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kmers.py
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kmers.py
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# Import
import re
import Bio.SeqIO
from collections import defaultdict
from joblib import Parallel, delayed
# Define a function to count the occurrences of k-mers in a single sequence
def count_seq(seq, k):
# Create an empty dictionary to store the counts for each k-mer
counts = defaultdict(int)
# Iterate through the sequence, considering k-mer windows of length k
for j in range(len(seq) - k + 1):
# Extract the k-mer from the sequence
kmer = seq[j:j+k]
# Increment the count for this k-mer
counts[kmer] += 1
# Return the dictionary of k-mer counts
return counts
# Define a function to get the k-mers from a list of sequences
def getKmers(D, k):
# Use the Parallel function to parallelize the counting of k-mers
# This will speed up the computation on multi-core CPUs
counts = Parallel(n_jobs=-1)(delayed(count_seq)(d[1], k) for d in D)
# Merge the counts into a single dictionary, with 0 as the default value
all_counts = defaultdict(int)
for count in counts:
all_counts.update(count)
# Return the dictionary of all k-mer counts
return all_counts
# Function to save the extracted k-mers
def saveExtractedKmers(k_mers_path, k_mers):
# Open file
file = open(k_mers_path, "w")
# Iterate through the k-mers
for i, k in enumerate(k_mers):
# Save the current k-mer
file.write(">" + str(i) + "\n" + k + "\n")
# Close the file
file.close()
# Function to load the set of k-mers from a fasta file
def loadKmers(k_mers_path):
# Initialize an empty dictionary for the k-mers
K = {}
# Iterate through the k-mers
for record in Bio.SeqIO.parse(k_mers_path, "fasta"):
# Save the current k-mer
K[str(record.seq.upper())] = 0
# Return the dictionary of k-mers
return K