Topostats
AFM image analysis Software
TopoStats is a Python toolkit that automates the processing and analysis of Atomic Force Microscopy data. The software batch processes raw AFM data and can then identify molecules or features and trace them, producing image outputs at different stages of the processing and summary statistics on molecule features in both tabular and graphical formats.
TopoStats has been written from the ground-up in Python and supports a number of AFM file formats (.spm, .ibw, .gwy and .tiff). Images are loaded and pre-processed to remove scars and flatten images prior to detection of features and derivation of masks.
The program can then convert these pixel masks into either circular or linear line traces of the backbone of the molecule and generates statistics based on these traces.
The tracing and analysis is discussed in the paper TopoStats – A program for automated tracing of biomolecules from AFM images.
Usage
The software is cross platform and runs on GNU/Linux, OSX (both x86 and ARM architectures) and Windows and leverages parallel processing to batch process images fast and has a simple command line interface with configuration via a dedicated configuration file.
If you would like to install and use the software the current stable version can be installed from the Python Package Index (PyPI) using pip install topostats. Extensive documentation is available here and includes guidelines on installing development versions and making contributions. A set of Jupyter Notebooks are also available which step through the individual steps.
User feedback is important as we continue to develop TopoStats and we are keen to hear about your experience and suggestions for improving TopoStats and any problems you may have encountered. Please report issues on the GitHub Issue tracker.
References
If you use TopoStats for your research please cite the following article in your publication (Citation File Format for TopoStats is available on GitHub see here).
- TopoStats – A program for automated tracing of biomolecules from AFM images Methods doi: 10.1016/j.ymeth.2021.01.008