Greedy Motif Search in Python - python-3.x

Question

I am studying the Bioinformatics course at Coursera, and have been stuck on the following problem for 5 days:
Implement GreedyMotifSearch.
Input: Integers k and t, followed by a collection of strings Dna.
Output: A collection of strings BestMotifs resulting from applying GreedyMotifSearch(Dna, k, t).
If at any step you find more than one Profile-most probable k-mer in a given string, use the
one occurring first.
Here's my attempt to solve this (I just copied it from my IDE, so pardon any print statements):
def GreedyMotifSearch(DNA, k, t):
"""
Documentation here
"""
import math
bestMotifs = []
bestScore = math.inf
for string in DNA:
bestMotifs.append(string[:k])
base = DNA[0]
for i in window(base, k):
newMotifs = []
for j in range(t):
profile = ProfileMatrix([i])
probable = ProfileMostProbable(DNA[j], k, profile)
newMotifs.append(probable)
if Score(newMotifs) <= bestScore:
bestScore = Score(newMotifs)
bestMotifs = newMotifs
return bestMotifs
The helper functions are these:
def SymbolToNumber(Symbol):
"""
Converts base to number (in lexicograpical order)
Symbol: the letter to be converted (str)
Returns: the number correspondinig to that base (int)
"""
if Symbol == "A":
return 0
elif Symbol == "C":
return 1
elif Symbol == "G":
return 2
elif Symbol == "T":
return 3
def NumberToSymbol(index):
"""
Finds base from number (in lexicographical order)
index: the number to be converted (int)
Returns: the base corresponding to index (str)
"""
if index == 0:
return str("A")
elif index == 1:
return str("C")
elif index == 2:
return str("G")
elif index == 3:
return str("T")
def HammingDistance(p, q):
"""
Finds the number of mismatches between 2 DNA segments of equal lengths
p: first DNA segment (str)
q: second DNA segment (str)
Returns: number of mismatches (int)
"""
return sum(s1 != s2 for s1, s2 in zip(p, q))
def window(s, k):
for i in range(1 + len(s) - k):
yield s[i:i+k]
def ProfileMostProbable(Text, k, Profile):
"""
Finds a k-mer that was most likely to be generated by profile among
all k-mers in Text
Text: given DNA segment (str)
k: length of pattern (int)
Profile: a 4x4 matrix (list)
Returns: profile-most probable k-mer (str)
"""
letter = [[] for key in range(k)]
probable = ""
hamdict = {}
index = 1
for a in range(k):
for j in "ACGT":
letter[a].append(Profile[j][a])
for b in range(len(letter)):
number = max(letter[b])
probable += str(NumberToSymbol(letter[b].index(number)))
for c in window(Text, k):
for x in range(len(c)):
y = SymbolToNumber(c[x])
index *= float(letter[x][y])
hamdict[c] = index
index = 1
for pat, ham in hamdict.items():
if ham == max(hamdict.values()):
final = pat
break
return final
def Count(Motifs):
"""
Documentation here
"""
count = {}
k = len(Motifs[0])
for symbol in "ACGT":
count[symbol] = []
for i in range(k):
count[symbol].append(0)
t = len(Motifs)
for i in range(t):
for j in range(k):
symbol = Motifs[i][j]
count[symbol][j] += 1
return count
def FindConsensus(motifs):
"""
Finds a consensus sequence for given list of motifs
motifs: a list of motif sequences (list)
Returns: consensus sequence of motifs (str)
"""
consensus = ""
for i in range(len(motifs[0])):
countA, countC, countG, countT = 0, 0, 0, 0
for motif in motifs:
if motif[i] == "A":
countA += 1
elif motif[i] == "C":
countC += 1
elif motif[i] == "G":
countG += 1
elif motif[i] == "T":
countT += 1
if countA >= max(countC, countG, countT):
consensus += "A"
elif countC >= max(countA, countG, countT):
consensus += "C"
elif countG >= max(countC, countA, countT):
consensus += "G"
elif countT >= max(countC, countG, countA):
consensus += "T"
return consensus
def ProfileMatrix(motifs):
"""
Finds the profile matrix for given list of motifs
motifs: list of motif sequences (list)
Returns: the profile matrix for motifs (list)
"""
Profile = {}
A, C, G, T = [], [], [], []
for j in range(len(motifs[0])):
countA, countC, countG, countT = 0, 0, 0, 0
for motif in motifs:
if motif[j] == "A":
countA += 1
elif motif[j] == "C":
countC += 1
elif motif[j] == "G":
countG += 1
elif motif[j] == "T":
countT += 1
A.append(countA)
C.append(countC)
G.append(countG)
T.append(countT)
Profile["A"] = A
Profile["C"] = C
Profile["G"] = G
Profile["T"] = T
return Profile
def Score(motifs):
"""
Finds score of motifs relative to the consensus sequence
motifs: a list of given motifs (list)
Returns: score of given motifs (int)
"""
consensus = FindConsensus(motifs)
score = 0.0000
for motif in motifs:
score += HammingDistance(consensus, motif)
#print(score)
return round(score, 4)
It seems fine to me. However, when I run this code for quiz problems, it gives an incorrect answer. Their code grading system shows this error:
Failed test #3. Your indexing may be off by one at the beginning of each string in Dna.
I have tried everything I can think of and run this code on all their sample data and debug data, but I simply can't figure out how to make this code work. Please help me with any possible solutions to this.

Answer 1

You have a few problems. I think this should address them all. I've included comments explaining each change along with your original code and a reference to the relevant Pseudocode in the debug data page you linked to.
def GreedyMotifSearch(DNA, k, t):
"""
Documentation here
"""
import math
bestMotifs = []
bestScore = math.inf
for string in DNA:
bestMotifs.append(string[:k])
base = DNA[0]
for i in window(base, k):
# Change here. Should start with one element in motifs and build up.
# As in the line "motifs ← list with only Dna[0](i,k)"
# newMotifs = []
newMotifs = [i]
# Change here to iterate over len(DNA).
# Should go through "for j from 1 to |Dna| - 1"
# for j in range(t):
for j in range(1, len(DNA)):
# Change here. Should build up motifs and build profile using them.
# profile = ProfileMatrix([i])
profile = ProfileMatrix(newMotifs)
probable = ProfileMostProbable(DNA[j], k, profile)
newMotifs.append(probable)
# Change to < rather < = to ensure getting the most recent hit. As referenced in the instructions:
# If at any step you find more than one Profile-most probable k-mer in a given string, use the one occurring **first**.
if Score(newMotifs) < bestScore:
#if Score(newMotifs) <= bestScore:
bestScore = Score(newMotifs)
bestMotifs = newMotifs
return bestMotifs