FREDDIE: A comprehensive tool for detecting exonization of retrotransposable elements in short and long RNA sequencing data
Bibliographic record
Abstract
Background Transposable elements (TEs) constitute a significant portion of mammalian genomes, accounting for about 50% of the total DNA. Intragenic TEs are of particular interest as they are co-transcribed with their host genes in pre-mRNA, potentially leading to the formation of novel chimeric transcripts and the exonization of TEs. The abundance of RNA sequencing data currently available offers a unique opportunity to explore transcriptomic variations. However, a significant limitation is the capability of existing computational tools. Here, we introduce FREDDIE, an innovative algorithm designed to detect the exonization of retrotransposable elements using RNA-seq data. FREDDIE can process short and long RNA sequencing data, assemble and quantify transcripts, evaluate coding potential, and identify protein domains in chimeric transcripts involving exonized TEs and retrocopies. Results To demonstrate the efficacy of FREDDIE, we analyzed and validated TE exonization in two human cancer cell lines, K562 and U251. We have identified 322 chimeric transcripts, of which 126 were from K562, and 196 were from U251. Among these chimeric transcripts, there were 35 that showed similar exonization patterns and host genes. These transcripts involve protein-coding genes of the host and exonization of LINE-1 (L1), Alu elements, and retrocopies of coding genes. We have selected some candidates and validated them experimentally through RT-PCR. The validation rate for these candidates was 70%, later confirmed by long-read sequencing. Additionally, we applied FREDDIE to analyze TE exonization across 157 glioblastoma samples, identifying 1,010 chimeric transcripts. The majority of these transcripts involved the exonization of Alu elements (69.8%), followed by L1 (20.6%) and retrocopies (9.6%). Notably, we discovered a highly expressed L1 exonization within the ROS gene, resulting in a truncated open reading frame (ORF) with the deletion of two protein domains. Conclusions FREDDIE is an efficient and user-friendly tool for identifying chimeric transcripts that involve exonization of intragenic TEs. Overall, FREDDIE enables comprehensive investigations into the contributions of TEs to transcriptome evolution, variation, and disease-associated abnormalities, and it operates effectively on standard computing systems. FREDDIE is publicly available: https://github.com/galantelab/freddie
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".