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Record W3115991508 · doi:10.1080/15476286.2020.1868150

An epigenetic ‘extreme makeover’: the methylation of flaviviral RNA (and beyond)

2020· review· en· W3115991508 on OpenAlexafffund
Alessia Ruggieri, Mark Helm, Laurent Chatel‐Chaix

Bibliographic record

VenueRNA Biology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsBiologyRNAFlavivirusRNA methylationEpigeneticsDengue feverViral replicationMethylationDNA methylationRNA editingGeneticsSubgenomic mRNATranslation (biology)VirologyComputational biologyMessenger RNAGeneVirusGene expression

Abstract

fetched live from OpenAlex

Beyond their high clinical relevance worldwide, flaviviruses (comprising dengue and Zika viruses) are of particular interest to understand the spatiotemporal control of RNA metabolism. Indeed, their positive single-stranded viral RNA genome (vRNA) undergoes in the cytoplasm replication, translation and encapsidation, three steps of the flavivirus life cycle that are coordinated through a fine-tuned equilibrium. Over the last years, RNA methylation has emerged as a powerful mechanism to regulate messenger RNA metabolism at the posttranscriptional level. Not surprisingly, flaviviruses exploit RNA epigenetic strategies to control crucial steps of their replication cycle as well as to evade sensing by the innate immune system. This review summarizes the current knowledge about vRNA methylation events and their impacts on flavivirus replication and pathogenesis. We also address the important challenges that the field of epitranscriptomics faces in reliably and accurately identifying RNA methylation sites, which should be considered in future studies on viral RNA modifications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.335
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2020
Admission routes2
Has abstractyes

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