Data analysis

File:Detection pics scipy signal find peaks.svg

Peak or event detection concepts support nanopore signal analysis. Source: Wikimedia Commons, CC BY-SA 4.0.

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.

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.

Common pitfalls

• Showing only attractive example traces.
• Changing thresholds until expected results appear.
• Publishing feature tables without raw-data provenance.

Related pages

• Event detection
• Noise and filtering
• Bioinformatics
• Experimental workflows

References

• Signal processing
• Data analysis