Theoretical estimation of drag tag lengths for direct quantitative analysis of multiple miRNAs (DQAMmiR)
Bibliographic record
Abstract
To better understand the regulatory roles of miRNA in biological functions and to use miRNA as molecular markers of diseases, we need to accurately measure amounts of multiple miRNAs in biological samples. Direct quantitative analysis of multiple miRNAs (DQAMmiR) has been recently developed by using a classical hybridization approach where miRNAs are hybridized with fluorescently labeled complementary DNA probes taken in excess, and the amounts of the hybrids and the unreacted probes are measured to calculate the amount of miRNAs. Capillary electrophoresis was used as an instrumental platform for analysis. The problem of separating the unreacted probes from the hybrids was solved by adding SSB to the run buffer. A more difficult problem of separating hybrids from each other was solved by attaching different drag tags to the probes. Biotin and a hairpin-forming extension on the probe were used as two drag tags in the proof-of-principle work. Making DQAMmiR a generic approach requires a generic solution for drag tags. Peptides have been suggested as drag tags for long oligonucleotides in DNA sequencing by electrophoresis. Here we theoretically consider short peptides of different lengths as drag tags for DQAMmiR. We find analytical equations that allow us to estimate mobilities of RNA-DNA hybrids with peptide drag tags of different lengths. Our calculations suggest that the mobility shifts required for DQAMmiR can be achieved with the length of peptide chains in the ranges of 5-20 residues for five miRNAs and 2-47 residues for nine miRNAs. Peptides of these lengths can be feasibly synthesized with good yield and purity. The results of this theoretical study will guide the design and production of hybridization probes for DQAMmiR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".