parse_region_for_vcf(region)
try:
vcf_reader = vcf_reader.fetch(chrom, start=beg, end=end)
except ValueError:
print("no data for region {} in vcf".format(region))
vcf_reader = []
data = []
for record in vcf_reader:
if append_chr:
chrom = 'chr{0}'.format(record.CHROM)
else:
chrom = record.CHROM
coord = record.POS
beg = coord - 100
beg = max(beg, 0)
end = coord + 100
result = map_reader.query(chrom, beg, end, 1)
if result is None:
mappability = 0
else:
mappability = result[0]['mean']
data.append({
'chrom': record.CHROM,
'coord': record.POS,
'mappability': mappability
})
data = pd.DataFrame(data)
csvutils.write_dataframe_to_csv_and_yaml(data, out_file, dtypes())
def main():
input_filename, output_filename, loc_filename, loc_key, chrom_col, start_col = sys.argv[
1:]
# open input, output, and bigwig files
location_file = LocationFile(loc_filename)
bigwig_filename = location_file.get_values(loc_key)
bwfh = open_or_die(bigwig_filename,
message='Error opening BigWig file %s' %
bigwig_filename)
bw = BigWigFile(file=bwfh)
ifh = open_or_die(input_filename,
message='Error opening input file %s' % input_filename)
ofh = open_or_die(output_filename,
mode='w',
message='Error opening output file %s' % output_filename)
# make column numbers 0-based
chrom_col = int(chrom_col) - 1
start_col = int(start_col) - 1
min_cols = max(chrom_col, start_col)
# add score column to imput file
line_number = 0
for line in ifh:
line_number += 1
line = line.rstrip('\r\n')
elems = line.split('\t')
if len(elems) > min_cols:
chrom = elems[chrom_col].strip()
# base-0 position in chrom
start = int(elems[start_col])
score_list = bw.get(chrom, start, start + 1)
score_list_len = len(score_list)
if score_list_len == 1:
beg, end, score = score_list[0]
score_val = '%1.3f' % score
elif score_list_len == 0:
score_val = 'NA'
else:
die('%s line %d: chrom=%s, start=%d, score_list_len = %d' %
(input_filename, line_number, chrom, start,
score_list_len))
print >> ofh, '\t'.join([line, score_val])
else:
print >> ofh, line
bwfh.close()
ifh.close()
ofh.close()
def extract_phastcons ( bedfile, phas_chrnames, width, pf_res ):
"""Extract phastcons scores from a bed file.
Return the average scores
"""
info("read bed file...")
bfhd = open(bedfile)
bed = parse_BED(bfhd)
# calculate the middle point of bed regions then extend left and right by 1/2 width
bchrs = bed.peaks.keys()
bchrs.sort()
chrs = []
for c in phas_chrnames:
if c in bchrs:
chrs.append(c)
sumscores = []
for chrom in chrs:
info("processing chromosome: %s" %chrom)
pchrom = bed.peaks[chrom]
bw = BigWigFile(open(chrom+'.bw', 'rb'))
for i in range(len(pchrom)):
mid = int((pchrom[i][0]+pchrom[i][1])/2)
left = int(mid - width/2)
right = int(mid + width/2)
if left < 0:
left = 0
right = width
summarize = bw.summarize(chrom, left, right, width/pf_res)
if not summarize:
continue
dat = summarize.sum_data / summarize.valid_count
#dat = dat.strip().split('\t')
sumscores.append(dat)
## a list with each element is a list of conservation score at the same coordinate
sumscores = map(list, zip(*sumscores))
## exclude na
sumscores = [[t2 for t2 in t if not math.isnan(t2)] for t in sumscores]
try:
conscores = [sum(t)/len(t) for t in sumscores]
except ZeroDivisionError:
conscores = [0] * (width/pf_res)
return conscores
def extract_phastcons ( bedfile, phas_chrnames, width, pf_res ):
"""Extract phastcons scores from a bed file.
Return the average scores
"""
info("read bed file...")
