A genome-wide telomere screen in yeast: The long and short of it all
Bibliographic record
Abstract
Following the identification of the first Saccharomyces cerevisiae gene whose mutation leads to ever-shorter telomeres ( EST1 ) (1), several dozen genes have been identified that play critical roles in telomere length regulation. Their functions can be loosely categorized into those that affect the action of the telomerase enzyme (e.g., EST1, EST2, EST3, TLC1, KU70 / KU80, PIF1 , and the MRX complex) (reviewed in ref. 2); those that affect stability of telomerase components such as the telomerase RNA TLC1 (e.g., Sm proteins, MTR10 ) (3, 4); and those that play a role in the regulation of telomeric heterochromatin, replication, or end protection (e.g., CDC13, STN1, TEN1, RAP1, RIF1, RIF2, MEC1 , and TEL1 ) (reviewed in ref. 2). Although these studies have vastly increased our understanding of telomere integrity and replication, a comprehensive telomere length analysis of the collection of haploid yeast strains containing a marked deletion at each known nonessential ORF has not yet been reported (5). In an article in this issue of PNAS, Askree et al. (6) are the first authors to publish such a genome-wide screen for deletions that affect average telomere length in S. cerevisiae . In an admirable feat, DNA was prepared from 4,852 strains comprising the haploid …
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".