def getPerReadAverage(args, in_fh):
for line in in_fh:
try:
line = line.decode('ascii')
except AttributeError:
pass
read = MethRead(line)
seqs = [x.seq for x in read.calldict.values()]
# filter out calls with unwanted seq
filteridx = [
idx for idx, s in enumerate(seqs) if args.exclude not in s
]
calls_all = np.array([x.call
for x in read.calldict.values()])[filteridx]
ratios = np.array([x.ratio for x in read.calldict.values()])[filteridx]
# filter out unconfident calls
calls = [x for x in calls_all if x != -1]
# drop empty call strings
if len(calls) > 0:
print('\t'.join([
str(x) for x in [
read.rname, read.start, read.end, read.qname,
np.mean(ratios), '.',
np.mean(calls),
len(calls)
]
]),
file=args.out)
def read_tabix(fpath, window):
with pysam.TabixFile(fpath) as tabix:
entries = [x for x in tabix.fetch(window)]
reads = [MethRead(x) for x in entries]
rdict = dict()
for read in reads:
try:
rdict[read.qname].append(read)
except:
rdict[read.qname] = [read]
return rdict
def read_tabix(fpath,window) :
with pysam.TabixFile(fpath) as tabix :
entries = [x for x in tabix.fetch(window)]
reads = [MethRead(x) for x in entries]
rdict = dict()
# for split-reads, multiple entries are recorded per read name
for meth in reads :
qname = meth.qname
if qname in rdict.keys() :
rdict[qname] = np.append(rdict[qname],meth.callarray,0)
else :
rdict[qname] = meth.callarray
return rdict
def getReadlevel(args, in_fh):
for line in in_fh:
try:
line = line.decode('ascii')
except AttributeError:
pass
read = MethRead(line)
callkeys = sorted(read.calldict)
for key in callkeys:
methcall = read.calldict[key]
outlist = [read.rname] + [str(x) for x in methcall]
outlist.insert(3, read.qname)
print("\t".join(outlist), file=args.out)
def readlevelHeatmap(datapath,reg,thr=0.1,window=10,verbose=False) :
heat = HeatmapRegion(reg)
if verbose : print(heat.coord,file=sys.stderr)
data = tabix(datapath,heat.coord)
for line in data :
read = MethRead(line)
if ( read.start <= heat.start and
read.end >= heat.end ) :
heat.addread(read)
if verbose :
print("{} reads covering the entire region out of total {} in the region".format(
heat.totreads,len(data)),file=sys.stderr)
heat.makeMatrix(window)
g = heat.plot(thr,window)
if verbose : print(heat.title,file=sys.stderr)
return g
def getFreq(args, in_fh):
if args.verbose: print("getting frequency", file=sys.stderr)
sites = dict()
n = 0
for line in in_fh:
try:
line = line.decode('ascii')
except AttributeError:
pass
n += 1
read = MethRead(line)
# # debug
# print(line,file=sys.stdout)
# print(read.ratios)
# print(read.keys)
# #
sitekeys = sorted(sites.keys())
# print(sitekeys)
try:
# print everything in sites if chromosome is different
if read.rname != sites[sitekeys[0]].rname:
printind = len(sitekeys)
else:
# get index of new position
printind = bisect.bisect_left(sitekeys, read.keys[0])
except IndexError:
printind = 0
if printind != 0:
for i in range(printind):
key = sitekeys[i]
sites[key].printFreq(args.motif, args.out)
sites.pop(key)
for key in read.keys:
if key not in sites.keys():
sites[key] = SiteStats(read.calldict[key], read.rname)
sites[key].update(read.calldict[key])
if args.verbose:
if n % 10000 == 0:
print("parsed {} lines".format(n), file=sys.stderr)
for key in sorted(sites.keys()):
sites[key].printFreq(args.motif, args.out)
def read_tabix(fpath,window) :
with pysam.TabixFile(fpath) as tabix :
entries = [x for x in tabix.fetch(window)]
reads = [MethRead(x) for x in entries]
cgdict = dict()
gcdict = dict()
# for split-reads, multiple entries are recorded per read name
for meth in reads :
qname = meth.qname
mod = meth.fields[-1]
if mod == "CG" :
if qname in cgdict.keys() :
cgdict[qname] = np.append(cgdict[qname],meth.callarray,0)
else :
cgdict[qname] = meth.callarray
elif mod == "GC" :
if qname in gcdict.keys() :
gcdict[qname] = np.append(gcdict[qname],meth.callarray,0)
else :
gcdict[qname] = meth.callarray
return cgdict,gcdict