MOSAIC: A Modular Single-Molecule Analysis Interface for Decoding Multistate Nanopore Data
Bibliographic record
Abstract
Biological and solid-state nanometer-scale pores are the basis for numerous emerging analytical technologies for use in precision medicine. We developed Modular Single-Molecule Analysis Interface (MOSAIC), an open source analysis software that improves the accuracy and throughput of nanopore-based measurements. Two key algorithms are implemented: ADEPT, which uses a physical model of the nanopore system to characterize short-lived events that do not reach their steady-state current, and CUSUM+, a version of the cumulative sum statistical method optimized for longer events that do. We show that ADEPT detects previously unreported conductance states that occur as double-stranded DNA translocates through a 2.4 nm solid-state nanopore and reveals new interactions between short single-stranded DNA and the vestibule of a biological pore. These findings demonstrate the utility of MOSAIC and the ADEPT algorithm, and offer a new tool that can improve the analysis of nanopore-based measurements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 0.002 |
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".