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Record W2108876548 · doi:10.1101/gad.1382806

Quantitative microarray profiling provides evidence against widespread coupling of alternative splicing with nonsense-mediated mRNA decay to control gene expression

2006· article· en· W2108876548 on OpenAlexaff
Qun Pan, Arneet L. Saltzman, Yoon Ki Kim, Christine Misquitta, Ofer Shai, Lynne E. Maquat, Brendan J. Frey, Benjamin J. Blencowe

Bibliographic record

VenueGenes & Development · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical Sciences
KeywordsNonsense-mediated decayBiologyAlternative splicingspliceRNA splicingGeneGene expressionMicroarrayGeneticsGene expression profilingDNA microarrayMessenger RNAComputational biologyRNA

Abstract

fetched live from OpenAlex

Sequence-based analyses have predicted that approximately 35% of mammalian alternative splicing (AS) events produce premature termination codon (PTC)-containing splice variants that are targeted by the process of nonsense-mediated mRNA decay (NMD). This led to speculation that AS may often regulate gene expression by activating NMD. Using AS microarrays, we show that PTC-containing splice variants are generally produced at uniformly low levels across diverse mammalian cells and tissues, independently of the action of NMD. Our results suggest that most PTC-introducing AS events are not under positive selection pressure and therefore may not contribute important functional roles.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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

Citations224
Published2006
Admission routes1
Has abstractyes

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