Identification of transposon insertion polymorphisms by computational comparative analysis of next generation personal genome data
Bibliographic record
Abstract
Structural variations (SVs) in a genome are now known as a prominent and important type of genetic variation. Among all types of SVs, the identification of transposon insertion polymorphisms (TIPs) is more challenging due to the highly repetitive nature of transposon sequences. We developed a computational method, TIP‐finder, to identify TIPs through analysis of next generation personal genome data and their extremely large copy numbers. We tested the efficiency of TIP‐finder with simulated data and are able to detect about 88% of TIPs with precision of ≥91%. Using TIP‐finder to analyze the Solexa pair‐end sequence data at deep coverage for six genomes representing two trio families, we identified a total of 5569 TIPs, consisting of 4881, 456, 91, and 141 insertions from Alu, L1, SVA and HERV, respectively, representing the most comprehensive analysis of such type of genetic variation.
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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 | 0.001 |
| 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) | 0.001 | 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".