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import gzip
import json
import requests
import numpy as np
import pandas as pd
from Bio import SeqIO
from Bio.Seq import Seq
from scipy.stats import mannwhitneyu, poisson
from statsmodels.stats.multitest import multipletests
DATA_DIR = "data"
RESULTS_DIR = "results"
K = 11
PROMOTER_LENGTH = 1000
DOWNSTREAM_LENGTH = 500
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(RESULTS_DIR, exist_ok=True)
ABC_TRANSPORTERS = {
"PDR5": {"type": "Drug efflux", "essential": False, "stress": True, "subfamily": "PDR"},
"SNQ2": {"type": "Drug efflux", "essential": False, "stress": True, "subfamily": "PDR"},
"YOR1": {"type": "Drug efflux", "essential": False, "stress": True, "subfamily": "PDR"},
"PDR10": {"type": "Drug efflux", "essential": False, "stress": True, "subfamily": "PDR"},
"PDR11": {"type": "Sterol transport", "essential": False, "stress": True, "subfamily": "PDR"},
"PDR12": {"type": "Weak acid efflux", "essential": False, "stress": True, "subfamily": "PDR"},
"PDR15": {"type": "Drug efflux", "essential": False, "stress": True, "subfamily": "PDR"},
"PDR1": {"type": "Transcription factor", "essential": False, "stress": True, "subfamily": "PDR"},
"PDR3": {"type": "Transcription factor", "essential": False, "stress": True, "subfamily": "PDR"},
"YCF1": {"type": "Vacuolar transport", "essential": False, "stress": True, "subfamily": "MRP"},
"YBT1": {"type": "Bile acid transport", "essential": False, "stress": False, "subfamily": "Other"},
"VMR1": {"type": "Vacuolar transport", "essential": False, "stress": False, "subfamily": "MRP"},
"ATM1": {"type": "Mitochondrial Fe-S", "essential": True, "stress": False, "subfamily": "ATM"},
"MDL1": {"type": "Mitochondrial peptide", "essential": False, "stress": False, "subfamily": "MDL"},
"MDL2": {"type": "Mitochondrial peptide", "essential": False, "stress": False, "subfamily": "MDL"},
"YEF3": {"type": "Translation elongation","essential": True, "stress": False, "subfamily": "Other"},
"GCN20": {"type": "Translation initiation","essential": False, "stress": False, "subfamily": "Other"},
"ARB1": {"type": "Ribosome biogenesis", "essential": True, "stress": False, "subfamily": "Other"},
"RLI1": {"type": "Ribosome biogenesis", "essential": True, "stress": False, "subfamily": "RLI"},
"BPT1": {"type": "Bile pigment transport","essential": False, "stress": False, "subfamily": "Other"},
"PDR16": {"type": "Phospholipid transport","essential": False, "stress": True, "subfamily": "PDR"},
"PDR17": {"type": "Transcription factor", "essential": False, "stress": True, "subfamily": "PDR"},
"PDR18": {"type": "Drug efflux", "essential": False, "stress": True, "subfamily": "PDR"},
"HMT1": {"type": "Heavy metal tolerance", "essential": False, "stress": True, "subfamily": "Other"},
"NMD5": {"type": "Protein import", "essential": False, "stress": False, "subfamily": "Other"},
"STE6": {"type": "a-factor export", "essential": False, "stress": False, "subfamily": "STE"},
}
GFF_URL = (
"https://ftp.ensembl.org/pub/release-110/gff3/saccharomyces_cerevisiae/"
"Saccharomyces_cerevisiae.R64-1-1.110.gff3.gz"
)
def load_nullomers(path):
with open(path) as f:
