Electronic Mapping of a Bacterial Genome with Dual Solid-State Nanopores and Active Single-Molecule Control
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We present an electronic mapping of a bacterial genome using solid-state nanopore technology. A dual-nanopore architecture and active control logic are used to produce single-molecule data that enables estimation of distances between physical tags installed at sequence motifs within double-stranded DNA. Previously developed “DNA flossing” control logic generates multiple scans of each captured DNA. We extended this logic in two ways: first, to automate “zooming out” on each molecule to progressively increase the number of tags scanned during flossing, and second, to automate recapture of a molecule that exited flossing to enable interrogation of the same and/or different regions of the molecule. Custom analysis methods were developed to produce consensus alignments from each multiscan event. The combined multiscanning and multicapture method was applied to the challenge of mapping from a heterogeneous mixture of single-molecule fragments that make up the Escherichia coli ( E. coli ) chromosome. Coverage of 3.1× across 2355 resolvable sites of the E. coli genome was achieved after 5.6 h of recording time. The recapture method showed a 38% increase in the merged-event alignment length compared to single-scan alignments. The observed intertag resolution was 150 bp in engineered DNA molecules and 166 bp natively within fragments of E. coli DNA, with detection of 133 intersite intervals shorter than 200 bp in the E. coli reference map. We present results on estimating distances in repetitive regions of the E. coli genome. With an appropriately designed array, higher throughput implementations could enable human-sized genome and epigenome mapping applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".