fileoutput = open(chrPrev+'_'+args.output, 'w')
for line in datafile:
words = line.split()
chr = str(words[0].split('_')[1])
pos = words[1]
# split chromosomes into separate files
if chr != chrPrev:
fileoutput.close()
fileoutput = open(chr+'_'+args.output, 'w')
chrPrev = chr
siteNumber = 1
# select samples
genotypes = calls.selectSamples(sampCol, words)
# count Ns
valueN = calls.countPerPosition(genotypes, 'N')
if valueN <= args.missing:
genotypesMerged = ''.join(str(e) for e in genotypes)
genotypesMergedP = genotypesMerged.replace('N', '?')
else:
continue
# count the number of called sites
if not calls.is_polymorphic(genotypes):
siteNumber += 1
else:
fileoutput.write("%s\t%s\t%s\t%s\n" % (chr, pos, siteNumber, genotypesMergedP))
sampCol = calls.indexSamples(sampleNames, header_words)
# count number of sample
nSample = len(sampleNames)
############################## perform counting ####################
print('Calculating ...')
nonMissDP = []
for line in datafile:
words = line.split()
Chr = words[0]
pos = int(words[1])
# select samples
sample_num = calls.selectSamples(sampCol, words)
# sum up
for i in sample_num:
if i.isdigit():
nonMissDP.append(float(i))
elif i == "NA":
continue
else:
warnings.warn("%s is not numeric at the line %s" %
(i, counter + 1))
# make output
depth = round(sum(nonMissDP) / float(len(nonMissDP)), 2)
outputFile.write("%s\t%s\t%s\n" % (Chr, pos, depth))
nonMissDP = []
counter = 0
# read the header
ances = open(args.ancestral, 'r')
ances_words = ances.readline()
output = open(args.output, 'w')
print('Opening the file...')
with open(args.input) as datafile:
header_words = datafile.readline().split()
sampCol = calls.indexSamples(sampleNames, header_words)
print('Creating the output file...')
samples_head = calls.selectSamples(sampCol, header_words)
samples_headP = '\t'.join(str(e) for e in samples_head)
output.write('%s\t%s\t%s\t%s\t%s\n' % ('CHROM', 'POS', 'ANC',
'DER', samples_headP))
# read the second line of the ancestral file
ances_words = ances.readline().split()
if '_' in ances_words[0]:
ances_ch = int(ances_words[0].split('_')[1])
else:
ances_ch = int(ances_words[0].split('chr')[1])
ances_pos = int(ances_words[1])
ances_gt = splitAncestral(ances_words[2])
for line in datafile:
words = line.split()
posE = ''
for line in datafile:
words = line.split()
Chr = words[0]
pos = int(words[1])
# to store the values of a previous line
if not ChrPrevious:
ChrPrevious = Chr
if not posS:
posS = pos
if not posE:
posE = pos
# select samples
sample_charaters = calls.selectSamples(sampCol, words)
# check if one- or two-character code
if any(["/" in gt for gt in sample_charaters]):
sample_charaters = calls.twoToOne(sample_charaters)
# if window size is reached output the results
if Chr > ChrPrevious: # if end of a chromosome
try:
HeterWindow = round(meanWindow(Hwindow, Twindow), 4)
except Exception:
HeterWindow = "NA"
calls.processWindow(ChrPrevious, posS, posE, HeterWindow,
outputFile)
windPosEnd = windSize
Hwindow = []
vars()[popName + "Index"] = calls.indexSamples(
vars()[popName + "samples"], header_words)
for line in datafile:
words = line.split()
GT = words[2:]
GTpair = [i for i in list(set(GT))
if i != 'N'] # get the set of alleles, skip missing alleles
popNum = 0
#if ("N" not in GT) and (len(GTpair) == 2) : # skip missing data or non-biallelic
if (len(GTpair) == 2): # skip non-biallelic
for popName in pops:
popNum += 1 # to make correct output. See below
# select genotypes
sGT = calls.selectSamples(vars()[popName + "Index"], words)
counts = Counter(sGT) # count alleles
bicounts = [counts[GTpair[0]],
counts[GTpair[1]]] # extract counts
bicountsP = ','.join(str(w) for w in bicounts)
# make output
if popNum == len(pops):
fileoutput.write("%s\n" % bicountsP)
else:
fileoutput.write("%s\t" % bicountsP)
# track progress
counter += 1
if counter % 1000000 == 0:
############################# program #############################
counter = 0
print('Opening the file...')
with open(args.input) as datafile:
header_words = datafile.readline().split()
# index samples
sampCol = calls.indexSamples(sampleNames, header_words)
# make output header
print('Creating the output file...')
