An Approach to Whole-Genome Identification of IRES Elements
Bibliographic record
Abstract
Spatial and temporal control of proteome expression is critical for cellular homeostasis. The ability to regulate polypeptide synthesis allows the cell to rapidly respond to changes in its environment. Under stress conditions, capdependent translation initiation is downregulated and alternative mechanisms of translation initiation are favoured for the production of critical proteins that ultimately determine whether the cell is able to overcome the stress. One such alternative mechanism of translation initiation is mediated by sequence elements located downstream of the 5 cap structure that are able to directly recruit ribosomes to a region proximal to the translation start site. Identifying the eukaryotic mRNAs that contain such internal ribosome entry sites (IRESes) is an important first step in cataloguing the cellular complement of proteins whose expression is translationally regulated during cellular stress and understanding how cells regulate translation under stress conditions. To date, no consensus sequence motif or structure has been identified as a signature of cellular IRES activity, making it difficult to identify the full complement of eukaryotic IRESes. This review will underscore the challenges faced in identifying IRESes on a genomic scale and potential solutions will be presented. Keywords: IRES, translation, stress, differentiation, microarray, polysome
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".