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Arena3D: visualizing time-driven phenotypic differences in biological systems.

Secrier M, Pavlopoulos GA, Aerts J, Schneider R - BMC Bioinformatics (2012)

Bottom Line: First, we analyze a medium scale dataset that looks at perturbation effects of the pluripotency regulator Nanog in murine embryonic stem cells.We also show how phenotypic patterning allows for extensive comparison and identification of high impact knockdown targets.The novel functionality implemented in Arena3D enables effective understanding and comparison of temporal patterns within morphological layers, to help with the system-wide analysis of dynamic processes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg 69117, Germany. secrier@embl.de

ABSTRACT

Background: Elucidating the genotype-phenotype connection is one of the big challenges of modern molecular biology. To fully understand this connection, it is necessary to consider the underlying networks and the time factor. In this context of data deluge and heterogeneous information, visualization plays an essential role in interpreting complex and dynamic topologies. Thus, software that is able to bring the network, phenotypic and temporal information together is needed. Arena3D has been previously introduced as a tool that facilitates link discovery between processes. It uses a layered display to separate different levels of information while emphasizing the connections between them. We present novel developments of the tool for the visualization and analysis of dynamic genotype-phenotype landscapes.

Results: Version 2.0 introduces novel features that allow handling time course data in a phenotypic context. Gene expression levels or other measures can be loaded and visualized at different time points and phenotypic comparison is facilitated through clustering and correlation display or highlighting of impacting changes through time. Similarity scoring allows the identification of global patterns in dynamic heterogeneous data. In this paper we demonstrate the utility of the tool on two distinct biological problems of different scales. First, we analyze a medium scale dataset that looks at perturbation effects of the pluripotency regulator Nanog in murine embryonic stem cells. Dynamic cluster analysis suggests alternative indirect links between Nanog and other proteins in the core stem cell network. Moreover, recurrent correlations from the epigenetic to the translational level are identified. Second, we investigate a large scale dataset consisting of genome-wide knockdown screens for human genes essential in the mitotic process. Here, a potential new role for the gene lsm14a in cytokinesis is suggested. We also show how phenotypic patterning allows for extensive comparison and identification of high impact knockdown targets.

Conclusions: We present a new visualization approach for perturbation screens with multiple phenotypic outcomes. The novel functionality implemented in Arena3D enables effective understanding and comparison of temporal patterns within morphological layers, to help with the system-wide analysis of dynamic processes. Arena3D is available free of charge for academics as a downloadable standalone application from: http://arena3d.org/.

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Time-resolved clustering and individual tracking of a gene. A subset of essential mitotic genes (see Additional file 4) is depicted on each layer as nodes colored according to the associated knockdown effect, from yellow to blue (low to high impact). Grey represents 0 impact. Each layer corresponds to one phenotype. Clustering of gene knockdown profiles and gene tracking are highlighted for three individual time points: (a) t = 2 h; (b) t = 7 h; (c) t = 33 h. Dynamic clustering of genes on different layers reveals more dynamic changes for the "grape", "large" and "dynamic" phenotypes compared to "mitotic delay" or "polylobed", which tend to stay more constant, indicating that these phenotypes may be more stable compared to the previous ones. The gene lsm14a is tracked by node expansion (also indicated using arrows for "mitotic delay" and "grape"). Its silencing has a mild to more pronounced impact for the "mitotic delay" phenotype (a-b), while having no influence on phenotype "grape" in the beginning (a) and high towards the end (c), indicating a latent impact on the cell upon this particular knockdown that determines it to adopt "grape" morphology after stagnation during mitosis.
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Figure 4: Time-resolved clustering and individual tracking of a gene. A subset of essential mitotic genes (see Additional file 4) is depicted on each layer as nodes colored according to the associated knockdown effect, from yellow to blue (low to high impact). Grey represents 0 impact. Each layer corresponds to one phenotype. Clustering of gene knockdown profiles and gene tracking are highlighted for three individual time points: (a) t = 2 h; (b) t = 7 h; (c) t = 33 h. Dynamic clustering of genes on different layers reveals more dynamic changes for the "grape", "large" and "dynamic" phenotypes compared to "mitotic delay" or "polylobed", which tend to stay more constant, indicating that these phenotypes may be more stable compared to the previous ones. The gene lsm14a is tracked by node expansion (also indicated using arrows for "mitotic delay" and "grape"). Its silencing has a mild to more pronounced impact for the "mitotic delay" phenotype (a-b), while having no influence on phenotype "grape" in the beginning (a) and high towards the end (c), indicating a latent impact on the cell upon this particular knockdown that determines it to adopt "grape" morphology after stagnation during mitosis.

Mentions: We visualize the effects of every gene knockdown (represented by nodes) for every resulting phenotype (each represented in one separate layer). The dynamic changes in gene knockdown impact are visualized through corresponding changes in node color as described for the previous experiment. The changes can be again tracked, as shown in Figure 4. The same visualization can also be applied to other datasets for changes in gene expression, protein concentration or any other kind of time-resolved variables.


