A Streamlined Method for Detecting Structural Variants in Cancer Genomes by Short Read Paired-End Sequencing
Bibliographic record
Abstract
Defining the architecture of a specific cancer genome, including its structural variants, is essential for understanding tumor biology, mechanisms of oncogenesis, and for designing effective personalized therapies. Short read paired-end sequencing is currently the most sensitive method for detecting somatic mutations that arise during tumor development. However, mapping structural variants using this method leads to a large number of false positive calls, mostly due to the repetitive nature of the genome and the difficulty of assigning correct mapping positions to short reads. This study describes a method to efficiently identify large tumor-specific deletions, inversions, duplications and translocations from low coverage data using SVDetect or BreakDancer software and a set of novel filtering procedures designed to reduce false positive calls. Applying our method to a spontaneous T cell lymphoma arising in a core RAG2/p53-deficient mouse, we identified 40 validated tumor-specific structural rearrangements supported by as few as 2 independent read pairs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".