bfhd = open(bedfile)
bed = parse_BED(bfhd)
# calculate the middle point of bed regions then extend left and right by 1/2 width
bchrs = bed.peaks.keys()
bchrs.sort()
chrs = []
for c in phas_chrnames:
if c in bchrs:
chrs.append(c)
sumscores = []
for chrom in chrs:
info("processing chromosome: %s" %chrom)
pchrom = bed.peaks[chrom]
bw = BigWigFile(open(chrom+'.bw', 'rb'))
for i in range(len(pchrom)):
mid = int((pchrom[i][0]+pchrom[i][1])/2)
left = int(mid - width/2)
right = int(mid + width/2)
if left < 0:
left = 0
right = width
summarize = bw.summarize(chrom, left, right, width/pf_res)
if not summarize:
continue
dat = summarize.sum_data / summarize.valid_count
#dat = dat.strip().split('\t')
sumscores.append(dat)
## a list with each element is a list of conservation score at the same coordinate
sumscores = map(list, zip(*sumscores))
## exclude na
sumscores = [[t2 for t2 in t if not math.isnan(t2)] for t in sumscores]
try:
conscores = [sum(t)/len(t) for t in sumscores]
except ZeroDivisionError:
conscores = [0] * (width/pf_res)
return conscores
def summary(bwfile1, bwfile2, bwfile_add, bedfile, topnumber, out):
total_result = []
p = BwIO(bwfile1)
q = BwIO(bwfile2)
chrom_len1 = {}
chrom_len2 = {}
for i in p.chromosomeTree['nodes']:
chrom_len1[i['key']] = i['chromSize']
for i in q.chromosomeTree['nodes']:
chrom_len2[i['key']] = i['chromSize']
bwHandle1 = BigWigFile(open(bwfile1, 'rb'))
bwHandle2 = BigWigFile(open(bwfile2, 'rb'))
inf = open(bedfile)
t = time.time()
for line in inf:
ll = line.split()
ll[3] = "-"
if chrom_len1.has_key(ll[0]) and chrom_len2.has_key(ll[0]):
summary = bwHandle1.summarize(ll[0], int(ll[1]), int(ll[2]), 1)
if summary.valid_count == 0:
mean_value1 = 0
else:
mean_value1 = (summary.sum_data / summary.valid_count)[0]
summary = bwHandle2.summarize(ll[0], int(ll[1]), int(ll[2]), 1)
if summary.valid_count == 0:
mean_value2 = 0
else:
mean_value2 = (summary.sum_data / summary.valid_count)[0]
total_result.append(ll + [mean_value1 + mean_value2])
inf.close()
total_result.sort(reverse=True, key=lambda x: x[-1])
bwHs = []
for i in bwfile_add:
bwHs.append(BigWigFile(open(i, 'rb')))
outf = open(out, 'w')
print "scaning 1st ", time.time() - t
t = time.time()
for i in range(min(len(total_result), topnumber)):
ll = total_result[i]
summary = bwHandle1.summarize(ll[0], int(ll[1]), int(ll[2]),
(int(ll[2]) - int(ll[1])))
additional_value1 = ",".join(map(str, list(summary.sum_data)))
summary = bwHandle2.summarize(ll[0], int(ll[1]), int(ll[2]),
(int(ll[2]) - int(ll[1])))
additional_value2 = ",".join(map(str, list(summary.sum_data)))
result = map(str, (ll + [additional_value1, additional_value2]))
for bwH in bwHs:
summary = bwH.summarize(ll[0], int(ll[1]), int(ll[2]),
(int(ll[2]) - int(ll[1])))
additional_value_add = ",".join(map(str, list(summary.sum_data)))
result.append(additional_value_add)
outf.write("\t".join(result) + "\n")
outf.close()
print "scaning 2nd ", time.time() - t
def check_position(chrom, start, end):
#is there 10% coverage of region [start, end]
valids = 0.
wrong = 0.
for directory in [x[0] for x in os.walk(DATAPATH + "data")]:
for filename in glob(directory + "/*.bigWig") + glob(directory +
"/*.bw"):
f = open(filename, "r")
bigwig = BigWigFile(file=f)
summary = bigwig.summarize(chrom, start, end + 1, 1)
if summary.valid_count * 10 < end - start + 1:
wrong += 1
else:
valids += 1
return (valids / (valids + wrong) >= 0.75)
def get_signal(inputfile,output,signalbw,extend):
signalbw = signalbw.strip().strip(',').split(',')
p=BwIO(signalbw[0])
chrom_len = {}
for i in p.chromosomeTree['nodes']:
chrom_len[i['key']] = i['chromSize']
bwHandle = []
for k in signalbw:
bwHandle.append(BigWigFile(open(k, 'rb')))
inf = open(inputfile)
outf = open(output,'w')
for line in inf:
ll = line.split()
inputlen = len(ll)
if not chrom_len.has_key(ll[0]):
continue
for bwH in bwHandle:
S = (int(ll[1]) + int(ll[2]))/2
E = (int(ll[1]) + int(ll[2]))/2 + 1
try:
signal=bwH.summarize(ll[0],max(0,S-extend),E+extend,1)
except:
break
if float(signal.valid_count) == 0:
ll.append('0')
else:
ll.append(str(float(signal.sum_data/signal.valid_count)))
if len(ll) == ( inputlen + len(bwHandle) ):
outf.write("\t".join(ll)+"\n")
inf.close()
outf.close()
def test_summaries_from_file():
bw = BigWigFile(file=open("test_data/bbi_tests/test.bw"))
def check_summary(line):
fields = line.split()
chrom = fields[0]
start = int(fields[1])
end = int(fields[2])
n = int(fields[3])
t = fields[4]
values = [float(v.replace('n/a', 'NaN')) for v in fields[5:]]
sd = bw.summarize(chrom, start, end, n)
if t == 'mean':
print sd.sum_data / sd.valid_count
print values
assert allclose(sd.sum_data / sd.valid_count, values)
elif t == 'min':
assert allclose(sd.min_val, values)
elif t == 'max':
assert allclose(sd.max_val, values)
#elif t == 'std':
# assert numpy.allclose( sd.max_val, values )
for line in open("test_data/bbi_tests/test.expectation"):
yield check_summary, line
class BigWigWrapper(object):
"""A wrapper for bx-python BigWig file"""
def __init__(self, filepath):
self.bw = BigWigFile(open(filepath))
def __getitem__(self, iv):
return self.bw.get_as_array(iv.chrom, iv.start, iv.end)
def build(self):
"""
Build the matrix.
