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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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Dynamic clustering of layered biological profiles. The network of ESC core genes is shown connected on each one of the four layers depicting phenotypic outcomes in terms of histone acetylation levels (HIS), RNA polymerase binding affinity (POL), mRNA production (RNA) and protein translation levels (PRO) upon knockdown of nanog. Nodes correspond to genes and are colored according to the values associated at every time point for each informational level, on a yellow-to-blue gradient as indicated. Clustering at three distinct time points is shown for each level: (a) day 1; (b) day 3; (c) day 5. There seems to be a transition in terms of dynamics based on the evolution of gene-associated values and clustering outcomes from the epigenetic levels (most dynamic) to the translational level (most stable). Genes with highest change in associated impact value between consecutive time points are connected between layers for better emphasis (b): close-up picture shows genes prmt1, smarcad1 and rnf2 as the ones displaying the highest change.
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Figure 2: Dynamic clustering of layered biological profiles. The network of ESC core genes is shown connected on each one of the four layers depicting phenotypic outcomes in terms of histone acetylation levels (HIS), RNA polymerase binding affinity (POL), mRNA production (RNA) and protein translation levels (PRO) upon knockdown of nanog. Nodes correspond to genes and are colored according to the values associated at every time point for each informational level, on a yellow-to-blue gradient as indicated. Clustering at three distinct time points is shown for each level: (a) day 1; (b) day 3; (c) day 5. There seems to be a transition in terms of dynamics based on the evolution of gene-associated values and clustering outcomes from the epigenetic levels (most dynamic) to the translational level (most stable). Genes with highest change in associated impact value between consecutive time points are connected between layers for better emphasis (b): close-up picture shows genes prmt1, smarcad1 and rnf2 as the ones displaying the highest change.

Mentions: The four layers of systems dynamics are visualized correspondingly: histone acetylation, chromatin bound RNA polymerase II, mRNA levels and nuclear protein abundance. On each layer, the ESC core network is represented, with nodes corresponding to genes/proteins and links to the interactions between them. Nodes are colored according to the level of acetylation, polymerase localization on chromatin, mRNA abundance or protein levels, respectively, for the corresponding gene. Values map to node color on a yellow-blue color scale, such that lowest values are coded in blue, highest in yellow and the intermediate ones according to the gradient in-between. Grey represents absolute 0. The changes in these values for the three days of measurement can be easily tracked using a slider that updates the network and the node colors for every time point. One can then further analyze snapshots of phenotypic profiles for different stages of the experiment (Figure 2).


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

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

Dynamic clustering of layered biological profiles. The network of ESC core genes is shown connected on each one of the four layers depicting phenotypic outcomes in terms of histone acetylation levels (HIS), RNA polymerase binding affinity (POL), mRNA production (RNA) and protein translation levels (PRO) upon knockdown of nanog. Nodes correspond to genes and are colored according to the values associated at every time point for each informational level, on a yellow-to-blue gradient as indicated. Clustering at three distinct time points is shown for each level: (a) day 1; (b) day 3; (c) day 5. There seems to be a transition in terms of dynamics based on the evolution of gene-associated values and clustering outcomes from the epigenetic levels (most dynamic) to the translational level (most stable). Genes with highest change in associated impact value between consecutive time points are connected between layers for better emphasis (b): close-up picture shows genes prmt1, smarcad1 and rnf2 as the ones displaying the highest change.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Dynamic clustering of layered biological profiles. The network of ESC core genes is shown connected on each one of the four layers depicting phenotypic outcomes in terms of histone acetylation levels (HIS), RNA polymerase binding affinity (POL), mRNA production (RNA) and protein translation levels (PRO) upon knockdown of nanog. Nodes correspond to genes and are colored according to the values associated at every time point for each informational level, on a yellow-to-blue gradient as indicated. Clustering at three distinct time points is shown for each level: (a) day 1; (b) day 3; (c) day 5. There seems to be a transition in terms of dynamics based on the evolution of gene-associated values and clustering outcomes from the epigenetic levels (most dynamic) to the translational level (most stable). Genes with highest change in associated impact value between consecutive time points are connected between layers for better emphasis (b): close-up picture shows genes prmt1, smarcad1 and rnf2 as the ones displaying the highest change.
Mentions: The four layers of systems dynamics are visualized correspondingly: histone acetylation, chromatin bound RNA polymerase II, mRNA levels and nuclear protein abundance. On each layer, the ESC core network is represented, with nodes corresponding to genes/proteins and links to the interactions between them. Nodes are colored according to the level of acetylation, polymerase localization on chromatin, mRNA abundance or protein levels, respectively, for the corresponding gene. Values map to node color on a yellow-blue color scale, such that lowest values are coded in blue, highest in yellow and the intermediate ones according to the gradient in-between. Grey represents absolute 0. The changes in these values for the three days of measurement can be easily tracked using a slider that updates the network and the node colors for every time point. One can then further analyze snapshots of phenotypic profiles for different stages of the experiment (Figure 2).

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