MultiPriDe: automated batch development of quantitative real-time PCR primers
Bibliographic record
Abstract
Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) is a commonly employed gene expression quantification technique. This requires the development of appropriately targeted oligonucleotide primers, which necessitates the identification of ideal amplicons, development of optimized oligonucleotide sequences under most favorable pre-determined reaction conditions, and management of the resultant target-oligonucleotide pair information for each gene to be studied. The Primer3 utility exists for development of oligonucleotide primers and fills that role effectively. However, the manual process of identifying target sites and individually generating primers is inefficient and prone to user-introduced error, especially when a large number of genes are to be examined. We have developed MultiPriDe (Multiple Primer Design), a Perl utility that accepts batch lists of Gene database identifiers, collects available intron and exon position data critical to qRT-PCR primer development, and supplies these sites as identified targets for the Primer3 utility. This automated 'gene to primer' procedure is coupled with a set of optimized hybridization conditions used by the Primer3 utility to maximize successful primer design. MultiPriDe and assembled repeat libraries are available upon request. Please direct requests to aziesel@emory.edu.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.026 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".