Biophysical instrumentation usually saves the output files with numbers and text. Data analysis is an integral process of the experiments leading to a final quantification of the studied phenomena. Working with the data by opening, copy-pasting to a spreadsheet is laborious and not efficient. We use Python for automated data reading, manipulation, non-linear curve fitting and plotting.
Usually biophysical experiments require scientific instrumentation which gives the output in text files with numbers and text information organized in the format developed by manufacturers. Standardization of the files structure is not possible, since each technique results in different data sets. However, the data analysis is an integral process of the experiments leading to a final quantification of the studied phenomena. In many cases, working with the data by opening, copy-pasting to a spreadsheet software for further analysis is laborious and not efficient. In our projects we use Python to analyze the data from fluorescence spectroscopy and microscopy experiments. We find it extremely useful for fluorescence cross-correlation spectroscopy (FCCS) experiments, a technique allowing i. a. for real-time reaction observation at the level of single molecules. We use numpy and scipy libraries for reading individual time points from the output files, data conversion and non-linear curve fitting. The data from individual time steps is conveniently plotted using. We use this approach in the studies of influenza polymerase, an enzyme catalyzing cleavage of RNA in infected cells. By using FCCS we designed the experiments to check the enzyme preferences of divalent ions, since the existing literature results are in contradiction. For each representative date sets we used several hundred individual fits, which without an automation step would be a tedious task to perform. Similarly, in spectroscopic experiments we used Python scripts for automatic data reading and further transformation such as subtraction, smoothing, non-linear curve fitting and plotting. They were applied in a project focusing on influenza protein, hemagglutinin, whose fragment acts as a mediator of fusion between two membranes, viral and cellular. The data analysis performed with Python allowed us for determination of the energies of hemagglutinin fragments interactions with artificial model membranes, mimicking the natural cellular plasma membrane. Supported by Scientific Polpharma Foundation, 2012/07/D/NZ1/04255 and Foundation for Polish Science grants.