Assisted transcriptome reconstruction and splicing orthology
Bibliographic record
Abstract
BACKGROUND: Transcriptome reconstruction, defined as the identification of all protein isoforms that may be expressed by a gene, is a notably difficult computational task. With real data, the best methods based on RNA-seq data identify barely 21 % of the expressed transcripts. While waiting for algorithms and sequencing techniques to improve - as has been strongly suggested in the literature - it is important to evaluate assisted transcriptome prediction; this is the question of how alternative transcription in one species performs as a predictor of protein isoforms in another relatively close species. Most evidence-based gene predictors use transcripts from other species to annotate a genome, but the predictive power of procedures that use exclusively transcripts from external species has never been quantified. The cornerstone of such an evaluation is the correct identification of pairs of transcripts with the same splicing patterns, called splicing orthologs. RESULTS: We propose a rigorous procedural definition of splicing orthologs, based on the identification of all ortholog pairs of splicing sites in the nucleotide sequences, and alignments at the protein level. Using our definition, we compared 24 382 human transcripts and 17 909 mouse transcripts from the highly curated CCDS database, and identified 11 122 splicing orthologs. In prediction mode, we show that human transcripts can be used to infer over 62 % of mouse protein isoforms. When restricting the predictions to transcripts known eight years ago, the percentage grows to 74 %. Using CCDS timestamped releases, we also analyze the evolution of the number of splicing orthologs over the last decade. CONCLUSIONS: Alternative splicing is now recognized to play a major role in the protein diversity of eukaryotic organisms, but definitions of spliced isoform orthologs are still approximate. Here we propose a definition adapted to the subtle variations of conserved alternative splicing sites, and use it to validate numerous accurate orthologous isoform predictions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".