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Record W1974662631 · doi:10.1139/o06-025

Harnessing asymmetrical substrate recognition by thermostable EndoV to achieve balanced linear amplification in multiplexed SNP typing

2006· article· en· W1974662631 on OpenAlexvenueno aff
Daniel J. Turner, Maneesh Pingle, Francis Barany

Bibliographic record

VenueBiochemistry and Cell Biology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMultiple displacement amplificationDNA polymeraseBiologyPrimer extensionEndonucleaseMolecular biologyApplications of PCRPrimer (cosmetics)Loop-mediated isothermal amplificationPolymeraseMultiplex ligation-dependent probe amplificationDNAPolymerase chain reactionGeneticsChemistryMultiplex polymerase chain reactionGeneBase sequenceDNA extractionExon

Abstract

fetched live from OpenAlex

Multiplexed amplification of specific DNA sequences, by PCR or by strand-displacement amplification, is an intrinsically biased process. The relative abundance of amplified DNA can be altered significantly from the original representation and, in extreme cases, allele dropout can occur. In this paper, we present a method of linear amplification of DNA that relies on the cooperative, sequence-dependent functioning of the DNA mismatch-repair enzyme endonuclease V (EndoV) from Thermotoga maritima (Tma) and Bacillus stearothermophilus (Bst) DNA polymerase. Tma EndoV can nick one strand of unmodified duplex DNA, allowing extension by Bst polymerase. By controlling the bases surrounding a mismatch and the mismatch itself, the efficiency of nicking by EndoV and extension by Bst polymerase can be controlled. The method currently allows 100-fold multiplexed amplification of target molecules to be performed isothermally, with an average change of <1.3-fold in their original representation. Because only a single primer is necessary, primer artefacts and nonspecific amplification products are minimized.

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 score0.015
Threshold uncertainty score0.629

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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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