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LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB

BACKGROUND: Biochemical networks play an essential role in systems biology. Rapidly growing network data and versatile research activities call for convenient visualization tools to aid intuitively perceiving abstract structures of networks and gaining insights into the functional implications of ne...

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Detalles Bibliográficos
Autores principales: He, Sheng, Mei, Juan, Shi, Guiyang, Wang, Zhengxiang, Li, Weijiang
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2825225/
https://www.ncbi.nlm.nih.gov/pubmed/20074382
http://dx.doi.org/10.1186/1471-2105-11-31
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author He, Sheng
Mei, Juan
Shi, Guiyang
Wang, Zhengxiang
Li, Weijiang
author_facet He, Sheng
Mei, Juan
Shi, Guiyang
Wang, Zhengxiang
Li, Weijiang
author_sort He, Sheng
collection PubMed
description BACKGROUND: Biochemical networks play an essential role in systems biology. Rapidly growing network data and versatile research activities call for convenient visualization tools to aid intuitively perceiving abstract structures of networks and gaining insights into the functional implications of networks. There are various kinds of network visualization software, but they are usually not adequate for visual analysis of complex biological networks mainly because of the two reasons: 1) most existing drawing methods suitable for biochemical networks have high computation loads and can hardly achieve near real-time visualization; 2) available network visualization tools are designed for working in certain network modeling platforms, so they are not convenient for general analyses due to lack of broader range of readily accessible numerical utilities. RESULTS: We present LucidDraw as a visual analysis tool, which features (a) speed: typical biological networks with several hundreds of nodes can be drawn in a few seconds through a new layout algorithm; (b) ease of use: working within MATLAB makes it convenient to manipulate and analyze the network data using a broad spectrum of sophisticated numerical functions; (c) flexibility: layout styles and incorporation of other available information about functional modules can be controlled by users with little effort, and the output drawings are interactively modifiable. CONCLUSIONS: Equipped with a new grid layout algorithm proposed here, LucidDraw serves as an auxiliary network analysis tool capable of visualizing complex biological networks in near real-time with controllable layout styles and drawing details. The framework of the algorithm enables easy incorporation of extra biological information, if available, to influence the output layouts with predefined node grouping features.
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spelling pubmed-28252252010-02-20 LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB He, Sheng Mei, Juan Shi, Guiyang Wang, Zhengxiang Li, Weijiang BMC Bioinformatics Software BACKGROUND: Biochemical networks play an essential role in systems biology. Rapidly growing network data and versatile research activities call for convenient visualization tools to aid intuitively perceiving abstract structures of networks and gaining insights into the functional implications of networks. There are various kinds of network visualization software, but they are usually not adequate for visual analysis of complex biological networks mainly because of the two reasons: 1) most existing drawing methods suitable for biochemical networks have high computation loads and can hardly achieve near real-time visualization; 2) available network visualization tools are designed for working in certain network modeling platforms, so they are not convenient for general analyses due to lack of broader range of readily accessible numerical utilities. RESULTS: We present LucidDraw as a visual analysis tool, which features (a) speed: typical biological networks with several hundreds of nodes can be drawn in a few seconds through a new layout algorithm; (b) ease of use: working within MATLAB makes it convenient to manipulate and analyze the network data using a broad spectrum of sophisticated numerical functions; (c) flexibility: layout styles and incorporation of other available information about functional modules can be controlled by users with little effort, and the output drawings are interactively modifiable. CONCLUSIONS: Equipped with a new grid layout algorithm proposed here, LucidDraw serves as an auxiliary network analysis tool capable of visualizing complex biological networks in near real-time with controllable layout styles and drawing details. The framework of the algorithm enables easy incorporation of extra biological information, if available, to influence the output layouts with predefined node grouping features. BioMed Central 2010-01-15 /pmc/articles/PMC2825225/ /pubmed/20074382 http://dx.doi.org/10.1186/1471-2105-11-31 Text en Copyright ©2010 He et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
He, Sheng
Mei, Juan
Shi, Guiyang
Wang, Zhengxiang
Li, Weijiang
LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
title LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
title_full LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
title_fullStr LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
title_full_unstemmed LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
title_short LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
title_sort luciddraw: efficiently visualizing complex biochemical networks within matlab
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2825225/
https://www.ncbi.nlm.nih.gov/pubmed/20074382
http://dx.doi.org/10.1186/1471-2105-11-31
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