def approximateMatches(Pattern, Text, d):
# Input: Strings Pattern and Text along with an integer d.
# Output: All starting positions where Pattern appears as a substring of Text with at most d mismatches.
positions = list()
k = len(Pattern)
for i in range(0, len(Text) - k + 1):
if (hammingDistance(Pattern, Text[i:i + k]) <= d):
positions.append(i)
return listToString(positions)
def findClumps(Text, k, L, t):
# Input: A string Genome, and integers k, L, and t.
# Output: All distinct k-mers forming (L, t)-clumps in Genome.
patterns = set()
for i in range(0, len(Text) - L):
textWindow = Text[i:i + L]
freqMap = frequencyTable(textWindow, k)
for key in freqMap:
if freqMap.get(key) >= t:
patterns.add(key)
return listToString(list(patterns))
while True:
profile = constructProfile(motifs, k, t)
motifs = Motifs(Dna, k, profile)
if score(motifs) < score(bestMotif):
bestMotif = motifs
else:
return bestMotif
def Motifs(Dna, k, profile):
motifs = []
for seq in Dna.split("\n"):
motifs.append(profileMostProbable(seq, k, profile))
return motifs
def iteration_RMS(Dna, k, t, iter):
bestMotifs = RandomizedMotifSearch(Dna, k, t)
for i in range(0, iter - 1):
motifs = RandomizedMotifSearch(Dna, k, t)
if score(motifs) < score(bestMotifs):
bestMotifs = motifs
return bestMotifs
if __name__ == "__main__":
bestMotifs = iteration_RMS(
"AGTCAACCCGGATCGCGGGTCGGTATGGCCACAGCACTCCGCGAACAGTCGGATAGGCAAGCACGGGGATCCGAACGCTCCAAGCGGCCATAAATTAGTTTCTTCTGGGTGCCATTAGAACTAGCCATCCTGCTAACCGCTCGGCGTAATGTGCACACTGCCTAAATTGCTTTAACGCATCCTATAAGTCAACCCGGATCG\nCGGGTCGGTATGGCCACAGCACTCCGCGAACAGTCGGATAGGCAAGCACGGGGATCCGAACGCTCCAAGCGGCCATATCGCTCCGGACCTCCAATTAGTTTCTTCTGGGTGCCATTAGAACTAGCCATCCTGCTAACCGCTCGGCGTAATGTGCACACTGCCTAAATTGCTTTAACGCATCCTATAAGTCAACCCGGATCG\nTGCGGGACTTGTAGATGGAATGCAGTTCTTGAGAGGTCCGTGCCTGGTTTTATCATTCCCGGGGAGCCCGATTGGGTGCCTGAGGCTTTCGGACTACAGGTTCCCCATAAACCACGAACCAATAGTAGCAGAAGCCAATAGCATCGAACTGGGCCCCCTTGCGAACGGACCTCCGCACGGGTGAGTATACTTCGGATACCC\nCGATATTGACACTGGTGACTCCCGCACCCTCATCGAGCGACCCCTGGGTTGCAGACATTAAGTTCTTGTTACACGTCATATTCATTTGGCTAAGGCTGCGGTAGTAAGCTACCCGGGACGATCTCAAAATCGAAAGTAACCTCCTTAGGGAAAACCTACTCACTCACCCACGGCCGTTCATCACTTTCGGACCATTCAGGT\nACTGTAGGCTTTTCAGGTTAAGACCACTACCCTTGGAAGCGCCAGGTGGGACCACCAATGAGATGATACATATGGGGCAGTTCTCTGGATGATTACACGCCACTCAGTCTAGCTTGTTTGCGCCAGTGAAAATTTGTCAACAAAGACGGCGATTGTAGGTCGAAACGGTAGTCCACTCGTGTTTCCATAAATGCCCTCGAA\nCATAGTTAGATCCACGTGCTGTTCCTAATAGAGACTTTACGCAATGCCGTATGGCTCTCTACGATGGGCTCAGGGGCATAGTTAAGTTACAAGTCAATTGAGATAGACTGGCGCAGACTTTGTGTCGAAACCTTCCTCCACTACGATGTTACCAACAGGGTTAGTGCAAGCATCCATCCATTATTTCACTCTGGCGGACTA\nAATGGCATGGTCCGCAGCCAAATATCGAGACACGAGTTGATACACCGTTCGATTAGCAACTCGAAAGTTACCTCCCTAGAGCGGCGTCGTCGGTTGATGTCATCTTTTCTCTGATCGCATTAATCTCGTGTAAAAAAATTAATGCATACTGAGGCACAGCTTCTTACAATCATGTGAGGTACTTCCGCCTGAATTCTGATG\nGCCTGTTGATAGGTCGGCTTTGTGTTGCCACTGCACAAGGAGGTAGCGTAGAGTGGCGTCACTTTCCAACTCGCGCTTTCAATGACCCCAACGTGCTGCAGATATGTGTAGAGTGAAATTACATAACGCCTTGCTATCCGTCCGTATAATTCCCAGTGGTTCGAAACGCTGCTCCAGGGATTAGGTGGCTAGCGAGGGCTT\nAAAGCTCTAAGATGTTCGCATGCGCAACGTGTAATTCAGGTACGCACGTCGAAACGGACCAAGTGACGATGGTTCACAACCGTTTGCCTAAGCGCGAGCAGTAACGTAAGCAAGTAGCGTGAACCAAGAGCATACCCTGTCTCTGGTTGAGACTATCGGCTTTGATAGAGCGTCTACAGTGCCACTCGTACATGTTCTCGA\nCAATAGCACTCGTTTTCCGATCGACCAGGACCTCCCCTTACCAGGCGGACTGTAATGAGCTGAACGGTTAAAAGGTCCAGTAGTAGCGTCGTGTCAATACCGGTCTTCTGACATCGGGGGAGGCCTGCGGAACAAAAAAAACAATATTATTTTGAATCGGGATCAGGGCACACACGAAATCATAGACACTACGTCTCGGCT\nGGTAGCATGCAAGACGGGGTCACCGTATGCAAGACAACTCACGTTAATAAAGGTCGAGCCAGCGGTTATAGGACACGAATTGAAGGTTTGTATAGACGGGCAAGCCTCATCCGTTGCCCATTGAGTCCCCTCGGGTCGACGAGGACCTCCTACACTGGATTTTTACCCAAGCCAAGACGAAAAACCTAGGCAAGTAACATT\nAGTTTTTGTATGCCTTTATTTGGGGGCGCAAAGGTTTGGATTTTAACTCAGGCGAGCTACGCCTACCGAAATTAAGCACCTGGTTAACGACGGGAAGAATATCTCACCTACAACTGACAAAGACCAGGCGCCACGTTCAGGTGACACGATTGGTAGGCGGTTCCTACCTTCCGATCGAAACGGACACACGATGTCTTGACA\nCCAGGCTGCTCCCAGTTATCGAGGCATCCTTGTGACTAGCGCCTCGAACGAGACCTCCTACCTATTGCTATATAAATCCAGCGTCGGATCTCACCCGAGACTCATCCCGTGCCTTCCCAGCCCTTGTTGGTCTAGTGCAGCTTAATTCTGTCGATAAAAGGGATGCTAGGTAAGTCATTCACGAATTTTCGTGTGTAGTAT\nACCTTGTAATATCTAATCGGCAGCTTCTCCAATACCATCGTCTCGGACCTCCAGTCGCTCGCTGCGTACGTACTCGTGTTCACACAGGGCGAACGCTGGCGGGCCGGGACGCTTCAGGGAGAGCGTTAGGCCATCAGCGTGACTCGGACGGTGACTATTGGATCTCACCCCCACTCAGGCGTCTCGGCACCCTCATAACCC\nCACTATCCTACCGTATCGGGCTCGAATATGCGCCATCCGAAGGGAGCGATTCAATAGGGACGGCCGGGTCGGCGCGCATATCTTGACTGCACGAAAATGAAACGGACCTCATATTCATGATCCACTGACTAGTAAATAGCTGCGAGGGCAGTTGTGGCAGGTCTTACAGGGTAAACGTTAACGAACTCCACTTTTCGTGGG\nTCACGCCCCTCGACGAAGTCGAAACGGAGACCCGTCCCACGCACATAGGAAGCCAGCGTCATCTGGCTTATGGGGCCGTAGGCTGGATGGTAGGCTCGACGTAGAGTGTCTCAATACAAGCTATTGGGCGCAGATTGATAAACCGCCCGCTTTTCAGACGTTAGTTACCTTCTGTGTGAGGATTGCAACATTACTTGCTAA\nACCCATCGCAGACCGAACTGGGGTTCTAGTACACGACGCCCACGCGATTTGTAGCGCACTTATTGGACTTGAGCCACTCAGCTCCCAGTCCATTCATATTTGTTACAATACGTCCCGTACCAGTGGAACCCCGTTTCCCCGACGGGCGTGCCCGGAGCATCTCGTAGAGGAAAAACGGACCTCCTGAAGGCGATTCGCCTG\nCTATATCCCTGGGTGCTTCACTCAGTCGAATGTGACCTCCAAGTAAAGAACCGGGATTACTTACCAACCTTAGCCGATAGCGATACTATAACGCTCCACCATCCTGACTGGCAACGCACCATTCCTCTTGCAACATGACGACCTGCCTTATTGGCCGATATTTAAGTCAAGAGACTCTGGTAAGTGCAGACGGTGTAAAAC\nGCTACGCCCGGGTGAGGAACGGGGTCCTCCACAGTTAACTATATTGCCAAATTCTTGGAGAAAGTCATAAACTTAAAATATCCATTGCAATACGAAGACCGTGGGGATGAACGCCGCGGGCTGTCTCTGTGTGGTGCTAAAACTCCCGGTGTAACCGATGCATGTTCTTTATCAGCACGGACCTCCCCATGATGCGCTCGC\nGGACATGATGGACTCAAGAGACTTCAGACGCCCTGGCCCCCGACTGCTTTTCTCGCCCGTGCGTAGCCCGCCGAAACGGACCTAAATAACCCCTTGACATACCAATGTACGATCTCGTCAATGTCCTTTTTACCCAACCCGATCAACAAGTCAGGGTCGAGCTACCGCCTGAAGTTGGCCACACACCGCAACCCAGACCGG",
15, 20, 1000)
print(listToString(bestMotifs).replace(" ", "\n"))
print(score(bestMotifs))
# Output: All integer(s) i minimizing Skewi (Genome) among all values of i (from 0 to |Genome|)
minVal = 999 # Is it best to start from 0?
