FRETBursts can analyze smFRET measurements from one or multiple excitation spots \cite{Ingargiola_2013}. The supported excitation schemes include single laser, alternating laser excitation (ALEX) with either CW lasers (μs-ALEX \cite{Kapanidis_2005}) or pulsed lasers (ns-ALEX \cite{Laurence_2005} or pulsed-interleaved excitation (PIE) \cite{M_ller_2005}).

The software implements both standard and novel algorithms for smFRET data analysis including background estimation as a function of time (including background accuracy metrics), sliding-window burst search \cite{Eggeling_1998}, dual-channel burst search (DCBS) \cite{Nir_2006} and modular burst selection methods based on user-defined criteria (including a large set of pre-defined selection rules). Novel features include burst size selection with \(\gamma\)-corrected burst sizes, burst weighting, burst search with background-dependent threshold (in order to guarantee a minimal signal-to-background ratio \cite{Michalet_2012}). Moreover, FRETBursts provides a large set of fitting options to characterize FRET subpopulations. In particular, distributions of burst quantities (such as \(E\) or \(S\)) can be assessed through (1) histogram fitting (with arbitrary model functions), (2) non-parametric weighted kernel density estimation (KDE), (3) weighted expectation-maximization (EM), (4) maximum likelihood fitting using Gaussian models or Poisson statistic. Finally FRETBursts includes a large number of predefined and customizable plot functions which (thanks to the matplotlib graphic library \cite{matplotlib}) produce publication quality plots in a wide range of formats.

Additionally, implementations of population dynamics analysis such as Burst Variance Analysis (BVA) \cite{Torella_2011} and two-channel kernel density distribution estimator (2CDE) \cite{Tomov_2012} are available as FRETBursts notebooks (BVA link, 2CDE link).

FRETBursts is hosted and openly developed on GitHub. FRETBursts homepage (link) contains links to the various resources. Pre-built packages are provided for Windows, OS X and Linux. Installation instructions can be found in the Reference Documentation (link). A description of FRETBursts execution using Jupyter notebooks is reported in SI \ref{sec:notebook}. Detailed information on development style, testing strategies and contributions guidelines are reported in SI \ref{sec:dev}. Finally, to facilitate evaluation and comparison with other software, we set up an on-line services allowing to execute FRETBursts without requiring any installation on the user’s computer (link).