HAltORF: a database of predicted out-of-frame alternative open reading frames in human
Bibliographic record
Abstract
Human alternative open reading frames (HAltORF) is a publicly available and searchable online database referencing putative products of out-of-frame alternative translation initiation (ATI) in human mRNAs. Out-of-frame ATI is a process by which a single mRNA encodes independent proteins, when distinct initiation codons located in different reading frames are recognized by a ribosome to initiate translation. This mechanism is largely used in viruses to increase the coding potential of small viral genomes. There is increasing evidence that out-of-frame ATI is also used in eukaryotes, including human, and may contribute to the diversity of the human proteome. HAltORF is the first web-based searchable database that allows thorough investigation in the human transcriptome of out-of-frame alternative open reading frames with a start codon located in a strong Kozak context, and are thus the more likely to be expressed. It is also the first large scale study on the human transcriptome to successfully predict the expression of out-of-frame ATI protein products that were previously discovered experimentally. HAltORF will be a useful tool for the identification of human genes with multiple coding sequences, and will help to better define and understand the complexity of the human proteome. Database URL: http://haltorf.roucoulab.com/.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.019 |
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".