A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis

Yang Liu; Jeehae Park; Karin A. Dahmen; Yann R. Chemla; Taekjip Ha

doi:10.1021/jp9057669

A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis

Yang Liu, Jeehae Park, Karin A. Dahmen, Yann R. Chemla, Taekjip Ha

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

Abstract

We compare two different types of hidden Markov modeling (HMM) algorithms, e.g., multivariate HMM (MHMM) and univariate HMM (UHMM), for the analysis of time-binned single-molecule fluorescence energy transfer (smFRET) data. In MHMM, the original two channel signals, i.e., the donor fluorescence intensity (I_D) and acceptor fluorescence intensity (I_A), are simultaneously analyzed. However, in UHMM, only the calculated FRET trajectory is analyzed. On the basis of the analysis of both synthetic and experimental data, we find that, if the noise in the signal is described with a proper probability distribution, MHMM generally outperforms UHMM. We also show that, in the case of multiple trajectories, analyzing them simultaneously gives better results than averaging over individual analysis results.

Original language	English (US)
Pages (from-to)	5386-5403
Number of pages	18
Journal	Journal of Physical Chemistry B
Volume	114
Issue number	16
DOIs	https://doi.org/10.1021/jp9057669
State	Published - Apr 29 2010
Externally published	Yes

ASJC Scopus subject areas

Physical and Theoretical Chemistry
Surfaces, Coatings and Films
Materials Chemistry

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title = "A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis",

abstract = "We compare two different types of hidden Markov modeling (HMM) algorithms, e.g., multivariate HMM (MHMM) and univariate HMM (UHMM), for the analysis of time-binned single-molecule fluorescence energy transfer (smFRET) data. In MHMM, the original two channel signals, i.e., the donor fluorescence intensity (ID) and acceptor fluorescence intensity (IA), are simultaneously analyzed. However, in UHMM, only the calculated FRET trajectory is analyzed. On the basis of the analysis of both synthetic and experimental data, we find that, if the noise in the signal is described with a proper probability distribution, MHMM generally outperforms UHMM. We also show that, in the case of multiple trajectories, analyzing them simultaneously gives better results than averaging over individual analysis results.",

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T1 - A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis

AU - Liu, Yang

AU - Park, Jeehae

AU - Dahmen, Karin A.

AU - Chemla, Yann R.

AU - Ha, Taekjip

PY - 2010/4/29

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AB - We compare two different types of hidden Markov modeling (HMM) algorithms, e.g., multivariate HMM (MHMM) and univariate HMM (UHMM), for the analysis of time-binned single-molecule fluorescence energy transfer (smFRET) data. In MHMM, the original two channel signals, i.e., the donor fluorescence intensity (ID) and acceptor fluorescence intensity (IA), are simultaneously analyzed. However, in UHMM, only the calculated FRET trajectory is analyzed. On the basis of the analysis of both synthetic and experimental data, we find that, if the noise in the signal is described with a proper probability distribution, MHMM generally outperforms UHMM. We also show that, in the case of multiple trajectories, analyzing them simultaneously gives better results than averaging over individual analysis results.

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A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis

Abstract

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