Evidence map›Paper›PMID 41993262›Full record

ArticlebioRxiv : the preprint server for biology2026

Beyond Invariable Sites: Using Evolutionary Stasis to Map Multi-Layered Constraints on the Evolution of Viral and Mammalian Genomes.

Sergei L Kosakovsky Pond, Hannah Verdonk, Steven Weaver, Gallean Brown, Danielle Callan, Anton Nekrutenko, Darren P Martin

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

7 authors.

Sergei L Kosakovsky PondDepartment of Biology, Temple University, Philadelphia, PA, USA.
Hannah VerdonkDepartment of Biology, Temple University, Philadelphia, PA, USA.
Steven WeaverDepartment of Biology, Temple University, Philadelphia, PA, USA.
Gallean BrownDepartment of Biology, Temple University, Philadelphia, PA, USA.
Danielle CallanDepartment of Biology, Temple University, Philadelphia, PA, USA.
Anton NekrutenkoDepartment of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA.
Darren P MartinInstitute of Infectious Disease and Molecular Virology, Division of Medical Virology, University of Cape Town, Cape Town, South Africa.

Funding

An in integrated platform for multiomic analyses of pathogen and host data using scalable public infrastructureU24AI183870 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI Kelsey M Beavers, Maximilian Haeussler · 2024 to 2026
$10.2M
Hypothesis Testing using Phylogenies for the 21st century (equipment supplement)R01GM151683 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI Spencer V. Muse, ANTON NEKRUTENKO · 2024 to 2026
$1.2M
NIAID NIH HHS U24 AI183870NIGMS NIH HHS R01 GM151683
6 · The paper itself

Abstract

The quantification of genomic conservation has progressed from foundational statistical modeling of evolutionary rates to state-of-the-art phylogeny-aware deep learning architectures. Yet, a fundamental resolution gap remains whenever evolutionary rates closely approach the "zero-rate origin," where standard selection inference tools will essentially ignore signals of extreme purifying section at invariant genome sites. We present B-STILL (Bayesian Significance Test of Invariant Low Likelihoods), a hierarchical Bayesian framework designed to resolve the selective landscape of protein-coding data by leveraging gene-level calibration and codon-site specific evolutionary opportunity. This framework is based on computationally efficient approximations using codon-substitution models which are scalable to alignments with thousands of sequences. By explicitly tuning the stasis radius around the near-zero evolutionary-rate regime, B-STILL distinguishes between stochastic invariance and functional constraint, identifying Evolutionary Stasis Anchors (ESAs) where the upper bound on permitted evolutionary change is statistically anomalous relative to the background of the gene. This hierarchical approach provides a signature of functional or structural constraint that is often difficult to detect using other tools. Validation against extensive pathogen and clinical databases confirms that ESAs are predictors of biological fitness and disease potential. Collectively, we identified thousands of significantly clustered ESAs that precisely footprint both known functional domains and currently uncharacterized structural motifs in mammalian and viral genomes. These findings establish B-STILL as a scalable statistical framework for high-resolution genomic annotation, transforming formerly ignored invariant genome and protein sites into informative markers of extreme purifying selection across both well-characterized and uncharacterized protein-coding genes from different domains of life.

Indexed as

B-STILLCodon ModelsComparative GenomicsEvolutionary StasisPurifying Selection

Identifiers

PMID41993262
PMCPMC13081978

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LicenceCC BY-NC-ND
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Registered trials

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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.