Arena3D: visualizing time-driven phenotypic differences in biological systems.

Secrier M, Pavlopoulos GA, Aerts J, Schneider R - BMC Bioinformatics (2012)

Time-resolved clustering and individual tracking of a gene. A subset of essential mitotic genes (see Additional file 4) is depicted on each layer as nodes colored according to the associated knockdown effect, from yellow to blue (low to high impact). Grey represents 0 impact. Each layer corresponds to one phenotype. Clustering of gene knockdown profiles and gene tracking are highlighted for three individual time points: (a) t = 2 h; (b) t = 7 h; (c) t = 33 h. Dynamic clustering of genes on different layers reveals more dynamic changes for the "grape", "large" and "dynamic" phenotypes compared to "mitotic delay" or "polylobed", which tend to stay more constant, indicating that these phenotypes may be more stable compared to the previous ones. The gene lsm14a is tracked by node expansion (also indicated using arrows for "mitotic delay" and "grape"). Its silencing has a mild to more pronounced impact for the "mitotic delay" phenotype (a-b), while having no influence on phenotype "grape" in the beginning (a) and high towards the end (c), indicating a latent impact on the cell upon this particular knockdown that determines it to adopt "grape" morphology after stagnation during mitosis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368716&req=5

Figure 4: Time-resolved clustering and individual tracking of a gene. A subset of essential mitotic genes (see Additional file 4) is depicted on each layer as nodes colored according to the associated knockdown effect, from yellow to blue (low to high impact). Grey represents 0 impact. Each layer corresponds to one phenotype. Clustering of gene knockdown profiles and gene tracking are highlighted for three individual time points: (a) t = 2 h; (b) t = 7 h; (c) t = 33 h. Dynamic clustering of genes on different layers reveals more dynamic changes for the "grape", "large" and "dynamic" phenotypes compared to "mitotic delay" or "polylobed", which tend to stay more constant, indicating that these phenotypes may be more stable compared to the previous ones. The gene lsm14a is tracked by node expansion (also indicated using arrows for "mitotic delay" and "grape"). Its silencing has a mild to more pronounced impact for the "mitotic delay" phenotype (a-b), while having no influence on phenotype "grape" in the beginning (a) and high towards the end (c), indicating a latent impact on the cell upon this particular knockdown that determines it to adopt "grape" morphology after stagnation during mitosis.
Mentions: We visualize the effects of every gene knockdown (represented by nodes) for every resulting phenotype (each represented in one separate layer). The dynamic changes in gene knockdown impact are visualized through corresponding changes in node color as described for the previous experiment. The changes can be again tracked, as shown in Figure 4. The same visualization can also be applied to other datasets for changes in gene expression, protein concentration or any other kind of time-resolved variables.

Bottom Line: First, we analyze a medium scale dataset that looks at perturbation effects of the pluripotency regulator Nanog in murine embryonic stem cells.We also show how phenotypic patterning allows for extensive comparison and identification of high impact knockdown targets.The novel functionality implemented in Arena3D enables effective understanding and comparison of temporal patterns within morphological layers, to help with the system-wide analysis of dynamic processes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg 69117, Germany. secrier@embl.de

ABSTRACT

Background: Elucidating the genotype-phenotype connection is one of the big challenges of modern molecular biology. To fully understand this connection, it is necessary to consider the underlying networks and the time factor. In this context of data deluge and heterogeneous information, visualization plays an essential role in interpreting complex and dynamic topologies. Thus, software that is able to bring the network, phenotypic and temporal information together is needed. Arena3D has been previously introduced as a tool that facilitates link discovery between processes. It uses a layered display to separate different levels of information while emphasizing the connections between them. We present novel developments of the tool for the visualization and analysis of dynamic genotype-phenotype landscapes.

Results: Version 2.0 introduces novel features that allow handling time course data in a phenotypic context. Gene expression levels or other measures can be loaded and visualized at different time points and phenotypic comparison is facilitated through clustering and correlation display or highlighting of impacting changes through time. Similarity scoring allows the identification of global patterns in dynamic heterogeneous data. In this paper we demonstrate the utility of the tool on two distinct biological problems of different scales. First, we analyze a medium scale dataset that looks at perturbation effects of the pluripotency regulator Nanog in murine embryonic stem cells. Dynamic cluster analysis suggests alternative indirect links between Nanog and other proteins in the core stem cell network. Moreover, recurrent correlations from the epigenetic to the translational level are identified. Second, we investigate a large scale dataset consisting of genome-wide knockdown screens for human genes essential in the mitotic process. Here, a potential new role for the gene lsm14a in cytokinesis is suggested. We also show how phenotypic patterning allows for extensive comparison and identification of high impact knockdown targets.

Conclusions: We present a new visualization approach for perturbation screens with multiple phenotypic outcomes. The novel functionality implemented in Arena3D enables effective understanding and comparison of temporal patterns within morphological layers, to help with the system-wide analysis of dynamic processes. Arena3D is available free of charge for academics as a downloadable standalone application from: http://arena3d.org/.

Show MeSH