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Record W2395959166 · doi:10.1007/978-1-61779-882-5_21

Rolling Circle Amplification-Mediated Long Hairpin RNA Library Construction in Plants

2012· article· en· W2395959166 on OpenAlexaff
Lei Wang, Yunliu Fan

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsBiotechnology Research Institute
Fundersnot available
KeywordsSmall hairpin RNAGeneComputational biologyGene silencingBiologyGeneticsRNARNA silencingRNA interferenceCloning (programming)Functional genomicsPopulationComplementary DNAGenomeComputer scienceGenomics

Abstract

fetched live from OpenAlex

Gene silencing has been used widely in gene function studies and crop plant modification. Long hairpin RNA (lhRNA) results in high efficiency of gene silencing; however, constructing multiple lhRNA vectors using traditional approaches is both time consuming and costly. Also, most of the existing approaches are based on sequence-specific cloning of individual sequences and are therefore not suitable for preparing hpRNA libraries from a pool of mixed target sequences. The rolling circle amplification (RCA)-mediated hairpin RNA (RMHR) construction system is suitable for generating hairpin libraries from any gene of interest or pool of genes. Using the RMHR system, a long-hairpin RNA (lhRNA) library is generated from an Arabidopsis cDNA population containing known and unknown genes. Our results indicate that the RMHR technique permits the rapid, efficient, and low-cost preparation of genome-wide lhRNA expression libraries.

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.013
GPT teacher head0.376
Teacher spread0.363 · 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
GenreMethods

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

Citations2
Published2012
Admission routes1
Has abstractyes

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