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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/.

Show MeSH
Recurrent correlations display from the epigenetic to the protein level. Phenotypic outcomes at the level of histone acetylation, chromatin binding, mRNA production and nuclear protein abundance are shown for the genes that form the ESC core network on each layer. Nodes are colored corresponding to the gene value on a yellow-blue color scale. Correlations between the vectors of time-resolved values associated to each gene are highlighted by connecting the corresponding nodes with a yellow line (for positive correlations) or a red line (for negative correlations). Left hand side: all correlations with p-value < 0.05 (i.e. coefficient greater than 0.997) are represented as connections between nodes for each layer. Right hand side: only recurrent correlations on at least two layers are displayed for the corresponding layers. The layer of chromatin bound RNA polymerase II is not shown because there are no recurrent correlations on that layer.
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Figure 3: Recurrent correlations display from the epigenetic to the protein level. Phenotypic outcomes at the level of histone acetylation, chromatin binding, mRNA production and nuclear protein abundance are shown for the genes that form the ESC core network on each layer. Nodes are colored corresponding to the gene value on a yellow-blue color scale. Correlations between the vectors of time-resolved values associated to each gene are highlighted by connecting the corresponding nodes with a yellow line (for positive correlations) or a red line (for negative correlations). Left hand side: all correlations with p-value < 0.05 (i.e. coefficient greater than 0.997) are represented as connections between nodes for each layer. Right hand side: only recurrent correlations on at least two layers are displayed for the corresponding layers. The layer of chromatin bound RNA polymerase II is not shown because there are no recurrent correlations on that layer.

Mentions: Even though for the given data there are only three time points (degree of freedom equal to 1), which could be considered insufficient for significant correlations, we do find several cases when the correlation coefficient is greater than 0.997, such that the p-value is less than 0.05, thus denoting significant correlations (Figure 3, left hand side). For illustration purposes we consider this sufficient. However, the assessment of whether the data volume is suitable for applying such calculations should be done on a case-by-case basis.


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

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

Recurrent correlations display from the epigenetic to the protein level. Phenotypic outcomes at the level of histone acetylation, chromatin binding, mRNA production and nuclear protein abundance are shown for the genes that form the ESC core network on each layer. Nodes are colored corresponding to the gene value on a yellow-blue color scale. Correlations between the vectors of time-resolved values associated to each gene are highlighted by connecting the corresponding nodes with a yellow line (for positive correlations) or a red line (for negative correlations). Left hand side: all correlations with p-value < 0.05 (i.e. coefficient greater than 0.997) are represented as connections between nodes for each layer. Right hand side: only recurrent correlations on at least two layers are displayed for the corresponding layers. The layer of chromatin bound RNA polymerase II is not shown because there are no recurrent correlations on that layer.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Recurrent correlations display from the epigenetic to the protein level. Phenotypic outcomes at the level of histone acetylation, chromatin binding, mRNA production and nuclear protein abundance are shown for the genes that form the ESC core network on each layer. Nodes are colored corresponding to the gene value on a yellow-blue color scale. Correlations between the vectors of time-resolved values associated to each gene are highlighted by connecting the corresponding nodes with a yellow line (for positive correlations) or a red line (for negative correlations). Left hand side: all correlations with p-value < 0.05 (i.e. coefficient greater than 0.997) are represented as connections between nodes for each layer. Right hand side: only recurrent correlations on at least two layers are displayed for the corresponding layers. The layer of chromatin bound RNA polymerase II is not shown because there are no recurrent correlations on that layer.
Mentions: Even though for the given data there are only three time points (degree of freedom equal to 1), which could be considered insufficient for significant correlations, we do find several cases when the correlation coefficient is greater than 0.997, such that the p-value is less than 0.05, thus denoting significant correlations (Figure 3, left hand side). For illustration purposes we consider this sufficient. However, the assessment of whether the data volume is suitable for applying such calculations should be done on a case-by-case basis.

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