Since bigWig files are essentially pre-summarized, this just extracts
the chrom/start/stop represented by each cell in the matrix and fills
it with the value from the bigWig file.
"""
self.bigwig = BigWigFile(open(self.file))
chrom_rc, chrom_bins = self.chrom2rc()
if self.chrom == 'genome':
chroms = self.chromdict.keys()
else:
chroms = [self.chrom]
for chrom in chroms:
rc = chrom_rc[chrom]
nbins = chrom_bins[chrom]
start, stop = self.chromdict[chrom]
results = self.bigwig.summarize(chrom, start, stop, nbins)
values = results.sum_data / results.valid_count
values[np.isnan(values)] = 0
self.matrix[rc[:,0], rc[:, 1]] = values
self._cleanup()
def test_summaries_from_file():
bw = BigWigFile(file=open("test_data/bbi_tests/test.bw", 'rb'))
def check_summary(line):
fields = line.split()
chrom = fields[0]
start = int(fields[1])
end = int(fields[2])
n = int(fields[3])
t = fields[4]
values = [float(v.replace('n/a', 'NaN')) for v in fields[5:]]
sd = bw.summarize(chrom, start, end, n)
if t == 'mean':
print(sd.sum_data / sd.valid_count)
print(values)
assert allclose(sd.sum_data / sd.valid_count, values)
elif t == 'min':
assert allclose(sd.min_val, values)
elif t == 'max':
assert allclose(sd.max_val, values)
# elif t == 'std':
# assert numpy.allclose( sd.max_val, values )
for i, line in enumerate(open("test_data/bbi_tests/test.expectation")):
f = partial(check_summary, line)
f.description = "Test summaries line %d: %s" % (i, line[:40])
yield (f, )
def load_annos(args):
"""
Populate a dictionary of Tabixfile handles for
each annotation file. Other modules can then
access a given handle and fetch data from it
as follows:
dbsnp_handle = annotations.annos['dbsnp']
hits = dbsnp_handle.fetch(chrom, start, end)
"""
anno_files = get_anno_files(args)
for anno in anno_files:
try:
# .gz denotes Tabix files.
if anno_files[anno].endswith(".gz"):
annos[anno] = pysam.Tabixfile(anno_files[anno])
# .bw denotes BigWig files.
elif anno_files[anno].endswith(".bw"):
annos[anno] = BigWigFile(open(anno_files[anno]))
except IOError:
sys.exit("Gemini cannot open this annotation file: %s. \n"
"Have you installed the annotation files? If so, "
"have they been moved or deleted? Exiting...\n\n"
"For more details:\n\t"
"http://gemini.readthedocs.org/en/latest/content/"
"#installation.html\#installing-annotation-files\n" %
anno_files[anno])
class BigWigWrapper(object):
"""A wrapper for bx-python BigWig file"""
def __init__(self, filepath):
self.bw = BigWigFile(open(filepath))
def __getitem__(self, iv):
return self.bw.get_as_array(iv.chrom, iv.start, iv.end)
def get_phastcons(bedtool, phastcons_location, species=None, index=None, ):
"""
Get phastcons scores for intervals in a bed tool
"""
if species is None and index is None:
print "Error, must select species or index"
f = open(phastcons_location, 'r')
bw = BigWigFile(file=f)
try:
#if its a line
#for each line fetch bigwig values
type(bedtool)
v = bedtool.chrom #is a single interval
vals = bw.get(bedtool.chrom, bedtool.start, bedtool.stop)
consvals = list(v[-1] for v in vals)
if len(consvals) > 0:
mean_phastcons = np.mean(consvals)
else:
mean_phastcons=0
data = mean_phastcons
except:
#if bedtool
for i, bedline in enumerate(bedtool):
data = np.ndarray(len(bedtool))
vals = bw.get(bedline.chrom, bedline.start, bedline.stop)
consvals = list(v[-1] for v in vals)
if len(consvals) > 0:
mean_phastcons = np.mean(consvals)
else:
mean_phastcons=0
data[i] = mean_phastcons
#returns mean phastcons score for each line
#returns inconistant data types, need to convert so it just returns an array
return data
def main():
input_filename, output_filename, loc_filename, loc_key, chrom_col, start_col = sys.argv[1:]
# open input, output, and bigwig files
location_file = LocationFile( loc_filename )
