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

Article in a journal

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

is not subject to a state or trade secret

References:

URL

Impact factor

Impact factor: 16.700

RIV identification code

RIV/00216224:14310/23:00131662

Organization unit

Faculty of Science

DOI

https://doi.org/10.1093/nar/gkad411

UT WoS

000989609300001

EID Scopus

2-s2.0-85163979078

Keywords in English

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

Abstract

In the original language

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

Cite

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://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 = {https://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://doi.org/10.1093/nar/gkad411.

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