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Record W3200919080 · doi:10.3390/ncrna7030059

A New Specific and Sensitive RT-qPCR Method Based on Splinted 5′ Ligation for the Quantitative Detection of RNA Species Shorter than microRNAs

2021· article· en· W3200919080 on OpenAlexafffund
Marine Lambert, Abderrahim Benmoussa, Patrick Provost

Bibliographic record

VenueNon-Coding RNA · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of Canada
KeywordsRNAAdapter (computing)BiologyComputational biologymicroRNALigationRibosomal RNAReverse transcriptaseMolecular biologySmall RNAReverse transcription polymerase chain reactionMessenger RNAGeneticsGeneComputer science

Abstract

fetched live from OpenAlex

Recently, we discovered a new family of unusually short RNAs mapping to 5.8S ribosomal RNA (rRNA) and which we named dodecaRNAs (doRNAs), according to the number of core nucleotides (12 nt) their members contain. To confirm these small RNA-sequencing (RNA-Seq) data, validate the existence of the two overly abundant doRNAs-the minimal core 12-nt doRNA sequence and its + 1-nt variant bearing a 5' Cytosine, C-doRNA-and streamline their analysis, we developed a new specific and sensitive splinted 5' ligation reverse transcription (RT)-quantitative polymerase chain reaction (qPCR) method. This method is based on a splint-assisted ligation of an adapter to the 5' end of doRNAs, followed by RT-qPCR amplification and quantitation. Our optimized protocol, which may discriminate between doRNA, C-doRNA, mutated and precursor sequences, can accurately detect as low as 240 copies and is quantitatively linear over a range of 7 logs. This method provides a unique tool to expand and facilitate studies exploring the molecular and cellular biology of RNA species shorter than microRNAs.

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 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: none
Teacher disagreement score0.679
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.299
Teacher spread0.268 · 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.

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

Citations3
Published2021
Admission routes2
Has abstractyes

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