An automated workflow for parallel processing of large multiview SPIM recordings.

Area of Science:

• Microscopy and Imaging Science
• Computational Biology
• Bioinformatics

Background:

• Selective Plane Illumination Microscopy (SPIM) generates massive 3D image datasets of developing organisms.
• Interactive processing via graphical user interface (GUI) applications is time-consuming.
• Automation and parallelization are crucial for handling large-scale SPIM data.

Purpose of the Study:

• To introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data.
• To enable efficient data analysis on single workstations or high-performance computing (HPC) clusters.
• To reduce the processing time for SPIM data.

Main Methods:

• Developed an automated data processing pipeline using snakemake.
• Designed the workflow for parallel processing on HPC clusters.
• Ensured adaptability to various cluster environments.

Main Results:

• The automated workflow significantly reduces the time required for SPIM data processing.
• The pipeline effectively handles large, complex SPIM datasets.
• The snakemake-based approach automates dependency resolution for consecutive processing steps.

Conclusions:

• The introduced automated workflow provides an efficient solution for analyzing large SPIM datasets.
• Parallel processing on HPC clusters accelerates the analysis of time-lapse SPIM data.
• This approach makes advanced SPIM data analysis more accessible and faster.