def main():
'''Main call. Parses, runs, and saves problem specific data.'''
# Parse the input data.
seq_list = ReadFASTA('data/rosalind_gc.txt')
highest_gc = map(str, max_gc_content(seq_list))
# Print and save the answer.
print '\n'.join(highest_gc)
with open('output/005_GC.txt', 'w') as output_data:
output_data.write('\n'.join(highest_gc))
def main():
'''Main call. Reads, runs, and saves problem specific data.'''
# Parse the two input protein strings.
s, t = [fasta[1] for fasta in ReadFASTA('data/rosalind_gaff.txt')]
# Get the alignment score.
score = global_alignment_affine_gap_penalty(s, t, BLOSUM62(), 11, 1)
# Print and save the answer.
print '\n'.join(score)
with open('output/096_GAFF.txt', 'w') as output_data:
output_data.write('\n'.join(score))
def main():
'''Main call. Reads, runs, and saves problem specific data.'''
# Read and parse the input data.
word1, word2 = [fasta[1] for fasta in ReadFASTA('data/rosalind_smgb.txt')]
# Get the alignment.
alignment = semiglobal_alignment(word1, word2, 1)
# Print and save the answer.
print '\n'.join(alignment)
with open('output/101_SMGB.txt', 'w') as output_data:
output_data.write('\n'.join(alignment))
def main():
'''Main call. Reads, runs, and saves problem specific data.'''
# Parse the input data.
v, w = [fasta[1] for fasta in ReadFASTA('data/rosalind_mgap.txt')]
# Get the maximum number of gaps.
max_gaps = str(maximum_gap_symbols(v, w))
# Print and save the answer.
print max_gaps
with open('output/083_MGAP.txt', 'w') as output_data:
output_data.write(max_gaps)
def main():
'''Main call. Reads, runs, and saves problem specific data.'''
# Parse the input data.
rna = ReadFASTA('data/rosalind_cat.txt')[0][1]
# Get the number of noncrossing perfect bondings.
noncrossing = str(noncrossing_perfect_bondings(rna))
# Print and save the answer.
print noncrossing
with open('output/033_CAT.txt', 'w') as output_file:
output_file.write(str(noncrossing))
def main():
'''Main call. Reads, runs, and saves problem specific data.'''
# Read and parse the input data.
word1, word2 = [fasta[1] for fasta in ReadFASTA('data/rosalind_sims.txt')]
# Get the fitting alignment.
alignment = fitting_alignment(word1, word2)
# Print and save the answer.
print '\n'.join(alignment)
with open('output/100_SIMS.txt', 'w') as output_data:
output_data.write('\n'.join(alignment))
from scripts import ReadFASTA
from protein_map import ProteinDictDNA
dlist = ReadFASTA('data/rosalind_splc.txt')
#print(dlist)
RNA = dlist[0][1]
lenDNA = len(dlist)
for i in range(1, lenDNA):
RNA = RNA.replace(dlist[i][1], '')
#print(RNA)
prot_dct = ProteinDictDNA()
protein = ''
for i in range(0, len(RNA), 3):
test = RNA[i:i + 3]
if (prot_dct[test] != 'Stop'):
protein += prot_dct[test]
print(protein)
# Quick lambda function to insert indels.
insert_indel = lambda word, i: word[:i] + '-' + word[i:]
# Insert indels to get the alignment.
while reduce(mul, current_index) != 0:
for i, perm_value in enumerate(
perm_list[backtrack[tuple(current_index)]]):
if perm_value == 0:
alignment[i] = insert_indel(alignment[i], current_index[i])
else:
current_index[i] -= 1
# Note: We don't need to prepend any indels because we forced a match at the start of all words.
# Remove the forced match from all alignments to recover the correct alignment.
return [str(max_score)] + [aligned[1:] for aligned in alignment]
if __name__ == '__main__':
from scripts import ReadFASTA
# Parse the input data.
words = [fasta[1] for fasta in ReadFASTA('data/rosalind_mult.txt')]
# Get the alignment.
words_aligned = multiple_alignment(words)
# Print and save the answer.
print '\n'.join(words_aligned)
with open('output/085_MULT.txt', 'w') as output_data:
output_data.write('\n'.join(words_aligned))
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Computing GC Content
Rosalind ID: GC
Rosalind #: 005
URL: http://rosalind.info/problems/gc/
'''
