import sys
import dna.utils as utils
def suffix_array(text):
return [
str(j[1]) for j in sorted([((s[i:], i)) for i in range(len(text))])
]
if __name__ == '__main__':
s = 'AACGATAGCGGTAGA$'
if len(sys.argv) >= 2:
s = utils.read_file_lines(sys.argv[1])[0].strip()
print(', '.join(suffix_array(s)))
with open('output/answer_21.txt', 'w') as out:
print(', '.join(map(str, suffix_array(s))), file=out)
print('Written to {}'.format(out.name))
expected = utils.read_file('output/expected_suff_arr.txt')
actual = utils.read_file('output/answer_21.txt')
print(actual == expected)
import sys
import dna.utils as utils
import suffix_array as sa
def inverse_burrows(text):
my_list = sorted(list(text))
for i in range(len(text) - 1):
my_list = sorted([text[i] + my_list[i] for i in range(len(s))])
return my_list[0][1:] + my_list[0][0]
if __name__ == '__main__':
s = 'TGTTGGAGGTTAAGTTGTCCGTGGTCCGGACCCATAAGTAGTACTGGCTCGCATCACCTTCCGTAAGGTTTATGGCAATATGGAAGTATACCCTAACTCTTTTAGTCAATCTTGGACAGATTAGCTCCTCGTTGATGCACCCCTTGAGCGCTCAGCTCCGCACGCGGGACCCGCTCTGCCTGTGTTCCTCCGACAAAGAAAAGGCACTGGAGCGATACAATGCGCTGTTTCCACGTCGGACTCCATGCGCAGCAGCATCAAGAATCTAGCGACGAGAACGGTGACCCAATCGTAGCGAACTTTGATTCGTTGAGGAACCCTCCATCGTCTGGTATTTGATGATTGTTGGCTGTCTTCGTCCACCAGGGACGTATTCGTACTGATGATGAGAGGTTATACAATCCAGTGAGTTGTCTGACCGGTTGATAGGATTTAGGTTAACTCTCAAAAACAATGCTGACGAAACTGTGCATTATAAGCAAGGTTCGGTAGGAG$ACAAGAGTGAGGGGGCAGTATTTATCCGTTCGCCCGTCCTCACGCTAGCCGGAGCAACCCCGTGTAGGATAAAGTAGTCGGGCTCTGAACGGCGGTGACTGATAACTTCGGCGTCCGAAACCTGGGTGCTATTTAGAGGGAGGATTTTACCACAGTACCTTTTTTACCGGCGTGGCTGCCCTCCGCTGTCTTCAATTCTGTACATAAAGACAGACGAGGCCATTCCTGCGATTATTAGTCCACCGCTTCCGGGCGACTGCTCATGTGCTCAGACGCAGAAAACACAAAGTAAGAGAAGGGTCTGGCTTCCATTTAGTTAACGGTCTCGGCTACTCCCTAACAGCAGCTGTATACCAATGCACCGCCCTGGAAACATATGAAGATGTATTATGGGCGCGAAGTACTCTCGCGTTCTCCACTCCGAGTAGAA'
if len(sys.argv) >= 2:
s = utils.read_file(sys.argv[1]).strip()
print(inverse_burrows(s))
expected = 'GATAATTATCGCAACGTATTAGCAGGTTCGCGCGTCTAACGAGCGTGTGGGAAATACATCCTCCCACATAATCTGTCCGGGTGTACCCAAGCTCTCCATCGGGGCCCCATACGTTCATGTACTTGACCCGGAGACCAAGGAAAATAACCGGGAGAGAATTCTGTCACCGCCATTTTGGGAATGGTCTCACTGAGGGGAGCTGGTAATGGATCGAATGTTGGCTATCGCATTAACTTGCAGCAATTCTCTGCAGAACGTTACTGCATACCAGGCGACGTCTCAAGATCCCAGCACTGCCTACGTGTCCTGTCGTATCATAACTACACTACTGTCGCGCATAGAACTATCGCGTTAGATAAGTACAGTATGTTAACCAGTTTGTCGTGTGATGGGATTTCTTTCACAATGGAACCTTCCATTTGGGGGGGTGGACGGCTGAGATCCTTTAAATGCTCCTAGGCGTAGCGTGGCCACTACGCTATAGGTGCACAGGGGAGGCTCTGAATGAAGACTTGCGCCAGACGGATAATGCTCAACGGGCATCGCCCGGAGTAGAAATGAAAGGGCGGCGCCCGTCCATCAACTTTATCGAAAGAGGTAACACAGTCCACAGATCGTCCCGCGAACATCGGGCACAGGATGGGTGACATTCTCCGCGTTTCTGGCGGATTTTAGCTGCATGGTCAACTTTAGATGTTCCACTACTGGGTTCAGGATGTTCTATCCTTCTTCTACCAATTACTGTCGGGGATTACTAGTATTTAGAACTCGATAGATACAGGTCTGTTGCATCTGCTGCCCGCGTTCTGGAGTTTGAGCGCCAGGAATCGTGACATAGCGGGATGCAGCTGTACGAAGACCCTAGGCTCAGTCTAAACATCGCCTCGCATCCCGCGGATTAAACAGCCTCTACTTGTACCACTTT$'
