Ångström- and Nano-scale Pore-Based Nucleic Acid Sequencing of Current and Emergent Pathogens
Bibliographic record
Abstract
State-of-the-art nanopore sequencing enables rapid and real-time identification of novel pathogens, which has wide application in various research areas and is an emerging diagnostic tool for infectious diseases including COVID-19. Nanopore translocation enables de novo sequencing with long reads (> 10 kb) of novel genomes, which has advantages over existing short-read sequencing technologies. Biological nanopore sequencing has already achieved success as a technology platform but it is sensitive to empirical factors such as pH and temperature. Alternatively, ångström- and nano-scale solid-state nanopores, especially those based on two-dimensional (2D) membranes, are promising next-generation technologies as they can surpass biological nanopores in the variety of membrane materials, ease of defining pore morphology, higher nucleotide detection sensitivity, and facilitation of novel and hybrid sequencing modalities. Since the discovery of graphene, atomically-thin 2D materials have shown immense potential for the fabrication of nanopores with well-defined geometry, rendering them viable candidates for nanopore sequencing membranes. Here, we review recent progress and future development trends of 2D materials and their ångström- and nano-scale pore-based nucleic acid (NA) sequencing including fabrication techniques and current and emerging sequencing modalities. In addition, we discuss the current challenges of translocation-based nanopore sequencing and provide an outlook on promising future research directions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".