bigwig_filename = location_file.get_values( loc_key )
bwfh = open_or_die( bigwig_filename, message='Error opening BigWig file %s' % bigwig_filename )
bw = BigWigFile( file=bwfh )
ifh = open_or_die( input_filename, message='Error opening input file %s' % input_filename )
ofh = open_or_die( output_filename, mode='w', message='Error opening output file %s' % output_filename )
# make column numbers 0-based
chrom_col = int( chrom_col ) - 1
start_col = int( start_col ) - 1
min_cols = max( chrom_col, start_col )
# add score column to imput file
line_number = 0
for line in ifh:
line_number += 1
line = line.rstrip( '\r\n' )
elems = line.split( '\t' )
if len( elems ) > min_cols:
chrom = elems[chrom_col].strip()
# base-0 position in chrom
start = int( elems[start_col] )
score_list = bw.get( chrom, start, start + 1 )
score_list_len = len( score_list )
if score_list_len == 1:
beg, end, score = score_list[0]
score_val = '%1.3f' % score
elif score_list_len == 0:
score_val = 'NA'
else:
die( '%s line %d: chrom=%s, start=%d, score_list_len = %d' % ( input_filename, line_number, chrom, start, score_list_len ) )
print('\t'.join( [line, score_val] ), file=ofh)
else:
print(line, file=ofh)
bwfh.close()
ifh.close()
ofh.close()
def getNumberOfFragmentsPerRegionFromBigWig(bw, chromSizes):
"""
Get the number of all mapped fragments per region in all chromosomes
from a bigWig. Utilizing bx-python.
Test dataset with two samples covering 200 bp.
>>> test = Tester()
Get number of fragments in sample.
>>> getNumberOfFragmentsPerRegionFromBigWig(test.bwFile1, [('3R', 200)])
3.0
>>> getNumberOfFragmentsPerRegionFromBigWig(test.bwFile2, [('3R', 200)])
4.0
"""
bwh = BigWigFile(open(bw, "rb"))
mapped = 0
for cname, csize in chromSizes:
regions = bwh.get(cname, 0, csize) # region = bwh.get(chrom_name, start, end)
for region in regions:
mapped += region[2]
return mapped
def get_GA_from_bw(self, plus, minus, GTF, filterfxn):
##bx-python 'get' method is 0 based, fully closed
ga = HTSeq.GenomicArray( "auto", typecode='d' , stranded = True)
with open(plus) as f:
bw_file = BigWigFile(file=f)
for GF in GTF:
if filterfxn( GF ) == False: continue
window = GF.iv
chrom, start, stop = window.chrom, window.start, window.end
vals = bw_file.get(chrom, start, stop)
for start, stop, value in vals:
ga[ HTSeq.GenomicPosition(chrom, start, '+') ] = value
with open(minus) as f:
bw_file = BigWigFile(file=f)
for GF in GTF:
if filterfxn( GF ) == False: continue
window = GF.iv
chrom, start, stop = window.chrom, window.start, window.end
vals = bw_file.get(chrom, start, stop)
for start, stop, value in vals:
ga[ HTSeq.GenomicPosition(chrom, start, '-') ] = value
return ga
def profile_bwfile(inbed,bwfile):
'''retrieve signal from bigwig file for each entry in input bed file'''
bw = BigWigFile( file=open( bwfile ) )
for line in open(inbed):
bw_signal=[]
try:
if line.startswith('#'):continue
if line.startswith('track'):continue
if line.startswith('browser'):continue
if not line.strip():
continue
else:
line = line.rstrip('\r\n')
fields = line.split()
chrom = fields[0]
start = int(fields[1])
end = int(fields[2])
except:
print >>sys.stderr,"Must be chrom [space] start [space] end: " + line,
continue
bw_signal.extend(bw.get_as_array(chrom,start,end))
print chrom +'\t'+ str(start) +'\t'+ str(end) + '\t' + ','.join(str(i) for i in bw_signal)
def findInsertions(bwFile, bedData, x):
if options.tn5 is not None:
bwFile = options.b + options.tn5 + "." + bedData[x][0] + ".Scores.bw"
sL = int(bedData[x][1]) - options.l
sR = int(bedData[x][2]) + options.r
# get signal data
f = open(bwFile, "rb")
bw = BigWigFile(f)
try:
signal = bw.get_as_array(bedData[x][0], sL, sR)
except OverflowError:
signal = np.array([np.nan] * (sR - sL))
f.close()
out = signal
