Hess, Martin ; Bremm, Sebastian ; Weissgraeber, Stephanie ; Hamacher, Kay ; Goesele, Michael ; Wiemeyer, Josef ; Landesberger von Antburg, Tatiana (2014)
Visual exploration of parameter influence on phylogenetic trees.
In: IEEE computer graphics and applications, 34 (2)
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Hess, Martin ; Bremm, Sebastian ; Weissgraeber, Stephanie ; Hamacher, Kay ; Goesele, Michael ; Wiemeyer, Josef ; Landesberger von Antburg, Tatiana |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual exploration of parameter influence on phylogenetic trees. |
| Sprache: | Englisch |
| Publikationsjahr: | 2014 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE computer graphics and applications |
| Jahrgang/Volume einer Zeitschrift: | 34 |
| (Heft-)Nummer: | 2 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 10 Fachbereich Biologie 10 Fachbereich Biologie > Computational Biology and Simulation |
| Hinterlegungsdatum: | 01 Jul 2014 11:14 |
| Letzte Änderung: | 09 Dez 2021 11:45 |
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