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Tanglegrams for rooted phylogenetic trees and networks.

Scornavacca C, Zickmann F, Huson DH - Bioinformatics (2011)

Bottom Line: We compare the performance of our method with existing tree tanglegram algorithms and also show a typical application to real biological datasets.For maximum usability, the algorithm does not require that the trees or networks are bifurcating or bicombining, or that they are on identical taxon sets.The algorithm is implemented in our program Dendroscope 3, which is freely available from www.dendroscope.org. scornava@informatik.uni-tuebingen.de; huson@informatik.uni-tuebingen.de.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics (ZBIT), Tübingen University, Sand 14, 72076 Tübingen, Germany. scornava@informatik.uni-tuebingen.de

ABSTRACT

Motivation: In systematic biology, one is often faced with the task of comparing different phylogenetic trees, in particular in multi-gene analysis or cospeciation studies. One approach is to use a tanglegram in which two rooted phylogenetic trees are drawn opposite each other, using auxiliary lines to connect matching taxa. There is an increasing interest in using rooted phylogenetic networks to represent evolutionary history, so as to explicitly represent reticulate events, such as horizontal gene transfer, hybridization or reassortment. Thus, the question arises how to define and compute a tanglegram for such networks.

Results: In this article, we present the first formal definition of a tanglegram for rooted phylogenetic networks and present a heuristic approach for computing one, called the NN-tanglegram method. We compare the performance of our method with existing tree tanglegram algorithms and also show a typical application to real biological datasets. For maximum usability, the algorithm does not require that the trees or networks are bifurcating or bicombining, or that they are on identical taxon sets.

Availability: The algorithm is implemented in our program Dendroscope 3, which is freely available from www.dendroscope.org.

Contact: scornava@informatik.uni-tuebingen.de; huson@informatik.uni-tuebingen.de.

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(a) Average running time (RT) and (b) performance ratio values (PR) for dataset D1. (c) Average running time (RT) and (d) performance ratio values (PR) for dataset D2.
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Figure 5: (a) Average running time (RT) and (b) performance ratio values (PR) for dataset D1. (c) Average running time (RT) and (d) performance ratio values (PR) for dataset D2.

Mentions: For each tanglegram and each method, we computed the performance ratio (PR), that is, the ratio (cn+1)/(cnopt+1), where cn and cnopt are the computed and the optimum number of crossings among connectors, respectively. The performance ratio values and the average running time for each method are shown in Figure 5.Fig. 5.


Tanglegrams for rooted phylogenetic trees and networks.

Scornavacca C, Zickmann F, Huson DH - Bioinformatics (2011)

(a) Average running time (RT) and (b) performance ratio values (PR) for dataset D1. (c) Average running time (RT) and (d) performance ratio values (PR) for dataset D2.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117342&req=5

Figure 5: (a) Average running time (RT) and (b) performance ratio values (PR) for dataset D1. (c) Average running time (RT) and (d) performance ratio values (PR) for dataset D2.
Mentions: For each tanglegram and each method, we computed the performance ratio (PR), that is, the ratio (cn+1)/(cnopt+1), where cn and cnopt are the computed and the optimum number of crossings among connectors, respectively. The performance ratio values and the average running time for each method are shown in Figure 5.Fig. 5.

Bottom Line: We compare the performance of our method with existing tree tanglegram algorithms and also show a typical application to real biological datasets.For maximum usability, the algorithm does not require that the trees or networks are bifurcating or bicombining, or that they are on identical taxon sets.The algorithm is implemented in our program Dendroscope 3, which is freely available from www.dendroscope.org. scornava@informatik.uni-tuebingen.de; huson@informatik.uni-tuebingen.de.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics (ZBIT), Tübingen University, Sand 14, 72076 Tübingen, Germany. scornava@informatik.uni-tuebingen.de

ABSTRACT

Motivation: In systematic biology, one is often faced with the task of comparing different phylogenetic trees, in particular in multi-gene analysis or cospeciation studies. One approach is to use a tanglegram in which two rooted phylogenetic trees are drawn opposite each other, using auxiliary lines to connect matching taxa. There is an increasing interest in using rooted phylogenetic networks to represent evolutionary history, so as to explicitly represent reticulate events, such as horizontal gene transfer, hybridization or reassortment. Thus, the question arises how to define and compute a tanglegram for such networks.

Results: In this article, we present the first formal definition of a tanglegram for rooted phylogenetic networks and present a heuristic approach for computing one, called the NN-tanglegram method. We compare the performance of our method with existing tree tanglegram algorithms and also show a typical application to real biological datasets. For maximum usability, the algorithm does not require that the trees or networks are bifurcating or bicombining, or that they are on identical taxon sets.

Availability: The algorithm is implemented in our program Dendroscope 3, which is freely available from www.dendroscope.org.

Contact: scornava@informatik.uni-tuebingen.de; huson@informatik.uni-tuebingen.de.

Show MeSH