Long Noncoding RNAs and Repetitive Elements: Junk or Intimate Evolutionary Partners?
Bibliographic record
Abstract
Our recent ability to sequence entire genomes, along with all of their transcribed RNAs, has led to the surprising finding that only ∼1% of the human genome is used to encode proteins. This finding has led to vigorous debate over the functional importance of the transcribed but untranslated portions of the genome. Currently, scientists tend to assume coding genes are functional until proven not to be, while the opposite is true for noncoding genes. This review takes a new look at the evidence for and against widespread noncoding gene functionality. We focus in particular on long noncoding RNA (noncoding RNAs longer than 200 nucleotides) genes and their 'junk' associates, transposable elements, and satellite repeats. Taken together, the suggestion put forward is that more of this junk DNA may be functional than nonfunctional and that noncoding RNAs and transposable elements act symbiotically to drive evolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".