Satoshi-Ogi/Nucleosome_Paper_2026: Nanopore Event Analysis Toolkit v1.0.0

Nanopore Event Analysis Toolkit — Initial Public Release (v1.0.0) This is the first public release of the Nanopore Event Analysis Toolkit, a Python software package for processing, classifying, and visualizing single-molecule nanopore sensing data. The toolkit was developed to support nucleosome translocation studies and provides a complete analysis pipeline from raw event output to publication-quality figures. Included Scripts | Script | Purpose | |--------|---------| | Re-definition_analysis_for_EWMAuto.py | Redefines event boundaries from EWMAuto output via baseline-crossing detection | | Re-definition_analysis_for_Nanolyzer.py | Redefines event boundaries from Nanolyzer output via the same algorithm | | GMM_analysis_for_Dwell_Time_Classification.py | Classifies events into Short/Long populations using a two-component Gaussian Mixture Model on log₁₀-transformed dwell times | | Overlay.py | Overlays multiple event waveforms with normalized time axis and blockage ratio for publication figures | Analysis Pipeline Raw Nanopore Data (EWMAuto / Nanolyzer output) │ ▼ Re-definition_analysis_for_.py ・Baseline-crossing detection to refine event boundaries ・Blockage ratio calculation (B_max, B_median) │ ├──────────────────────────┐ ▼ ▼ GMM_analysis_.py Overlay.py ・log₁₀ transformation ・Normalized time axis ・EM algorithm fitting ・Blockage ratio overlay ・Short / Long classification ・SVG export Key Features Boundary Redefinition: Corrects event start/end positions by tracing baseline crossings from the deepest signal point, improving consistency across events detected by different software tools. Dual Software Compatibility: Supports both EWMAuto and Nanolyzer output formats with identical algorithmic logic. Statistical Classification: Two-component GMM on log₁₀(dwell time) separates transient and stable translocation events with posterior probability assignments. Reproducible Visualization: Overlay plots normalize waveform duration to [0, 1], enabling direct visual comparison of events with varying dwell times. Requirements Python ≥ 3.10 pandas, numpy, matplotlib, scikit-learn Associated Publication This software was developed to support the following manuscript: Ogihara, S. et al. (manuscript in preparation, 2026) If you use this software in your research, please cite both this repository (via the Zenodo DOI) and the associated paper when available. License MIT License © 2026 Satoshi Ogihara