A comparative study of multivariate and univariate hidden Markov modelings in time-binned single-molecule FRET data analysis.
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.
Liu
Yang
Y
Center for the Physics of Living Cell, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
Park
Jeehae
J
Dahmen
Karin A
KA
Chemla
Yann R
YR
Ha
Taekjip
T
D000465:Algorithms; D001483:Base Sequence; D004247:DNA / Q000235:genetics / Q000378:metabolism; D031541:Fluorescence Resonance Energy Transfer / Q000706:statistics & numerical data*; D008390:Markov Chains; D015999:Multivariate Analysis; D011938:Rec A Recombinases / Q000378:metabolism; D013997:Time Factors
D004247:DNA; D011938:Rec A Recombinases