Retroelement Distributions in the Human Genome: Variations Associated With Age and Proximity to Genes
Bibliographic record
Abstract
Remnants of more than 3 million transposable elements, primarily retroelements, comprise nearly half of the human genome and have generated much speculation concerning their evolutionary significance. We have exploited the draft human genome sequence to examine the distributions of retroelements on a genome-wide scale. Here we show that genomic densities of 10 major classes of human retroelements are distributed differently with respect to surrounding GC content and also show that the oldest elements are preferentially found in regions of lower GC compared with their younger relatives. In addition, we determined whether retroelement densities with respect to genes could be accurately predicted based on surrounding GC content or if genes exert independent effects on the density distributions. This analysis revealed that all classes of long terminal repeat (LTR) retroelements and L1 elements, particularly those in the same orientation as the nearest gene, are significantly underrepresented within genes and older LTR elements are also underrepresented in regions within 5 kb of genes. Thus, LTR elements have been excluded from gene regions, likely because of their potential to affect gene transcription. In contrast, the density of Alu sequences in the proximity of genes is significantly greater than that predicted based on the surrounding GC content. Furthermore, we show that the previously described density shift of Alu repeats with age to domains of higher GC was markedly delayed on the Y chromosome, suggesting that recombination between chromosome pairs greatly facilitates genomic redistributions of retroelements. These findings suggest that retroelements can be removed from the genome, possibly through recombination resulting in re-creation of insert-free alleles. Such a process may provide an explanation for the shifting distributions of retroelements with time.
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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) | 0.001 | 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".