Identification of highly-disrupted tRNA genes in nuclear genome of the red alga, Cyanidioschyzon merolae 10D
Bibliographic record
Abstract
The limited locations of tRNA introns are crucial for eukaryal tRNA-splicing endonuclease recognition. However, our analysis of the nuclear genome of an early-diverged red alga, Cyanidioschyzon merolae, demonstrated the first evidence of nuclear-encoded tRNA genes that contain ectopic and/or multiple introns. Some genes exhibited both intronic and permuted structures in which the 3'-half of the tRNA coding sequence lies upstream of the 5'-half, and an intron is inserted into either half. These highly disrupted tRNA genes, which account for 63% of all nuclear tRNA genes, are expressed via the orderly and sequential processing of bulge-helix-bulge (BHB) motifs at intron-exon junctions and termini of permuted tRNA precursors, probably by a C. merolae tRNA-splicing endonuclease with an unidentified subunit architecture. The results revealed a considerable diversity in eukaryal tRNA intron properties and endonuclease architectures, which will help to elucidate the acquisition mechanism of the BHB-mediated disrupted tRNA genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".