Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Secondary structure of the SARS-CoV-2 genome is predictive of nucleotide substitution frequency.

eLife - 28 Feb 2025

Hensel Zach

Abstract excerpt

Accurate estimation of the effects of mutations on SARS-CoV-2 viral fitness can inform public-health responses such as vaccine development and predicting the impact of a new variant; it can also illuminate biological mechanisms including those underlying the emergence of variants of concern. Recently, Lan et al. reported a model of SARS-CoV-2 secondary structure and its underlying dimethyl sulfate reactivity data...

Read the complete abstract on PubMed

Topics

SARS-CoV-2
Genome, Viral
Humans
COVID-19
Mutation
Nucleic Acid Conformation
Phylogeny
RNA, Viral

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Genetic Fitness

Source

Open in PubMed Open full text

Identifiers

PMID: 40019136
DOI: 10.7554/eLife.98102
PMCID: PMC11870649

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 25 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Secondary structure of the SARS-CoV-2 genome is predictive of nucleotide substitution frequency.PMID 40019136Read ↗

Secondary structure of the SARS-CoV-2 genome is predictive of nucleotide substitution frequencySemantic proximity Read ↗

Evolution of the SARS-CoV-2 mutational spectrumSemantic proximity Read ↗

Evolution of the SARS-CoV-2 Mutational Spectrum.Semantic proximity Read ↗

An entropy-based study on the mutational landscape of SARS-CoV-2 in USA: Comparing different variants and revealing co-mutational behavior of proteins.Semantic proximity Read ↗

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks.Semantic proximity Read ↗

The mutation rate of SARS-CoV-2 is highly variable between sites and is influenced by sequence context, genomic region, and RNA structure.Semantic proximity Read ↗

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phaseSemantic proximity Read ↗

An accelerating, decreasing phylogenetic trend in SARS-CoV-2 genome compositional heterogeneity during the pandemicSemantic proximity Read ↗

Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase.Semantic proximity Read ↗

The mutational landscape of SARS-CoV-2 provides new insight into viral evolution and fitnessSemantic proximity Read ↗

Fitness effects of mutations to SARS-CoV-2 proteinsSemantic proximity Read ↗

Enrichment of G-to-U Substitution in SARS-CoV-2 Functional Regions and Its Compensation via Concurrent Mutations.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.