try:
if bedData[x][3] == "-":
out = out[::-1]
except IndexError:
pass
return out
def get_phastcons(bedtool, species=None, index=None):
"""
Get phastcons scores for intervals in a bed tool
"""
if species is None and index is None:
print "Error, must select species or index"
if species is not None and index is None:
if species == "mm9":
index= basedir + "/yeolab/Conservation/phastCons/mm9_30way/placental/mm9_phastcons.bw"
elif species == "hg19":
index = basedir + "/yeolab/Conservation/phastCons/hg19_46way/placentalMammals/reformat/hg19_phastcons.bw"
f = open(index, 'r')
bw = BigWigFile(file=f)
try:
type(bedtool)
v = bedtool.chrom #is a single interval
vals = bw.get(bedtool.chrom, bedtool.start, bedtool.stop)
consvals = list(v[-1] for v in vals)
if len(consvals) > 0:
mean_phastcons = np.mean(consvals)
else:
mean_phastcons=0
data = mean_phastcons
except:
for i, bedline in enumerate(bedtool):
data = np.ndarray(len(bedtool))
vals = bw.get(bedline.chrom, bedline.start, bedline.stop)
consvals = list(v[-1] for v in vals)
if len(consvals) > 0:
mean_phastcons = np.mean(consvals)
else:
mean_phastcons=0
data[i] = mean_phastcons
return data
class TestBigWig(unittest.TestCase):
def setUp(self):
f = open( "test_data/bbi_tests/test.bw" )
self.bw = BigWigFile(file=f)
def test_get_summary(self):
data = self.bw.query("chr1", 10000, 20000, 10)
means = [ x['mean'] for x in data ]
print means
assert numpy.allclose( map(float, means), [-0.17557571594973645, -0.054009292602539061, -0.056892242431640622, -0.03650328826904297, 0.036112907409667966, 0.0064466032981872557, 0.036949024200439454, 0.076638259887695306, 0.043518108367919923, 0.01554749584197998] )
# Summarize variant
sd = self.bw.summarize( "chr1", 10000, 20000, 10)
assert numpy.allclose( sd.sum_data / sd.valid_count, [-0.17557571594973645, -0.054009292602539061, -0.056892242431640622, -0.03650328826904297, 0.036112907409667966, 0.0064466032981872557, 0.036949024200439454, 0.076638259887695306, 0.043518108367919923, 0.01554749584197998] )
# Test min and max for this entire summary region
data = self.bw.query("chr1", 10000, 20000, 1)
maxs = [ x['max'] for x in data ]
mins = [ x['min'] for x in data ]
self.assertEqual( map(float, maxs), [0.289000004529953] )
self.assertEqual( map(float, mins), [-3.9100000858306885] )
def test_get_leaf(self):
data = self.bw.query("chr1", 11000, 11005, 5)
means = [ x['mean'] for x in data ]
assert numpy.allclose( map(float, means), [0.050842501223087311, -2.4589500427246094, 0.050842501223087311, 0.050842501223087311, 0.050842501223087311] )
# Test min and max for this entire leaf region
data = self.bw.query("chr1", 11000, 11005, 1)
maxs = [ x['max'] for x in data ]
mins = [ x['min'] for x in data ]
self.assertEqual( map(float, maxs), [0.050842501223087311] )
self.assertEqual( map(float, mins), [-2.4589500427246094] )
def test_wrong_nochrom(self):
data = self.bw.query("chr2", 0, 10000, 10)
self.assertEqual( data, None )
import numpy as np
fl=sys.argv[1]
dist=int(sys.argv[2])
from bx.bbi.bigwig_file import BigWigFile
genes=read.dat("/home/ssaberi/resources/list.genes.txt",'\t')
table=read.dat("/projects/epigenomics/MarcoJuliaPon/peaks.txt",'\t')
mygenes=read.dat("/projects/epigenomics/MarcoJuliaPon/mygenes.txt",'\t')
ens=[]
for i in mygenes:
for gn in genes:
if i in gn[0]:
ens.append(gn[1])
break
genespos=read.read_gene_pos('/home/ssaberi/resources/hg19v69_genes.TSS_2000.pc.A03480.H3K27me3.GE02.coverage')
genesbed=bedtools.makebed_genpos(ens,genespos,100000)
f = open(fl)
bw = BigWigFile(file=f)
mat=[]
for bed_i in genesbed:
vals = bw.get( bed_i[0], bed_i[1], bed_i[2])
mat.append(np.array(vals))
mat=np.array(mat)
plt.matshow(mat,aspect='auto',cmap='YlOrBr')
fl=fl[-fl[::-1].index('/'):-fl[::-1].index('.')]
