Developing messenger RNA biomarkers: A workflow to characterise and identify transcript target sequences unaffected by alternative splicing for reproducible gene transcript quantification by reverse transcriptase quantitative polymerase chain reaction
Bibliographic record
Abstract
Abstract Most eukaryotic genes generate multiple messenger RNA (mRNA) transcript variants by alternative splicing. The incomplete annotation of gene transcripts in genomic databases can result in improper primer design, adversely affecting the reliability of gene expression measurements by reverse transcriptase quantitative polymerase chain reaction (RT‐qPCR). Hence, we present a workflow combining bioinformatics analyses, to select two to three evolutionarily conserved constitutive exons in rats, mice and humans as target sequences for PCR primer design, with experimental RT‐PCR amplification and amplicon sequencing to confirm the expression and identity of gene transcript targets. The application of this workflow to the characterization of neurodevelopmental biomarker genes identified an unannotated exon in the rat Map2 gene, illustrating the importance of target sequence validation for the development of translational mRNA biomarkers for toxicological and biomedical studies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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.
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".