TranscriptDB: a transcript-centric database to study eukaryotic transcript conservation and evolution
Bibliographic record
Abstract
Eukaryotic genes can encode multiple distinct transcripts through the alternative splicing (AS) of genes. Interest in the AS mechanism and its evolution across different species has stimulated numerous studies, leading to several databases that provide information on AS and transcriptome data across multiple eukaryotic species. However, existing resources do not offer information on transcript conservation and evolution between genes of multiple species. Similarly to genes, identifying conserved transcripts-those from homologous genes that have retained a similar exon composition-is useful for determining transcript homology relationships, studying transcript functions and reconstructing transcript phylogenies. To address this gap, we have developed TranscriptDB, a database dedicated to studying the conservation and evolution of transcripts within gene families. TranscriptDB offers an extensive catalog of conserved transcripts and phylogenies for 317 annotated eukaryotic species, sourced from Ensembl database version 111. It serves multiple purposes, including the exploration of gene and transcript evolution. Users can access TranscriptDB through various browsing and querying tools, including a user-friendly web interface. The incorporated web servers enable users to retrieve information on transcript evolution using their own data as input. Additionally, a REST application programming interface is available for programmatic data retrieval. A data directory is also available for bulk downloads. TranscriptDB and its resources are freely accessible at https://transcriptdb.cobius.usherbrooke.ca.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".