MAJIQlopedia: an encyclopedia of RNA splicing variations in human tissues and cancer
Bibliographic record
Abstract
Quantification of RNA splicing variations based on RNA-Sequencing can reveal tissue- and disease-specific splicing patterns. To study such splicing variations, we introduce MAJIQlopedia, an encyclopedia of splicing variations that encompasses 86 human tissues and 41 cancer datasets. MAJIQlopedia reports annotated and unannotated splicing events for a total of 486 175 alternative splice junctions in normal tissues and 338 317 alternative splice junctions in cancer. This database, available at https://majiq.biociphers.org/majiqlopedia/, includes a user-friendly interface that provides graphical representations of junction usage quantification for each junction across all tissue or cancer types. To demonstrate case usage of MAJIQlopedia, we review splicing variations in genes WT1, MAPT and BIN1, which all have known tissue or cancer-specific splicing variations. We also use MAJIQlopedia to highlight novel splicing variations in FDX1 and MEGF9 in normal tissues, and we uncover a novel exon inclusion event in RPS6KA6 that only occurs in two cancer types. Users can download the database, request the addition of data to the webtool, or install a MAJIQlopedia server to integrate proprietary data. MAJIQlopedia can serve as a reference database for researchers seeking to understand what splicing variations exist in genes of interest, and those looking to understand tissue- or cancer-specific splice isoform usage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".