fileoutput = open(args.output, 'w')
sampHeader = calls.selectSamples([0, 1] + sampCol, header_words)
sampHeaderP = '\t'.join(str(el) for el in sampHeader)
fileoutput.write(sampHeaderP + '\n')
for line in datafile:
words = line.split()
chr_pos = words[0:2]
# select samples
genotypes = calls.selectSamples(sampCol, words)
# make output
chr_posP = '\t'.join(str(el) for el in chr_pos)
genotypesP = '\t'.join(str(el) for el in genotypes)
fileoutput.write('%s\t%s\n' % (chr_posP, genotypesP))
fileoutput = open(chrPrev + '_' + args.output, 'w')
for line in datafile:
words = line.split()
chr = str(words[0].split('_')[1])
pos = words[1]
# split chromosomes into separate files
if chr != chrPrev:
fileoutput.close()
fileoutput = open(chr + '_' + args.output, 'w')
chrPrev = chr
siteNumber = 1
# select samples
genotypes = calls.selectSamples(sampCol, words)
# count Ns
valueN = calls.countPerPosition(genotypes, 'N')
if valueN <= args.missing:
genotypesMerged = ''.join(str(e) for e in genotypes)
genotypesMergedP = genotypesMerged.replace('N', '?')
else:
continue
# count the number of called sites
if not calls.is_polymorphic(genotypes):
siteNumber += 1
else:
fileoutput.write("%s\t%s\t%s\t%s\n" %
return all(x == items[0] for x in items[1:])
counter = 0
output = open(args.output, 'w')
output.write("#CHR\tPOS\tCommon_alleles\tRare_alleles\n")
print('Opening the file...')
with open(args.input) as datafile:
header_words = datafile.readline().split()
# index samples
sIndex = calls.indexSamples(sNames, header_words)
# create lists for output
sNames = calls.selectSamples(sIndex, header_words)
for line in datafile:
words = line.split()
chr_pos = words[0:2]
chr_posP = '\t'.join(str(e) for e in chr_pos)
# select samples
sGT = calls.selectSamples(sIndex, words)
# define two or one character code
if all(len(i) == 1 for i in sGT):
alleles = calls.OneToTwo(sGT)
elif all("/" in i for i in sGT):
alleles = sGT
# index samples
sampCol = calls.indexSamples(sampleNames, header_words)
for line in datafile:
# track progress
counter += 1
if counter % 1000000 == 0:
print str(counter), "lines processed"
words = line.split()
chr_pos = words[0:2]
ch = int(words[0].split('_')[1])
pos = int(words[1])
# select samples
alleles = calls.selectSamples(sampCol, words)
# count Ns
valueN = calls.countPerPosition(alleles, 'N')
if valueN <= args.missing:
Allalleles = [i for i in alleles if i != 'N']
else:
continue
# find overlap with the ancestor
while ch > ref_ch or (ch == ref_ch and pos > ref_pos):
words2 = ref.readline().split()
if words2 == []:
ancest = 'N'
break
counter = 0
print('Opening the file...\n')
with open(args.input) as datafile:
header_words = datafile.readline().split()
ChrPos = header_words[0:2]
ChrPosP = '\t'.join(str(e) for e in ChrPos)
# index samples
sampCol = calls.indexSamples(sampleNames, header_words)
# create lists for output
sampColnames = calls.selectSamples(sampCol, header_words)
# create merged column names
idsheader = []
for i in range(len(sampColnames)):
if i % 2 == 0:
name1 = sampColnames[i].split("_")[0]
name2 = sampColnames[i + 1].split("_")[0]
if name1 == name2:
idsheader.append(name1)
else:
raise KeyError(
"Sample is not paired. Sample name %s doesn't equal to sample name %s"
% (name1, name2))
header = '\t'.join(str(e) for e in idsheader)
############################# program #############################
counter = 0
print('Opening the file...')
with open(args.input) as datafile:
header_line = datafile.readline()
header_words = header_line.split()
# index samples
sampCol = calls.indexSamples(sampleNames, header_words)
# create lists for output
sampColnames = calls.selectSamples(sampCol, header_words)
sampNs = [0 for i in sampColnames]
print('Counting Ns ...')
