DNA Word Design Strategy for Creating Sets of Non-interacting Oligonucleotides for DNA Microarrays
Bibliographic record
Abstract
A template−map design strategy for generating sets of non-interacting DNA oligonucleotides for applications in DNA arrays and biosensors is demonstrated. This strategy is used to create a set of oligonucleotides of size s with length l that possess at least n base mismatches with the complements of all the other members in the set. These “DNA word” sets are denoted as n bm l -mers or l: n sets. To regularize the thermodynamic stability of the perfectly matched hybridized DNA duplexes, the l -mers chosen for all the sets are required to have an approximately 50% G/C content. To achieve good discrimination between each DNA word in each set generated using the template−map strategy, it is required that n should be approximately equal to l /2 or higher. The template−map strategy can be used in a straightforward manner to create DNA word sets for cases when l = 4 k and n = 2 k, where k is an integer. Specific examples of 4 k:2 k sets are designed: an 8:4 set ( s = 224), a 12:6 set ( s = 528), a 16:8 set ( s = 960), and a 20:10 set ( s = 1520). These sets are further optimized to achieve the narrowest possible distribution of melting temperatures by selecting the best set after permutation of the templates and maps over all possible configurations. To demonstrate the viability of this methodology, a non-interacting set of four specific 6bm 12mers have been chosen, synthesized, and used in an SPR imaging measurement of the hybridization adsorption onto a DNA array. The template−map strategy is also applied to generate DNA word sets for cases where l ≠ 4 k . In these cases, the creation of the maps and templates is more complicated, but possible. The templates and maps for three additional types of sets are created: (4 k − 1):(2 k − 1), (4 k + 1):2 k, and (4 k − 2):(2 k − 1). Specific examples are given for l = 7, 9, and 10: DNA word sets of 7:3 ( s = 224), 9:4 ( s = 360), and 10:5 ( s = 132).
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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 | 0.000 | 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".