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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
Similarity scoring of gene knockdown impact profiles. Scoring the overall impact of individual gene knockdowns on the prevalence of different phenotypes. We look at the span of one cell cycle, approximately 50 time points. Nodes correspond to gene knockdown events and are colored according to the scoring scale, as indicated (white-dark red, low-high). A set of 1067 essential mitotic genes is represented on each layer. One gene has the same position on all layers. Two alternative scoring schemes are presented: (a) averaging the values in the gene knockdown vector; (b) the lower bound of Wilson score confidence interval. A line chart of timeline evolution of knockdown values for each phenotype can be obtained by clicking on a particular node of interest, as shown for genes incenp and ranbp3, both of which display increasingly higher signal for the phenotype "polylobed" (green line) throughout the time course.
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Figure 5: Similarity scoring of gene knockdown impact profiles. Scoring the overall impact of individual gene knockdowns on the prevalence of different phenotypes. We look at the span of one cell cycle, approximately 50 time points. Nodes correspond to gene knockdown events and are colored according to the scoring scale, as indicated (white-dark red, low-high). A set of 1067 essential mitotic genes is represented on each layer. One gene has the same position on all layers. Two alternative scoring schemes are presented: (a) averaging the values in the gene knockdown vector; (b) the lower bound of Wilson score confidence interval. A line chart of timeline evolution of knockdown values for each phenotype can be obtained by clicking on a particular node of interest, as shown for genes incenp and ranbp3, both of which display increasingly higher signal for the phenotype "polylobed" (green line) throughout the time course.

Mentions: The score based on averaging (Figure 5a) is revealing some genes with high effect upon knockdown on the cell phenotypic landscape. The highest peaking signals overall are found for the polylobed phenotype, which is indeed a strongly prevalent phenotype in many of the screens. This scoring scheme thus allows selective decisions about potentially interesting targets for further experiments.


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

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

Similarity scoring of gene knockdown impact profiles. Scoring the overall impact of individual gene knockdowns on the prevalence of different phenotypes. We look at the span of one cell cycle, approximately 50 time points. Nodes correspond to gene knockdown events and are colored according to the scoring scale, as indicated (white-dark red, low-high). A set of 1067 essential mitotic genes is represented on each layer. One gene has the same position on all layers. Two alternative scoring schemes are presented: (a) averaging the values in the gene knockdown vector; (b) the lower bound of Wilson score confidence interval. A line chart of timeline evolution of knockdown values for each phenotype can be obtained by clicking on a particular node of interest, as shown for genes incenp and ranbp3, both of which display increasingly higher signal for the phenotype "polylobed" (green line) throughout the time course.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Similarity scoring of gene knockdown impact profiles. Scoring the overall impact of individual gene knockdowns on the prevalence of different phenotypes. We look at the span of one cell cycle, approximately 50 time points. Nodes correspond to gene knockdown events and are colored according to the scoring scale, as indicated (white-dark red, low-high). A set of 1067 essential mitotic genes is represented on each layer. One gene has the same position on all layers. Two alternative scoring schemes are presented: (a) averaging the values in the gene knockdown vector; (b) the lower bound of Wilson score confidence interval. A line chart of timeline evolution of knockdown values for each phenotype can be obtained by clicking on a particular node of interest, as shown for genes incenp and ranbp3, both of which display increasingly higher signal for the phenotype "polylobed" (green line) throughout the time course.
Mentions: The score based on averaging (Figure 5a) is revealing some genes with high effect upon knockdown on the cell phenotypic landscape. The highest peaking signals overall are found for the polylobed phenotype, which is indeed a strongly prevalent phenotype in many of the screens. This scoring scheme thus allows selective decisions about potentially interesting targets for further experiments.

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