Inferring Clusters of Orthologous and Paralogous Transcripts
Bibliographic record
Abstract
Abstract The alternative processing of eukaryote genes allows producing multiple distinct transcripts from a single gene, thereby contributing to the transcriptome diversity. Recent studies suggest that more than 90% of human genes are concerned, and the transcripts resulting from alternative processing are highly conserved between orthologous genes. In this paper, we first present a model to define orthology and paralogy relationships at the transcriptome level, then we present an algorithm to infer clusters of orthologous and paralogous transcripts. Gene-level homology relationships are used to define different types of homology relationships between transcripts and a Reciprocal Best Hits approach is used to infer clusters of isoorthologous and recent paralogous transcripts. We applied the method to transcripts of gene families from the Ensembl-Compara database. The results are agreeing with those from previous studies comparing orthologous gene transcripts. The results also provide evidence that searching for conserved transcripts beyond orthologous genes will likely yield valuable information. The results obtained on the Ensembl-Compara gene families are available at https://github.com/UdeS-CoBIUS/TranscriptOrthology . Supplementary material can be found at https://doi.org/10.5281/zenodo.7750949 .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".