Fully Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection
| Autor: | Axel Munk, Inder Tecuapetla-Gómez, Florian Pein, Claudia Steinem, Ole M. Schütte |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Patch-Clamp Techniques Computer science Biomedical Engineering Pharmaceutical Science Medicine (miscellaneous) Markov process Bioengineering Models Biological Ion Channels 03 medical and health sciences symbols.namesake Humans Segmentation Electrical and Electronic Engineering Hidden Markov model Flicker Gramicidin Computational Biology Signal Processing Computer-Assisted Filter (signal processing) Inverse problem Thresholding Computer Science Applications 030104 developmental biology 13. Climate action symbols Deconvolution Algorithm Algorithms Biotechnology |
| Zdroj: | IEEE Transactions on NanoBioscience. 17:300-320 |
| ISSN: | 1558-2639 1536-1241 |
| DOI: | 10.1109/tnb.2018.2845126 |
| Popis: | We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods. This is confirmed theoretically and in a comprehensive simulation study. In addition, JULES can be applied as a preprocessing method for a refined hidden Markov analysis. Our new methodology allows us to show that gramicidin A flickering events have the same amplitude as the slow gating events. JULES is available as an R function jules in the package clampSeg . |
| Databáze: | OpenAIRE |
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