skewness = 0
positions = list()
skewValues = {
"G": 1,
"C": -1,
"A": 0,
"T": 0,
"g": 1,
"c": -1,
"a": 0,
"t": 0
}
for i in range(0, len(Genome)):
skewness = skewness + skewValues.get(Genome[i])
if (minVal > skewness):
positions = list()
minVal = skewness
if (minVal == skewness):
positions.append(i + 1)
return (skewness, positions)
if __name__ == "__main__":
with open('dataset_369238_6.txt', 'r') as file:
data = file.read().replace("\n", "")
print(listToString(minimumSkew(data)[1]))
def profile_random_probability_kmer(string, k, profile):
translation = {"A": 0, "C": 1, "G": 2, "T": 3}
probabilities = {}
for i in range(0, len(string) - k + 1):
kmer = string[i:i + k]
probabilities[kmer] = 1.0
for j in range(0, len(kmer)):
probabilities[kmer] = probabilities[kmer] * profile[translation[
kmer[j]]][j] * 1.0
random_choice = random.choices(list(probabilities.keys()),
weights=probabilities.values())
return string.join(random_choice)
def iteration_GS(Dna, k, t, N, iter):
bestMotifs = GibbsSampler(Dna, k, t, N)
for i in range(0, iter - 1):
motifs = GibbsSampler(Dna, k, t, N)
if score(motifs) < score(bestMotifs):
bestMotifs = motifs
return bestMotifs
if __name__ == "__main__":
print(
listToString(
iteration_GS(
"GCGCCCCGCCCGGACAGCCATGCGCTAACCCTGGCTTCGATGGCGCCGGCTCAGTTAGGGCCGGAAGTCCCCAATGTGGCAGACCTTTCGCCCCTGGCGGACGAATGACCCCAGTGGCCGGGACTTCAGGCCCTATCGGAGGGCTCCGGCGCGGTGGTCGGATTTGTCTGTGGAGGTTACACCCCAATCGCAAGGATGCATTATGACCAGCGAGCTGAGCCTGGTCGCCACTGGAAAGGGGAGCAACATC\nCCGATCGGCATCACTATCGGTCCTGCGGCCGCCCATAGCGCTATATCCGGCTGGTGAAATCAATTGACAACCTTCGACTTTGAGGTGGCCTACGGCGAGGACAAGCCAGGCAAGCCAGCTGCCTCAACGCGCGCCAGTACGGGTCCATCGACCCGCGGCCCACGGGTCAAACGACCCTAGTGTTCGCTACGACGTGGTCGTACCTTCGGCAGCAGATCAGCAATAGCACCCCGACTCGAGGAGGATCCCG\nACCGTCGATGTGCCCGGTCGCGCCGCGTCCACCTCGGTCATCGACCCCACGATGAGGACGCCATCGGCCGCGACCAAGCCCCGTGAAACTCTGACGGCGTGCTGGCCGGGCTGCGGCACCTGATCACCTTAGGGCACTTGGGCCACCACAACGGGCCGCCGGTCTCGACAGTGGCCACCACCACACAGGTGACTTCCGGCGGGACGTAAGTCCCTAACGCGTCGTTCCGCACGCGGTTAGCTTTGCTGCC\nGGGTCAGGTATATTTATCGCACACTTGGGCACATGACACACAAGCGCCAGAATCCCGGACCGAACCGAGCACCGTGGGTGGGCAGCCTCCATACAGCGATGACCTGATCGATCATCGGCCAGGGCGCCGGGCTTCCAACCGTGGCCGTCTCAGTACCCAGCCTCATTGACCCTTCGACGCATCCACTGCGCGTAAGTCGGCTCAACCCTTTCAAACCGCTGGATTACCGACCGCAGAAAGGGGGCAGGAC\nGTAGGTCAAACCGGGTGTACATACCCGCTCAATCGCCCAGCACTTCGGGCAGATCACCGGGTTTCCCCGGTATCACCAATACTGCCACCAAACACAGCAGGCGGGAAGGGGCGAAAGTCCCTTATCCGACAATAAAACTTCGCTTGTTCGACGCCCGGTTCACCCGATATGCACGGCGCCCAGCCATTCGTGACCGACGTCCCCAGCCCCAAGGCCGAACGACCCTAGGAGCCACGAGCAATTCACAGCG\nCCGCTGGCGACGCTGTTCGCCGGCAGCGTGCGTGACGACTTCGAGCTGCCCGACTACACCTGGTGACCACCGCCGACGGGCACCTCTCCGCCAGGTAGGCACGGTTTGTCGCCGGCAATGTGACCTTTGGGCGCGGTCTTGAGGACCTTCGGCCCCACCCACGAGGCCGCCGCCGGCCGATCGTATGACGTGCAATGTACGCCATAGGGTGCGTGTTACGGCGATTACCTGAAGGCGGCGGTGGTCCGGA\nGGCCAACTGCACCGCGCTCTTGATGACATCGGTGGTCACCATGGTGTCCGGCATGATCAACCTCCGCTGTTCGATATCACCCCGATCTTTCTGAACGGCGGTTGGCAGACAACAGGGTCAATGGTCCCCAAGTGGATCACCGACGGGCGCGGACAAATGGCCCGCGCTTCGGGGACTTCTGTCCCTAGCCCTGGCCACGATGGGCTGGTCGGATCAAAGGCATCCGTTTCCATCGATTAGGAGGCATCAA\nGTACATGTCCAGAGCGAGCCTCAGCTTCTGCGCAGCGACGGAAACTGCCACACTCAAAGCCTACTGGGCGCACGTGTGGCAACGAGTCGATCCACACGAAATGCCGCCGTTGGGCCGCGGACTAGCCGAATTTTCCGGGTGGTGACACAGCCCACATTTGGCATGGGACTTTCGGCCCTGTCCGCGTCCGTGTCGGCCAGACAAGCTTTGGGCATTGGCCACAATCGGGCCACAATCGAAAGCCGAGCAG\nGGCAGCTGTCGGCAACTGTAAGCCATTTCTGGGACTTTGCTGTGAAAAGCTGGGCGATGGTTGTGGACCTGGACGAGCCACCCGTGCGATAGGTGAGATTCATTCTCGCCCTGACGGGTTGCGTCTGTCATCGGTCGATAAGGACTAACGGCCCTCAGGTGGGGACCAACGCCCCTGGGAGATAGCGGTCCCCGCCAGTAACGTACCGCTGAACCGACGGGATGTATCCGCCCCAGCGAAGGAGACGGCG\nTCAGCACCATGACCGCCTGGCCACCAATCGCCCGTAACAAGCGGGACGTCCGCGACGACGCGTGCGCTAGCGCCGTGGCGGTGACAACGACCAGATATGGTCCGAGCACGCGGGCGAACCTCGTGTTCTGGCCTCGGCCAGTTGTGTAGAGCTCATCGCTGTCATCGAGCGATATCCGACCACTGATCCAAGTCGGGGGCTCTGGGGACCGAAGTCCCCGGGCTCGGAGCTATCGGACCTCACGATCACC",
15, 10, 2000, 20)).replace(" ", "\n"))
# 1.8 Some Hidden Messages are More Elusive than Others
from Ex7 import hammingDistance
from Ex4 import listToString
from Ex2 import MaxMap
from Ex10 import Neighbors
def FrequentWordsWithMismatches(Text, k, d):
# Input: A string Text as well as integers k and d. (You may assume k <= 12 and d <= 3.)
# Output: All most frequent k-mers with up to d mismatches in Text.
Patterns = list()
freqMap = {}
n = len(Text)
for i in range(0, n-k):
pattern = Text[i:i+k]
neighborhood = list(Neighbors(pattern, d))
for j in range(0, len(neighborhood)-1):
neighbor = neighborhood[j]
if neighbor not in freqMap.keys():
freqMap[neighbor] = 1
else:
freqMap[neighbor] = freqMap.get(neighbor) + 1
m = MaxMap(freqMap)
for key in freqMap:
if freqMap[key] == m:
Patterns.append(key)
return Patterns
if __name__ == "__main__":
print(listToString(FrequentWordsWithMismatches("GGTAATATCTTAAATCTTGGTAGGTAATAAATTAAAGGTAATAATAAATTGGTAAATTAAAATAGGTAAAAATAGGTAATAAAAATAAATTAAAAATTATAATAAAAATATCTTTCTTATAATAAAAAAATCTTGGTATCTTTCTTAAAAATTAAAATAAATTAATTTCTTAAATCTTATAGGTAGGTAGGTAAATTAATTAAAAAAGGTAATATCTTAAAAAAAAAAAATCTTTCTTTCTTGGTATCTTTCTTAATTGGTATCTTATAGGTAATATCTTTCTTGGTAATAAAAAATTTCTTTCTTGGTAATAGGTAAATTAAAAATTAATTATAAATTAAATCTTGGTAGGTAATATCTTTCTTAAATCTTAATT", 6, 3)))