Evaluation of simulation models to mimic the distortions introduced into squiggles by nanopore sequencers and segmentation algorithms
Bibliographic record
Abstract
Nucleotides ratcheted through the biomolecular pores of nanopore sequencers generate raw picoamperage currents, which are segmented into step-current level signals representing the nucleotide sequence. These 'squiggles' are a noisy, distorted representation of the underlying true stepped current levels due to experimental and algorithmic factors. We were interested in developing a simulation model to support a white-box approach to identify common distortions, rather than relying on commonly used black box neural network techniques for basecalling nanopore signals. Dynamic time warped-space averaging (DTWA) techniques can generate a consensus from multiple noisy signals without introducing key feature distortions that occur with standard averaging. As a preprocessing tool, DTWA could provide cleaner and more accurate current signals for direct RNA or DNA analysis tools. However, DTWA approaches need modification to take advantage of the a-priori knowledge regarding a common, underlying gold-standard RNA / DNA sequence. Using experimental data, we derive a simulation model to provide known squiggle distortion signals to assist in validating the performance of analysis tools such as DTWA. Simulation models were evaluated by comparing mocked and experimental squiggle characteristics from one Enolase mRNA squiggle group produced by an Oxford MinION nanopore sequencer, and cross-validated using other Enolase, Sequin R1_71_1 and Sequin R2_55_3 mRNA studies. New techniques identified high inserted but low deleted base rates, generating consistent x1.7 squiggle event to base called ratios. Similar probability density and cumulative distribution functions, PDF and CDF, were found across all studies. Experimental PDFs were not the normal distributions expected if squiggle distortion arose from segmentation algorithm artefacts, or through individual nucleotides randomly interacting with individual nanopores. Matching experimental and mocked CDFs required the assumption that there are unique features associated with individual raw-current data streams. Z-normalized signal-to-noise ratios suggest intrinsic sensor limitations being responsible for half the gold standard and noisy squiggle DTW differences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".