Data analysis
Data analysis is part of the Nanopore Wiki learning path. Nanopore data analysis extracts reliable events, features, and conclusions from raw current traces or sequencing output.
Core concepts
- For sensing, raw data are often current-versus-time traces.
- For sequencing, data may include POD5, FAST5, FASTQ, BAM, and reports.
- Analysis should preserve true event features while controlling noise, drift, and artifacts.
- Quality control should be performed before and after feature extraction.
Scientific basis
- For sensing, raw data are often current-versus-time traces.
- For sequencing, data may include POD5, FAST5, FASTQ, BAM, and reports.
- Interpretations should be tied to measurable quantities, experimental controls, and documented analysis settings.
- Claims should distinguish direct observations from downstream inference.
Analysis workflow
| Step | Description |
|---|---|
| 1 | Organize metadata and raw files. |
| 2 | Assess baseline, noise, and drift. |
| 3 | Filter or detrend only with documented parameters. |
| 4 | Detect events and extract features such as blockade depth and dwell time. |
| 5 | Visualize both representative traces and population statistics. |
Typical applications
- Single-molecule sensing.
- Sequencing quality control.
- Method comparison and troubleshooting.
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
- Showing only attractive example traces.
- Changing thresholds until expected results appear.
- 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.