ef-synonymous

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English

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.

Install

pip install ef-synonymous

CLI

# 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 --json

Human-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

Library

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)

Ensembl VEP plugin

cp vep_plugin/EFSynonymous.pm ~/.vep/Plugins/
vep -i input.vcf --plugin EFSynonymous

Adds 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.

How it works (and what it is not)

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.

Method and license

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.

Español

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.

Instalación

pip install ef-synonymous

CLI

# 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 --json

Salida 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

Librería

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)

Plugin Ensembl VEP

cp vep_plugin/EFSynonymous.pm ~/.vep/Plugins/
vep -i input.vcf --plugin EFSynonymous

Añade 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.

Cómo funciona (y qué NO es)

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.

Método y licencia

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.