MétaCan
Menu
Back to cohort
Record W4200115150 · doi:10.1016/j.semcdb.2021.11.018

Mechanisms of epigenetic regulation by C. elegans nuclear RNA interference pathways

2021· review· en· W4200115150 on OpenAlexafffund
Uri Seroussi, Chengyin Li, Adam E. Sundby, Tammy L. Lee, Julie M. Claycomb, Arneet L. Saltzman

Bibliographic record

VenueSeminars in Cell and Developmental Biology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsRNA interferenceBiologyArgonauteEpigeneticsChromatinGeneticsRNARasiRNATrans-acting siRNATranscription (linguistics)GeneCell biology

Abstract

fetched live from OpenAlex

RNA interference (RNAi) is a highly conserved gene regulatory phenomenon whereby Argonaute/small RNA (AGO/sRNA) complexes target transcripts by antisense complementarity to modulate gene expression. While initially appreciated as a cytoplasmic process, RNAi can also occur in the nucleus where AGO/sRNA complexes are recruited to nascent transcripts. Nuclear AGO/sRNA complexes recruit co-factors that regulate transcription by inhibiting RNA Polymerase II, modifying histones, compacting chromatin and, in some organisms, methylating DNA. C. elegans has a longstanding history in unveiling the mechanisms of RNAi and has become an outstanding model to delineate the mechanisms underlying nuclear RNAi. In this review we highlight recent discoveries in the field of nuclear RNAi in C. elegans and the roles of nuclear RNAi in the regulation of gene expression, chromatin organization, genome stability, and transgenerational epigenetic inheritance.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.245
Teacher spread0.228 · 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

Citations42
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSeminars in Cell and Developmental BiologySame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207