Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular...

Area of Science:

• Structural Biology
• Biophysics
• Bioinformatics

Background:

• Automated segmentation tools are advancing biological imaging data interpretation.
• Public repositories increasingly support sharing and visualizing segmentation data.
• There is a growing need for interactive web-based visualization of 3D volume segmentations.

Purpose of the Study:

• To develop an integrated solution for visualizing multimodal cellular imaging data.
• To enable interactive, web-based visualization of 3D volumes and segmentations.
• To support data from various microscopy techniques and biological annotations.

Main Methods:

• Developed Mol* Volumes and Segmentations (Mol*VS) integrated into Mol* Viewer.
• Supported visualization of electron and light microscopy data.
• Enabled local instance deployment for custom datasets in various formats (.ccp4, .mrc, .map, EMDB-SFF, Amira, iMod, Segger).

Main Results:

• Mol*VS provides interactive visualization of cellular imaging data with macromolecular context.
• All EMDB and EMPIAR entries with segmentation datasets are accessible.
• The tool supports a wide range of microscopy data and common file formats.

Conclusions:

• Mol*VS addresses the challenge of integrating and visualizing multimodal biological imaging data.
• The open-source tool enhances accessibility and sharing of 3D segmentation data.
• Mol*VS is a valuable resource for researchers working with complex biological imaging datasets.