ArticleGenome biology and evolution2018
The Effect of RNA Substitution Models on Viroid and RNA Virus Phylogenies.
Article in Genome biology and evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- "Fertile" Mutations in SARS-CoV-2 RNA More Frequently Occurred in Hairpin Loops That Determine Virus Evolution.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2025Article
- Viroid-like colonists of human microbiomes.Cell · 2024Article
- Viroid-like colonists of human microbiomes.bioRxiv : the preprint server for biology · 2024Article
- Covariance predicts conserved protein residue interactions important to the emergence and continued evolution of SARS-CoV-2 as a human pathogen.bioRxiv : the preprint server for biology · 2022Article
- Article
- Comparative Mitogenomics inGenes · 2021Article
- Host Plant Strategies to Combat Against Viruses Effector Proteins.Current genomics · 2020Review
- Alternative analyses of compensatory base changes in an ITS2 phylogeny of Corydalis (Papaveraceae).Annals of botany · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many viroids and RNA viruses have genomes that exhibit secondary structure, with paired nucleotides forming stems and loops. Such structures violate a key assumption of most methods of phylogenetic reconstruction, that sequence change is independent among sites. However, phylogenetic analyses of these transmissible agents rarely use evolutionary models that account for RNA secondary structure. Here, we assess the effect of using RNA-specific nucleotide substitution models on the phylogenetic inference of viroids and RNA viruses. We obtained data sets comprising full-genome nucleotide sequences from six viroid and ten single-stranded RNA virus species. For each alignment, we inferred consensus RNA secondary structures, then evaluated different DNA and RNA substitution models. We used model selection to choose the best-fitting model and evaluate estimated Bayesian phylogenies. Further, for each data set we generated and compared Robinson-Foulds (RF) statistics in order to test whether the distributions of trees generated under alternative models are notably different to each other. In all alignments, the best-fitting model was one that considers RNA secondary structure: RNA models that allow a nonzero rate of double substitution (RNA16A and RNA16C) fitted best for both viral and viroid data sets. In 14 of 16 data sets, the use of an RNA-specific model led to significantly longer tree lengths, but only in three cases did it have a significant effect on RFs. In conclusion, using RNA model when undertaking phylogenetic inference of viroids and RNA viruses can provide a better model fit than standard approaches and model choice can significantly affect branch length estimates.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.