A general approach to the analysis of errors and failure modes in the base-calling function in automated fluorescent DNA sequencing
Bibliographic record
Abstract
We describe the analysis of errors and failure modes in the base-calling function in automated DNA sequencing, on instruments in which fluorescently-labeled Sanger dideoxy-sequencing ladders are detected via their times of migration past a fixed detector. A general approach entails the joint use of: (i) well-defined control samples such as M13mp18, and (ii) mathematical simulation of sequencing electropherograms, with the deliberate introduction of different types of distortion and noise. An algorithm, the electrophoretic trace simulator (ETS), is used to calculate electrophoresis traces corresponding to the output data stream of an automated fluorescent DNA sequencer. The ETS accepts a user-defined sequence of nucleotide bases (A, C, G, T) as input, and employs user-adjustable functions to compute the following critical parameters of an electropherogram: peak intensity, peak spacing, peak shape as a function of base number; background, noise, and spectral cross-talk correction (for a sequencer using multiple dyes). We use a combination of M13mp18 controls and simulated electropherograms to analyze two problems of considerable practical importance: (i) variation in electrophoretic migration rates between different lanes of a gel, and (ii) variation in signal intensity due to user-dependent loading artifacts. The issue of base-calling errors and failure modes, for electropherograms that contain noise and distortion, is addressed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".