"""
# Brute Force (Naive Search) solution presented.
# For more optimized algorithm, see Rabin–Karp algorithm which is the most suited
# for multiple pattern search that uses hashed values.
from reader_FASTA import read_FASTA
def common_finder(n):
possible_commons = [first_piece[i:n+i] for i in range(len(first_piece) - n + 1)]
for common_str in possible_commons:
for ind, dna_list in enumerate(all_dna_pieces[1:]):
if common_str not in dna_list:
break
elif ind == len(all_dna_pieces) - 2:
return common_str
else:
continue
return
file = open('/Users/Anuar_the_great/desktop/rosalind_lcsm.txt')
data = file.readlines()
all_dna_pieces = read_FASTA(data).values()
first_piece = all_dna_pieces[0]
file.close()
for i in range(len(min(all_dna_pieces)), 1, -1 ):
if common_finder(i):
least_common_str = common_finder(i)
print least_common_str
break
>Rosalind_12
ATCGGTCGAA
>Rosalind_15
ATCGGTCGAGCGTGT
Sample output:
MVYIADKQHVASREAYGHMFKVCA
"""
from reader_FASTA import read_FASTA
from rna_to_prot import RNA_to_protein
def RNA_splicing(data, main_dna):
for id, intron in data.items():
main_dna[1] = main_dna[1].replace(intron, "")
main_dna[1] = main_dna[1].replace("T", "U")
main_dna[1] = RNA_to_protein(main_dna[1])
return main_dna
file = open("/Users/Anuar_the_great/desktop/Data_files/rosalind_splc.txt")
main_dna = [file.readline()[1:].strip("\n"), file.readline().strip("\n")]
data = file.readlines()
file.close()
# Alternative version for read_FASTA (returns a list instead):
# no_introns = filter(lambda line: line[0] != '>', data)
data = read_FASTA(data)
print RNA_splicing(data, main_dna)
Rosalind_0498 Rosalind_2391
Rosalind_0498 Rosalind_0442
Rosalind_2391 Rosalind_2323
"""
from reader_FASTA import read_FASTA
file = open('/Users/Anuar_the_great/desktop/rosalind_grph(1).txt')
data = file.readlines()
file.close()
def adjacency_list1(dict):
leest = []
for key1, value1 in dict.items():
for key2, value2 in dict.items():
if key1 != key2:
if value1.endswith(value2[0:3]):
leest.append([key1, key2])
return leest
# More optimized code (same logic but uses list comprehension)
def adjacency_list2(dict):
return [[key1, key2] for key1, value1 in dict.items() for key2, value2 in dict.items()\
if key1 != key2 if value1.endswith(value2[0:3])]
dict = read_FASTA(data)
leest1 = adjacency_list1(dict)
leest2 = adjacency_list2(dict)
for nodes1, nodes2 in zip(leest1, leest2):
print nodes1[0], nodes1[1], '; ', nodes2[0], nodes2[1]
