Computational identification of regions that influence activity of transposable elements in the human genome
Bibliographic record
Abstract
As the most abundant active transposable elements, Alu elements have 1.1 million copies and occupy 6% of the human genome. Recent evidence indicates that 22 AluY and 6 AluS subfamilies have been the most active Alu elements in recent human history, whose transposition has been implicated in several inherited human diseases and in various forms of cancer by integrating into genes; therefore, understanding the transpositional activity and factors that change the activity level of these TEs is very important. There has been some work done to quantify and analyze the transposition of active Alu transposable elements in mobile assays. Based on this activity data, a method/simulation was created in this paper to computationally identify the regions on a TE consensus sequence that may change the transpositional activity. This method was applied to AluY, the youngest and most active Alu subfamily, to identify the harmful regions laying in its consensus. Mutations occurring within these regions have crucial effects in decreasing the elements' transposition. The identified regions were then verified by the secondary structure of the AluY RNA, where the harmful regions overlapped with the AluY RNA major SRP9/14 contact sites. An additional simulation also showed that the identified harmful regions covering the AluY RNA functional regions is not by chance. Therefore, we conclude that mutations occurring within the harmful regions identified alter the mobile activity levels of active AluY elements.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".