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Record W2054856719 · doi:10.1186/1756-0500-3-185

Optimus Primer: A PCR enrichment primer design program for next-generation sequencing of human exonic regions

2010· article· en· W2054856719 on OpenAlexafffund
Andrew Brown, Ken Sin Lo, Paul Guelpa, Mélissa Beaudoin, John D. Rioux, Jean‐Claude Tardif, Michael Phillips, Guillaume Lettre

Bibliographic record

VenueBMC Research Notes · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsMontreal Heart InstituteUniversité de Montréal
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesInstitut de Cardiologie de MontréalCrohn's and Colitis FoundationGenome CanadaCrohn's and Colitis Foundation of America
KeywordsAmpliconPrimer (cosmetics)Computational biologyContext (archaeology)Polymerase chain reactionGeneticsHuman genomeComputer scienceGeneExonGenomeBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Polymerase chain reaction (PCR) remains a simple, flexible, and inexpensive method for enriching genomic regions of interest for next-generation sequencing. In order to utilize PCR in this context, a major challenge facing researchers is how to generate a very large number of functional PCR primers that will successfully generate useable amplicons. For instance, in an exon-only re-sequencing project targeting 100 genes, each with 10 exons, 1,000 pairs of primers are required. In fact, the reality is often more complex as each gene might have several isoforms and large exons need to be divided to maintain the desired amplicon size. With only a list of gene names, our program Optimus Primer (OP) automatically takes into account all these variables, and can generate primers with no need to provide genome coordinates. More importantly however, OP, unlike other primer design programs, uniquely utilizes Primer3 in an iterative manner that allows the user to progressively design up to four iterations of primer designs. Through a single interface, the user can specify up to four different design parameters with different stringencies, thus increasing the probability that a functional PCR primer pair will be designed for all regions of interest in a single pass of the pipeline. FINDINGS: To demonstrate the effectiveness of the program, we designed PCR primers against 77 genes located in loci associated with ulcerative colitis as part of a candidate gene re-sequencing experiment. We achieved an experimental success rate of 93% or 472 out of 508 amplicons spanning the exonic regions of the 77 genes. Moreover, by automatically passing amplicons that failed primer design through three additional iterations of design parameters, we achieved an additional 170 successful primer pairs or 34% more in a single pass of OP than by conventional methods. CONCLUSION: With only a gene list and PCR parameters, a user can produce hundreds of PCR primer designs for regions of interest with a high probability of success in a very short amount of time. Optimus Primer is an essential tool for researchers who want to pursue PCR-based enrichment strategies for next-generation re-sequencing applications. The program can be accessed via website at http://op.pgx.ca.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.007

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 head0.297
GPT teacher head0.444
Teacher spread0.147 · 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 designSimulation or modeling
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

Citations5
Published2010
Admission routes2
Has abstractyes

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