print(inverse_burrows(s) == expected)
import sys
import dna.utils as utils
def calculate_mendels_probability(k, m, n):
total = k + m + n
# two reccessive genes
rec_rec = (n / total) * ((n - 1) / (total - 1))
# two hetero genes
het_het = (m / total) * ((m - 1) / (total - 1))
# a hetero and recessive
het_rec = (m / total) * (n / (total - 1)) + (n / total) * (m / (total - 1))
probability = rec_rec + het_het / 4 + het_rec / 2
return 1 - probability
if __name__ == '__main__':
dominant, heterozygous, homozygous = [2, 2, 2]
if len(sys.argv) >= 2:
dominant, heterozygous, homozygous = map(int, utils.read_file(sys.argv[1]).split(' '))
print(calculate_mendels_probability(dominant, heterozygous, homozygous))
import sys
import dna.utils as utils
from Bio import pairwise2
from Bio.SubsMat import MatrixInfo as matlist
def local_alignment(s, t):
"""local_alignment(s, t) performs a local alignment using the PAM250 matrix,
aligns protein strings s and t and returns a possible alignment array
"""
matrix = matlist.pam250
return pairwise2.align.localds(s, t, matrix, -5, -5)
if __name__ == '__main__':
file_content = '>Rosalind_67\nMEANLYPRTEINSTRING\n>Rosalind_17\nPLEASANTLYEINSTEIN'
if len(sys.argv) >= 2:
file_content = utils.read_file(sys.argv[1])
s, t = utils.parse_fasta(file_content)
alignment = local_alignment(s, t)[0]
score = int(alignment[2])
span = (alignment[3], alignment[4])
new_s = alignment[0][span[0]:span[1]]
new_t = alignment[1][span[0]:span[1]]
print('\n'.join(
[str(score),
new_s.replace('-', ''),
new_t.replace('-', '')]))
import dna.utils as utils
import reverse_complement as rvc
def edge(elmmt, k):
return '(' + elmmt[0:k - 1] + ',' + elmmt[1:k] + ')'
def de_bruijn(sequences):
edge_nodes = set()
for s in sequences:
edge_nodes.add(s)
edge_nodes.add(rvc.complementary_nucleotide(s))
k = len(sequences[0])
edge_nodes = [edge(element, k) for element in edge_nodes]
return edge_nodes
if __name__ == '__main__':
sequences = ['TGAT', 'CATG', 'TCAT', 'ATGC', 'CATC', 'CATC']
if len(sys.argv) >= 2:
data = utils.read_file(sys.argv[1])
sequences = data.splitlines()
graph = de_bruijn(sequences)
with open('output/answer_13.txt', 'w') as answer:
print('\n'.join(graph), file=answer)