blik: an extensible napari plugin for cryo-ET data visualisation, annotation and analysis

Abstract

AbstractPowerful, workflow-agnostic and interactive visualisation is essential for the ad-hoc, human-in-the-loop workflows typical of cryo-electron tomography (cryo-ET). While several tools exist for visualisation and annotation of cryo-ET data, they are often integrated as part of monolithic processing pipelines, or focused on a specific task and offering limited reusability and extensibility. With each software suite presenting its own pros and cons and often tools tailored to address specific challenges, seamless integration between available pipelines is often a difficult task. As part of the effort to enable such flexibility and move the software ecosystem towards a more collaborative and modular approach, we developedblik, an open-sourcenapariplugin for visualisation and annotation of cryo-ET data (source code:https://github.com/brisvag/blik).blikoffers fast, interactive, and user-friendly 3D visualisation thanks tonapari, and is built with extensibility and modularity at the core. Data is handled and exposed through well-established scientific Python libraries such asnumpyarrays andpandasdataframes. Reusable components (such as data structures, file read/write, and annotation tools) are developed as independent Python libraries to encourage reuse and community contribution. By easily integrating with established image analysis tools – even outside of the cryo-ET world –blikprovides a versatile platform for interacting with cryo-ET data. On top of core visualisation features – interactive and simultaneous visualisation of tomograms, particle picks and segmentations –blikprovides an interface for interactive tools such as manual particle picking, surface-based and filament-based particle picking and image segmentation, as well as simple filtering tools. Additional self-containednapariplugins developed as part of this work also implement interactive plotting and selection based on particle features, and label interpolation for easier segmentation. Finally, we highlight the differences with existing software and showcaseblik’s applicability in biological research.