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. 2010 Feb 22;3(1):1.
doi: 10.1186/1756-0381-3-1.

A reference guide for tree analysis and visualization

Affiliations

A reference guide for tree analysis and visualization

Georgios A Pavlopoulos et al. BioData Min. .

Abstract

The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis.

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Figures

Figure 1
Figure 1
A) An example of a cladogram representation: a branching diagram assumed to be an estimate of a phylogeny. B) An example of a phylogram. A phylogram is different from a cladogram with respect to the fact that the branch lengths are proportional to the amount of inferred evolutionary change. C) An example of an unrooted cladogram. An unrooted tree can be rooted on any of its branches, and so there are many rooted trees that can be derived from a single unrooted tree. D) An example of a circular cladogram. These kinds of layout types place the nodes in concentric rings around the center. E) An example of a slanted cladogram. The sloped version of the rectangular layout remains equally informative and efficient. F) An example of a hyperbolic tree. G) 3D Trees by 3DPE (3D Phylogeny Explorer) tool. H) 3D tree visualized by Arena3D [67] visualization tool.
Figure 2
Figure 2
A simple tree example described in Newick format: (((A:0.2, B:0.3):0.3,(C:0.5, D:0.3):0.2):0.3, E:0.7):1.0;

References

    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–29. doi: 10.1038/75556. - DOI - PMC - PubMed
    1. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004. pp. D267–270. - DOI - PMC - PubMed
    1. Darwin C. The Origin of Species. The Modern Library, New York. 1872. pp. 170–171.
    1. Pennisi E. Modernizing the tree of life. Science. 2003;300(5626):1692–1697. doi: 10.1126/science.300.5626.1692. - DOI - PubMed
    1. Jain AK, Murty MN, Flynn PJ. Data Clustering: A review. ACM Comp Surv. 1999.

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