def main():
args = get_args()
if args.dupefile:
dupes = get_dupes(args.dupefile)
else:
dupes = None
#pdb.set_trace()
# get dbSNP data
all_snps = get_xml_data(args.xml)
used = set()
# iterate over intersections
args.output.write('rsid,pos,maf,1000g\n')
for row in args.dbsnp:
if not row.startswith('UCE'):
uce, chromo, start, end, snp, snps, snpe = row.strip('\n').split(',')
start, end, snps, snpe = map(int, [start, end, snps, snpe])
# get relative position
if not snpe - snps > 1 and snp not in used and not uce in dupes:
middle = int(round((start + end)/2, 0))
rel_snp_pos = snps - middle
# lookup data for snps
if all_snps[snp.strip('rs')].val_1000G and all_snps[snp.strip('rs')].val_1000G.lower() == 'true':
thousandg = True
else:
thousandg = False
if not all_snps[snp.strip('rs')].freq_freq:
freq = 0.0
else:
freq = float(all_snps[snp.strip('rs')].freq_freq)
args.output.write("{0},{1},{2},{3}\n".format(
snp,
rel_snp_pos,
freq,
thousandg
)
)
# make sure we skip any duplicates
used.add(snp)
def main():
args = get_args()
if args.dupefile:
dupes = get_dupes(args.dupefile)
else:
dupes = None
used = set()
mx = max([int(row.strip('\n').split(',')[3]) \
- int(row.strip('\n').split(',')[2]) \
for row in open(args.dbsnp,'rU') if not row.startswith('UCE')])
# get the SNP metadata
all_snps = get_xml_data(args.xml)
# find the middle
overall_middle = int(round(mx / 2, 0))
# list to hold results
l = numpy.zeros(mx + 1)
positions = copy.deepcopy(l)
# create a dict to hold the results by position in longest array
#differences = dict((d,numpy.array([])) for d in range(-middle, middle + 1))
# iterate over intersections
d = {}
if args.output2:
args.output2.write(
'UCE,chromo,uce-start,uce-end,snp-name,snp-start,snp-end,1000gvalidated,freq\n'
)
for row in open(args.dbsnp, 'rU'):
if not row.startswith('UCE'):
uce, chromo, start, end, snp, snps, snpe = row.strip('\n').split(
',')
start, end, snps, snpe = map(int, [start, end, snps, snpe])
# get middle of this UCE
middle = int(round((start + end) / 2, 0))
#pdb.set_trace()
if snp not in used:
if not snpe - snps > 1 \
and (uce not in dupes) \
and all_snps[snp.strip('rs')].val_1000G == 'true' \
and all_snps[snp.strip('rs')].freq_freq is not None:
if not uce in d.keys():
d[uce] = numpy.zeros(mx + 1)
rel_snp_pos = snps - middle
d[uce][overall_middle +
rel_snp_pos] = all_snps[snp.strip('rs')].freq_freq
if args.output2 and not snpe - snps > 1 and (
snp not in used) and (uce not in dupes):
args.output2.write("{},{},{},{},{},{},{},{},{}\n".format(
uce, chromo, start, end, snp, snps, snpe,
all_snps[snp.strip('rs')].val_1000G,
all_snps[snp.strip('rs')].freq_freq))
used.add(snp)
stack = numpy.array([d[uce] for uce in d.keys()])
#pdb.set_trace()
# compute the running average
win = 25
data = sum(stack > 0)
weightings = numpy.repeat(1.0, win) / win
running = numpy.convolve(data, weightings)[win - 1:-(win - 1)]
args.output.write("pos,avg,ci,datatype\n")
for base in range(len(running)):
pos = base - overall_middle
args.output.write("{},{},,running\n".format(pos, running[base]))
