def predict(inFileName, outFileName, strain, binSize):
"""Walks through a file of site calls and predicts
Whether the region is M, N, or H based on a sliding
window of the given binSize.
Returns a tuple with the number of chromosomes and regions predicted"""
with open(inFileName) as sitesFile, open(outFileName, 'w') as predFile:
#Load whole file into memory since it is only a couple of MBs
reader = csv.reader(sitesFile, delimiter='\t')
#dictionary of lists
chromeMap = defaultdict(list)
#We really only want to work with sites that have been called as N,M,H
#so just count up the number of U sites between the more meaningful
#sites for summary stats later
uCount = 0
for row in reader:
if (row[2] == 'U'):
uCount = uCount + 1
else:
chromeMap[row[0]].append((row[2], int(row[1]), uCount))
uCount = 0
#Write header
if TABLE_PRINT:
predFile.write(','.join(
("Strain", "Chromosome", "Start", "End", "Size (kbp)",
"Number of SNPs", "N2/Genotyped", "MY14/Genotyped",
"Heterozygous/Genotyped", "Incongruities/Genotyped",
"Unknown/All", "Previous Prediction", "Prediction",
"Next Prediction", "Link")) + '\n')
else:
predFile.write('\t'.join(
("Chromosome", "Breakpoint Lower", "Breakpoint Upper",
"Prediction", "Number of SNPs", "N2/Genotyped",
"MY14/Genotyped", "Heterozygous/Genotyped", "Unknown/All")) +
'\n')
#Gather up thresholds for the binSize and binSize/2 because sometimes
#we break the window in half for more positional knowledge
fullBinThresh = util.cutoffs(binSize)
halfBinThresh = util.cutoffs(binSize / 2)
thresholds = (fullBinThresh[0], fullBinThresh[1], halfBinThresh[0],
halfBinThresh[1])
#The total number of chromosomes and regions predicted
chromeCount = 0
regCount = 0
#Make predictions for each chromosome
for chrome in sorted(chromeMap.keys()):
chromes, regs = predictChrome(strain, chrome, chromeMap[chrome],
binSize, thresholds, predFile)
chromeCount += chromes
regCount += regs
return chromeCount, regCount
def predict(inFileName, outFileName, strain, binSize):
"""Walks through a file of site calls and predicts
Whether the region is M, N, or H based on a sliding
window of the given binSize.
Returns a tuple with the number of chromosomes and regions predicted"""
with open(inFileName) as sitesFile, open(outFileName, 'w') as predFile:
#Load whole file into memory since it is only a couple of MBs
reader = csv.reader(sitesFile, delimiter='\t')
#dictionary of lists
chromeMap = defaultdict(list)
#We really only want to work with sites that have been called as N,M,H
#so just count up the number of U sites between the more meaningful
#sites for summary stats later
uCount = 0
for row in reader:
if(row[2] == 'U'):
uCount = uCount + 1
else:
chromeMap[row[0]].append((row[2], int(row[1]), uCount))
uCount = 0
#Write header
if TABLE_PRINT:
predFile.write(','.join(("Strain", "Chromosome", "Start", "End", "Size (kbp)", "Number of SNPs", "N2/Genotyped", "MY14/Genotyped", "Heterozygous/Genotyped", "Incongruities/Genotyped", "Unknown/All", "Previous Prediction", "Prediction", "Next Prediction", "Link")) + '\n')
else:
predFile.write('\t'.join(("Chromosome", "Breakpoint Lower", "Breakpoint Upper", "Prediction", "Number of SNPs", "N2/Genotyped", "MY14/Genotyped", "Heterozygous/Genotyped", "Unknown/All")) + '\n')
#Gather up thresholds for the binSize and binSize/2 because sometimes
#we break the window in half for more positional knowledge
fullBinThresh = util.cutoffs(binSize)
halfBinThresh = util.cutoffs(binSize / 2)
thresholds = (fullBinThresh[0], fullBinThresh[1], halfBinThresh[0], halfBinThresh[1])
#The total number of chromosomes and regions predicted
chromeCount = 0
regCount = 0
#Make predictions for each chromosome
for chrome in sorted(chromeMap.keys()):
chromes, regs = predictChrome(strain, chrome, chromeMap[chrome], binSize, thresholds, predFile)
chromeCount += chromes
regCount += regs
return chromeCount, regCount
"""Clean up mpileup calls so that we are left with just the bases,
not the extra stuff it adds"""
# Easy stuff first
reads = re.sub(r"[$*<>]|\^.", "", reads)
# Now do the trickier inserts/deletes
# First find the regex
while(1):
match = re.search(r"[+-](\d+)", reads)
if not match:
break
num = int(match.group(1))
# now find the index and keep the string before and after the insert/delete
startIndex = reads.find(match.group(0))
endIndex = startIndex + 1 + len(match.group(1)) + num
reads = reads[0:startIndex] + reads[endIndex:]
return reads
if __name__ == '__main__':
# print clean('*A$C>G<T+23ACACACACACACACACACAGGGGT^JC-2TTC-11AAAAAAAAAAAC+4TTTTC')
hetCutoffs = {}
for i in range(1, 500):
hetCutoffs[i] = util.cutoffs(i)
callReads(hetCutoffs, *sys.argv[1:])
"""Clean up mpileup calls so that we are left with just the bases,
not the extra stuff it adds"""
# Easy stuff first
reads = re.sub(r"[$*<>]|\^.", "", reads)
# Now do the trickier inserts/deletes
# First find the regex
while (1):
match = re.search(r"[+-](\d+)", reads)
if not match:
break
num = int(match.group(1))
# now find the index and keep the string before and after the insert/delete
startIndex = reads.find(match.group(0))
endIndex = startIndex + 1 + len(match.group(1)) + num
reads = reads[0:startIndex] + reads[endIndex:]
return reads
if __name__ == '__main__':
# print clean('*A$C>G<T+23ACACACACACACACACACAGGGGT^JC-2TTC-11AAAAAAAAAAAC+4TTTTC')
hetCutoffs = {}
for i in range(1, 500):
hetCutoffs[i] = util.cutoffs(i)
callReads(hetCutoffs, *sys.argv[1:])