Somrit: The Somatic Retrotransposon Insertion Toolkit
Bibliographic record
Abstract
Abstract Mobile elements, such as retrotransposons, have the ability to express and re-insert themselves into the genome, with over half the human genome being made up of mobile element sequence. Somatic mobile element insertions (MEIs) have been shown to cause disease, including some cancers. Accurate identification of where novel retrotransposon insertion events occur in the genome is crucial to understand the functional consequence of an insertion event. In this paper we describe somrit, a modular toolkit for detecting somatic MEIs from long reads aligned to a reference genome. We identify the initial read-to-reference mapping step as a potential source of error when the insertion is similar to a nearby repeat in the reference genome and develop a consensus-realignment procedure to resolve this. We show how somrit improves the sensitivity of detection for rare somatic retrotransposon insertion events compared to existing tools, and how the local realignment procedure can reduce false positive translocation calls caused by mis-mapped reads bearing MEIs. Somrit is openly available at: https://github.com/adcosta17/somrit
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".