Sequence composition similarities with the 7SL RNA are highly predictive of functional genomic features
Bibliographic record
Abstract
Transposable elements derived from the 7SL RNA gene, such as Alu elements in primates, have had remarkable success in several mammalian lineages. The results presented here show a broad spectrum of functions for genomic segments that display sequence composition similarities with the 7SL RNA gene. Using thoroughly documented loci, we report that DNaseI-hypersensitive sites can be singled out in large genomic sequences by an assessment of sequence composition similarities with the 7SL RNA gene. We apply a root word frequency approach to illustrate a distinctive relationship between the sequence of the 7SL RNA gene and several classes of functional genomic features that are not presumed to be of transposable origin. Transposable elements that show noticeable similarities with the 7SL sequence include Alu sequences, as expected, but also long terminal repeats and the 5'-untranslated regions of long interspersed repetitive elements. In sequences masked for repeated elements, we find, when using the 7SL RNA gene as query sequence, distinctive similarities with promoters, exons and distal gene regulatory regions. The latter being the most notoriously difficult to detect, this approach may be useful for finding genomic segments that have regulatory functions and that may have escaped detection by existing methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".