def shared_kmers(k, dna1, dna2):
'''Returns a list of positions for shared k-mers (up to reverse complement) in dna1 and dna2.'''
# Store the starting index of all k-mers from dna1 in a dictionary keyed to the k-mer.
dna1_dict = defaultdict(list)
for i in xrange(len(dna1) - k + 1):
dna1_dict[dna1[i:i + k]].append(i)
# Check k-mers in dna2 against those in dna1, add matching index pairs to a set to remove possible duplicate entries.
return {
(i, j)
for j in xrange(len(dna2) - k + 1)
for i in dna1_dict[dna2[j:j + k]] + dna1_dict[rev_comp(dna2[j:j + k])]
}
def shared_kmers(dna1, dna2, k):
'''Returns a list of positions for shared kmers (up to reverse complement) in dna1 and dna2.'''
from scripts import ReverseComplementDNA as rev_comp
# Initialize the dictionary to store kmers.
dna_dict = {}
# Store the starting index of all kmers contained in dna1 in a list keyed to the kmer.
for i in xrange(len(dna1) - k + 1):
# Add the ith kmer.
if dna1[i:i + k] in dna_dict:
dna_dict[dna1[i:i + k]].append(i)
else:
dna_dict[dna1[i:i + k]] = [i]
# Add the reverse complement of the ith kmer.
if rev_comp(dna1[i:i + k]) in dna_dict:
dna_dict[rev_comp(dna1[i:i + k])].append(i)
else:
dna_dict[rev_comp(dna1[i:i + k])] = [i]
# Use a set to remove possible duplicate entries.
common_kmers = set()
# Check kmers in dna2 against those in dna1, adding matching indices to common_kmers.
for j in xrange(len(dna2) - k + 1):
# Check the jth kmer.
if dna2[j:j + k] in dna_dict:
for x in dna_dict[dna2[j:j + k]]:
common_kmers.add((x, j))
# Check the reverse complement of the jth kmer.
if rev_comp(dna2[j:j + k]) in dna_dict:
for x in dna_dict[rev_comp(dna2[j:j + k])]:
common_kmers.add((x, j))
return common_kmers
def shared_kmers(dna1, dna2, k):
'''Returns a list of positions for shared kmers (up to reverse complement) in dna1 and dna2.'''
from scripts import ReverseComplementDNA as rev_comp
# Initialize the dictionary to store kmers.
dna_dict = {}
# Store the starting index of all kmers contained in dna1 in a list keyed to the kmer.
for i in xrange(len(dna1) - k + 1):
# Add the ith kmer.
if dna1[i:i+k] in dna_dict:
dna_dict[dna1[i:i+k]].append(i)
else:
dna_dict[dna1[i:i+k]] = [i]
# Add the reverse complement of the ith kmer.
if rev_comp(dna1[i:i+k]) in dna_dict:
dna_dict[rev_comp(dna1[i:i+k])].append(i)
else:
dna_dict[rev_comp(dna1[i:i+k])] = [i]
# Use a set to remove possible duplicate entries.
common_kmers = set()
# Check kmers in dna2 against those in dna1, adding matching indices to common_kmers.
for j in xrange(len(dna2) - k + 1):
# Check the jth kmer.
if dna2[j:j+k] in dna_dict:
for x in dna_dict[dna2[j:j+k]]:
common_kmers.add((x,j))
# Check the reverse complement of the jth kmer.
if rev_comp(dna2[j:j+k]) in dna_dict:
for x in dna_dict[rev_comp(dna2[j:j+k])]:
common_kmers.add((x,j))
return common_kmers
def shared_kmers(k, dna1, dna2):
'''Returns a list of positions for shared k-mers (up to reverse complement) in dna1 and dna2.'''
# Store the starting index of all k-mers from dna1 in a dictionary keyed to the k-mer.
dna1_dict = defaultdict(list)
for i in xrange(len(dna1) - k + 1):
dna1_dict[dna1[i:i+k]].append(i)
# Check k-mers in dna2 against those in dna1, add matching index pairs to a set to remove possible duplicate entries.
shared_kmer_indices = set()
for j in xrange(len(dna2) - k + 1):
shared_kmer_indices |= set(map(lambda x: (x,j), dna1_dict[dna2[j:j+k]]))
shared_kmer_indices |= set(map(lambda x: (x,j), dna1_dict[rev_comp(dna2[j:j+k])]))
return shared_kmer_indices
def freq_words_with_mm_and_rev_comp(seq, k, d):
"""Returns all most frequent k-mers with up to d mismatches in the dna sequence seq."""
# Frequency analysis so we don't generate mismatches for the same k-mer more than once.
kmer_freq = defaultdict(int)
for i in xrange(len(seq) - k + 1):
kmer_freq[seq[i : i + k]] += 1
kmer_freq[rev_comp(seq[i : i + k])] += 1
# Get all of the mismatches for each unique k-mer in the sequence, appearing freq times.
mismatch_count = defaultdict(int)
for kmer, freq in kmer_freq.iteritems():
for mismatch in kmer_mismatches(kmer, d):
mismatch_count[mismatch] += freq
# Computing the maximum value is somewhat time consuming to repeat, so only do it once!
max_count = max(mismatch_count.values())
return sorted([kmer for kmer, count in mismatch_count.iteritems() if count == max_count])
def freq_words_with_mm_and_rev_comp(seq, k, d):
"""Returns all most frequent k-mers with up to d mismatches in the dna sequence seq."""
# Frequency analysis so we don't generate mismatches for the same k-mer more than once.
kmer_freq = defaultdict(int)
for i in xrange(len(seq) - k + 1):
kmer_freq[seq[i:i + k]] += 1
kmer_freq[rev_comp(seq[i:i + k])] += 1
# Get all of the mismatches for each unique k-mer in the sequence, appearing freq times.
mismatch_count = defaultdict(int)
for kmer, freq in kmer_freq.iteritems():
for mismatch in kmer_mismatches(kmer, d):
mismatch_count[mismatch] += freq
# Computing the maximum value is somewhat time consuming to repeat, so only do it once!
max_count = max(mismatch_count.values())
return sorted([
kmer for kmer, count in mismatch_count.iteritems()
if count == max_count
])
