Simulation of Next Generation Sequencing Short Reads for Mutation Spectrum Analysis
Bibliographic record
Abstract
Next generation sequencing (NGS) of mutant reporter transgenes is increasingly being used for mutation spectrum analysis (MSA) to characterize the genomic effects of mutagens.The ability to simulate NGS-MSA experimental data will permit the tuning of various parameters in the downstream analysis pipeline.However, no simulator currently exists that is capable of producing the read depths (up to 100,000x) required for MSA experiments.In this study, we introduce MutSim, a short read mutation simulator that enables researchers to generate NGS data for simulated samples exposed to a mutagen.MutSim generates data following the Ion Proton™ instrument error model and also a mutational model of a given mutagen.MutSim is shown to be highly scalable both with respect to genome length and read depth coverage.MutSim simulated data is validated against real experimental data in several aspects including genotype content, quality scores, read depth coverage and read length.iii
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".