Article

Arena3D: visualizing time-driven phenotypic differences in biological systems

Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg 69117, Germany.
BMC Bioinformatics (Impact Factor: 2.67). 03/2012; 13:45. DOI: 10.1186/1471-2105-13-45
Source: PubMed

ABSTRACT 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.
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.
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/.

Download full-text

Full-text

Available from: Georgios A Pavlopoulos, Jul 08, 2015
0 Followers
 · 
189 Views
  • Source
    • "Our combined approach, exploiting the information of interacting duplexes, eases the task of characterizing the regulatory role of newly discovered sRNAs. While some research has been made for the visual mining of biological networks, like signaling network [5], protein-protein network [18] or metabolic network [33] (for a review see [27]), there has been, to the best of our knowledge, no previously published work on providing visual support for the prediction of sRNA-mRNA interactions at a genome scale. Integration of biological databases is currently one the main challenges in the data mining community, and many works have focused on enrichment analyses for driving the integration of multipurpose omics or "
    Dataset: dubois-rNAV
  • Source
    • "anticoagulants). The network created by these data was visualized using Arena3D [51] [52]. Arena 3D uses staggered layers in 3D space, allowing the user to group related data into separate layers; in this case, the proteins, the drugs and the indications/diseases. "
    [Show abstract] [Hide abstract]
    ABSTRACT: A major part of membrane function is conducted by proteins, both integral and peripheral. Peripheral membrane proteins temporarily adhere to biological membranes, either to the lipid bilayer or to integral membrane proteins with non-covalent interactions. The aim of this study was to construct and analyze the interactions of the human plasma membrane peripheral proteins (peripherome hereinafter). For this purpose, we collected a dataset of peripheral proteins of the human plasma membrane. We also collected a dataset of experimentally verified interactions for these proteins. The interaction network created from this dataset has been visualized using Cytoscape. We grouped the proteins based on their subcellular location and clustered them using the MCL algorithm in order to detect functional modules. Moreover, functional and graph theory based analyses have been performed to assess biological features of the network. Interaction data with drug molecules show that ~10% of peripheral membrane proteins are targets for approved drugs, suggesting their potential implications in disease. In conclusion, we reveal novel features and properties regarding the protein-protein interaction network created by peripheral proteins of the human plasma membrane.
    BioMed Research International 03/2014; 2014. DOI:10.1155/2014/397145 · 2.71 Impact Factor
  • Source
    • "Our combined approach, exploiting the information of interacting duplexes, eases the task of characterizing the regulatory role of newly discovered sRNAs. While some research has been made for the visual mining of biological networks, like signaling network [5], protein-protein network [18] or metabolic network [33] (for a review see [27]), there has been, to the best of our knowledge, no previously published work on providing visual support for the prediction of sRNA-mRNA interactions at a genome scale. Integration of biological databases is currently one the main challenges in the data mining community, and many works have focused on enrichment analyses for driving the integration of multipurpose omics or "
    [Show abstract] [Hide abstract]
    ABSTRACT: The central dogma in molecular biology postulated that 'DNA makes RNA makes protein', however this dogma has been recently extended to integrate new biological activities involving small bacterial noncoding RNAs, called sRNAs. Accordingly, increasing attention has been given to these molecules over the last decade and related experimental works have shown a wide range of functional activities for these molecules. In this paper, we present rNAV (for rna NAVigator), a new tool for the visual exploration and analysis of bacterial sRNA-mediated regulatory networks. rNAV has been designed to help bioinformaticians and biologists to identify, from lists of thousands of predictions, pertinent and reasonable sRNA target candidates for carrying out experimental validations. We propose a list of dedicated algorithms and interaction tools that facilitate the exploration of such networks. These algorithms can be gathered into pipelines which can then be saved and reused over several sessions. To support exploration awareness, rNAV also provides an exploration tree view that allows to navigate through the steps of the analysis but also to select the sub-networks to visualize and compare. These comparisons are facilitated by the integration of multiple and fully linked views. We demonstrate the usefulness of our approach by a case study on Escherichia coli bacteria performed by domain experts.
    Biological Data Visualization (BioVis), 2013 IEEE Symposium on; 10/2013