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import numpy as np
import pandas as pd
from itertools import product as iproduct
from Bio.Seq import Seq
from scipy.stats import spearmanr, mannwhitneyu
RESULTS_DIR = "results"
STRESS_ELEMENTS = {
"PDRE": {
"consensus": "TCCGCGGA",
"variants": ["TCCGCGGA", "TCCGYGGA", "TCCACGGA", "TCCGTGGA"],
"factors": "Pdr1/Pdr3",
},
"STRE": {
"consensus": "AGGGG",
"variants": ["AGGGG", "CCCCT", "AGGGA", "TCCCT"],
"factors": "Msn2/Msn4",
},
"HSE": {
"consensus": "nGAAn",
"variants": ["AGAAA", "TGAAT", "CGAAA", "GGAAT", "AGAAT", "TGAAA"],
"factors": "Hsf1",
},
"AP1": {
"consensus": "TGASTCA",
"variants": ["TGACTCA", "TGAGTCA", "TTAGTCA", "TTACTAA", "TGACTAA"],
"factors": "Yap1",
},
}
IUPAC = {
"R": ["A", "G"], "Y": ["C", "T"], "S": ["G", "C"],
"W": ["A", "T"], "K": ["G", "T"], "M": ["A", "C"],
"B": ["C", "G", "T"], "D": ["A", "G", "T"],
"H": ["A", "C", "T"], "V": ["A", "C", "G"],
"N": ["A", "C", "G", "T"],
}
DRUG_EFFLUX_TYPES = {"Drug efflux", "Weak acid efflux"}
def expand_iupac(motif):
positions = [IUPAC.get(c, [c]) for c in motif.upper()]
return ["".join(combo) for combo in iproduct(*positions)]
def find_motif(sequence, motif):
positions = []
seq = sequence.upper()
for expanded in expand_iupac(motif):
ml = len(expanded)
for i in range(len(seq) - ml + 1):
if seq[i:i + ml] == expanded:
positions.append({"position": i, "strand": "+", "sequence": expanded})
rc = str(Seq(expanded).reverse_complement())
for i in range(len(seq) - len(rc) + 1):
if seq[i:i + len(rc)] == rc:
positions.append({"position": i, "strand": "-", "sequence": rc})
seen = set()
unique = []
for p in positions:
key = (p["position"], p["strand"])
if key not in seen:
unique.append(p)
seen.add(key)
return unique
def scan_stress_elements(abc_sequences):
results = {}
for gene, seqs in abc_sequences.items():
results[gene] = {}
for etype, einfo in STRESS_ELEMENTS.items():
all_pos = []
for motif in [einfo["consensus"]] + einfo["variants"]:
all_pos.extend(find_motif(seqs["promoter"], motif))
seen = set()
unique = []
for p in all_pos:
key = (p["position"], p["strand"])
if key not in seen:
unique.append(p)
seen.add(key)
results[gene][etype] = unique
return results
def build_correlation_df(abc_sequences, nem_results, stress_results, abc_info):
rows = []
for gene in abc_sequences:
info = abc_info[gene]
prom_len = len(abc_sequences[gene]["promoter"])
prom_nems = len(nem_results.get(gene, {}).get("promoter", []))
nem_density = (prom_nems / prom_len) * 1000 if prom_len > 0 else 0
counts = {etype: len(stress_results[gene][etype]) for etype in STRESS_ELEMENTS}
rows.append({
"gene": gene,
"promoter_length": prom_len,
"promoter_nems": prom_nems,
"nem_density_per_kb": nem_density,
"total_stress_elements": sum(counts.values()),
**counts,
"type": info["type"],
"essential": info["essential"],
"stress": info["stress"],
"subfamily": info["subfamily"],
"is_drug_efflux": info["type"] in DRUG_EFFLUX_TYPES,
})
return pd.DataFrame(rows)
def motif_disruption(abc_sequences, nem_results, stress_results):
rows = []
for gene in abc_sequences:
prom_nems = nem_results.get(gene, {}).get("promoter", [])
for nem in prom_nems:
pos = nem["position"]
for etype, einfo in STRESS_ELEMENTS.items():
ml = len(einfo["consensus"])
for elem in stress_results[gene][etype]:
estart = elem["position"]
if estart <= pos < estart + ml:
rows.append({
"gene": gene,
"nem_position": pos,
"nem_mutation": nem["mutation"],
"element_type": etype,
"motif_position": estart,
"motif_strand": elem["strand"],
"position_in_motif": pos - estart,
})
break
return pd.DataFrame(rows)
def main():
from importlib.util import spec_from_file_location, module_from_spec
nem_csv = os.path.join(RESULTS_DIR, "nem_comprehensive_summary.csv")
if not os.path.exists(nem_csv):
raise FileNotFoundError(f"{nem_csv} not found. Run 02_nem_analysis.py first.")
spec = spec_from_file_location("nem_mod", "02_nem_analysis.py")
mod = module_from_spec(spec)
spec.loader.exec_module(mod)
nullomers = mod.load_nullomers(os.path.join(RESULTS_DIR, "nullomers_k11.txt"))
gene_coords = mod.parse_gff(os.path.join("data", "yeast.gff3.gz"))
genome_dict = mod.load_genome_dict(os.path.join("data", "yeast_genome.fsa"))
abc_info = mod.ABC_TRANSPORTERS
abc_sequences = {}
for gene in abc_info:
if gene in gene_coords:
seqs = mod.extract_sequences(
gene, gene_coords, genome_dict, mod.PROMOTER_LENGTH, mod.DOWNSTREAM_LENGTH
)
if seqs:
abc_sequences[gene] = seqs
nem_results = {}
for gene in abc_sequences:
nem_results[gene] = {
"gene": mod.find_nems(abc_sequences[gene]["gene"], nullomers, mod.K),
"promoter": mod.find_nems(abc_sequences[gene]["promoter"], nullomers, mod.K),
"downstream": mod.find_nems(abc_sequences[gene]["downstream"], nullomers, mod.K),
}
stress_results = scan_stress_elements(abc_sequences)
corr_df = build_correlation_df(abc_sequences, nem_results, stress_results, abc_info)
corr_df.to_csv(os.path.join(RESULTS_DIR, "stress_element_nem_correlation.csv"), index=False)
disrupt_df = motif_disruption(abc_sequences, nem_results, stress_results)
if not disrupt_df.empty:
disrupt_df.to_csv(os.path.join(RESULTS_DIR, "motif_disruption_by_nems.csv"), index=False)
rho, p = spearmanr(corr_df["PDRE"], corr_df["nem_density_per_kb"])
drug = corr_df[corr_df["is_drug_efflux"]]["nem_density_per_kb"].values
other = corr_df[~corr_df["is_drug_efflux"]]["nem_density_per_kb"].values
_, p_drug = mannwhitneyu(drug, other, alternative="two-sided")
print(f"PDRE-NEM rho={rho:.3f} p={p:.2e} drug efflux p={p_drug:.4f} "
f"disruptions={len(disrupt_df):,}")
if __name__ == "__main__":
main()
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