Software for the analysis of single-molecule FRET experiments
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|PAM - PIE Analysis with MATLAB||PAM is short for PIE Analysis with MATLAB – it is a GUI-based software package for the analysis of fluorescence experiments and supports a large number of analysis methods ranging from single-molecule methods to imaging.||PAM is a framework for integrated and robust analysis of fluorescence ensemble, single-molecule, and imaging data. Although it was originally developed for the analysis of pulsed interleaved excitation experiments, PAM has since been extended to support most types of data collection modalities. It combines a multitude of powerful analysis algorithms, ranging from time- and space-correlation analysis, over single-molecule burst analysis, to lifetime imaging microscopy, while offering intrinsic support for multicolor experiments. For more information, read the publication, documentation or see the git repository.
W. Schrimpf, A. Barth, J. Hendrix, D. C. Lamb, PAM: A Framework for Integrated Analysis of Imaging, Single-Molecule, and Ensemble Fluorescence Data. Biophys J 114, 1518–1528 (2018). https://doi.org/10.1016/j.bpj.2018.02.035
PAM currently supports the following analysis methods:
|TIRF||MASH-FRET||MASH-FRET is a Matlab-based software package for the analysis of single-molecule FRET videos and trajectories.||The framework encompasses the entire workflow from localizing single molecule on videos to characterizing molecule dynamics.|
|TIRF||TwoTone||A TIRF-FRET analysis package for the automatic analysis of single-molecule FRET movies.||TwoTone is written by Seamus J Holden, Oliver J Britton and Stephan Uphoff, in association with our paper on TIRF-FRET measurement and data analysis .
TwoTone is written in a combination of MATLAB and C++, works on Windows and Linux, and is distributed with complete source code.
 S. J. Holden, et al., Defining the Limits of Single-Molecule FRET Resolution in TIRF Microscopy. Biophys J 99, 3102–3111 (2010).
|Confocal||OpenSMFS||A collection of tools for solution based single molecule fluorescence spectroscopy, including single-molecule FRET, FCS, MC-DEPI.||OpenSMFS was developed in the lab of Shimon Weiss at UCLA. More information can be found on the GitHub and the website of the Lerner lab.
It currently includes the following tools:
|ChiSurf||ChiSurf is a fluorescence analysis platform for the analysis of time-resolved fluorescence decays.||1) ChiSurf allows users to easily compose complex model functions for multiple datasets using a set of predefined model functions (e.g for FRET sensitized emission, donor-donor energy migration, quenching of the donor fluorescence, fluorescence lifetime models, ...)
2) Pre-processes TTTR data streams (correlation, trace-processing, and time-resolved fluorescence, ...)
3) ChiSurf provides multiple tools for structural modeling using FRET data (computation of orientation factor distributions, FRET restrained sampling protein conformations, ...)
4) ChiSurf implements a Bayesian workflow for the composed models to yield more accurate estimates of the model parameters
|TIRF||ebFRET||ebFRET performs combined analysis on multiple single-molecule FRET time series to learn a set of rates and states.||ebFRET is a library written in Matlab for the analysis of single-molecule FRET time series with hidden Markov models.
ebFRET is the successor of vbFRET, which it improves upon by performing combined analysis of multiple time series at once. This approach, known as empirical Bayes, is both more accurate and statistically robust. It also enables more advanced analysis use cases such as sub-population detection.
“Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data.” Jan-Willem van de Meent (@jwvdm), Jonathan E. Bronson, Frank Wood, Ruben L. Gonzalez Jr., Chris H. Wiggins. International Conference on Machine Learning 2013
“Empirical Bayes methods enable advanced population-level analyses of single-molecule FRET experiments.” Jan-Willem van de Meent (@jwvdm), Jonathan E. Bronson, Chris H. Wiggins, Chris H. Wiggins, Ruben L. Gonzalez Jr. Accepted for publication in the Biophysical Journal
|TIRF||smFRET analysis (Zhuang lab)|
|Confocal||MFD Spectroscopy and Imaging||A software package for fluorescence spectroscopy and imaging experiments using Multiparameter Fluorescence Detection (MFD).||We present a software package for fluorescence data analysis in Multiparameter Fluorescence Detection (MFD) . In this technique time-resolved observation of intrinsic properties of chromophores (i.e. spectral properties of absorption and fluorescence, fluorescence quantum yield, fluorescence lifetime, and anisotropy) is recorded. Selective analysis of molecular subensembles and time dependent parameter traces from single molecule are possible .
Subsequent software-correlation yields full correlation curves covering more than 12 orders of magnitude in time . An advanced time-correlated single photon counting (TCSPC) technique simultaneously provides traditional autocorrelation fluorescence correlation (FCS) or cross correlation (FCCS) from selected molecules which makes analysis more powerful.
