Experimental workflows
Experimental workflows is part of the Nanopore Wiki learning path. Nanopore experiments require coordinated control of sample quality, buffer chemistry, pore or membrane state, electrical recording, metadata, and analysis.
Core concepts
- A useful SOP includes acceptance criteria and troubleshooting logic, not only steps.
- Blank controls and pre-sample baselines are essential for interpreting events.
- Metadata must record voltage, buffer, pore, sample, bandwidth, sampling rate, and time points.
- Real-time judgement prevents poor traces from becoming misleading datasets.
Scientific basis
- A useful SOP includes acceptance criteria and troubleshooting logic, not only steps.
- Blank controls and pre-sample baselines are essential for interpreting events.
- Interpretations should be tied to measurable quantities, experimental controls, and documented analysis settings.
- Claims should distinguish direct observations from downstream inference.
Standard workflow
| Step | Description |
|---|---|
| 1 | Define the target, question, pore system, and platform. |
| 2 | Prepare sample, buffer, blank controls, and recording setup. |
| 3 | Verify baseline and pore state before adding sample. |
| 4 | Record data while monitoring event frequency, drift, and pore stability. |
| 5 | Save raw data, metadata, scripts, and failure notes. |
Typical applications
- Custom nanopore sensing.
- Sequencing library preparation and run monitoring.
- Training new laboratory members.
Quality checks
- Record platform, pore or flow-cell type, chemistry, voltage or acquisition settings, and software versions.
- Use negative controls, positive controls, and replicate measurements where the claim depends on specificity.
- Report limitations such as noise, bias, sample degradation, pore instability, or model-dependent analysis.
- Keep laboratory metadata linked to the raw signal or sequencing run before interpreting results.
Interpretation limits
- Running samples without blank baselines.
- Changing buffer or voltage without documenting it.
- A single result should not be generalized beyond the sample, protocol, instrument state, and analysis pipeline used.
- Medical, legal, regulatory, or commercial decisions require domain-specific validation beyond this educational summary.