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3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations

Leonid Andronov 1 Jonathan Michalon 1 Khalid Ouararhni 1 Igor Orlov 1 Ali Hamiche 1 Jean-Luc Vonesch 1 Bruno P. Klaholz 1, * 
* Corresponding author
1 Inserm U964 - CNRS UMR7104 - IGBMC - Centre for Integrative Biology - CBI
IGBMC - Institut de Génétique et de Biologie Moléculaire et Cellulaire
Abstract : Motivation: Single-molecule localization microscopy (SMLM) can play an important role in integrated structural biology approaches to identify, localize and determine the 3D structure of cellular structures. While many tools exist for the 3D analysis and visualization of crystal or cryo-EM structures little exists for 3D SMLM data, which can provide unique insights but are particularly challenging to analyze in three dimensions especially in a dense cellular context. Results: We developed 3DClusterViSu, a method based on 3D Voronoi tessellations that allows local density estimation, segmentation and quantification of 3D SMLM data and visualization of protein clusters within a 3D tool. We show its robust performance on microtubules and histone proteins H2B and CENP-A with distinct spatial distributions. 3DClusterViSu will favor multi-scale and multi-resolution synergies to allow integrating molecular and cellular levels in the analysis of macromolecular complexes. Availability and impementation: 3DClusterViSu is available under Supplementary information: Supplementary figures are available at Bioinformatics online.
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Submitted on : Friday, December 13, 2019 - 3:25:19 PM
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Leonid Andronov, Jonathan Michalon, Khalid Ouararhni, Igor Orlov, Ali Hamiche, et al.. 3DClusterViSu: 3D clustering analysis of super-resolution microscopy data by 3D Voronoi tessellations. Bioinformatics, Oxford University Press (OUP), 2018, 34 (17), pp.3004-3012. ⟨10.1093/bioinformatics/bty200⟩. ⟨inserm-02409748⟩



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