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Code1_11_FrequentWordsWithMismatches.py
131 lines (113 loc) · 3.62 KB
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Code1_11_FrequentWordsWithMismatches.py
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#Chunyu Zhao 20150909
import sys,Code1_8_HammingDistance as hd
def immediateNeighbor(pattern):
neighbor = [pattern]
for i in range(len(pattern)):
symbol = pattern[i]
for x in ['A','C','G','T']:
if x != symbol:
neighbor.append(pattern[:i]+x+pattern[i+1:])
return neighbor
def neighbors(pattern,d):
if d == 0:
return pattern
if len(pattern) == 1:
return ['A','C','G','T']
neighborhood = []
suffixNeighbors = neighbors(pattern[1:],d)
for text in suffixNeighbors:
if hd.hamming_distance(pattern[1:],text) < d:
for x in ['A','C','G','T']:
neighborhood.append(x+text)
else:
neighborhood.append(pattern[:1]+text)
neighborhood = list(set(neighborhood))
return neighborhood
def approximatePatternCount(text,pattern,d):
count = 0
for i in range(len(text)-len(pattern)+1):
if hd.hamming_distance(text[i:i+len(pattern)],pattern) <= d:
count += 1
return count
def frequentWordsWithMismatches(text,k,d):
frequentPatterns = []
close = [0] * (4**k)
frequencyArray = [0] * (4**k)
for i in range(len(text)-k+1):
neighborhood = neighbors(text[i:i+k],d)
for pattern in neighborhood:
index = PatternToNumber(pattern)
close[index] = 1
'''only consider k-mers that are close to a k-mer in text'''
for i in range(4**k):
if close[i] == 1:
pattern = NumberToPattern(i,k)
frequencyArray[i] = approximatePatternCount(text,pattern,d)
maxCount = max(frequencyArray)
for i in range(4**k):
if frequencyArray[i] == maxCount:
pattern = NumberToPattern(i,k)
frequentPatterns.append(pattern)
return list(set(frequentPatterns))
def frequentWordsWithMismatchesBySorting(text,k,d):
neighborhoods = []
for i in range(len(text)-k+1):
reverseString = reverse_complement(text[i:i+k])
neighborhoods = neighborhoods + neighbors(text[i:i+k],d) + neighbors(reverseString,d)
count = [0] * len(neighborhoods)
index = [0] * len(neighborhoods)
for i in range(len(neighborhoods)):
pattern = neighborhoods[i]
index[i] = PatternToNumber(pattern)
count[i] = 1
sortedIndex = sorted(index)
for i in range(len(neighborhoods)-1):
if sortedIndex[i] == sortedIndex[i+1]:
count[i+1] = count[i] + 1
maxCount = max(count)
frequentPatterns = [NumberToPattern(sortedIndex[i],k) for i,c in enumerate(count) if c == maxCount]
return frequentPatterns
def SymbolToNumber(symbol):
mapping = {'A':0,'C':1,'G':2,'T':3}
return mapping[symbol]
def NumberToSymbol(number):
mapping = {'A':0,'C':1,'G':2,'T':3}
invmapping = {val:key for key, val in mapping.items()}
return invmapping[number]
def PatternToNumber(pattern):
''' beautiful recursion '''
if len(pattern) == 0 :
return 0
symbol = pattern[-1]
prefix = pattern[:-1]
return 4 * PatternToNumber(prefix) + SymbolToNumber(symbol)
def NumberToPattern(index,k):
if k == 1:
return NumberToSymbol(index)
prefixIndex = index / 4
r = index % 4
symbol = NumberToSymbol(r)
prefixPattern = NumberToPattern(prefixIndex, k-1)
return prefixPattern + symbol
def reverse_complement(dna):
dnadict = {'A':'T','C':'G','G':'C','T':'A'}
reverseDna = [ dnadict[c] for c in dna ]
return ''.join(reverseDna[::-1])
if __name__ == '__main__':
if len(sys.argv) == 2:
filename = sys.argv[1]
with open(filename) as f:
lines = f.read().splitlines()
text = lines[0]
k = int(lines[1].split(' ')[0])
d = int(lines[1].split(' ')[1])
else:
text = 'ACGTTGCATGTCGCATGATGCATGAGAGCT'
k = 4
d = 1
print "Frequent Words with Mismatches Problem:"
freqpattern = frequentWordsWithMismatches(text,k,d)
print ' '.join(freqpattern)
print "Frequent Words with Mismatches and Reverse Complements Problem:"
freqpattern = frequentWordsWithMismatchesBySorting(text,k,d)
print ' '.join(freqpattern)