Somatic microindels: analysis in mouse soma and comparison with the human germline
Bibliographic record
Abstract
Microindels, defined as mutations that result in a colocalized microinsertion and microdeletion with a net gain or loss of between 1 and 50 nucleotides, may be an important contributor to cancer. We report the first comprehensive analysis of somatic microindels. Our large database of mutations in the lacI transgene of Big Blue((R)) mice contains 0.5% microindels, 2.8% pure microinsertions, and 11.5% pure microdeletions. There appears to be no age, gender, or tissue-type specificity in the frequency of microindels. Of the independent somatic mutations that result in a net in-frame insertion or deletion, microindels are responsible for 13% of protein expansions and 6% of protein contractions. These in-frame microindels may play a crucial role in oncogenesis and evolution via "protein tinkering" (i.e., modest expansion or contraction of proteins). Four characteristics suggest that microindels are caused by unique mechanisms, not just simple combinations of the same mechanisms that cause pure microinsertions and pure microdeletions. First, microinsertions and microdeletions commonly occur at hotspots, but none of the 30 microindels are recurrent. Second, the sizes of the deletions and insertions in microindels are larger and more varied than in pure microdeletions and pure microinsertions. Third, microinsertions overwhelmingly repeat the adjacent base (97%) while the insertions in microindels do so only infrequently (17%). Fourth, analysis of the sequence contexts of microindels is consistent with unique mechanisms including recruitment of translesion DNA synthesis polymerases. The mouse somatic microindels have characteristics similar to those of human germline microindels, consistent with similar causative mechanisms in mouse and human, and in soma and germline.
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 | 0.000 | 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".