Evidence map›Paper›PMID 36597548›Full record

ArticleResearch square2025

Viral genome sequence datasets display pervasive evidence of strand-specific substitution biases that are best described using non-reversible nucleotide substitution models.

Rita Sianga-Mete, Penelope Hartnady, Wimbai Caroline Mandikumba, Kayleigh Rutherford, Christopher Brian Currin, Florence Phelanyane, Sabina Stefan, Steven Weaver, Sergei L Kosakovsky Pond, Darren P Martin

Open access · greenAbstract readPreprint
In one paragraph

Article in Research square, 2025. 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Rita Sianga-MeteDivision of Computational Biology, Institute of Infectious Diseases and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Anzio Road Observatory 7549, Cape Town, South Africa.
Penelope HartnadyDivision of Computational Biology, Institute of Infectious Diseases and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Anzio Road Observatory 7549, Cape Town, South Africa.
Wimbai Caroline MandikumbaDivision of Computational Biology, Institute of Infectious Diseases and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Anzio Road Observatory 7549, Cape Town, South Africa.
Kayleigh RutherfordDivision of Computational Biology, Institute of Infectious Diseases and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Anzio Road Observatory 7549, Cape Town, South Africa.
Christopher Brian CurrinDepartment of Human Biology, Faculty of Health Sciences, University of Cape Town, Anzio Road Observatory 7549, Cape Town, South Africa.
Florence PhelanyaneCentre for Infectious Disease and Epidemiology Research, School of Public Health and Family Medicine, University of Cape Town, South Africa.
Sabina StefanCentre for Biomedical Engineering, School of Engineering, Brown University, Providence, RI 02912, USA.
Steven WeaverInstitute for Genomics and Evolutionary Medicine, Department of Biology, Temple University, Pennsylvania, USA.
Sergei L Kosakovsky PondInstitute for Genomics and Evolutionary Medicine, Department of Biology, Temple University, Pennsylvania, USA.
Darren P MartinDivision of Computational Biology, Institute of Infectious Diseases and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Anzio Road Observatory 7549, Cape Town, South Africa.
University of Cape Town · ZAJohn Brown University · USTemple College · US

Funding

Turning big data analysis infrastructure for HIV researchR01AI134384 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI NEKRUTENKO, ANTON, POND, SERGEI L KOSAKOVSKY · 2017 to 2021
$4.2M
HIV Evolution Defines Virus-Host/Drug Interactions In Viremic and Aviremic PeopleR01AI140970 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Ronald I Swanstrom, Shuntai Zhou · 2018 to 2026
$4.1M
Reinventing dN/dS and the study of natural selectionR01GM144468 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI HEY, EMANUEL, POND, SERGEI L KOSAKOVSKY · 2022 to 2025
$1.3M
NIAID NIH HHS R01 AI134384NIAID NIH HHS R01 AI140970NIGMS NIH HHS R01 GM144468Wellcome Trust
6 · The paper itself

Abstract

Most phylogenetic trees are inferred using time-reversible evolutionary models that assume that the relative rates of substitution for any given pair of nucleotides are the same regardless of the direction of the substitutions. However, there is no reason to assume that the underlying biochemical mutational processes that cause substitutions are similarly symmetrical. We consider two non-reversible nucleotide substitution models: (1) a 6-rate non-reversible model (NREV6) that is applicable to analyzing mutational processes in double-stranded genomes in that complementary substitutions occur at identical rates; and (2) a 12-rate non-reversible model (NREV12) that is applicable to analyzing mutational processes in single-stranded (ss) genomes in that all substitution types are free to occur at different rates. Using likelihood ratio and Akaike Information Criterion-based model tests, we show that, surprisingly, NREV12 provided a significantly better fit than the General Time Reversible (GTR) and NREV6 models to 21/31 dsRNA and 20/30 dsDNA datasets. As expected, however, NREV12 provided a significantly better fit to 24/33 ssDNA and 40/47 ssRNA datasets. We tested how non-reversibility impacts the accuracy with which phylogenetic trees are inferred. As simulated degrees of non-reversibility (DNR) increased, the tree topology inferences using both NREV12 and GTR became more accurate, whereas inferred tree branch lengths became less accurate. We conclude that while non-reversible models should be helpful in the analysis of mutational processes in most virus species, there is no pressing need to use these models for routine phylogenetic inference.

Indexed as

Models of evolutionMutationsNon-reversibilityReversibility

Identifiers

PMID36597548
PMCPMC9810213
OpenAlexW4313329922

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.