from scripts import ReadFASTA
# Our data is in FASTA form.
dna_list = ReadFASTA('data/rosalind_gc.txt')
highest_GC = -1
highest_GC_name = ''
for index, dna_seq in enumerate(dna_list):
GC_count = 0
for nucleotide in dna_seq[1]:
if nucleotide == 'G' or nucleotide == 'C':
GC_count += 1
GC_amount = ( (GC_count*100.0)/len(dna_seq[1]) )
if GC_amount > highest_GC:
highest_GC = GC_amount
highest_GC_name = dna_list[index][0]
# Print the solution.
print highest_GC_name, '\n', highest_GC
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Open Reading Frames
Rosalind ID: ORF
Rosalind #: 018
URL: http://rosalind.info/problems/orf/
'''
from scripts import ReadFASTA, ReverseComplementDNA, ProteinDictDNA
dna_list = [ReadFASTA('data/rosalind_orf.txt')[0][1]]
dna_list.append(ReverseComplementDNA(dna_list[0]))
dna_dict = ProteinDictDNA()
# Use a set since we want to return distinct protein.
# Sets keep track of distinct elements without us needing to worry about adding duplicates.
protein_orf = set()
for dna in dna_list:
for i in range(len(dna) - 2):
# Check for the Start codon.
if dna[i:i + 3] == 'ATG':
# Use a new index since we'll want to return to the ith position of the strand in case there are multiple start codons in a row.
j = i
current_protein = ''
# Continue, if necessary, until we hit the end of the DNA sequence.
while j + 3 < len(dna) - 1:
# Add the protein and break if we hit a Stop codon.
if dna_dict[dna[j:j + 3]] == 'Stop':
protein_orf.add(current_protein)
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Locating Restriction Sites
Rosalind ID: REVP
Rosalind #: 021
URL: http://rosalind.info/problems/revp/
'''
from scripts import ReadFASTA, ReverseComplementDNA
dna = ReadFASTA('data/rosalind_revp.txt')[0][1]
locations = []
for length in range(4, 13):
for index in range(len(dna) - length + 1):
if dna[index:index + length] == ReverseComplementDNA(dna[index:index +
length]):
print index + 1, length
locations.append(str(index + 1) + ' ' + str(length))
with open('output/021_REVP.txt', 'w') as output_data:
for location in locations:
output_data.write(location + '\n')
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Maximum Matchings and RNA Secondary Structures
Rosalind ID: MMCH
Rosalind #: 040
URL: http://rosalind.info/problems/mmch/
'''
from math import factorial
from scripts import ReadFASTA
def nPr(n, k):
'''Returns the number of k-pernumatations of n.'''
return factorial(n) / factorial(n - k)
rna = ReadFASTA('data/rosalind_mmch.txt')[0][1]
# Counts the number of each times each nucleotide appears in the RNA string.
AU_num = [rna.count(nucleotide) for nucleotide in 'AU']
GC_num = [rna.count(nucleotide) for nucleotide in 'GC']
# There are nPr(max, min) edges for each AU, CG. Total number of edges is then the product.
max_matchings = nPr(max(AU_num), min(AU_num)) * nPr(max(GC_num), min(GC_num))
print max_matchings
with open('output/040_MMCH.txt', 'w') as output_data:
output_data.write(str(max_matchings))
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Transitions and Transversions
Rosalind ID: TRAN
Rosalind #: 031
URL: http://rosalind.info/problems/tran/
'''
from scripts import ReadFASTA
dna1, dna2 = [fasta[1] for fasta in ReadFASTA('data/rosalind_tran.txt')]
transitions = transversions = 0.0
for i in xrange(len(dna1)):
if dna1[i] == dna2[i]:
pass
# Check if the nucleotides are in the same purine/pyrimidine group.
elif dna1[i] in [['A', 'G'], ['C', 'T']][dna2[i] in ['C', 'T']]:
transitions += 1
else:
transversions += 1
print transitions / transversions
with open('output/031_TRAN.txt', 'w') as output_data:
output_data.write(str(transitions / transversions))
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Perfect Matchings and RNA Secondary Structures
Rosalind ID: PMCH
Rosalind #: 026
URL: http://rosalind.info/problems/pmch/
'''
from math import factorial
from scripts import ReadFASTA
rna = ReadFASTA('data/rosalind_pmch.txt')[0][1]
pmch = factorial(rna.count('A'))*factorial(rna.count('C'))
print pmch
with open('output/026_PMCH.txt', 'w') as output_data:
output_data.write(str(pmch))
subintervals.append([rna[1:i], rna[i + 1:]])
if subintervals == []:
# If we didn't find any subintervals, there are no possible noncrossing matchings.
noncross_dict[rna] = 0
else:
# Reduce the problem to noncrossing matchings over the substrings.
noncross_dict[rna] = sum([
Noncrossing(subint[0]) * Noncrossing(subint[1])
for subint in subintervals
]) % 1000000
return noncross_dict[rna]
def check_subinterval(subint):
'''Checks if a given subinterval has the same number of matching nucleotides.'''
