MétaCan
Menu
Back to cohort

RNA‐modifying machines in archaea

2003· review· en· W1894304989 on OpenAlexafffund
Arina D. Omer, Sonia Ziesche, Wayne A. Decatur, Maurille J. Fournier, Patrick P. Dennis

Bibliographic record

VenueMolecular Microbiology · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsBiologyRibonucleoproteinRNAComputational biologyRNA splicingArchaeaRibosomeIntronBiogenesisNon-coding RNAGeneticsRibosomal RNACell biologyGene

Abstract

fetched live from OpenAlex

It has been known for nearly half a century that coding and non-coding RNAs (mRNA, and tRNAs and rRNAs respectively) play critical roles in the process of information transfer from DNA to protein. What is both surprising and exciting, are the discoveries in the last decade that cells, particularly eukaryotic cells, contain a plethora of non-coding RNAs and that these RNAs can either possess catalytic activity or can function as integral components of dynamic ribonucleoprotein machines. These machines appear to mediate diverse, complex and essential processes such as intron excision, RNA modification and editing, protein targeting, DNA packaging, etc. Archaea have been shown to possess RNP complexes; some of these are authentic homologues of the eukaryotic complexes that function as machines in the processing, modification and assembly of rRNA into ribosomal subunits. Deciphering how these RNA-containing machines function will require a dissection and analysis of the component parts, an understanding of how the parts fit together and an ability to reassemble the parts into complexes that can function in vitro. This article summarizes our current knowledge about small-non-coding RNAs in Archaea, their roles in ribosome biogenesis and their relationships to the complexes that have been identified in eukaryotic cells.

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 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 score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.022
GPT teacher head0.292
Teacher spread0.269 · 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

Citations96
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueMolecular MicrobiologySame topicRNA and protein synthesis mechanismsFrench-language works237,207