Fluorescence recovery after photobleaching data analysis software tools | Advanced fluorescence microscopy
Fluorescence recovery after photobleaching (FRAP) is now widely used to investigate binding interactions in live cells. Although various idealized solutions have been identified for the reaction-diffusion equations that govern FRAP, there has been no comprehensive analysisor systematic approach to serve as a guide for extracting binding information from an arbitrary FRAP curve.
Analyzes, processes and visualizes multi-dimensional microscopy images. BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes.
Identifies cells of interest on imaging of large cell numbers in quantitative microscopy. Micropilot can automatically process complex fluorescence microscopy-based imaging assays. Users can train the software to detect objects in a fast low-resolution prescanning mode. This software is able to execute more complex imaging assays on selected object positions by following an online reconfiguration of the microscope system.
Provides assistance for the qualitative and quantitative analysis of fluorescence recovery after photobleaching (FRAP) data. easy-FRAP allows data visualization, normalization of the raw recovery curves and curve fitting. This software can also exploit large data sets of raw data under different experimental conditions, exclude low quality data and perform batch analysis. All of this tasks can be run simultaneously.
Fits numerical simulations of three-dimensional models to fluorescence recovery after photobleaching (FRAP)/inverse FRAP (iFRAP) data and accounts for bleaching/photoconversion inhomogeneities. PyFRAP is able to apply post-bleach image as initial condition, and numerically simulates the FRAP experiment in realistic two-dimensional or three-dimensional experiment geometries. Moreover, it can determine the diffusion coefficients of fluorescent molecules ranging from 3 to 500 kDa in both artificial and biological contexts.
A graphical user interface for the fitting of linear and non-linear decay functions to data from fluorescence decay after photoconversion (FDAP) experiments. PyFDAP structures and analyses large FDAP datasets and features multiple fitting and plotting options.
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