A Fast Algorithm for Detecting Frame Shifts in DNA sequences
Why this work is in the frame
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Bibliographic record
Abstract
Sequencing technologies used to generate long strands of DNA are susceptible to laboratory errors that may result in several DNA nucleotides being deleted from the genome. Detecting such deletions in the protein coding regions is of utmost importance. Missing even a single nucleotide may lead to frame shifts with all the following codons (and consequently the encoded amino acids) being identified incorrectly. In addition to the deletion of nucleotides during sequencing, frame shifts can occur because of a variety of other reasons including mutations. In this paper, we present a fast computational technique to identify frame shifts in protein coding regions in DNA sequences. Our technique is based on Fourier spectral characteristics of coding regions in DNA sequences. We provide two applications of our technique - detecting deletions in DNA sequences in coding regions and also detecting frame shifts in viral DNA
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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 it