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Visual exploration of parameter influence on phylogenetic trees.

Hess, Martin and Bremm, Sebastian and Weissgraeber, Stephanie and Hamacher, Kay and Goesele, Michael and Wiemeyer, Josef and von Landesberger, Tatiana (2014):
Visual exploration of parameter influence on phylogenetic trees.
In: IEEE computer graphics and applications, 34 (2), pp. 48-56. ISSN 1558-1756,
[Article]

Abstract

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.

Item Type: Article
Erschienen: 2014
Creators: Hess, Martin and Bremm, Sebastian and Weissgraeber, Stephanie and Hamacher, Kay and Goesele, Michael and Wiemeyer, Josef and von Landesberger, Tatiana
Title: Visual exploration of parameter influence on phylogenetic trees.
Language: English
Abstract:

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.

Journal or Publication Title: IEEE computer graphics and applications
Journal volume: 34
Number: 2
Divisions: 10 Department of Biology
10 Department of Biology > Computational Biology and Simulation
Date Deposited: 01 Jul 2014 11:14
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