Distributions of Transposable Elements Reveal Hazardous Zones in Mammalian Introns
Bibliographic record
Abstract
Comprising nearly half of the human and mouse genomes, transposable elements (TEs) are found within most genes. Although the vast majority of TEs in introns are fixed in the species and presumably exert no significant effects on the enclosing gene, some markedly perturb transcription and result in disease or a mutated phenotype. Factors determining the likelihood that an intronic TE will affect transcription are not clear. In this study, we examined intronic TE distributions in both human and mouse and found several factors that likely contribute to whether a particular TE can influence gene transcription. Specifically, we observed that TEs near exons are greatly underrepresented compared to random distributions, but the size of these "underrepresentation zones" differs between TE classes. Compared to elsewhere in introns, TEs within these zones are shorter on average and show stronger orientation biases. Moreover, TEs in extremely close proximity (<20 bp) to exons show a strong bias to be near splice-donor sites. Interestingly, disease-causing intronic TE insertions show the opposite distributional trends, and by examining expressed sequence tag (EST) databases, we found that the proportion of TEs contributing to chimeric TE-gene transcripts is significantly higher within their underrepresentation zones. In addition, an analysis of predicted splice sites within human long terminal repeat (LTR) elements showed a significantly lower total number and weaker strength for intronic LTRs near exons. Based on these factors, we selectively examined a list of polymorphic mouse LTR elements in introns and showed clear evidence of transcriptional disruption by LTR element insertions in the Trpc6 and Kcnh6 genes. Taken together, these studies lend insight into the potential selective forces that have shaped intronic TE distributions and enable identification of TEs most likely to exert transcriptional effects on genes.
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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 | 0.000 |
| 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) | 0.001 | 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".