Data Dictionary
Input Data
All input data is downloaded automatically by the scripts at runtime. Nothing needs to be uploaded manually.
Genome & Annotation Files (cached to data/ at runtime)
| File | Source | Size |
|---|---|---|
data/yeast_genome.fsa |
Ensembl R64-1-1 (release 110) | 12.16 Mb |
data/yeast.gff3.gz |
Ensembl release 110 | — |
Genome assembly: S. cerevisiae S288C, R64-1-1, 12,157,105 bp across 17 chromosomes including mitochondria.
Output Data
All output files are written to results/ by running the scripts in order.
Nullomer List
results/nullomers_k11.txt — generated by scripts/01_nullomer_identification.py
- One 11-mer sequence per line, sorted alphabetically
- 463,220 sequences (11.04% of the 4,194,304 theoretical 11-mers)
- Mean GC content: 65.7% (vs 38.3% genome-wide)
- Both forward and reverse-complement strands are accounted for
NEM Analysis
results/nem_comprehensive_summary.csv — generated by scripts/02_nem_analysis.py
| Column | Type | Description |
|---|---|---|
gene |
string | Gene name (e.g. PDR5) |
region |
string | gene, promoter, or downstream |
nem_count |
integer | Number of nullomer-emerging mutations |
seq_length |
integer | Length of the region in bp |
nem_density_per_kb |
float | NEMs per kilobase |
type |
string | Functional classification |
essential |
boolean | Gene essentiality |
stress |
boolean | Stress-responsive classification |
subfamily |
string | ABC transporter subfamily |
78 rows (26 genes × 3 regions). Total NEMs across all regions: 174,799.
results/nem_enrichment_analysis.csv — generated by scripts/02_nem_analysis.py
| Column | Type | Description |
|---|---|---|
gene |
string | Gene name |
region |
string | gene, promoter, or downstream |
observed_nems |
integer | Observed NEM count |
expected_nems |
float | Expected under Poisson null |
enrichment_ratio |
float | Observed / expected |
p_value |
float | Poisson p-value |
p_adjusted |
float | Bonferroni-corrected p-value |
significant |
boolean | p_adjusted < 0.05 |
results/stress_permutation_test.json — generated by scripts/02_nem_analysis.py
Permutation test results (10,000 iterations) comparing NEM density between stress-responsive and non-stress genes. Fields: stress_mean, stress_std, nonstress_mean, nonstress_std, observed_diff_nems_per_kb, mannwhitney_u, mannwhitney_p, permutation_p, cohens_d, n_stress, n_nonstress.
Stress Element Analysis
results/stress_element_nem_correlation.csv — generated by scripts/03_stress_element_analysis.py
| Column | Type | Description |
|---|---|---|
gene |
string | Gene name |
promoter_length |
integer | Promoter length in bp (1000 bp) |
promoter_nems |
integer | NEM count in promoter |
nem_density_per_kb |
float | Promoter NEM density |
total_stress_elements |
integer | Sum of all binding sites |
PDRE |
integer | Pleiotropic Drug Response Element count |
STRE |
integer | Stress Response Element count |
HSE |
integer | Heat Shock Element count |
AP1 |
integer | AP-1 element count |
type |
string | Functional classification |
essential |
boolean | Gene essentiality |
stress |
boolean | Stress-responsive classification |
is_drug_efflux |
boolean | Drug efflux gene flag |
26 rows (one per gene). Key result: PDRE count correlates with NEM density at Spearman ρ=0.685, p=1.1×10⁻⁴.
results/motif_disruption_by_nems.csv — generated by scripts/03_stress_element_analysis.py
| Column | Type | Description |
|---|---|---|
gene |
string | Gene name |
nem_position |
integer | Position in promoter sequence |
nem_mutation |
string | Mutation notation (e.g. A142G) |
element_type |
string | PDRE, STRE, HSE, or AP1 |
motif_position |
integer | Motif start position |
motif_strand |
string | + or - |
position_in_motif |
integer | Position of NEM within the motif |
Records where a single mutation both creates a nullomer and falls inside a known TF binding site. Total: 16,480 disruptions across all elements.
