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Record W4387473048 · doi:10.2144/btn-2023-0042

Playback Cloning: Simple, Reversible, Cost-effective Cloning for the Combinatorial Assembly of Complex Expression Constructs

2023· article· en· W4387473048 on OpenAlexafffund
Gregory Foran, Ryan D. Hallam, Marvel Megaly, Anel Turgambayeva, Aleksandar Necakov

Bibliographic record

VenueBioTechniques · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCloning (programming)Computer scienceComputational biologyPlasmidConstruct (python library)UsabilityMultiple cloning siteCloning vectorBiologyExpression vectorRecombinant DNAVector (molecular biology)GeneticsDNAProgramming languageHuman–computer interactionGene

Abstract

fetched live from OpenAlex

With advancements in multicomponent molecular biological tools, the need for versatile, rapid and cost-effective cloning that enables successful combinatorial assembly of DNA plasmids of interest is becoming increasingly important. Unfortunately, current cloning platforms fall short regarding affordability, ease of combinatorial assembly and, above all, the ability to iteratively remove individual cassettes at will. Herein we construct, implement and make available a broad set of cloning vectors, called PlayBack vectors, that allow for the expression of several different constructs simultaneously under separate promoters. Overall, this system is substantially cheaper than other multicomponent cloning systems, has usability for a wide breadth of experimental paradigms and includes the novel feature of being able to selectively remove components of interest at will at any stage of the cloning platform.

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: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.483

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 head0.023
GPT teacher head0.347
Teacher spread0.325 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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