Evidence map›Paper›PMID 40502937›Full record

ArticleComputational and structural biotechnology journal2025

Over time analysis of the codon usage of SARS-CoV-2 and its variants.

Alma Davidson, Marina Parr, Franziska Totzeck, Alexander Churkin, Danny Barash, Dmitrij Frishman, Tamir Tuller

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alma DavidsonDepartment of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.
Marina ParrDepartment of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany.
Franziska TotzeckDepartment of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany.
Alexander ChurkinDepartment of Software Engineering, Sami Shamoon College of Engineering, Beer-Sheva 84100, Israel.
Danny BarashDepartment of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, Israel.
Dmitrij FrishmanDepartment of Bioinformatics, School of Life Sciences, Technical University of Munich, Freising, Germany.
Tamir TullerDepartment of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As viruses evolve over time with their host, they adapt to multiple cellular functions to ensure efficient long-term transmission. Their ability to survive and function efficiently depends on optimizing their genetic code to effectively recruit the host's gene expression machinery, particularly the translation machinery. Codon usage bias (CUB) measures the level of adaptation at the codon level, considering multiple factors such as the host's tRNA pool.By estimating the adaptation scores of viruses to their host, we can gain insight into the changes that occur on the genomic level throughout their evolution. In our study, we propose tracking the viral evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using CUB to estimate adaptation. By comparing different strains and analyzing changes over time, we demonstrate an increased adaptation of the Omicron SARS-COV-2 variant. In addition, we observe fluctuations in CUB scores, with periods of decreased and then increased adaptation over time, offering detailed insights at a fine temporal resolution. This analysis further contributes to understanding how different mutations influence viral adaptation, as well as the evolution of other human-infecting viruses.

Indexed as

Codon usageComparative AnalysisOmicronOver Time AnalysisSARS-CoV-2

Identifiers

PMID40502937
PMCPMC12152341

What OpenQuestion holds

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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.