Efficient algorithms for the discovery of DNA oligonucleotide barcodes from sequence databases
Bibliographic record
Abstract
Efficient design of barcode oligonucleotides can lead to significant cost reductions in the manufacturing of DNA arrays. Previous methods are based on either a preliminary alignment, which reduces their efficiency for intron-rich regions, or on a brute force approach, not feasible for large-scale problems or on data structures with very poor performance in the worst case. One of the algorithms we propose uses 'oligonucleotide sorting' for the discovery of oligonucleotide barcodes of given sizes, with good asymptotic performance. Specific barcode oligonucleotides with at least one base difference from other sequences in a database are found for each individual sequence. With another algorithm, specific oligonucleotides can also be found for groups or clades in the database, which have 100% homology for all oligonucleotide sequences within the group or clade while having differences with the rest of the data. By re-organizing the sequences/groups in the database, oligonucleotides for different hierarchical levels can be found. The oligonucleotides or polymorphism locations identified as species or clade specific by the new algorithm are refined and screened further for hybridization thermodynamic properties with third party software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".