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Record W4410879367 · doi:10.1016/j.xpro.2025.103859

Split-and-pool synthesis to generate scalable combinatorial oligonucleotide libraries on magnetic nanoparticles

2025· article· en· W4410879367 on OpenAlexafffund
John Nguyen, Lidija Malic, Christina Nassif, Dillon Da Fonte, Maryam Tabrizian

Bibliographic record

VenueSTAR Protocols · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University Health CentreNational Research Council CanadaMedical Council of CanadaMcGill University
FundersFonds de Recherche du Québec - SantéNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOligonucleotideScalabilityNanoparticleComputer scienceMagnetic nanoparticlesComputational biologyCombinatorial synthesisNanotechnologyCombinatorial chemistryChemistryBiologyMaterials scienceGeneticsDNAOperating system

Abstract

fetched live from OpenAlex

The generation of combinatorial oligonucleotide libraries is desirable for applications such as DNA aptamers, data storage, DNA origami, or synthetic genomes, but conventional libraries present challenges in detection and analysis. Synthesis of unique oligonucleotide sequences on magnetic nanoparticles would enhance the ability to manipulate, recover, and detect them. Here, we present a protocol for generating a scalable combinatorial oligonucleotide library on magnetic nanoparticles using split-and-pool synthesis. We then describe the process for preparing the library for conventional and next-generation sequencing (NGS) DNA sequencing. For complete details on the use and execution of this protocol, please refer to Nguyen et al. 1

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 score0.043
Threshold uncertainty score0.674

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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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