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Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations

Segmentation helps interpret imaging data in a biological context. With the development of powerful tools for automated segmentation, public repositories for imaging data have added support for sharing and visualizing segmentations, creating the need for interactive web-based visualization of 3D vol...

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Autores principales: Chareshneu, Aliaksei, Midlik, Adam, Ionescu, Crina-Maria, Rose, Alexander, Horský, Vladimír, Cantara, Alessio, Svobodová, Radka, Berka, Karel, Sehnal, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320116/
https://www.ncbi.nlm.nih.gov/pubmed/37194693
http://dx.doi.org/10.1093/nar/gkad411
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author Chareshneu, Aliaksei
Midlik, Adam
Ionescu, Crina-Maria
Rose, Alexander
Horský, Vladimír
Cantara, Alessio
Svobodová, Radka
Berka, Karel
Sehnal, David
author_facet Chareshneu, Aliaksei
Midlik, Adam
Ionescu, Crina-Maria
Rose, Alexander
Horský, Vladimír
Cantara, Alessio
Svobodová, Radka
Berka, Karel
Sehnal, David
author_sort Chareshneu, Aliaksei
collection PubMed
description Segmentation helps interpret imaging data in a biological context. With the development of powerful tools for automated segmentation, public repositories for imaging data have added support for sharing and visualizing segmentations, creating the need for interactive web-based visualization of 3D volume segmentations. To address the ongoing challenge of integrating and visualizing multimodal data, we developed Mol* Volumes and Segmentations (Mol*VS), which enables the interactive, web-based visualization of cellular imaging data supported by macromolecular data and biological annotations. Mol*VS is fully integrated into Mol* Viewer, which is already used for visualization by several public repositories. All EMDB and EMPIAR entries with segmentation datasets are accessible via Mol*VS, which supports the visualization of data from a wide range of electron and light microscopy experiments. Additionally, users can run a local instance of Mol*VS to visualize and share custom datasets in generic or application-specific formats including volumes in .ccp4, .mrc, and .map, and segmentations in EMDB-SFF .hff, Amira .am, iMod .mod, and Segger .seg. Mol*VS is open source and freely available at https://molstarvolseg.ncbr.muni.cz/.
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spelling pubmed-103201162023-07-06 Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations Chareshneu, Aliaksei Midlik, Adam Ionescu, Crina-Maria Rose, Alexander Horský, Vladimír Cantara, Alessio Svobodová, Radka Berka, Karel Sehnal, David Nucleic Acids Res Web Server Issue Segmentation helps interpret imaging data in a biological context. With the development of powerful tools for automated segmentation, public repositories for imaging data have added support for sharing and visualizing segmentations, creating the need for interactive web-based visualization of 3D volume segmentations. To address the ongoing challenge of integrating and visualizing multimodal data, we developed Mol* Volumes and Segmentations (Mol*VS), which enables the interactive, web-based visualization of cellular imaging data supported by macromolecular data and biological annotations. Mol*VS is fully integrated into Mol* Viewer, which is already used for visualization by several public repositories. All EMDB and EMPIAR entries with segmentation datasets are accessible via Mol*VS, which supports the visualization of data from a wide range of electron and light microscopy experiments. Additionally, users can run a local instance of Mol*VS to visualize and share custom datasets in generic or application-specific formats including volumes in .ccp4, .mrc, and .map, and segmentations in EMDB-SFF .hff, Amira .am, iMod .mod, and Segger .seg. Mol*VS is open source and freely available at https://molstarvolseg.ncbr.muni.cz/. Oxford University Press 2023-05-17 /pmc/articles/PMC10320116/ /pubmed/37194693 http://dx.doi.org/10.1093/nar/gkad411 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server Issue
Chareshneu, Aliaksei
Midlik, Adam
Ionescu, Crina-Maria
Rose, Alexander
Horský, Vladimír
Cantara, Alessio
Svobodová, Radka
Berka, Karel
Sehnal, David
Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
title Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
title_full Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
title_fullStr Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
title_full_unstemmed Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
title_short Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
title_sort mol* volumes and segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320116/
https://www.ncbi.nlm.nih.gov/pubmed/37194693
http://dx.doi.org/10.1093/nar/gkad411
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