A step towards making serial femtosecond X-ray crystallography data processing user-friendly.
What is it about?
Serial femtosecond crystallography (SFX) is a technique for determining the structure of biological molecules at atomic resolution. However, this technique requires highly intense, ultra-short X-ray pulses, experiment-specific methods of introducing the biological samples into the X-ray beam, and novel detectors for capturing the data. This results in often complicated analysis for extracting useful images, and the Pixel Anomaly Detection Tool (PADT) provides a user-friendly interface that makes this analysis amendable to the lay programmer by making use of established machine learning algorithms.
Why is it important?
An SFX experiment often requires a large team of scientists with various expertise, while generating large amounts of image data (often multiple terrabytes). From sample preparation, sample injection, X-ray instrumentation to data analysis; everything influences the collected data and different scientists are interested in studying different effects. PADT provides an interactive interface to easily train machine learning models for filtering out the images containing the requested information, without having to write any code.