Investigation of fast and efficient lossless compression algorithms for macromolecular crystallography experiments

Area of Science:

• Structural biology
• Synchrotron radiation science
• Data science

Background:

• State-of-the-art synchrotron facilities enable high-framerate data collection in macromolecular crystallography (MX).
• High-volume data throughput (up to 25 GB/s) necessitates efficient data compression for practical management.
• Current default compression algorithms for detectors like DECTRIS Eiger are effective but may be surpassed by newer methods.

Purpose of the Study:

• To investigate the performance of recent lossless compression algorithms.
• To evaluate their effectiveness on diffraction data from advanced MX beamlines.

Main Methods:

• Testing of advanced lossless compression algorithms.
• Application to diverse diffraction datasets from synchrotron MX experiments.

Main Results:

• Newer lossless compression algorithms demonstrate superior performance compared to default methods.
• These algorithms effectively reduce data volume for high-framerate synchrotron diffraction data.

Conclusions:

• The adoption of advanced lossless compression algorithms can enhance the efficiency and cost-effectiveness of MX data collection.
• Further research into compression techniques is crucial for managing the increasing data rates in structural biology.