plt.save(fl+".pdf")
def output(fragmentsMap , fragmentList, fragmentPairs, fragmentCount, fragmentsChrom):
'''
outputs 2 files, the first containing
"chr extraField fragmentMid marginalizedContactCount mappable? (0/1)"
and the second containing:
"chr1 fragmentMid1 chr2 fragmentMid2 contactCount"
optionally output the 2D contact matrix
'''
if (options.verbose):
print >> sys.stdout, "- %s START : output data " % (timeStamp())
if ( options.outputFilename != "" ):
outfile1 = gzip.open(options.outputDir+options.outputFilename+".fragmentLists.gz","wb")
else:
outfile1 = gzip.open(options.outputDir+os.path.basename(args[0])+".fragmentLists.gz","wb")
fragmentIds = fragmentsMap.keys()
fragmentIds.sort()
# lookup mean mappability ratio
bw = ""
if (options.mappability != ""):
# lazy load
from bx.intervals.io import GenomicIntervalReader
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile( open( options.mappability ) )
for fragmentId in fragmentIds:
contactCounts = 0
chrom = fragmentsMap[fragmentId][0]
midpoint = fragmentsMap[fragmentId][1]
if (options.vverbose):
print >> sys.stdout, "- process %s %d " % (chrom, midpoint)
if (fragmentList.has_key(fragmentId)):
contactCounts = fragmentList[fragmentId]
if (bw != ""):
try:
mappable = bw.query(chrom, midpoint-options.resolution/2, midpoint+options.resolution/2, 1)[0]["mean"]
except:
mappable = 0
# problem with invalid values
if (options.vverbose):
print >> sys.stderr, "Problem with bw file at %s %d-%d" % (chrom, midpoint-options.resolution/2, midpoint+options.resolution/2)
print traceback.format_exc()
elif (contactCounts>0):
mappable=1
outfile1.write("%s\t%d\t%s\t%f\n" % (chrom, midpoint, "NA", mappable))
outfile1.close()
if ( options.outputFilename != "" ):
outfile2 = gzip.open(options.outputDir+options.outputFilename+".contactCounts.gz","wb")
else:
outfile2 = gzip.open(options.outputDir+os.path.basename(args[0])+".contactCounts.gz","wb")
for fragmentIds, contactCounts in fragmentPairs.iteritems():
chrom1 = fragmentsMap[fragmentIds[0]][0]
midpoint1 = fragmentsMap[fragmentIds[0]][1]
chrom2 = fragmentsMap[fragmentIds[1]][0]
midpoint2 = fragmentsMap[fragmentIds[1]][1]
outfile2.write("%s\t%d\t%s\t%d\t%d\n" % (chrom1, midpoint1, chrom2, midpoint2, contactCounts))
outfile2.close()
if (options.create2DMatrix or options.create2DMatrixPerChr):
# lazy loading
from scipy.sparse import lil_matrix
import numpy
# populate sparse matrix
A = lil_matrix((fragmentCount, fragmentCount), dtype='i')
for fragmentIds, contactCounts in fragmentPairs.iteritems():
A[fragmentIds[0],fragmentIds[1]] = contactCounts
A[fragmentIds[1],fragmentIds[0]] = contactCounts
# convert to coordinate format
B = A.tocoo()
if (options.create2DMatrix):
if ( options.outputFilename != "" ):
outfile3 = options.outputDir+options.outputFilename+".matrix"
else:
outfile3 = options.outputDir+os.path.basename(args[0])+".matrix"
if (options.verbose):
print >> sys.stdout, "- save 2Dmatrix to %s " % (outfile3)
f_handle=open(outfile3,'w')
C = B.tocsr()
for i in xrange(fragmentCount):
numpy.savetxt(f_handle, C[i].toarray(),fmt='%i', delimiter='\t')
f_handle.close()
if (options.create2DMatrixPerChr):
for chr in fragmentsChrom.keys():
C = B.tocsc()[:,fragmentsChrom[chr][0]:fragmentsChrom[chr][1]].tocsr()[fragmentsChrom[chr][0]:fragmentsChrom[chr][1],:]
fragmentRange=fragmentsChrom[chr][1]-fragmentsChrom[chr][0]
header=['d']+[ "%s%d" % i for i in zip(['r']*fragmentRange,range(fragmentRange))]
if ( options.outputFilename != "" ):
outfile3 = options.outputDir+options.outputFilename+"."+chr+".matrix"
else:
outfile3 = options.outputDir+os.path.basename(args[0])+"."+chr+".matrix"
if (options.verbose):
print >> sys.stdout, "- save 2Dmatrix for chromosome %s to %s " % (chr, outfile3)
f_handle=open(outfile3,'w')
f_handle.write('\t'.join(header)+"\n")
for i in xrange(fragmentRange):
f_handle.write(header[i+1]+"\t")
numpy.savetxt(f_handle, C[i].toarray(),fmt='%i', delimiter='\t')
f_handle.close()
if (options.verbose):
print >> sys.stdout, "- %s FINISHED: output data" % (timeStamp())
#data = np.array(range(1,10));
#for f in allFunctions:
# data = applyFunction(data,f);
# print(data);
#print(allFunctions);
#exit;
chromSizesFile = MYUTILS.smartGZOpen(args.chrsFile,'r');
chromSizes = {};
for line in chromSizesFile:
if line is None or line == "" or line[0]=="#": continue
data=line.rstrip().split("\t");
chromSizes[data[0]]=int(data[1]);
curBW = BigWigFile(open(args.inBW))
outStream = MYUTILS.smartGZOpen("%s.wig.gz"%(args.outFPre),"w");
outStream.write("track type=wiggle_0\n")
for chr in chromSizes.keys():
last = 0;
final = chromSizes[chr];
sys.stderr.write("Outputting data for %s:\n"%(chr));
