An adaptable dry lab for <scp>SYBR</scp> based <scp>RT‐qPCR</scp> primer design to reinforce concepts in molecular biology and nucleic acids
Bibliographic record
Abstract
The real time PCR (qPCR) method provides a powerful method to assess levels of particular species of DNA. When combined with reverse transcription (RT-qPCR) it is the predominate technique to measure expression of gene transcripts. While this approach is very powerful, particular care must be taken in the design of the primers to facilitate specific and sensitive detection. Herein describes the framework for an undergraduate assignment which aims to teach primer design for SYBR based RT-qPCR. Beyond gaining direct experience with primer design, students will gain familiarity with important bioinformatic resources as well as a deeper theoretical understanding of the RT-qPCR approach and potential limitations. Moreover, as students' progress through the assignment they re-encounter many important concepts in molecular biology, gene expression, and nucleic acids, creating an opportunity for spiral learning. As this exercise only requires access to free web-based resources and does not require a laboratory it can be used in most science education settings. Despite not being a wet lab, this is a highly authentic research experience as this design process is commonplace in a molecular biology laboratory. Furthermore, the assignment is highly adaptable for different learning outcomes, time frames, and student background and ability. This article seeks to highlight connections and expanded learning outcomes for those already teaching such material, as well as a step-by-step guide for those new to teaching such content.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.060 | 0.054 |
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".