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

J 2023

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

CHARESHNEU, Aliaksei, Adam MIDLIK, Crina-Maria IONESCU, Alexander ROSE, Vladimír HORSKÝ et. al.

Basic information

Original name

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

Authors

CHARESHNEU, Aliaksei (112 Belarus, belonging to the institution), Adam MIDLIK (703 Slovakia, belonging to the institution), Crina-Maria IONESCU (203 Czech Republic, belonging to the institution), Alexander ROSE, Vladimír HORSKÝ (203 Czech Republic, belonging to the institution), Alessio CANTARA (380 Italy, belonging to the institution), Radka SVOBODOVÁ (203 Czech Republic, belonging to the institution), Karel BERKA (203 Czech Republic) and David SEHNAL (203 Czech Republic, guarantor, belonging to the institution)

Edition

Nucleic Acids Research, Oxford University Press, 2023, 0305-1048

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 14.900 in 2022

RIV identification code

RIV/00216224:14310/23:00131662

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.1093/nar/gkad411

UT WoS

000989609300001

Keywords in English

3D structure; visualization; biomacromolecules; organelle- and cell-sized models

Abstract

V originále

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/.

Links

GM22-30571M, research and development project

Name: Cell*: webová platforma pro vizualizaci, modelování a dynamiku organelových a buněčných struktur (Acronym: Cell*)

Investor: Czech Science Foundation

LM2023054, research and development project

Name: e-Infrastruktura CZ

Investor: Ministry of Education, Youth and Sports of the CR

90255, large research infrastructures

Name: ELIXIR CZ III

Citovat

CHARESHNEU, Aliaksei, Adam MIDLIK, Crina-Maria IONESCU, Alexander ROSE, Vladimír HORSKÝ, Alessio CANTARA, Radka SVOBODOVÁ, Karel BERKA and David SEHNAL. Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations. Nucleic Acids Research. Oxford University Press, 2023, vol. 51, W1, p. "W326"-"W330", 5 pp. ISSN 0305-1048. Available from: https://dx.doi.org/10.1093/nar/gkad411.

@article{2309937,
   author = {Chareshneu, Aliaksei and Midlik, Adam and Ionescu, CrinaandMaria and Rose, Alexander and Horský, Vladimír and Cantara, Alessio and Svobodová, Radka and Berka, Karel and Sehnal, David},
   article_number = {W1},
   doi = {http://dx.doi.org/10.1093/nar/gkad411},
   keywords = {3D structure; visualization; biomacromolecules; organelle- and cell-sized models},
   language = {eng},
   issn = {0305-1048},
   journal = {Nucleic Acids Research},
   title = {Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations},
   url = {https://doi.org/10.1093/nar/gkad411},
   volume = {51},
   year = {2023}
}

TY  - JOUR
ID  - 2309937
AU  - Chareshneu, Aliaksei - Midlik, Adam - Ionescu, Crina-Maria - Rose, Alexander - Horský, Vladimír - Cantara, Alessio - Svobodová, Radka - Berka, Karel - Sehnal, David
PY  - 2023
TI  - Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations
JF  - Nucleic Acids Research
VL  - 51
IS  - W1
SP  - "W326"-"W330"
EP  - "W326"-"W330"
PB  - Oxford University Press
SN  - 03051048
KW  - 3D structure
KW  - visualization
KW  - biomacromolecules
KW  - organelle- and cell-sized models
UR  - https://doi.org/10.1093/nar/gkad411
N2  - 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/.
ER  -

CHARESHNEU, Aliaksei, Adam MIDLIK, Crina-Maria IONESCU, Alexander ROSE, Vladimír HORSKÝ, Alessio CANTARA, Radka SVOBODOVÁ, Karel BERKA and David SEHNAL. Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations. \textit{Nucleic Acids Research}. Oxford University Press, 2023, vol.~51, W1, p.~''W326''-''W330'', 5 pp. ISSN~0305-1048. Available from: https://dx.doi.org/10.1093/nar/gkad411.

Detailed Information on Publication Record