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Record W4390971272 · doi:10.1371/journal.ppat.1011905

Hidden viral proteins: How powerful are they?

2024· article· en· W4390971272 on OpenAlexaff
Fangfang Li, Mingxuan Jia, Aiming Wang

Bibliographic record

VenuePLoS Pathogens · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsAgriculture and Agri-Food Canada
FundersChinese Academy of Agricultural SciencesNational Natural Science Foundation of China
KeywordsComputational biologyVirologyBiology

Abstract

fetched live from OpenAlex

Viruses are submicroscopic obligate intracellular parasites and usually have tiny genomes.Based on the nature of their genomic nucleic acid and replicative intermediates, viruses may be classified into 7 classes, including positive-sense single-stranded RNA (+ssRNA), negativesense single-stranded RNA (-ssRNA), double-stranded RNA (dsRNA), single-stranded DNA (ssDNA), and double-stranded DNA (dsDNA) viruses, retroviruses, and pararetroviruses.The vast majority of known viruses belong to the +ssRNA class with monopartite, bipartite, or tripartite ssRNA genomes (https://talk.ictvonline.org/taxonomy/).Infection by +ssRNA viruses can cause substantial damage to their host organisms, often leading to severe economic losses and, in some cases, even catastrophic mortality.Examples include human and animal infecting viruses such as acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the ongoing COVID-19 pandemic, dengue, Zika, yellow fever, hepatitis C, foot-and-mouth disease, polio, chikungunya, SARS, and MERS viruses [1].Many plant-infecting +ssRNA viruses, such as tomato brown rugose fruit virus, potato virus Y, plum pox virus, and maize dwarf mosaic virus, cause devastating diseases in diverse crops and threaten global food security.The ssDNA viruses include some of the smallest and simplest viruses, with genomes only approximately 2 to 6 kb in length.Geminiviruses and nanoviruses are 2 known plant ssDNA viruses belonging to the families Geminiviridae and Nanoviridae, commonly known as geminiviruses and nanoviruses, are among the most economically important plant-infecting ssDNA viruses.Due to the small genome size with limited coding capacity, viruses have to rely on host cellular pathways and hijack host factors for infection.On the other hand, viruses have evolved a variety of expression strategies to maximize the coding capacity of their compact genome.These noncanonical translation strategies adopted by viruses include internal ribosome entry, leaky scanning, non-AUG initiation, ribosome shunting, reinitiation, ribosomal frameshift, and stop-codon readthrough [2].In recent years, "hidden" protein-coding open reading frames (ORFs) with neglected noncanonical translation strategies have been discovered in the genomes of various viruses [1-6].For example, a short ORF encoding a small peptide named P3N-PIPO that is required for viral cell-to-cell movement was found to be embedded within the 5 0 terminal region of viruses in the genus Potyvirus of the family Potyviridae [5]. +ssRNA viruses encode hidden proteins in their -RNAGong and colleaguesAU : Pleasenotethatallinstancesof }etal:}inthemaintexthavebeenchangedto}andcol recently revealed that +ssRNA viruses encode additional functional proteins in their (-)-strand replication intermediates (-RNA) [6], which challenges the current consensus that -RNA lacks coding capacity.This surprising discovery raises an important question: To what extent is this expression mode shared among viruses?

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0050.014
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.246
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations14
Published2024
Admission routes1
Has abstractno

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