return set(line.strip() for line in f if line.strip())
def download_gff(gff_gz):
if not os.path.exists(gff_gz):
r = requests.get(GFF_URL, timeout=180)
if r.status_code != 200:
raise RuntimeError(f"GFF download failed: HTTP {r.status_code}")
with open(gff_gz, "wb") as f:
f.write(r.content)
def parse_gff(gff_gz):
coords = {}
with gzip.open(gff_gz, "rt") as f:
for line in f:
if line.startswith("#") or not line.strip():
continue
fields = line.strip().split("\t")
if len(fields) < 9 or fields[2] != "gene":
continue
attrs = {}
for attr in fields[8].split(";"):
if "=" in attr:
k, v = attr.split("=", 1)
attrs[k] = v
name = attrs.get("Name", "")
if name:
coords[name] = {
"chrom": fields[0],
"start": int(fields[3]),
"end": int(fields[4]),
"strand": fields[6],
}
return coords
def load_genome_dict(genome_file):
records = list(SeqIO.parse(genome_file, "fasta"))
return {r.id: str(r.seq).upper() for r in records}
def extract_sequences(gene_name, coords, genome_dict, promoter_len, downstream_len):
c = coords[gene_name]
chrom_seq = genome_dict.get(c["chrom"], "")
if not chrom_seq:
return None
start, end, strand = c["start"], c["end"], c["strand"]
gene_seq = chrom_seq[start - 1:end]
if strand == "+":
prom_seq = chrom_seq[max(0, start - promoter_len - 1):start - 1]
down_seq = chrom_seq[end:end + downstream_len]
else:
prom_seq = str(Seq(chrom_seq[end:min(len(chrom_seq), end + promoter_len)]).reverse_complement())
down_seq = str(Seq(chrom_seq[max(0, start - downstream_len - 1):start - 1]).reverse_complement())
gene_seq = str(Seq(gene_seq).reverse_complement())
return {"gene": gene_seq, "promoter": prom_seq, "downstream": down_seq}
def find_nems(sequence, nullomer_set, k=11):
nems = []
seq = str(sequence).upper()
bases = ["A", "C", "G", "T"]
for pos in range(len(seq)):
orig = seq[pos]
for new in bases:
if new == orig:
continue
check_start = max(0, pos - k + 1)
check_end = min(len(seq) - k, pos)
for ks in range(check_start, check_end + 1):
kmer = seq[ks:ks + k]
if ks <= pos < ks + k:
mp = pos - ks
mutated = kmer[:mp] + new + kmer[mp + 1:]
if mutated in nullomer_set:
nems.append({
"position": pos,
"original": orig,
"mutant": new,
"mutation": f"{orig}{pos+1}{new}",
"nullomer": mutated,
})
return nems
def permutation_test(group1, group2, n_perm=10000, seed=42):
rng = np.random.default_rng(seed)
obs = np.mean(group1) - np.mean(group2)
combined = np.concatenate([group1, group2])
n1 = len(group1)
diffs = []
for _ in range(n_perm):
rng.shuffle(combined)
diffs.append(np.mean(combined[:n1]) - np.mean(combined[n1:]))
p = np.mean(np.abs(diffs) >= np.abs(obs))
return float(obs), float(p), diffs
def main():
nullomers_path = os.path.join(RESULTS_DIR, f"nullomers_k{K}.txt")
if not os.path.exists(nullomers_path):
raise FileNotFoundError(f"{nullomers_path} not found. Run 01_nullomer_identification.py first.")
nullomers = load_nullomers(nullomers_path)
gff_gz = os.path.join(DATA_DIR, "yeast.gff3.gz")
download_gff(gff_gz)
gene_coords = parse_gff(gff_gz)
genome_file = os.path.join(DATA_DIR, "yeast_genome.fsa")
if not os.path.exists(genome_file):
raise FileNotFoundError("Genome not found. Run 01_nullomer_identification.py first.")
