Deep SAGE analysis of the Caenorhabditis elegans transcriptome
Bibliographic record
Abstract
We employed the Tag-seq technique to generate global transcription profiles for different strains and life stages of the nematode C. elegans. Tag-seq generates cDNA tags as does Serial Analysis of Gene Expression (SAGE), but the method yields a much larger number of tags, generating much larger data sets than SAGE. We examined differences in the performance of SAGE and Tag-seq by comparing gene expression data for 13 pairs of libraries. We identified genes for which expression was consistently changed in long-lived worms. Additional genes emerged in the deeper Tag-seq profiles, including several 'signature' genes found among those zup-regulated in long-lived dauer larvae (cki-1, aak-2 and daf-16). Fifty to sixty percent of the genes differentially expressed in daf-2(-) versus daf-2(+) adults had fragmentary or no functional annotation, suggesting the involvement of as yet unstudied pathways in aging. We were able to distinguish between changes in gene expression associated with altered genotype or altered growth conditions. We found 62 cases of possible mRNA isoform switching in the 13 Tag-seq libraries, whereas the 13 SAGE libraries allowed detection of only 15 such occurrences. We observed strong expression of anti-sense transcripts for several mitochondrial genes, but nuclear anti-sense transcripts were neither abundant nor consistently expressed among the libraries.
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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 | 0.001 | 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 | 0.001 |
| 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".