N = [subint.count(nucleotide) for nucleotide in 'AUCG']
if N[0] == N[1] and N[2] == N[3]:
return True
return False
rna = ReadFASTA('data/rosalind_cat.txt')[0][1]
noncross_dict = {}
matchings = {'A': 'U', 'U': 'A', 'C': 'G', 'G': 'C'}
noncross = Noncrossing(rna)
print noncross
with open('output/033_CAT.txt', 'w') as output_file:
output_file.write(str(noncross))
from scripts import ReadFASTA
def reverseComplementDNA(acid):
out = "".maketrans("TAGC", "ATCG")
return acid.translate(out)[::-1].lstrip()
dna_list = ReadFASTA("data/corr_data.txt")
dna_groups = []
for dna_tuple in dna_list:
in_group = False
dna = dna_tuple[1]
for index, group in enumerate(dna_groups):
if dna in group or reverseComplementDNA(str(dna)) in group:
dna_groups[index].append(dna)
in_group = True
break
if not in_group:
dna_groups.append([dna])
dna_groups += [[], []] + dna_groups
while len(dna_groups) > 2:
if len(dna_groups[len(dna_groups) - 1]) > 1:
dna_groups[0].append(dna_groups.pop(len(dna_groups) - 1))
else:
dna_groups[1] += dna_groups.pop(len(dna_groups) - 1)
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Creating a Distance Matrix
Rosalind ID: PDST
Rosalind #: 041
URL: http://rosalind.info/problems/PDST/
'''
from numpy import zeros
from scripts import ReadFASTA
dna_list = [fasta[1] for fasta in ReadFASTA('data/rosalind_pdst.txt')]
# All seqences have the same length.
dna_len = len(dna_list[0])
M = zeros((len(dna_list), len(dna_list)))
for i in range(len(dna_list)):
for j in range(len(dna_list)):
if i < j:
for k in range(dna_len):
if dna_list[i][k] != dna_list[j][k]:
M[i][j] += 1.0 / dna_len
elif i > j:
M[i][j] = M[j][i]
print M
return 1
else:
# If we've already computed the value, return it!
if rna in noncross_dict:
return noncross_dict[rna]
# Otherwise, calculate the value, add it to the dictionary, and return it.
else:
subintervals = []
for i in xrange(1, len(rna)):
if rna[0] == matchings[rna[i]]:
subintervals.append([rna[1:i], rna[i + 1:]])
# Reduce the problem to noncrossing matchings over the matching substrings, and the matchings for the next starting point.
noncross_dict[rna] = (sum([
Noncrossing(subint[0]) * Noncrossing(subint[1])
for subint in subintervals
]) + Noncrossing(rna[1:])) % 1000000
return noncross_dict[rna]
rna = ReadFASTA('data/rosalind_motz.txt')[0][1]
matchings = {'A': 'U', 'U': 'A', 'C': 'G', 'G': 'C'}
noncross_dict = {}
noncross = Noncrossing(rna)
print noncross
with open('output/048_MOTZ.txt', 'w') as output_file:
output_file.write(str(noncross))
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Maximum Matchings and RNA Secondary Structures
Rosalind ID: MMCH
Rosalind #: 040
URL: http://rosalind.info/problems/mmch/
'''
from math import factorial
from scripts import ReadFASTA
def nPr(n, k):
'''Returns the number of k-pernumatations of n.'''
return factorial(n)/factorial(n-k)
rna = ReadFASTA('data/rosalind_mmch.txt')[0][1]
