MétaCan
Menu
Back to cohort
Record W1983807948 · doi:10.4161/rna.22638

RNA helicases

2012· review· en· W1983807948 on OpenAlexafffund
George W. Owttrim

Bibliographic record

VenueRNA Biology · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRNAeIF4ABiologyRNA Helicase ARibosome biogenesisHelicaseRiboswitchDegradosomeRibosomeNon-coding RNACell biologyGeneticsGene

Abstract

fetched live from OpenAlex

Similar to proteins, RNA molecules must fold into the correct conformation and associate with protein complexes in order to be functional within a cell. RNA helicases rearrange RNA secondary structure and RNA-protein interactions in an ATP-dependent reaction, performing crucial functions in all aspects of RNA metabolism. In prokaryotes, RNA helicase activity is associated with roles in housekeeping functions including RNA turnover, ribosome biogenesis, translation and small RNA metabolism. In addition, RNA helicase expression and/or activity are frequently altered during cellular response to abiotic stress, implying they perform defined roles during cellular adaptation to changes in the growth environment. Specifically, RNA helicases contribute to the formation of cold-adapted ribosomes and RNA degradosomes, implying a role in alleviation of RNA secondary structure stabilization at low temperature. A common emerging theme involves RNA helicases acting as scaffolds for protein-protein interaction and functioning as molecular clamps, holding RNA-protein complexes in specific conformations. This review highlights recent advances in DEAD-box RNA helicase association with cellular response to abiotic stress in prokaryotes.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.015

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 head0.060
GPT teacher head0.334
Teacher spread0.274 · 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 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

Citations103
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueRNA BiologySame topicRNA and protein synthesis mechanismsFrench-language works237,207