Evidence map›Paper›PMID 38798625›Full record

ArticlebioRxiv : the preprint server for biology2025

Symmetry, gauge freedoms, and the interpretability of sequence-function relationships.

Anna Posfai, David M McCandlish, Justin B Kinney

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Gauge fixing for sequence-function relationships.PLoS computational biology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Anna PosfaiSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory.
David M McCandlishSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory.
Justin B KinneySimons Center for Quantitative Biology, Cold Spring Harbor Laboratory.ORCID 0000-0003-1897-3778

Funding

A unified quantitative modeling strategy for multiplex assays of variant effectR01HG011787 · NHGRI · COLD SPRING HARBOR LABORATORY · PI JUSTIN B. KINNEY · 2022 to 2026
$4.1M
Computational analysis of complex genetic interactionsR35GM133613 · NIGMS · COLD SPRING HARBOR LABORATORY · PI David Martin McCandlish · 2019 to 2026
$3.5M
Biophysical modeling of cis-regulatory complexes in transcription and splicing using massively parallel reporter assaysR35GM133777 · NIGMS · COLD SPRING HARBOR LABORATORY · PI KINNEY, JUSTIN B. · 2019 to 2023
$2.4M
NHGRI NIH HHS R01 HG011787NIGMS NIH HHS R35 GM133613NIGMS NIH HHS R35 GM133777
6 · The paper itself

Abstract

Quantitative models that describe how biological sequences encode functional activities are ubiquitous in modern biology. One important aspect of these models is that they commonly exhibit gauge freedoms, i.e., directions in parameter space that do not affect model predictions. In physics, gauge freedoms arise when physical theories are formulated in ways that respect fundamental symmetries. However, the connections that gauge freedoms in models of sequence-function relationships have to the symmetries of sequence space have yet to be systematically studied. In this work we study the gauge freedoms of models that respect a specific symmetry of sequence space: the group of position-specific character permutations. We find that gauge freedoms arise when model parameters transform under redundant irreducible matrix representations of this group. Based on this finding, we describe an "embedding distillation" procedure that enables both analytic calculation of the number of independent gauge freedoms and efficient computation of a sparse basis for the space of gauge freedoms. We also study how parameter transformation behavior affects parameter interpretability. We find that in many (and possibly all) nontrivial models, the ability to interpret individual model parameters as quantifying intrinsic allelic effects requires that gauge freedoms be present. This finding establishes an incompatibility between two distinct notions of parameter interpretability. Our work thus advances the understanding of symmetries, gauge freedoms, and parameter interpretability in models of sequence-function relationships.

Identifiers

PMID38798625
PMCPMC11118426

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.