Evidence map›Paper›PMID 37903545›Full record

ArticleRNA (New York, N.Y.)2023

Motifs in SARS-CoV-2 evolution.

Christopher Barrett, Andrei C Bura, Qijun He, Fenix W Huang, Thomas J X Li, Christian M Reidys

Abstract read
In one paragraph

Article in RNA (New York, N.Y.), 2023. 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. Article
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

6 authors.

Christopher BarrettBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA.
Andrei C BuraBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA.
Qijun HeBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA.
Fenix W HuangBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA.
Thomas J X LiBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA.
Christian M ReidysBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia 22904, USA duckcr@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of coevolving sites in the alignment (motifs), identified via coevolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the coevolutionary coupling of pairs and manifest as coevolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across 21 lineages and 27 countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness, and robustness. Lastly, we study how well each lineage is classified by such a system.

Indexed as

COVID-19SARS-CoV-2GenomicsHumansPandemicsRetrospective Studiescoevolutiongenomic surveillancerelational structureSARS-CoV-2site motif

Identifiers

PMID37903545
PMCPMC10726165

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.