Software gives opportunity for all potential applications of technique using pulsed or cw laser excitation: antibunching effects, rotational diffusion and fluorescence resonance energy transfer (FRET) as are discussed.
The combination of FCS and fluorescence lifetime (FLCS)  is implemented into software correlator. Direct generation of necessary filters for polarisation dependent signals is possible.
Software for probability distribution analysis (PDA) is presented for quantitative and precise description of the photon counting histograms (PCH) from fluorescence resonance energy transfer experiments (FRET) . An accurate description of the histogram profile based on the statistical distributions of fluorescence and background signals is sensitive to small changes in fluorescence signal even when signal counts are low. The PDA formalism allows monitoring the changes in the emission spectra of single molecules, dynamical changes of the system, etc. PDA harbours the potential for Molecular Ångström Optics.
Software for confocal Multiparameter Fluorescence Imaging (MFIS) is developed. Each pixel of MFIS corresponds to “single burst” in solution experiments and all MFD type analysis are applied. Intensity, lifetime, anisotropy, correlation times, correlation amplitudes or any MFD parameter images can be reconstructed [6,7].
 Kühnemuth et al., Single Molecules, 2, 251 (2001).
 Antonik M., et al., J. Phys. Chem. B 110, 6970 (2006).
 Widengren J., et al., Anal. Chem. 78, 2039 (2006).
 Gaiduk et al., Chem.Phys.Chem. 5, 976 (2005).
 Felekyan S., et al., Rev. Sci. Instr. 76, 083104 (2005).
 Kudryavtsev et al., Anal.&Bioanal.Chem., in press.
 Böhmer M., et al., Chem. Phys. Lett. 353, 439 (2002).
|TIRF||vbFRET||vbFRET uses variational Bayesian inference to learn hidden Markov models from individual, single-molecule fluorescence resonance energy transfer efficiency time trajectories.||The software is described in Bronson et al (2009) and Bronson et al (2010). The software is implemented in MATLAB.|
|TIRF||iSMS||iSMS is a user-interfaced software package for the visualization and analysis of wide-field, single-molecule FRET (smFRET) microscopy using immobilized molecules.||iSMS is a user-interfaced software package for the visualization and analysis of wide-field, single-molecule FRET (smFRET) microscopy using immobilized molecules.
In smFRET microscopy, the structural conformations and dynamic equilibria of hundreds of single biomolecules are monitored in real-time. This provides us with insight into the conformational landscapes and transitional dynamics of DNA, RNA and proteins, being important for the functioning of these biomolecules. Equally important, smFRET provides a direct view into the population heterogeneity of biological samples which traditionally has been hidden behind an ensemble average.
iSMS is designed to quickly process, analyse and share smFRET data with peers and collaborators. Ultimately we hope to unify and standardize the analysis of smFRET microscopy across different laboratories.
The open-source version is written in MATLAB (Mathworks) but is compiled into a standalone executable software. The compiled version does not require MATLAB nor a MATLAB license.
Please cite the following paper if you make use of iSMS in your work:
Preus, S., Noer, S.L., Hildebrandt, L.L., Gudnason, D., Birkedal, V., "iSMS: single-molecule FRET microscopy software", Nature Methods 12, 593–594 (2015)
|TIRF||HaMMy||smFRET Analysis and Hidden Markov Modeling (Ha lab)||Our code treats single-molecule time-binned FRET trajectories as hidden Markov processes, allowing one to determine the most likely FRET-value distributions of states and their interconversion rates based on probability alone. Source code available upon request.|
|Modeling||LabelLib||LabelLib is a C++ library for the simulation of the accessible volume (AV) of small probes flexibly coupled to biomolecules.||LabelLib is a low-level C++ library for the simulation of small probes flexibly coupled to biomolecules for the development of higher-level applications and libraries. LabelLib can calculate the distribution of flexible labels around attachment points. Such probes are for instance dyes for fluorescence spectroscopy, spin-labels for EPR and NMR, or chemical cross-links for mass-spectrometry. Typically, these labels are fluorescent dyes. For such dyes LabelLib can calculate FRET observables.
LabelLib uses a coarse-grained approach to simulate the spatial distribution of probes around their attachment point. In this coarse-grained approach, LabelLib determines the sterically accessible volume of the probe considering the linker length and the spatial dimensions of the probe. The linker connecting the probe to the biomolecule and the probe are approximated by a tube and soft sphere, respectively. Details are provided in the following publications:
1. S. Kalinin, et al., A toolkit and benchmark study for FRET-restrained high-precision structural modeling. Nat Meth 9, 1218–1225 (2012).