Thermodynamic Analysis
results/nullomer_thermodynamics.csv — generated by scripts/04_thermodynamic_analysis.py
| Column | Type | Description |
|---|---|---|
sequence |
string | 11-mer sequence |
group |
string | nullomer or random |
Tm |
float | Melting temperature (°C) |
dG |
float | Gibbs free energy at 37°C (kcal/mol) |
GC |
float | GC fraction (0–1) |
hairpin |
boolean | Palindromic hairpin potential |
g4 |
boolean | G-quadruplex motif (GGGG) present |
imotif |
boolean | i-motif motif (CCCC) present |
20,000 rows (10,000 nullomers + 10,000 random controls). Parameters: SantaLucia (1998) nearest-neighbour, 37°C, 1 M NaCl.
results/thermodynamic_summary.json — generated by scripts/04_thermodynamic_analysis.py
Key values confirmed against the manuscript:
| Metric | Nullomers | Random |
|---|---|---|
| Mean Tm | 41.73 ± 5.70 °C | 35.56 ± 6.63 °C |
| Mean ΔG | −13.96 ± 1.52 kcal/mol | −12.13 ± 1.80 kcal/mol |
| ΔΔG | 1.83 kcal/mol | — |
| Boltzmann fold disadvantage | 19.4× | — |
| GC–Tm Pearson r | 0.803 | — |
| Very stable (ΔG < −10) | 99.7% | — |
| Hairpin potential | 22.4% | — |
| G-quadruplex | 1.0% | — |
| i-motif | 1.2% | — |
ML and Network Analysis
results/ml_feature_importance.csv — generated by scripts/05_ml_and_network_analysis.py
| Column | Type | Description |
|---|---|---|
feature |
string | Feature name |
importance |
float | Random Forest mean decrease in impurity |
Top features: at_content (0.359), gc_content (0.356), cg_dinuc (0.153).
results/ml_model_performance.json — generated by scripts/05_ml_and_network_analysis.py
Random Forest performance (100 bp windows, 50 bp step, 26 genes):
| Metric | Value |
|---|---|
| Test R² | 0.760 |
| Test RMSE | 41.46 NEMs |
| CV R² (5-fold) | 0.717 ± 0.045 |
Also contains Gaussian Process fitness landscape results: R²=0.896, RMSE=93.8 NEMs/kb.
results/network_topology.csv — generated by scripts/05_ml_and_network_analysis.py
| Column | Type | Description |
|---|---|---|
gene |
string | Gene name |
nem_density |
float | NEM density (NEMs/kb) |
degree |
integer | Number of STRING interaction partners |
betweenness |
float | Betweenness centrality |
closeness |
float | Closeness centrality |
eigenvector |
float | Eigenvector centrality |
is_drug_efflux |
boolean | Drug efflux gene flag |
Network: 26 nodes, 13 edges (STRING v11.5, score ≥ 400, physical interactions only).
results/fragility_scores.csv — generated by scripts/05_ml_and_network_analysis.py
| Column | Type | Description |
|---|---|---|
gene |
string | Gene name |
fragility_score |
float | F = 0.4×(NEM/5000) + 0.3×(degree/n) + 0.3×(neighbor_NEM/5000) |
Top 5: PDR15 (1.402), PDR10 (1.330), PDR5 (1.238), SNQ2 (1.161), PDR12 (1.076).
Statistical Synthesis
results/statistical_synthesis.json — generated by scripts/06_statistical_synthesis.py
Contains all four hypothesis tests and Fisher's combined p-value:
| Test | Result |
|---|---|
| H1: Stress vs non-stress NEM density | Mann-Whitney p=0.019, permutation p=0.006, d=1.36 |
| H2: PDRE–NEM correlation | Spearman ρ=0.685, p=1.1×10⁻⁴, slope=85.5 NEMs/kb per PDRE |
| H3: Drug efflux vs other | Mann-Whitney p=0.018, Cohen's d=1.08 |
| H4: Promoter vs gene body density | Wilcoxon p=0.003, enrichment=22.6% |
| Meta-analysis (Fisher) | χ²=51.32, df=8, combined p=2.28×10⁻⁸ |
ABC Transporters Analyzed
26 genes spanning: drug efflux pumps (PDR5, SNQ2, YOR1, PDR10, PDR11, PDR12, PDR15, PDR18, YCF1), transcriptional regulators of drug resistance (PDR1, PDR3, PDR16, PDR17), mitochondrial transporters (ATM1, MDL1, MDL2), translation-related (YEF3, GCN20, ARB1, RLI1), and others (VMR1, YBT1, BPT1, HMT1, NMD5, STE6).
Promoter length used throughout: 1000 bp upstream of each start codon.
File Formats
All CSV files use comma separation, UTF-8 encoding, and a header row. JSON files use UTF-8 with two-space indentation. The nullomer list (nullomers_k11.txt) has one 11-mer per line, sorted lexicographically.