Event detection
Event detection is part of the Nanopore Wiki learning path. Event detection identifies real blockade or signal-change events in nanopore recordings and extracts comparable features from them.
Core concepts
- Detection thresholds should be tied to baseline and local noise.
- Event boundaries affect dwell time, area, and shape metrics.
- Weak, overlapping, or multi-level events need careful validation.
- False positives can arise from noise, drift, bubbles, clogging, and analysis settings.
Scientific basis
- Detection thresholds should be tied to baseline and local noise.
- Event boundaries affect dwell time, area, and shape metrics.
- Interpretations should be tied to measurable quantities, experimental controls, and documented analysis settings.
- Claims should distinguish direct observations from downstream inference.
Event detection workflow
| Step | Description |
|---|---|
| 1 | Annotate experimental phases and sample addition times. |
| 2 | Estimate local baseline and noise. |
| 3 | Set candidate thresholds and minimum duration rules. |
| 4 | Merge, split, or reject events using documented criteria. |
| 5 | Export event tables and plots for review. |
Typical applications
- Quantifying blockade depth and dwell time.
- Comparing samples or pore variants.
- Building training data for classification models.
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
- Using one threshold for recordings with strong drift.
- Failing to test false positives on blank controls.
- 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.