Proteogenomics-enabled discovery of novel small open reading frame (sORF)-encoded polypeptides in human and mouse tissues
Bibliographic record
Abstract
Small open reading frames (sORFs) encode an emerging class of functional proteins less than 100 amino acids in length. However, sORFs are incompletely characterized in mice and humans. The development of proteomics and Ribo-seq techniques has enabled the discovery of a number of sORF-encoded peptides (SEPs), but previous proteogenomics studies have been limited to a few cell lines or tissues. Given these limitations, a potentially vast number of sORFs remains to be discovered. We collected community-scale previously published proteomics data including one billion experimental spectra derived from a wide range of mouse and human tissues in order to identify novel sORFs and reveal the tissue expression status of novel and recently annotated sORF-encoded proteins. We have detected several novel sORFs in specific tissues, including a conserved protein-coding upstream overlapping ORF in HNRNPUL2 expressed in human lymphocytes, which may hold important biological functions. This work introduces a simple and efficient filtration strategy to detect novel sORFs. Our workflow will likely prove useful for future studies on sORFs in humans and other animals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".