PCRTiler: automated design of tiled and specific PCR primer pairs
Bibliographic record
Abstract
Efficiency and specificity of PCR amplification is dependent on several parameters, such as amplicon length, as well as hybridization specificity and melting temperature of primer oligonucleotides. Primer design is thus of critical importance for the success of PCR experiments, but can be a time-consuming and repetitive task, for example when large genomic regions are to be scanned for the presence of a protein of interest by chromatin immunoprecipitation experiments. We present here a webserver that allows the automated design of tiled primer pairs for any number of genomic loci. PCRTiler splits the target DNA sequences into smaller regions, and identifies candidate primers for each sub-region by running the well-known program Primer3 followed by the elimination of primers with a high cross-hybridization potential via BLAST. Tiling density and primer characteristics are specified by the user via a simple and user-friendly interface. The webserver can be accessed at http://pcrtiler.alaingervais.org:8080/PCRTiler. Additionally, users may download a standalone Java-based implementation of this software. Experimental validation of PCRTiler has demonstrated that it produces correct results. We have tiled a region of the human genome, in which 96 of 123 primer pairs worked in the first attempt, and 105 of 123 (85%) could be made to work by optimizing the conditions of the PCR assay.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".