2. S. Sindbert, et al., Accurate distance determination of nucleic acids via Förster resonance energy transfer: implications of dye linker length and rigidity. J. Am. Chem. Soc. 133, 2463–2480 (2011).
|Confocal||H2MM||H2MM is a maximum likelihood estimation algorithm for photon-by-photon analysis of single-molecule FRET experiments.||Applied to simulations of freely diffusing molecules, H2MM was shown to retrieve accurately reaction times from ~1 second to ~10 microseconds and even faster (Pirchi et al., J. Phys. Chem. B. 2016, 120, 13065−13075, DOI: 10.1021/acs.jpcb.6b10726). It has been applied to study protein and nucleic acid dynamics by us and other groups, with two recent example being a study of the dynamics of the disaggregation machine ClpB (Mazal et al., NATURE COMMUNICATIONS, (2019) 10:1438, DOI: 10.1038/s41467-019-09474-6) and a study of the transcription bubble (Lerner et al., J. Chem. Phys. 148, 123315 (2018), https://doi.org/10.1063/1.5004606) .
The code for H2MM can be found in the SI of the publication.
|TIRF||SMACKS||SMACKS (Single Molecule Analysis of Complex Kinetic Sequences) is a maximum likelihood approach to extract kinetic rate models from noisy single molecule data.||Please find the full paper here: Single-Molecule Analysis beyond Dwell Times: Demonstration and Assessment in and out of Equilibrium.
While fine-tuned for FRET data, SMACKS is applicable to single molecule time traces of any kind. It is based on multidimensional semi-ensemble HMM.
The SMACKS software tool comes with the complete source code, a detailed manual, an ascii importer & example data for testing.
|rFRET||rFRET is a comprehensive, Matlab-based program for analyzing ratiometric microscopic FRET experiments.||rFRET, a Matlab application, evaluates intensity-based FRET measurements. The analysis can be restricted to certain areas of images using masks and gating on histograms. The program includes different estimation approaches and correction for fluorophore saturation phenomena.
The web site and the program contains comprehensive help. The original publication describing the tool:
Peter Nagy, Ágnes Szabó, Tímea Váradi, Tamás Kovács, Gyula Batta, János Szöllősi
rFRET: A comprehensive, Matlab-based program for analyzing ratiometric microscopic FRET experiments
Cytometry A 89: 376-84. (2016)
|Confocal||ALiX||ALiX is developed for basic research on diffusing two-color single-molecule FRET in single or multiple spot geometries.||ALiX is written in LabVIEW and released as a standalone Windows 64 bit executable, which can be installed from the Download & Installation Instructions page of the website.
To get an overview of the software capability, please check the ALiX Overview page of the online manual.
The ALiX Workflow page provides a bird's eye view of the different steps involved in the analysis of a data file.
Tutorial pages are available and use example files from published papers to illustrate typical analysis workflows.
This software was developed as part of research performed in the Single Molecule Biophysics group (Weiss Lab), Department of Chemistry & Biochemistry at UCLA. As such, it owes much to interactions with students, postdocs and researchers from within and outside the Weiss laboratory. Special thanks are in order for Dr. Antonino Ingargiola (developer of FRETBursts) and Dr. Eitan Lerner.
The software code includes open source code from the Open G Library, HDF5 binding for LabVIEW, and correlation routines kindly provided by Dr. Ted Laurence.
This work was funded by a grant from the NIH (R01-GM95904) and in part by DOE (DE-FC02-02ER63421-00).
LabVIEW is a trademark of National Instruments.
Citation: If you use ALiX in your work, please consider citing the following paper, which first introduced it:
A. Ingargiola, E. Lerner, S. Chung, F. Panzeri, A. Gulinatti, I. Rech, M. Ghioni, S. Weiss, X. Michalet. Multispot single-molecule FRET: high-throughput analysis of freely diffusing molecules. PLoS ONE 12 (2017) e0175766. https://doi.org/10.1371/journal.pone.0175766
|TIRF||SPARTAN||SPARTAN is a suite of tools, written in MATLAB, to facilitate single molecule FRET data analysis, including trace extraction from wide-field movies, trace selection with user-defined criteria, hidden Markov modeling, and data visualization.||The advent of scientific CMOS (sCMOS) cameras has drastically increased the potential imaging throughput to record more than 10,000 single molecules simultaneously. SPARTAN is designed to take full advantage of this large volume of data. It is also intended to make the data analysis process more user friendly and accessible to non-expert users.
