J 2023

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

ULBRICH, Pavol, Manuela WALDNER, Katarína FURMANOVÁ, Sérgio Manuel MARQUES, David BEDNÁŘ et. al.

Basic information

Original name

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration

Authors

ULBRICH, Pavol (703 Slovakia, guarantor, belonging to the institution), Manuela WALDNER (40 Austria), Katarína FURMANOVÁ (703 Slovakia, belonging to the institution), Sérgio Manuel MARQUES (620 Portugal, belonging to the institution), David BEDNÁŘ (203 Czech Republic, belonging to the institution), Barbora KOZLÍKOVÁ (203 Czech Republic, belonging to the institution) and Jan BYŠKA (203 Czech Republic, belonging to the institution)

Edition

IEEE Transactions on Visualization and Computer Graphics, United States, IEEE Computer Society, 2023, 1077-2626

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 5.200 in 2022

RIV identification code

RIV/00216224:14330/23:00130033

Organization unit

Faculty of Informatics

DOI

UT WoS

001266848400001

Keywords in English

Molecular dynamics;structure;node-based visualization;progressive analytics

Tags

International impact, Reviewed
Změněno: 25/7/2024 07:58, Mgr. Marie Šípková, DiS.

Abstract

V originále

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Links

GJ20-15915Y, research and development project
Name: Studium molekulováho rozpoznávání a vývoj nových softwarových nástrojů pro identifikaci a design přístupových cest v proteinech
Investor: Czech Science Foundation
LM2018131, research and development project
Name: Česká národní infrastruktura pro biologická data (Acronym: ELIXIR-CZ)
Investor: Ministry of Education, Youth and Sports of the CR, Czech National Infrastructure for Biological Data
LM2018140, research and development project
Name: e-Infrastruktura CZ (Acronym: e-INFRA CZ)
Investor: Ministry of Education, Youth and Sports of the CR
MUNI/A/1230/2021, interní kód MU
Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 22 (Acronym: SKOMU)
Investor: Masaryk University
MUNI/A/1339/2022, interní kód MU
Name: Rozvoj technik pro zpracování dat pro podporu vyhledávání, analýz a vizualizací rozsáhlých datových souborů s využitím umělé inteligence
Investor: Masaryk University, Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence