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Glass-box pathogenicity scoring for synonymous variants from a single interpretable physical observable: the signed nearest-neighbor stacking ΔΔG (Turner RNA parameters) plus the G>A mutational bias.
A 2-variable model that reproduces the dedicated CNN of Paper 1
(AUC 0.671 pooled / 0.680 leave-genes-out; dedicated CNN 0.709, on par on the gene-matched set). Inference is
numpy-only — no PyTorch, no GPU, no external services. It is the same logic
as the web prototype and core/synonymous_sigma_predictor.py, verified to 1e-6.
⚠ Research use only. Not a medical device. The ACMG mapping thresholds are illustrative and not clinically calibrated.
pip install ef-synonymous# literal CDS + HGVS
ef-syn score --cds ATGGTC...TAG --hgvs c.213G>A --gene CACNA1C
# from a local FASTA
ef-syn score --cds-file NM_000719.cds.fasta --pos 213 --ref G --alt A
# fetching the CDS of a transcript (Ensembl REST, user's network)
ef-syn score --transcript ENST00000399655 --hgvs c.213G>A
# auditable JSON dossier (the SaMD argument)
ef-syn score --cds-file cds.fasta --hgvs c.213G>A --jsonHuman-readable output:
EF-Synonymous · c.213G>A (CACNA1C)
score_sigma (P pathogenic): 0.688
ACMG: PP3 (supporting pathogenic) [illustrative thresholds, uncalibrated]
codon: GCG(A) -> GCA(A) — synonymous ✓
glass-box (contributions to the logit):
intercept -0.062
sigma_signed 1.67 +0.071
is_GtoA 1 +0.782
logit = 0.791 -> P = 0.688
from ef_synonymous import SynonymousSigmaPredictor
clf = SynonymousSigmaPredictor.load()
clf.predict_proba(cds, cds_pos=213, ref="G", alt="A") # 0.688...
clf.score(cds, 213, "G", "A", gene="CACNA1C") # full dossier (dict)cp vep_plugin/EFSynonymous.pm ~/.vep/Plugins/
vep -i input.vcf --plugin EFSynonymousAdds EF_sigma, EF_score and EF_acmg to synonymous_variant records. The
plugin delegates to the CLI (same engine). At scale, the production path is a
precomputed per-transcript cache or a REST endpoint — this plugin is the
reference implementation of the integration.
The model is a logistic regression over two physical observables that matches the dedicated CNN of Paper 1 (AUC 0.709, Turner RNA):
- σ (sigma_signed): signed sum of nearest-neighbor stacking ΔΔG (Turner RNA parameters) over ±10 nt around the variant — local thermodynamic destabilization of the mRNA.
- is_GtoA: G>A transition (CpG mutational bias, 2.60× enriched in pathogenic).
Everything runs locally with numpy. Each prediction is reproducible, auditable and traceable variant by variant — the glass-box argument for SaMD/ACMG. AUC ~0.68 = supporting evidence, not a verdict; wide gray zone; illustrative, uncalibrated ACMG thresholds. Do not use for clinical decisions.
Code under the MIT license. The method (mRNA nearest-neighbor thermodynamic fingerprinting) is covered by patent P202630522 (OEPM, Spain); commercial use of the method may require a separate license. Paper 1: Zenodo 10.5281/zenodo.20275792.
QMetrika Labs · Jose Antonio Vilar Sánchez.
Scoring glass-box de patogenicidad de variantes sinónimas a partir de un único observable físico interpretable: el ΔΔG de apilamiento nearest-neighbor con signo (parámetros Turner de RNA) más el sesgo mutacional G>A.
Un modelo de 2 variables que reproduce a la CNN dedicada del Paper 1
(AUC 0.671 pooled / 0.680 leave-genes-out; CNN dedicada 0.709, a la par en el set gene-matched). Inferencia solo con
numpy — sin PyTorch, sin GPU, sin servicios externos. Es la misma lógica del
prototipo web y de core/synonymous_sigma_predictor.py, verificada a 1e-6.
⚠ Research use only. No es un dispositivo médico. Los umbrales de mapeo ACMG son ilustrativos y no están calibrados clínicamente.
pip install ef-synonymous# CDS literal + HGVS
ef-syn score --cds ATGGTC...TAG --hgvs c.213G>A --gene CACNA1C
# desde un FASTA local
ef-syn score --cds-file NM_000719.cds.fasta --pos 213 --ref G --alt A
# descargando el CDS de un transcrito (Ensembl REST, red del usuario)
ef-syn score --transcript ENST00000399655 --hgvs c.213G>A
# dossier auditable JSON (el argumento SaMD)
ef-syn score --cds-file cds.fasta --hgvs c.213G>A --jsonSalida humana:
EF-Synonymous · c.213G>A (CACNA1C)
score_sigma (P patogénica): 0.688
ACMG: PP3 (supporting pathogenic) [umbrales ilustrativos, sin calibrar]
codón: GCG(A) -> GCA(A) — sinónima ✓
glass-box (contribuciones al logit):
intercepto -0.062
sigma_signed 1.67 +0.071
is_GtoA 1 +0.782
logit = 0.791 -> P = 0.688
from ef_synonymous import SynonymousSigmaPredictor
clf = SynonymousSigmaPredictor.load()
clf.predict_proba(cds, cds_pos=213, ref="G", alt="A") # 0.688...
clf.score(cds, 213, "G", "A", gene="CACNA1C") # dossier completo (dict)cp vep_plugin/EFSynonymous.pm ~/.vep/Plugins/
vep -i input.vcf --plugin EFSynonymousAñade EF_sigma, EF_score y EF_acmg a las variantes synonymous_variant. El
plugin delega en la CLI (mismo motor). Para escala, la vía de producción es una
cache precomputada por transcrito o un endpoint REST — este plugin es la
implementación de referencia de la integración.
El modelo es una regresión logística de dos observables físicos que iguala a la CNN dedicada del Paper 1 (AUC 0.709, Turner RNA):
- σ (sigma_signed): suma con signo del ΔΔG de apilamiento nearest-neighbor (parámetros Turner de RNA) en ±10 nt alrededor de la variante — desestabilización termodinámica local del mRNA.
- is_GtoA: transición G>A (sesgo mutacional CpG, enriquecido 2.60× en patogénicas).
Todo corre en local con numpy. Cada predicción es reproducible, auditable y trazable variante a variante — el argumento glass-box para SaMD/ACMG. AUC ~0.68 = evidencia de apoyo, no un veredicto; zona gris amplia; umbrales ACMG ilustrativos y sin calibrar. No usar para decisiones clínicas.
Código bajo licencia MIT. El método (huella termodinámica nearest-neighbor del mRNA) está cubierto por la patente P202630522 (OEPM); su uso comercial puede requerir licencia. Paper 1: Zenodo 10.5281/zenodo.20275792.
QMetrika Labs · Jose Antonio Vilar Sánchez.