Data analysis: Difference between revisions
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[[File: | [[File:Oxford_Nanopore_MinION_top_open.jpg|thumb|right|320px|Nanopore data analysis links instruments, file formats, and computational pipelines.]] | ||
'''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. | '''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. | ||
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== 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. | |||
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== Analysis workflow == | == Analysis workflow == | ||
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== Typical applications == | == Typical applications == | ||
* Single-molecule sensing. | * Single-molecule sensing. | ||
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* Method comparison and troubleshooting. | * Method comparison and troubleshooting. | ||
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== | == 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. | |||
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== Interpretation limits == | |||
* Showing only attractive example traces. | * Showing only attractive example traces. | ||
* Changing thresholds until expected results appear. | * 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. | |||
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== Related pages == | == Related pages == | ||
* [[Event detection]] | * [[Event detection]] | ||
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* [[Experimental workflows]] | * [[Experimental workflows]] | ||
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== References == | == References == | ||
* [https://en.wikipedia.org/wiki/Signal_processing Signal processing] | * [https://en.wikipedia.org/wiki/Signal_processing Signal processing] | ||
Latest revision as of 09:25, 15 July 2026
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.