Evidence map›Paper›PMID 38798671›Full record

ArticlebioRxiv : the preprint server for biology2024

Gauge fixing for sequence-function relationships.

Anna Posfai, Juannan Zhou, David M McCandlish, Justin B Kinney

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Anna PosfaiSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724.
Juannan ZhouSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724.
David M McCandlishSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724.
Justin B KinneySimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724.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 of sequence-function relationships are ubiquitous in computational biology, e.g., for modeling the DNA binding of transcription factors or the fitness landscapes of proteins. Interpreting these models, however, is complicated by the fact that the values of model parameters can often be changed without affecting model predictions. Before the values of model parameters can be meaningfully interpreted, one must remove these degrees of freedom (called "gauge freedoms" in physics) by imposing additional constraints (a process called "fixing the gauge"). However, strategies for fixing the gauge of sequence-function relationships have received little attention. Here we derive an analytically tractable family of gauges for a large class of sequence-function relationships. These gauges are derived in the context of models with all-order interactions, but an important subset of these gauges can be applied to diverse types of models, including additive models, pairwise-interaction models, and models with higher-order interactions. Many commonly used gauges are special cases of gauges within this family. We demonstrate the utility of this family of gauges by showing how different choices of gauge can be used both to explore complex activity landscapes and to reveal simplified models that are approximately correct within localized regions of sequence space. The results provide practical gauge-fixing strategies and demonstrate the utility of gauge-fixing for model exploration and interpretation.

Indexed as

epistasismodel interpretabilitynon-identifiabilityregressionsequence space

Identifiers

PMID38798671
PMCPMC11118547

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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.