Size fractionated NET‐Seq reveals a conserved architecture of transcription units around yeast genes
Bibliographic record
Abstract
Genomes from yeast to humans are subject to pervasive transcription. A single round of pervasive transcription is sufficient to alter local chromatin conformation, nucleosome dynamics and gene expression, but is hard to distinguish from background signals. Size fractionated native elongating transcript sequencing (sfNET-Seq) was developed to precisely map nascent transcripts independent of expression levels. RNAPII-associated nascent transcripts are fractionation into different size ranges before library construction. When anchored to the transcription start sites (TSS) of annotated genes, the combined pattern of the output metagenes gives the expected reference pattern. Bioinformatic pattern matching to the reference pattern identified 9542 transcription units in Saccharomyces cerevisiae, of which 47% are coding and 53% are noncoding. In total, 3113 (33%) are unannotated noncoding transcription units. Anchoring all transcription units to the TSS or polyadenylation site (PAS) of annotated genes reveals distinctive architectures of linked pairs of divergent transcripts approximately 200nt apart. The Reb1 transcription factor is enriched 30nt downstream of the PAS only when an upstream (TSS -60nt with respect to PAS) noncoding transcription unit co-occurs with a downstream (TSS +150nt) coding transcription unit and acts to limit levels of upstream antisense transcripts. The potential for extensive transcriptional interference is evident from low abundance unannotated transcription units with variable TSS (median -240nt) initiating within a 500nt window upstream of, and transcribing over, the promoters of protein-coding genes. This study confirms a highly interleaved yeast genome with different types of transcription units altering the chromatin landscape in distinctive ways, with the potential to exert extensive regulatory control.
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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 | 0.000 |
| 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) | 0.001 | 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".