| import os |
| 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() |
|
|