genome_dict = load_genome_dict(genome_file)
abc_sequences = {}
for gene in ABC_TRANSPORTERS:
if gene in gene_coords:
seqs = extract_sequences(gene, gene_coords, genome_dict, PROMOTER_LENGTH, DOWNSTREAM_LENGTH)
if seqs:
abc_sequences[gene] = seqs
nem_results = {}
for gene, seqs in abc_sequences.items():
nem_results[gene] = {
"gene": find_nems(seqs["gene"], nullomers, K),
"promoter": find_nems(seqs["promoter"], nullomers, K),
"downstream": find_nems(seqs["downstream"], nullomers, K),
}
rows = []
for gene in nem_results:
info = ABC_TRANSPORTERS[gene]
for region in ["gene", "promoter", "downstream"]:
count = len(nem_results[gene][region])
length = len(abc_sequences[gene][region])
rows.append({
"gene": gene, "region": region,
"nem_count": count, "seq_length": length,
"nem_density_per_kb": (count / length) * 1000 if length > 0 else 0,
"type": info["type"], "essential": info["essential"],
"stress": info["stress"], "subfamily": info["subfamily"],
})
nem_df = pd.DataFrame(rows)
nem_df.to_csv(os.path.join(RESULTS_DIR, "nem_comprehensive_summary.csv"), index=False)
nullomer_rate = len(nullomers) / (4 ** K)
enrich_rows = []
for gene in abc_sequences:
info = ABC_TRANSPORTERS[gene]
for region in ["gene", "promoter", "downstream"]:
length = len(abc_sequences[gene][region])
observed = len(nem_results[gene][region])
expected = length * 3 * K * nullomer_rate
ratio = observed / expected if expected > 0 else 0
p = 1 - poisson.cdf(observed - 1, expected)
enrich_rows.append({
"gene": gene, "region": region,
"observed_nems": observed, "expected_nems": expected,
"enrichment_ratio": ratio, "p_value": p,
"seq_length": length, "type": info["type"],
"essential": info["essential"], "stress": info["stress"],
})
enrich_df = pd.DataFrame(enrich_rows)
enrich_df["p_adjusted"] = multipletests(enrich_df["p_value"], method="bonferroni")[1]
enrich_df["significant"] = enrich_df["p_adjusted"] < 0.05
enrich_df.to_csv(os.path.join(RESULTS_DIR, "nem_enrichment_analysis.csv"), index=False)
stress_density, nonstress_density = [], []
for gene in abc_sequences:
info = ABC_TRANSPORTERS[gene]
total_len = len(abc_sequences[gene]["gene"]) + len(abc_sequences[gene]["promoter"])
total_nems = len(nem_results[gene]["gene"]) + len(nem_results[gene]["promoter"])
density = (total_nems / total_len) * 1000 if total_len > 0 else 0
(stress_density if info["stress"] else nonstress_density).append(density)
u_stat, p_mw = mannwhitneyu(stress_density, nonstress_density, alternative="two-sided")
obs_diff, p_perm, _ = permutation_test(np.array(stress_density), np.array(nonstress_density))
pooled_std = np.sqrt((np.std(stress_density) ** 2 + np.std(nonstress_density) ** 2) / 2)
cohens_d = obs_diff / pooled_std if pooled_std > 0 else 0
perm_out = {
"stress_mean": round(float(np.mean(stress_density)), 2),
"stress_std": round(float(np.std(stress_density)), 2),
"nonstress_mean": round(float(np.mean(nonstress_density)), 2),
"nonstress_std": round(float(np.std(nonstress_density)), 2),
"observed_diff_nems_per_kb": round(obs_diff, 2),
"mannwhitney_u": float(u_stat),
"mannwhitney_p": float(p_mw),
"permutation_p": p_perm,
"cohens_d": round(float(cohens_d), 3),
"n_stress": len(stress_density),
"n_nonstress": len(nonstress_density),
}
with open(os.path.join(RESULTS_DIR, "stress_permutation_test.json"), "w") as f:
json.dump(perm_out, f, indent=2)
total_nems = nem_df["nem_count"].sum()
print(f"Total NEMs: {total_nems:,} stress p={p_mw:.4f} perm p={p_perm:.4f} d={cohens_d:.3f}")
if __name__ == "__main__":
main()
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