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single-molecule smFRET experiments.
With FRETBursts we provide a tool that is available to any scientist
to use, study and modify. Furthermore, FRETBursts execution model
based on Juyter Notebook~\cite{Shen_2014} is designed to facilitate
computational reproducibility.
FRETBursts is hosted and openly developed on GitHub~\cite{Blischak_2016,Prli__2012},
where users can send comments, report issues or contribute code.
...
the fundamental steps of burst analysis, highlighting key parameters
and algorithms available. The aim is not covering all FRETBursts
features and options but providing an overview detailed enough for starting
using
FRETbursts FRETBursts and customizing the analysis. For additional information,
we refer the reader to the
\href{http://fretbursts.readthedocs.org/}{FRETBursts FRETBursts Reference
Documentation}
. Documentation
(\href{http://fretbursts.readthedocs.org/}{link}) and to
the FRETBursts µs-ALEX notebook
(\href{http://nbviewer.ipython.org/github/tritemio/FRETBursts_notebooks/blob/master/notebooks/FRETBursts\%20-\%20us-ALEX\%20smFRET\%20burst\%20analysis.ipynb}{link})
Finally, usage questions can be
asked on the mailing list or on GitHub posted by opening an
Issue. Issue
on GitHub (\href{https://github.com/tritemio/FRETBursts}{link}).
\subsection{Paper overview}
...
In the next section~\ref{sec:overview} we give an overview of the software features,
the modality of execution and the development style.
In section~\ref{sec:concepts}, we
introduce review a few preliminary concepts and
some specific terminology needed
to understand the smFRET burst analysis.
In section~\ref{sec:analysis}, we detail the execution the
several main steps involved
in smFRET burst analysis: data loading (section~\ref{sec:dataload}), defining
excitation alternation periods (section~\ref{sec:alternation}), background
correction (section~\ref{sec:bg_calc}), burst search (section~\ref{sec:burstsearch}),
burst selection (section~\ref{sec:burstsel}) and FRET fitting (section~\ref{sec:fretfit}).
The aim is to provide enough information to understand the specificities of
the different algorithms and to be able to adapt the analysis to new situations.
In section~\ref{sec:bva}, we show how to implement a new burst analysis in FRETBursts,
taking as an example the Burst Variance Analysis (BVA)~\cite{Torella_2011}.
Finally, in section~\ref{sec:conclusions}, we summarize what we believe to be
the strengths of FRETBursts software.
In addition to this paper, we refer the interested readers to the
\href{http://nbviewer.ipython.org/github/tritemio/FRETBursts_notebooks/blob/master/notebooks/FRETBursts\%20-\%20us-ALEX\%20smFRET\%20burst\%20analysis.ipynb}{FRETBursts µs-ALEX tutorial}
for a single complete example of µs-ALEX data analysis and to the
\href{http://fretbursts.readthedocs.org/}{FRETBursts Reference Documentation}
for a more in-depth description of the software. Throughout this paper,
we provide direct
links to relevant sections of
the FRETBursts documentation and
display them other web resources
are displayed as ``(link)''.
Finally, in In order to make
this paper the text accessible to the widest number of readers,
we concentrated python-specific details in special subsections titled
\textit{Python details}. These subsections provide deeper insights for readers
already familiar with python and can be safely skipped otherwise.
Note also Finally, note that all commands
shown in this paper here reported can be found in the
FRETBursts tutorial notebook accompanying notebooks and, in general, do not need to be re-typed.