# Counts the number of each times each nucleotide appears in the RNA string.
AU_num = [rna.count(nucleotide) for nucleotide in 'AU']
GC_num = [rna.count(nucleotide) for nucleotide in 'GC']
# There are nPr(max, min) edges for each AU, CG. Total number of edges is then the product.
max_matchings = nPr(max(AU_num), min(AU_num))*nPr(max(GC_num), min(GC_num))
print max_matchings
with open('output/040_MMCH.txt', 'w') as output_data:
output_data.write(str(max_matchings))
from scripts import ReadFASTA
from protein_map import ProteinDictDNA
dna_list = ReadFASTA('data/rna_splicing.txt')
exon = dna_list[0][1]
for intron in dna_list[1:]:
# print(intron[1])
exon = exon.replace(intron[1], '')
# print(exon)
proteinDict = ProteinDictDNA()
# print(proteinDict)
# for index in range(0, len(exon), 3):
# print(str(exon[index:index+3]))
# print(proteinDict[exon[index:index+3]])
exon_protein = ''
index = 3
while index < len(exon):
codon = exon[index:index + 3]
p = proteinDict[codon]
if p != 'Stop':
exon_protein += proteinDict[codon]
index = index + 3
print(exon_protein)
from scripts import ReadFASTA
DNA1, DNA2 = [fasta[1] for fasta in ReadFASTA('data/rosalind_tran.txt')]
transitions = transversions = 0.0
for i in xrange(len(DNA1)):
if DNA1[i] == DNA2[i]:
pass
# Check if the nucleotides are in the same purine/pyrimidine group.
elif DNA1[i] in [['A', 'G'], ['C', 'T']][DNA2[i] in ['C', 'T']]:
transitions += 1
else:
transversions += 1
print transitions / transversions
with open('output/031_TRAN.txt', 'w') as output_data:
output_data.write(str(transitions / transversions))
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: k-Mer Composition
Rosalind ID: KMER
Rosalind #: 036
URL: http://rosalind.info/problems/kmer/
'''
from itertools import product
from scripts import ReadFASTA
dna = ReadFASTA('data/rosalind_kmer.txt')[0][1]
# Get a list of all 4-mers in lexiographic order.
kmer_list = [''.join(kmer) for kmer in list(product('ACGT', repeat=4))]
# Initialize the count of each 4-mer at zero for each 4-mer.
kmer_count = [0] * (4**4)
# Count each 4-mer
for i in range(len(dna) - 3):
kmer_count[kmer_list.index(dna[i:i + 4])] += 1
print ' '.join(map(str, kmer_count))
with open('output/036_KMER.txt', 'w') as output_data:
output_data.write(' '.join(map(str, kmer_count)))
# Backtrack to start of the local alignment starting at the highest scoring cell.
while backtrack[i][j] != 3 and i * j != 0:
if backtrack[i][j] == 0:
i -= 1
elif backtrack[i][j] == 1:
j -= 1
elif backtrack[i][j] == 2:
i -= 1
j -= 1
# Cut the strings at the ending point of the backtrack.
v_aligned = v_aligned[i:]
w_aligned = w_aligned[j:]
return max_score, v_aligned, w_aligned
if __name__ == '__main__':
# Parse the two input protein strings.
s, t = [fasta[1] for fasta in ReadFASTA('input/rosalind_loca.txt')]
# Get the local alignment (given sigma = 5 in problem statement).
alignment = local_alignment(s, t, PAM250(), 5)
# Print and save the answer.
print '\n'.join(alignment)
with open('output/local_alignment.txt', 'w') as output_data:
output_data.write('\n'.join(alignment))
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Error Correction in Reads
Rosalind ID: CORR
Rosalind #: 034
URL: http://rosalind.info/problems/corr/
'''
from scripts import ReadFASTA, ReverseComplementDNA as RevComp, HammingDistance as Hamm
# Group together identical DNA sequences, up to reverse complement.
dna_groups = []
for dna in [fasta[1] for fasta in ReadFASTA('data/rosalind_corr.txt')]:
in_group = False
for index, group in enumerate(dna_groups):
if dna in group or RevComp(dna) in group:
dna_groups[index].append(dna)
in_group = True
break
if not in_group:
dna_groups.append([dna])
# Sort the DNA groups as either being a correct read in index 0, or incorrect read in index 1.
dna_groups = [[], []] + dna_groups
while len(dna_groups) > 2:
if len(dna_groups[len(dna_groups) - 1]) > 1:
# Convert to set to eliminate repeats.
dna_groups[0].append(dna_groups.pop(len(dna_groups) - 1))
# Backtrack to start of the local alignment starting at the highest scoring cell.
while backtrack[i][j] != 3 and i * j != 0:
if backtrack[i][j] == 0:
i -= 1
elif backtrack[i][j] == 1:
j -= 1
elif backtrack[i][j] == 2:
i -= 1
j -= 1
# Cut the strings at the ending point of the backtrack.
v_aligned = v_aligned[i:]
w_aligned = w_aligned[j:]
return max_score, v_aligned, w_aligned
if __name__ == '__main__':
