Highly Recurrent Multinucleotide Mutations in SARS-CoV-2

Multinucleotide mutations simultaneously replace multiple nucleotides. They are a significant contributor to evolution and disease, as well as to misdiagnosis, misannotation and other biases in genome data analysis. Multinucleotide mutations are generally thought to be rare and random events. However, by processing over 2 million publicly shared genomes, we show that certain multinucleotide mutations are highly recurrent in SARS-CoV-2: they repeatedly and consistently modify the same multiple nucleotides at the same genome position in the same way. The most frequent of these multinucleotide mutations have independently occurred hundreds of times across all SARS-CoV-2 lineages. We find evidence that the vast majority of these recurrent multinucleotide mutations (14 out of 15, corresponding to 97.6% of all individual occurrences) are linked to transcription regulatory sequences. We propose a mechanism that can explain them through template switching as part of the natural transcription process of the virus. This previously unknown mutational pattern increases our understanding of the evolution of SARS-CoV-2 and potentially many other nidoviruses. It also has important consequences for computational evolutionary biology: we show that for example recurrent multinucleotide mutations cause approximately 12% of false positives during inference of recombination in SARS-CoV-2.

Authors

Institutions

Publication Details

Journal
Molecular Biology and Evolution
Published
2025-10-23
DOI
https://doi.org/10.1093/molbev/msaf272
Primary Topic
CRISPR and Genetic Engineering
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Highly Recurrent Multinucleotide Mutations in SARS-CoV-2

Nick Goldman, Nicola De Maio, Kyle Smith, Yatish Turakhia et al.
Molecular Biology and Evolution
CRISPR and Genetic Engineering
article

Highly Recurrent Multinucleotide Mutations in SARS-CoV-2

Nick Goldman, Nicola De Maio, Kyle Smith, Yatish Turakhia, Olivier Anoufa
article en

Abstract

Multinucleotide mutations simultaneously replace multiple nucleotides. They are a significant contributor to evolution and disease, as well as to misdiagnosis, misannotation and other biases in genome data analysis. Multinucleotide mutations are generally thought to be rare and random events. However, by processing over 2 million publicly shared genomes, we show that certain multinucleotide mutations are highly recurrent in SARS-CoV-2: they repeatedly and consistently modify the same multiple nucleotides at the same genome position in the same way. The most frequent of these multinucleotide mutations have independently occurred hundreds of times across all SARS-CoV-2 lineages. We find evidence that the vast majority of these recurrent multinucleotide mutations (14 out of 15, corresponding to 97.6% of all individual occurrences) are linked to transcription regulatory sequences. We propose a mechanism that can explain them through template switching as part of the natural transcription process of the virus. This previously unknown mutational pattern increases our understanding of the evolution of SARS-CoV-2 and potentially many other nidoviruses. It also has important consequences for computational evolutionary biology: we show that for example recurrent multinucleotide mutations cause approximately 12% of false positives during inference of recombination in SARS-CoV-2.

Molecular Biology and EvolutionVol. 42(11)
European Bioinformatics Institute (GB), University of San Diego (US), University of California San Diego (US), Université Lille Nord de France (FR)
Openalex Percentile: Top 73%
CRISPR and Genetic Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.