Juette, M. F.*, Terry, D. S.*, Wasserman, M. R., Altman, R. B., Zhou, Z., Zhao, H. & Blanchard, S. C. Single-molecule imaging of non-equilibrium molecular ensembles on the millisecond scale. Nature Methods 13, p. 341 (2016). http://dx.doi.org/10.1038/nmeth.3769
|Confocal||Fretica||Fretica is a user-extendable toolbox for analyzing single-molecule fluorescence data in Wolfram Mathematica, with a backend written in C++. It supports both the analysis of experiments on freely diffusing and immobilized molecules.||Fretica is a user-extendable toolbox for analyzing single-molecule fluorescence data. It is a Wolfram Mathematica package with a backend written in C++, which has been actively developed since 2010 by D. Nettels, B. Schuler, and co-workers. From within Mathematica Notebooks, raw data can be accessed and analyzed with almost three hundred highly optimized commands provided by the package on top of the vast number of commands provided by Mathematica itself, which enable versatile high-level coding and cover many fields of mathematics, data science, visualization, machine learning, etc.
Fretica supports both the analysis of experiments on freely diffusing and immobilized molecules. For free-diffusion experiments, functions range from photon burst identification, corrections, and analysis in the spirit of multi-parameter fluorescence detection (MFD), to PIE/ALEX data analysis, PCH, FIDA, PDA, recurrence analysis, fluorescence lifetime fitting, FLIM, etc., global analysis schemes for equilibrium and time-resolved data (e.g. from microfluidic mixing experiments), and many correlation techniques, such as multi-channel FCS, Fluorescence Lifetime Correlation Spectroscopy (FLCS), dual-focus FCS, and nsFCS. For immobilized molecules, both photon-by-photon and binned trajectories can be analyzed using hidden Markov models (HMMs) with arbitrary numbers of states and detection channels. Model parameters can be found by maximum-likelihood optimization. For testing purposes, Fretica can simulate realistic photon data given any HMM with Poissonian or super-Poissonian photon statistics, both for immobilized and freely diffusing molecules, including mixtures of species with heterogeneous diffusion coefficients in open or confined volumes.
Fretica’s extensive documentation is integrated into the Mathematica documentation system. Notebooks (and raw data) can be easily shared or deposited on archives. All analysis steps from loading the raw data to the final results are well documented and can be re-executed any time. Fretica reads data in the PTU file format developed by PicoQuant, Berlin.
|Confocal||gSMFRETda||gSMFRETda is a C++/CUDA library for photon distribution analysis (PDA) of smFRET experiments.||gSMFRETda is a smFRET PDA program written under C++/CUDA. It can use GPUs to accelerate Monte Carlo simulations of PDA. Due to the drastically reduced calculation time, people can sample dwell time and other parameters more densely. It also enables PDA to analyze very fast dynamic interconversion of the system or some other complex TCSPC setup forms requesting lots of PDA calculation.|
|Modeling||FRETraj||gSMFRETda is a Python module for predicting FRET efficiencies and distributions based on multiple accessible-contact volumes (multi-ACV). The package features a user-friendly PyMOL plugin for FRET-assisted structural modeling and integrates with the Jupyter ecosystem.||FRETraj is a Python module for predicting FRET efficiencies and distributions based on multiple accessible-contact volumes (multi-ACV). The package features a user-friendly PyMOL plugin for FRET-assisted structural modeling and integrates with the Jupyter ecosystem. It further interfaces with the LabelLib library for fast computation of ACVs and also has its own Python implementation of the algorithm. FRETraj is specifically designed towards
- planning FRET experiments by optimizing label positions
- interpreting FRET-based distance measurements on biomolecules
- linking FRET experiments with molecular dynamics simulations
|Confocal||BurstH2MM||BurstH2MM is a package for easy processing of multiparameter photon-by-photon FRETBursts data by hidden Markov Modeling (mpH2MM), that removes the need to manually recode details, and calculates of things such as E, S, transition rates, and other dwell and model parameters. BurstH2MM includes an updated/improved FRETbursts version (see OpenSMFS ) and is documented at (bursth2mm and h2mmpythonlib. H2MM was developed by Pirchi and Tuskanov et. al 2016 J. Phys. Chem. B, and extended to mpH2MM in Harris et.al 2022 Nat. Comm. BurstH2MM requires the H2MM_C package (Harris et.al), and FRETBursts (Ingargiola et. al 2016 PLoS One) to make the analysis process easier for single-molecule FRET data.||Documentation of BurstH2MM can be found in https://bursth2mm.readthedocs.io/en/latest/
and in https://h2mmpythonlib.readthedocs.io/en/latest/
Who to cite:
H2MM was developed by Pirchi and Tuskanov et. al 2016 J. Phys. Chem. B, and extended to mpH2MM in Harris et.al 2022 Nat. Comm.
This module requires the H2MM_C package, introduced in Harris et.al, and FRETBursts, developed by Ingargiola et. al 2016 PLoS One, to make the analysis process easier for single-molecule FRET data.
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