# Parse the two input protein strings.
s, t = [fasta[1] for fasta in ReadFASTA('data/rosalind_loca.txt')]
# Get the local alignment (given sigma = 5 in problem statement).
alignment = local_alignment(s, t, PAM250(), 5)
# Print and save the answer.
print '\n'.join(alignment)
with open('output/081_LOCA.txt', 'w') as output_data:
output_data.write('\n'.join(alignment))
from scripts import ReadFASTA, ProteinDictDNA
dna_list = ReadFASTA('../data/rosalind_splc.txt')
exon = dna_list[0][1]
# Remove the introns.
for intron in dna_list[1:]:
print(intron)
exon = exon.replace(intron[1], '')
# Translate the exons.
dna_dict = ProteinDictDNA()
exon_protein = ''
for index in range(0, len(exon), 3):
exon_protein += dna_dict[exon[index:index +
3]] if dna_dict[exon[index:index +
3]] != 'Stop' else ''
print exon_protein
with open('../data/SPLC.txt', 'w') as output_data:
output_data.write(exon_protein)
'''Extracts all substrings from the first string in a list, and sends longest substring candidates to be checked.'''
longest = ''
for start_index in xrange(len(string_list[0])):
for end_index in xrange(len(string_list[0]), start_index, -1):
# Break if the length becomes too small, as it will only get smaller.
if end_index - start_index <= len(longest):
break
elif CheckSubstring(string_list[0][start_index:end_index], string_list):
longest = string_list[0][start_index:end_index]
return longest
def CheckSubstring(find_string, string_list):
'Checks if a given substring appears in all members of a given collection of strings and returns True/False.'
for string in string_list:
if (len(string) < len(find_string)) or (find_string not in string):
return False
return True
if __name__ == '__main__':
fasta_list = ReadFASTA('data/rosalind_lcsm.txt')
dna = []
for fasta in fasta_list:
dna.append(fasta[1])
lcsm = LongestSubstring(dna)
print lcsm
with open('output/014_LCSM.txt', 'w') as output_data:
output_data.write(lcsm)
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Finding a Spliced Motif
Rosalind ID: SSEQ
Rosalind #: 030
URL: http://rosalind.info/problems/sseq/
'''
from scripts import ReadFASTA
dna, sub_seq = [fasta[1] for fasta in ReadFASTA('data/rosalind_sseq.txt')]
sseq_indicies, i = [], 0
for nucleotide in sub_seq:
# In practice: Use exception handling/additional constraints as such a subsequence does not necessarily exist.
while dna[i] != nucleotide:
i += 1
# Use i+1 as the indicies because Rosalind starts at i=1 instead of i=0.
sseq_indicies.append(str(i + 1))
i += 1
print ' '.join(sseq_indicies)
with open('output/030_SSEQ.txt', 'w') as output_data:
output_data.write(' '.join(sseq_indicies))
from scripts import ReadFASTA
# from string import maketrans
def reverseComplementDNA(dna):
intable = "ATGC"
outtable = "TACG"
out = "".maketrans(intable, outtable)
return dna.translate(out)
dna_list = ReadFASTA('data/corr.txt')
dna_dict = {}
for dna in dna_list[1:]:
dna_dict[dna[1]] = 0
dna_groups = []
for dna_tuple in dna_list:
in_group = False
dna = dna_tuple[1]
complementDNA = reverseComplementDNA(dna[1])
for index, group in enumerate(dna_groups):
if in_group
print(dna_dict)
print(reverseComplementDNA("AGGGGGA"))
#!/usr/bin/env python
'''
A solution to a ROSALIND bioinformatics problem.
Problem Title: Consensus and Profile
Rosalind ID: SUBS
Rosalind #: 009
URL: http://rosalind.info/problems/subs/
'''
from numpy import zeros
from scripts import ReadFASTA
# Data is in FASTA form
dna_list = ReadFASTA('data/rosalind_cons.txt')
# Setup an array and count into the array
M = zeros((4, len(dna_list[0][1])), dtype=int)
snp_dict = {'A': 0, 'C': 1, 'G': 2, 'T': 3}
for dna in dna_list:
for index, snp in enumerate(dna[1]):
M[snp_dict[snp]][index] += 1
# Determine the consensus string
consensus = ''
to_snp = {0: 'A', 1: 'C', 2: 'G', 3: 'T'}
for i in range(0, len(dna_list[0][1])):
maxval = [-1, -1]
for j in range(0, 4):
if maxval[1] < M[j][i]:
maxval = [j, M[j][i]]