Ns = []
for line in datafile:
words = line.split()
chr_pos = words[0:2]
# select samples
sample_charaters = calls.selectSamples(sampCol, words)
# count Ns per position
contNsOnly = calls.countPerPosition(sample_charaters, 'N')
sampleNames = calls.checkSampleNames(args.stats, args.input)
############################# program #############################
output = open(args.output, 'w')
counter = 0
print('Opening the file...')
with open(args.input) as datafile:
header_line = datafile.readline()
header_words = header_line.split()
sampCol = calls.indexSamples(sampleNames, header_words)
sampColnames = calls.selectSamples(sampCol, header_words)
sampColnamesP = '\t'.join(str(e) for e in sampColnames)
output.write("%s\t%s\t%s\n" %
(header_words[0], header_words[1], sampColnamesP))
print('Adding noise ...')
for line in datafile:
words = line.split()
chr_pos = words[0:2]
sample_scores = calls.selectSamples(sampCol, words)
sample_scoresNoise = []
for s in sample_scores:
fieldsIndex = calls.indexSamples(fieldsNames, annotOptions)
sift_words = siftFile.readline().split()
sift_chr = int(sift_words[0].split('_')[1])
sift_pos = int(sift_words[1])
with open(args.tab) as datafile:
header_words = datafile.readline().split()
# index samples
sampCol = calls.indexSamples(sampleNames, header_words)
# make output header
print('Creating the output file...')
output = open(args.output, 'w')
ouput_header = header_words[0:2] + calls.selectSamples(sampCol, header_words)
ouput_headerP = '\t'.join(str(el) for el in ouput_header)
output.write('%s\n' % ouput_headerP)
############################### perform counting ####################
for line in datafile:
words = line.split()
ch = int(words[0].split('_')[1])
pos = int(words[1])
# select samples
tab_charaters = calls.selectSamples(sampCol, words)
# find overlap in genomic position
while (ch > sift_chr) or (ch == sift_chr and pos > sift_pos):
with open(args.callsFile) as callsFile:
header_words = callsFile.readline().split()
# index samples according to the header
for groupName in groups:
vars()[groupName + "Index"] = calls.indexSamples(
vars()[groupName + "samples"], header_words)
for line in callsFile:
words = line.split()
CHR = words[0]
POS = int(words[1])
# prepare the genotypes
for popName in groups:
sGT = calls.selectSamples(vars()[popName + "Index"],
words) # select genotypes per group
sGTnoN = [i for i in sGT if i != 'N'] # remove missing data
if sGTnoN != []: # if not all GT are missing
sGTnoNset = list(set(sGTnoN)) # find set of alleles
random.shuffle(
sGTnoNset) # shuffle list to deal with a tie case.
mostFreqGT = max(
sGTnoNset, key=sGTnoN.count
) # find the most frequent allele. A tie is solved randomly.
else:
mostFreqGT = 'NA'
if CHR != CHRprev:
try:
fastaSeqP = ''.join(
str(w) for w in vars()[popName + "fastaSeq"])
ANCindex = calls.indexSamples(['ANC'], header_words)
DERindex = calls.indexSamples(['DER'], header_words)
FamilyIndex = {}
phasedLines = {}
for family in familySamples:
FamilyIndex[family] = calls.indexSamples(familySamples[family],
header_words)
phasedLines[family] = []
for line in datafile:
words = line.split()
CHR = words[0]
POS = words[1]
chr_pos = CHR + '_' + POS
ANC = calls.selectSamples(ANCindex, words)[0]
DER = calls.selectSamples(DERindex, words)[0]
allSeq = calls.selectSamples(sampleIndex, words)
if calls.is_biallelic(allSeq):
for family in FamilyIndex:
famSeq = calls.selectSamples(FamilyIndex[family], words)
GTsplit = []
splitfamSeq = []
for GT in famSeq:
GT = GT.replace('/', ' ')
GT = GT.replace('|', ' ')
GT = GT.replace('.', '9')
GTsplit.append(GT)
splitSeq = ' '.join(str(e) for e in GTsplit)
splitfamSeq.append(splitSeq)
parser.add_argument('-s', '--samples', help = 'column names of the samples to process (optional)', type=str, required=False)
args = parser.parse_args()
# check if samples names are given and if all sample names are present in a header
sampleNames = calls.checkSampleNames(args.samples, args.input)
############################# program #############################
CHRprev = 'NA'
with open(args.input) as datafile:
header_words = datafile.readline().split()
# index a sample
sampCol = calls.indexSamples(sampleNames, header_words)
header_samples = calls.selectSamples(sampCol, header_words)
header_samplesP = '\t'.join(str(e) for e in header_samples)
for line in datafile:
words = line.split()
CHR = words[0]
Pos = words[1]
samples = calls.selectSamples(sampCol, words)
samplesP = '\t'.join(str(e) for e in samples)
# find chromosome border
if CHRprev == CHR:
output.write('%s\t%s\t%s\n' % (CHR, Pos, samplesP))
else:
CHRprev = CHR
