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Record W2048975746 · doi:10.1139/g99-087

Properties of sequence-tagged-site primer sets influencing repeatability

2000· article· en· W2048975746 on OpenAlexvenueno aff
Arunrut Vanichanon, N. K. Blake, John M. Martin, L. E. Talbert

Bibliographic record

VenueGenome · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPrimer (cosmetics)RepeatabilityBiologyGeneticsPolymerase chain reactionPrimer dimerComputational biologyGeneMultiplex polymerase chain reactionMathematicsStatisticsChemistry

Abstract

fetched live from OpenAlex

The polymerase chain reaction (PCR) has become a standard procedure in plant genetics, and is the basis for many emerging genomics approaches to mapping and gene identification. One advantage of PCR is that sequence information for primer sets can be exchanged between laboratories, obviating the need for exchange and maintenance of biological materials. Repeatability of primer sets, whereby the same products are amplified in different laboratories using the same primer set, is important to successful exchange and utilization. We have developed several hundred sequence-tagged site (STS) primer sets for wheat and barley. The ability of the primer sets to generate reproducible amplifications in other laboratories has been variable. We wished to empirically determine the properties of the primer sets that most influenced repeatability. A total of 96 primer sets were tested with four genomic DNA samples on each of four thermocyclers. All major bands were repeatable across all four thermocylers for approximately 50% of the primer sets. Characteristics most often associated with differences in repeatability included primer GC content and 3'-end stability of the primers. The propensity for primer-dimer formation was not a factor in repeatability. Our results provide empirical direction for the development of repeatable primer sets.

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 score0.073
Threshold uncertainty score0.375

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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

Citations20
Published2000
Admission routes1
Has abstractyes

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