# also output the average heterozygosity of 1000 Genome validated, hetero SNPs.
for base in range(len(stack[0])):
pos = base - overall_middle
values = numpy.where(stack[:, base] != 0)[0]
# reindex
avg = numpy.mean(stack[:, base][values])
ci = 1.96 * (numpy.std(stack[:, base][values], ddof=1) /
numpy.sqrt(len(stack[:, base][values])))
args.output.write("{},{},{},mean_hetero\n".format(pos, avg, ci))
win = 25
data = numpy.mean(stack, axis=1)
weightings = numpy.repeat(1.0, win) / win
running = numpy.convolve(data, weightings)[win - 1:-(win - 1)]
for base in range(len(stack[0])):
pos = base - overall_middle
args.output.write("{},{},,running_hetero\n".format(pos, running[base]))
def main():
args = get_args()
if args.dupefile:
dupes = get_dupes(args.dupefile)
else:
dupes = None
used = set()
mx = max([int(row.strip('\n').split(',')[3]) \
- int(row.strip('\n').split(',')[2]) \
for row in open(args.dbsnp,'rU') if not row.startswith('UCE')])
# get the SNP metadata
all_snps = get_xml_data(args.xml)
# find the middle
overall_middle = int(round(mx/2, 0))
# list to hold results
l = numpy.zeros(mx + 1)
positions = copy.deepcopy(l)
# create a dict to hold the results by position in longest array
#differences = dict((d,numpy.array([])) for d in range(-middle, middle + 1))
# iterate over intersections
d = {}
if args.output2:
args.output2.write('UCE,chromo,uce-start,uce-end,snp-name,snp-start,snp-end,1000gvalidated,freq\n')
for row in open(args.dbsnp, 'rU'):
if not row.startswith('UCE'):
uce, chromo, start, end, snp, snps, snpe = row.strip('\n').split(',')
start, end, snps, snpe = map(int, [start, end, snps, snpe])
# get middle of this UCE
middle = int(round((start + end)/2, 0))
#pdb.set_trace()
if snp not in used:
if not snpe - snps > 1 \
and (uce not in dupes) \
and all_snps[snp.strip('rs')].val_1000G == 'true' \
and all_snps[snp.strip('rs')].freq_freq is not None:
if not uce in d.keys():
d[uce] = numpy.zeros(mx + 1)
rel_snp_pos = snps - middle
d[uce][overall_middle + rel_snp_pos] = all_snps[snp.strip('rs')].freq_freq
if args.output2 and not snpe - snps > 1 and (snp not in used) and (uce not in dupes):
args.output2.write("{},{},{},{},{},{},{},{},{}\n".format(
uce, chromo, start, end, snp, snps,
snpe, all_snps[snp.strip('rs')].val_1000G,
all_snps[snp.strip('rs')].freq_freq))
used.add(snp)
stack = numpy.array([d[uce] for uce in d.keys()])
#pdb.set_trace()
# compute the running average
win = 25
data = sum(stack > 0)
weightings = numpy.repeat(1.0, win) / win
running = numpy.convolve(data, weightings)[win-1:-(win-1)]
args.output.write("pos,avg,ci,datatype\n")
for base in range(len(running)):
pos = base - overall_middle
args.output.write("{},{},,running\n".format(pos,running[base]))
# also output the average heterozygosity of 1000 Genome validated, hetero SNPs.
for base in range(len(stack[0])):
pos = base - overall_middle
values = numpy.where(stack[:,base] != 0)[0]
# reindex
avg = numpy.mean(stack[:,base][values])
ci = 1.96 * (numpy.std(stack[:,base][values], ddof = 1)/numpy.sqrt(len(stack[:,base][values])))
args.output.write("{},{},{},mean_hetero\n".format(pos, avg, ci))
win = 25
data = numpy.mean(stack, axis = 1)
weightings = numpy.repeat(1.0, win) / win
running = numpy.convolve(data, weightings)[win-1:-(win-1)]
for base in range(len(stack[0])):
pos = base - overall_middle
args.output.write("{},{},,running_hetero\n".format(pos,running[base]))