while last!=final: # this breaks it up into chunks so that I'm not piping entire (human) chromosomes at once
if args.verbose>0: sys.stderr.write(" Section %i - %i:\n"%(last,curLast));
curLast = np.min([last+args.chunks,final]);
curEnd = np.min([curLast+additionalFlankSize, final]);
curSt = np.max([last-additionalFlankSize,0]);
values = curBW.get_as_array( chr, curSt, curEnd )
useThese = {};
totalLength = 0
for chr in chrOrder:
#sample positions
useThese[chr] = np.random.random_sample((chromSizes[chr]))<args.sample;
totalLength = totalLength + np.sum(useThese[chr]);
#make a matrix of the data
allData = np.empty([totalLength,len(IDs)]);
if args.eliminateMissing>0:
keepThese = np.ones([totalLength]).astype(bool); # onlt those for which data was observed in all tracks
for i in range(0,len(IDs)):
#input GB tracks
curBW = BigWigFile(open(files[i]))
curTot = 0;
if args.verbose>1: sys.stderr.write("Inputting data for %s.\n"%(IDs[i]));
for chr in chrOrder:
if args.verbose>1: sys.stderr.write(" Inputting data for %s.\n"%(chr));
if args.verbose>2: sys.stderr.write(" Getting data from BW.\n");
values = curBW.get_as_array( chr, 0, chromSizes[chr] )
if values is None:
sys.stderr.write("%s is missing %s... skipping it for all\n"%(IDs[i],chr));
chrOrder.remove(chr)
allData = np.delete(allData, [range(curTot, (curTot+np.sum(useThese[chr])))],0);
if args.eliminateMissing>0:
keepThese = np.delete(keepThese, [range(curTot, (curTot+np.sum(useThese[chr])))],0);
totalLength = totalLength - np.sum(useThese[chr]);
del useThese[chr]
del chromSizes[chr]
opts.add_option("-a", help="<bw> Accepts a bigwig file")
opts.add_option("-g", help="<Genome Size file>")
opts.add_option("-w", default=150,type='int', help="<Int> window size")
opts.add_option("-s", default=20,type='int', help="<Int> step size (span)")
options, arguments = opts.parse_args()
# return usage information if no argvs given
if len(sys.argv)==1:
os.system(sys.argv[0]+" --help")
sys.exit()
##### DEFINE FUNCTIONS #####
##### INPUTS AND OUTPUTS #####
# open bigwig
bw = BigWigFile(open(options.a))
# get gSize file
gSizes = np.loadtxt(options.g,'str')
chunkSize = 1000000
padLen = 5000
# open out file
outName = os.path.join(os.path.dirname(options.a), 'out.smooth.bed')
try: os.remove(outName)
except OSError: pass
print "Saving to %s.."%outName
outF = file(outName, 'a')
def setUp(self):
f = open( "test_data/bbi_tests/test.bw" )
self.bw = BigWigFile(file=f)
if line is None or line == "" or line[0]=="#": continue
data=line.rstrip().split("\t");
for i in range(0,len(data)):
oldToNew[data[i]] = data[0];
transChrs.append(data[i]);
inFile.close();
chromSizesFile = MYUTILS.smartGZOpen(args.chrsFile,'r');
chromSizes = {};
for line in chromSizesFile:
if line is None or line == "" or line[0]=="#": continue
data=line.rstrip().split("\t");
chromSizes[data[0]]=int(data[1]);
curBW = BigWigFile(open(args.inBW))
outStream = MYUTILS.smartGZOpen("%s.wig.gz"%(args.outFPre),"w");
outStream.write("track type=wiggle_0\n")
for chr in transChrs:
values = curBW.get_as_array( chr, 0, chromSizes[oldToNew[chr]] )
#print(chr);
if values is not None:
sys.stderr.write("Adding %s -> %s\n"%(chr, oldToNew[chr]));
outStream.write("fixedStep chrom=%s start=1 step=1\n"%(oldToNew[chr]))
outStream.write("\n".join(map(str,values)));
outStream.write("\n");
toBW = subprocess.Popen(["wigToBigWig","%s.wig.gz"%(args.outFPre),args.chrsFile,"%s.bw"%(args.outFPre)])
temp = toBW.communicate()
if tokens[3] == '+':
annotated_5p.add((tokens[0], int(tokens[1]) - 1))
annotated_3p.add((tokens[0], int(tokens[2]) - 1))
elif tokens[3] == '-':
annotated_3p.add((tokens[0], int(tokens[1]) - 1))
annotated_5p.add((tokens[0], int(tokens[2]) - 1))
else:
raise RuntimeError(
'Invalid line in annotation file: "{}".'.format(line)
)
unannotated_fivep_splice_site_counts = defaultdict(int)
unannotated_threep_splice_site_counts = defaultdict(int)
annotated_fivep_splice_site_counts = defaultdict(int)
annotated_threep_splice_site_counts = defaultdict(int)
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile(open(args.phylop_bw, 'rb'))
print >>sys.stderr, '\x1b[KDone. Computing/writing matrix elements...'
with open(
allincidence
) as incidence_stream, open(
args.out, 'w'
) as output_stream:
unannotated_line_counts = defaultdict(int)
annotated_line_counts = defaultdict(int)
splice_sites = 0
for key, group in itertools.groupby(
incidence_stream, lambda x: x.split('\t')[0]
):
for line in group:
print >>sys.stderr, (
'Processed {} splice sites...\r'.format(
Create a site profile vector showing the average signal accumulated from a
bigwig file around the center of each interval from a BED file.
