Osprey: a comprehensive tool employing novel methods for the design of oligonucleotides for DNA sequencing and microarrays
Bibliographic record
Abstract
We have developed a software package called Osprey for the calculation of optimal oligonucleotides for DNA sequencing and the creation of microarrays based on either PCR-products or directly spotted oligomers. It incorporates a novel use of position-specific scoring matrices, for the sensitive and specific identification of secondary binding sites anywhere in the target sequence. Using accelerated hardware is faster and more efficient than the traditional pairwise alignments used in most oligo-design software. Osprey consists of a module for target site selection based on user input, novel utilities for dealing with problematic sequences such as repeats, and a common code base for the identification of optimal oligonucleotides from the target list. Overall, these improvements provide a program that, without major increases in run time, reflects current DNA thermodynamics models, improves specificity and reduces the user's data preprocessing and parameterization requirements. Using a TimeLogic hardware accelerator, we report up to 50-fold reduction in search time versus a linear search strategy. Target sites may be derived from computer analysis of DNA sequence assemblies in the case of sequencing efforts, or genome or EST analysis in the case of microarray development in both prokaryotes and eukaryotes.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".