Computational identification of harmful mutation regions to the activity of transposable elements
Bibliographic record
Abstract
BACKGROUND: Transposable elements (TEs) are interspersed DNA sequences that can move or copy to new positions within a genome. TEs are believed to promote speciation and their activities play a significant role in human disease. In the human genome, the 22 AluY and 6 AluS TE subfamilies have been the most recently active, and their transposition has been implicated in many inherited human diseases and in various forms of cancer. Therefore, understanding their transposition activity is very important and identifying the factors that affect their transpositional activity is of great interest. Recently, there has been some work done to quantify the activity levels of active Alu TEs based on variation in the sequence. Given this activity data, an analysis of TE activity based on the position of mutations is conducted. RESULTS: A method/simulation is created to computationally predict so-called harmful mutation regions in the consensus sequence of a TE; that is, mutations that occur in these regions decrease the transpositional activity dramatically. The methods are applied to the most active subfamily, AluY, to identify the harmful regions, and seven harmful regions are identified within the AluY consensus with q-values less than 0.05. A supplementary simulation also shows that the identified harmful regions covering the AluYa5 RNA functional regions are not occurring by chance. This method is then applied to two additional TE families: the Alu family and the L1 family, to computationally detect the harmful regions in these elements. CONCLUSIONS: We use a computational method to identify a set of harmful mutation regions. Mutations within the identified harmful regions decrease the transpositional activity of active elements. The correlation between the mutations within these regions and the transpositional activity of TEs are shown to be statistically significant. Verifications are presented using the activity of AluY elements and the secondary structure of the AluYa5 RNA, providing evidence that the method is successfully identifying harmful mutation regions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".