Output is the average signal value at that relative position across the
intervals.
usage: %prog bigwig_file.bw padding < bed_file.bed
"""
import sys
from numpy import *
from bx.intervals.io import GenomicIntervalReader
from bx.bbi.bigwig_file import BigWigFile
bw = BigWigFile( open( sys.argv[1] ) )
padding = int( sys.argv[2] )
totals = zeros( padding*2, dtype=float64 )
valid = zeros( padding*2, dtype=int32 )
for interval in GenomicIntervalReader( sys.stdin ):
center = floor( ( interval.start + interval.end ) / 2 )
values = bw.get_as_array( interval.chrom, center - padding, center + padding )
# Determine which positions had data and mask the rest for totalling
invalid = isnan( values )
values[ invalid ] = 0
totals += values
valid += ( ~ invalid )
savetxt( sys.stdout, totals/valid )
lengthCount+=1;
lengthSum+=curLen;
if scanThese[i][GENOMEDATA.STR]!="+" and scanThese[i][GENOMEDATA.STR]!="-":
numStrandless+=1
avgLength = int(round(lengthSum/lengthCount))
if numStrandless>0 and args.inFile2 is not None:
raise Exception("Error: loci contain strandless entries, but genome tracks provided for each strand!!");
if numStrandless>0:
sys.stderr.write("Warning: Strandless loci detected; assuming forward orientation.\n");
padding = int(args.flank);
#read in track file(s)
if args.format=="BIGWIG" or args.format=="BW":
from bx.bbi.bigwig_file import BigWigFile
inFile1 = BigWigFile(open(args.inFile))
if args.inFile2 is not None:
inFile2 = BigWigFile(open(args.inFile2))
elif args.format=="BIGBED" or args.format=="BB":
from bx.bbi.bigwig_file import BigBedFile
inFile1 = BigBedFile(open(args.inFile))
if args.inFile2 is not None:
inFile2 = BigBedFile(open(args.inFile2))
elif args.format=="WIG" or args.format=="W":
from bx.arrays.wiggle import WiggleReader
inFile1 = WiggleReader(open(args.inFile))
if args.inFile2 is not None:
inFile2 = WiggleReader(open(args.inFile2))
elif args.format=="BEDGR" or args.format=="BG":
from bx.arrays.bed import BedReader
inFile1 = BedReader(open(args.inFile))
def main(args):
bw_file = BigWigFile( open(args.bigWigFile) )
bw_file.get_as_array(chrom, st, end)
class HilbertMatrixBigWig(HilbertMatrix):
# Need to override build(), but otherwise just like a HilbertMatrix
def __init__(self, *args, **kwargs):
"""
Subclass of HilbertMatrix specifically for bigWig format files
"""
super(HilbertMatrixBigWig, self).__init__(*args, **kwargs)
def build(self):
"""
Build the matrix.
Since bigWig files are essentially pre-summarized, this just extracts
the chrom/start/stop represented by each cell in the matrix and fills
it with the value from the bigWig file.
"""
self.bigwig = BigWigFile(open(self.file))
chrom_rc, chrom_bins = self.chrom2rc()
if self.chrom == 'genome':
chroms = self.chromdict.keys()
else:
chroms = [self.chrom]
for chrom in chroms:
rc = chrom_rc[chrom]
nbins = chrom_bins[chrom]
start, stop = self.chromdict[chrom]
results = self.bigwig.summarize(chrom, start, stop, nbins)
values = results.sum_data / results.valid_count
values[np.isnan(values)] = 0
self.matrix[rc[:,0], rc[:, 1]] = values
self._cleanup()
def chrom2rc(self):
"""
Return a dictionary of {chrom: (rows, cols)} and {chrom: nbins}
"""
precomputed = np.load(
os.path.join(
os.path.dirname(__file__),
'precomputed.npz'))
rc = precomputed['_%s' % self.matrix_dim]
d = {}
bins = {}
last_stop = 0
for chrom, startstop in self.chromdict.items():
start, stop = startstop
frac = self.chromdict[chrom][1] / float(self.chrom_length)
nbins = int(frac * (self.matrix_dim * self.matrix_dim))
d_start = last_stop
d_stop = d_start + nbins
d[chrom] = rc[d_start:d_stop, :]
bins[chrom] = nbins
last_stop += nbins
return d, bins