File size: 2,523 Bytes
94b0695 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import os
import gzip
import time
import requests
from itertools import product
from Bio import SeqIO
from Bio.Seq import Seq
import numpy as np
RESULTS_DIR = "results"
DATA_DIR = "data"
K = 11
GENOME_URL = "https://ftp.ensembl.org/pub/release-110/fasta/saccharomyces_cerevisiae/dna/Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa.gz"
os.makedirs(RESULTS_DIR, exist_ok=True)
os.makedirs(DATA_DIR, exist_ok=True)
def download_genome(genome_file):
gz_file = genome_file + ".gz"
if not os.path.exists(genome_file):
response = requests.get(GENOME_URL, timeout=300, stream=True)
if response.status_code != 200:
raise RuntimeError(f"Genome download failed: HTTP {response.status_code}")
with open(gz_file, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
with gzip.open(gz_file, "rt") as f_in, open(genome_file, "w") as f_out:
f_out.write(f_in.read())
os.remove(gz_file)
def load_genome(genome_file):
records = list(SeqIO.parse(genome_file, "fasta"))
whole_genome = "".join(str(r.seq).upper() for r in records)
return whole_genome, records
def find_present_kmers(sequence, k):
present = set()
for i in range(len(sequence) - k + 1):
kmer = sequence[i:i + k]
if all(b in "ACGT" for b in kmer):
present.add(kmer)
present.add(str(Seq(kmer).reverse_complement()))
return present
def identify_nullomers(present_kmers, k):
bases = ["A", "C", "G", "T"]
theoretical = set("".join(p) for p in product(bases, repeat=k))
return theoretical - present_kmers, theoretical
def compute_gc(seq):
return (seq.count("G") + seq.count("C")) / len(seq)
def main():
genome_file = os.path.join(DATA_DIR, "yeast_genome.fsa")
download_genome(genome_file)
whole_genome, _ = load_genome(genome_file)
t0 = time.time()
present_kmers = find_present_kmers(whole_genome, K)
nullomers, theoretical = identify_nullomers(present_kmers, K)
pct = 100 * len(nullomers) / len(theoretical)
sample = list(nullomers)[:10000]
mean_gc = np.mean([compute_gc(s) for s in sample])
out_path = os.path.join(RESULTS_DIR, f"nullomers_k{K}.txt")
with open(out_path, "w") as f:
for nm in sorted(nullomers):
f.write(nm + "\n")
print(f"Nullomers: {len(nullomers):,} ({pct:.2f}%) GC: {mean_gc*100:.1f}% "
f"time: {time.time()-t0:.1f}s")
if __name__ == "__main__":
main()
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