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  Fully-automatic multiresolution idealization for filtered ion channel recordings: Flickering event detection

Pein, F., Tecuapetla-Gómez, I., Schütte, O. M., Steinem, C., & Munk, A. (2018). Fully-automatic multiresolution idealization for filtered ion channel recordings: Flickering event detection. IEEE Transactions on NanoBioscience, 17(3), 300-320. doi:10.1109/TNB.2018.2845126.

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 Creators:
Pein, F., Author
Tecuapetla-Gómez, I., Author
Schütte, O. M., Author
Steinem, Claudia1, Author           
Munk, A., Author
Affiliations:
1Max Planck Fellow Group Membrane-based biomimetic nano- and micro-compartments, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2586691              

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Free keywords: IEEE Keywords: Hidden Markov models; Ions; Deconvolution; Markov processes; Data models; Nanobioscience; Signal resolution; INSPEC: Controlled Indexing: deconvolution; hidden Markov models; statistical analysis INSPEC: Non-Controlled Indexing: filtered ion channel recordings; flickering event detection; model-free segmentation method; local deconvolution; multiresolution criterion; sampling rate; flickering events; temporal scales; filter frequency; deconvolution step; precise determination; dwell times; amplitude levels; common thresholding methods; comprehensive simulation study; preprocessing method; refined hidden Markov analysis; slow gating events; R function jules; JULES; statistical multiresolution techniques; fully automatic multiresolution idealization Author Keywords: Amplitude reconstruction; deconvolution; dynamic programming; gramicidin A; inverse problems; m-dependency; model-free; peak detection; planar patch clamp; statistical multiresolution criterion
 Abstract: 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 methodolodgy 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.

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Language(s): eng - English
 Dates: 2018-06-072018-07-31
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TNB.2018.2845126
 Degree: -

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Title: IEEE Transactions on NanoBioscience
Source Genre: Journal
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Pages: - Volume / Issue: 17 (3) Sequence Number: - Start / End Page: 300 - 320 Identifier: -