I am trying to solve the "Consensus and Profile" challenge on Rosalind. The challenge instructions are as follows:
Given: A collection of at most 10 DNA strings of equal length (at most 1 kbp) in FASTA format.
Return: A consensus string and profile matrix for the collection. (If several possible consensus strings exist, then you may return any one of them.)
My code is as follows (I got most of it from another user on this website). My only issue is that some of the DNA strands are broken down into multiple separate lines, so they are being appended to the "allstrings" list as separate strings. I am trying to figure out how to write each consecutive line that does not contain ">" as a single string.
import numpy as np
seq = []
allstrings = []
temp_seq = []
matrix = []
C = []
G = []
T = []
A = []
P = []
consensus = []
position = 1
file = open("C:/Users/knigh/Documents/rosalind_cons (3).txt", "r")
conout = open("C:/Users/knigh/Documents/consensus.txt", "w")
# Right now, this is reading and writing each as an individual line. Thus, it
# is splitting each sequence into multiple small sequences. You need to figure
# out how to read this in FASTA format to prevent this from occurring
desc = file.readlines()
for line in desc:
allstrings.append(line)
for string in range(1, len(allstrings)):
if ">" not in allstrings[string]:
temp_seq.append(allstrings[string])
else:
seq.insert(position, temp_seq[0])
temp_seq = []
position += 1
# This last insertion into the sequence must be performed after the loop to empty
# out the last remaining string from temp_seq
seq.insert(position, temp_seq[0])
for base in seq:
matrix.append([pos for pos in base])
M = np.array(matrix).reshape(len(seq), len(seq[0]))
for base in range(len(seq[0])):
A_count = 0
C_count = 0
G_count = 0
T_count = 0
for pos in M[:, base]:
if pos == "A":
A_count += 1
elif pos == "C":
C_count += 1
elif pos == "G":
G_count += 1
elif pos == "T":
T_count += 1
A.append(A_count)
C.append(C_count)
G.append(G_count)
T.append(T_count)
profile_matrix = {"A": A, "C": C, "G": G, "T": T}
P.append(A)
P.append(C)
P.append(G)
P.append(T)
profile = np.array(P).reshape(4, len(A))
for pos in range(len(A)):
if max(profile[:, pos]) == profile[0, pos]:
consensus.append("A")
elif max(profile[:, pos]) == profile[1, pos]:
consensus.append("C")
elif max(profile[:, pos]) == profile[2, pos]:
consensus.append("G")
elif max(profile[:, pos]) == profile[3, pos]:
consensus.append("T")
conout.write("".join(consensus) + "\n")
for k, v in profile_matrix.items():
conout.write(k + ": " + " ".join(str(x) for x in v) + "\n")
conout.close()