def find_kmer_locations(sequence: str, kmer: str,
options: Options = Options()) -> List[int]:
# Construct test kmers
test_kmers = set()
test_kmers.add(kmer)
[
test_kmers.add(alt_kmer)
for alt_kmer in find_all_dna_kmers_within_hamming_distance(
kmer, options.hamming_distance)
]
if options.reverse_complement:
rc_kmer = reverse_complement(kmer)
[
test_kmers.add(alt_rc_kmer)
for alt_rc_kmer in find_all_dna_kmers_within_hamming_distance(
rc_kmer, options.hamming_distance)
]
# Slide over the sequence's kmers and check for matches against test kmers
k = len(kmer)
idxes = []
for seq_kmer, i in slide_window(sequence, k):
if seq_kmer in test_kmers:
idxes.append(i)
return idxes
def neighborhood(kmer: str) -> Set[str]:
neighbourhood = find_all_dna_kmers_within_hamming_distance(
kmer, options.hamming_distance)
if options.reverse_complement:
kmer_rc = reverse_complement(kmer)
neighbourhood = find_all_dna_kmers_within_hamming_distance(
kmer_rc, options.hamming_distance)
return neighbourhood
def kmer_frequency_with_mismatches_and_reverse_complements(
data: str, k: int, min_hamming_dist: int) -> Counter[str]:
counter = Counter()
for i in range(0, len(data) - k + 1):
kmer = data[i:i + k]
neighbourhood = find_all_dna_kmers_within_hamming_distance(
kmer, min_hamming_dist)
for neighbouring_kmer in neighbourhood:
counter[neighbouring_kmer] += 1
kmer_rc = reverse_complement(kmer)
neighbourhood = find_all_dna_kmers_within_hamming_distance(
kmer_rc, min_hamming_dist)
for neighbouring_kmer in neighbourhood:
counter[neighbouring_kmer] += 1
return counter
def motif_enumeration(
dnas: List[str], # dna strings to search in for motif
k: int, # k-mer length
max_mismatches: int # max num of mismatches for motif (hamming dist)
) -> Set[str]:
found_kmers = set()
kmers_to_check = set()
for dna in dnas:
for kmer, _ in slide_window(dna, k):
neighbouring_kmers = find_all_dna_kmers_within_hamming_distance(
kmer, max_mismatches)
kmers_to_check |= neighbouring_kmers
for kmer_to_check in kmers_to_check:
found_count = 0
for dna in dnas:
for other_kmer, _ in slide_window(dna, k):
if hamming_distance(kmer_to_check,
other_kmer) <= max_mismatches:
found_count += 1
break
if found_count == len(dnas):
found_kmers.add(kmer_to_check)
return found_kmers
def count_kmers(data: str, k: int,
options: Options = Options()) -> Counter[str]:
counter = Counter()
for kmer, i in slide_window(data, k):
neighbourhood = find_all_dna_kmers_within_hamming_distance(
kmer, options.hamming_distance)
for neighbouring_kmer in neighbourhood:
counter[neighbouring_kmer] += 1
if options.reverse_complement:
kmer_rc = reverse_complement(kmer)
neighbourhood = find_all_dna_kmers_within_hamming_distance(
kmer_rc, options.hamming_distance)
for neighbouring_kmer in neighbourhood:
counter[neighbouring_kmer] += 1
return counter
import hashlib
import textwrap
import matplotlib.pyplot as plt
from CountASequencesKmersWithMismatchesAndReverseComplement import \
kmer_frequency_with_mismatches_and_reverse_complements
from FindAllDnaKmersWithinHammingDistance import find_all_dna_kmers_within_hamming_distance
from GCSkew import gc_skew
with open('/home/user/Downloads/dataset_240229_4.txt',
mode='r',
encoding='utf-8') as f:
data = f.read()
lines = data.split('\n')
kmer = lines[0]
hamming_dist = int(lines[1])
kmer_variations = find_all_dna_kmers_within_hamming_distance(
kmer, hamming_dist)
print(f'{" ".join(kmer_variations)}')