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Endoribonucleases – enzymes gaining spotlight in mRNA metabolism

2009· review· en· W1502471552 on OpenAlexaff
Wai Ming Li, Chow H. Lee

Bibliographic record

VenueFEBS Journal · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsMessenger RNAEnzymeRNABiologyMechanism (biology)CleaveGene expressionGeneBiochemistryMetabolismComputational biologyCell biology

Abstract

fetched live from OpenAlex

The efficient turnover of messenger RNA represents an important mechanism that allows the cell to control gene expression. Until recently, the mechanism of mRNA decay was mainly attributed to exonucleases, comprising enzymes that degrade RNAs from the ends of the molecules. This article summarizes the endoribonucleases, comprising enzymes that cleave RNA molecules internally, which were identified in more recent years in eukaryotic mRNA metabolism. Endoribonucleases have received little attention in the past, based on the difficulty in their identification and a lack of understanding of their physiological significance. This review aims to compare the similarities and differences among this group of enzymes, as well as their known cellular functions. Despite the many differences in protein structure, and thus difficulties in identifying them based on amino acid sequence, most endoribonucleases possess essential cellular functions and have been shown to play an important role in mRNA turnover.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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.

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

Citations47
Published2009
Admission routes1
Has abstractyes

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