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Single Molecule Analysis Research Tool (SMART): An Integrated Approach for Analyzing Single Molecule Data

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  • Max Greenfeld
  • Dmitri S Pavlichin
  • Hideo Mabuchi
  • Daniel Herschlag

Abstract

Single molecule studies have expanded rapidly over the past decade and have the ability to provide an unprecedented level of understanding of biological systems. A common challenge upon introduction of novel, data-rich approaches is the management, processing, and analysis of the complex data sets that are generated. We provide a standardized approach for analyzing these data in the freely available software package SMART: Single Molecule Analysis Research Tool. SMART provides a format for organizing and easily accessing single molecule data, a general hidden Markov modeling algorithm for fitting an array of possible models specified by the user, a standardized data structure and graphical user interfaces to streamline the analysis and visualization of data. This approach guides experimental design, facilitating acquisition of the maximal information from single molecule experiments. SMART also provides a standardized format to allow dissemination of single molecule data and transparency in the analysis of reported data.

Suggested Citation

  • Max Greenfeld & Dmitri S Pavlichin & Hideo Mabuchi & Daniel Herschlag, 2012. "Single Molecule Analysis Research Tool (SMART): An Integrated Approach for Analyzing Single Molecule Data," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-12, February.
    • Handle: RePEc:plo:pone00:0030024
    DOI: 10.1371/journal.pone.0030024
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    References listed on IDEAS

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    1. Sergey V. Solomatin & Max Greenfeld & Steven Chu & Daniel Herschlag, 2010. "Multiple native states reveal persistent ruggedness of an RNA folding landscape," Nature, Nature, vol. 463(7281), pages 681-684, February.
    2. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 217-244.
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    1. Markus Götz & Anders Barth & Søren S.-R. Bohr & Richard Börner & Jixin Chen & Thorben Cordes & Dorothy A. Erie & Christian Gebhardt & Mélodie C. A. S. Hadzic & George L. Hamilton & Nikos S. Hatzakis &, 2022. "A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Simon Wanninger & Pooyeh Asadiatouei & Johann Bohlen & Clemens-Bässem Salem & Philip Tinnefeld & Evelyn Ploetz & Don C. Lamb, 2023. "Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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