Relatively frequent switching of transcription start sites during cerebellar development
Bibliographic record
Abstract
BACKGROUND: Alternative transcription start site (TSS) usage plays important roles in transcriptional control of mammalian gene expression. The growing interest in alternative TSSs and their role in genome diversification spawned many single-gene studies on differential usages of tissue-specific or temporal-specific alternative TSSs. However, exploration of the switching usage of alternative TSS usage on a genomic level, especially in the central nervous system, is largely lacking. RESULTS: In this study, We have prepared a unique set of time-course data for the developing cerebellum, as part of the FANTOM5 consortium ( http://fantom.gsc.riken.jp/5/ ) that uses their innovative capturing of 5' ends of all transcripts followed by Helicos next generation sequencing. We analyzed the usage of all transcription start sites (TSSs) at each time point during cerebellar development that provided information on multiple RNA isoforms that emerged from the same gene. We developed a mathematical method that systematically compares the expression of different TSSs of a gene to identify temporal crossover and non-crossover switching events. We identified 48,489 novel TSS switching events in 5433 genes during cerebellar development. This includes 9767 crossover TSS switching events in 1511 genes, where the dominant TSS shifts over time. CONCLUSIONS: We observed a relatively high prevalence of TSS switching in cerebellar development where the resulting temporally-specific gene transcripts and protein products can play important regulatory and functional roles.
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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 | 0.001 |
| 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 | 0.000 |
| 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".