Character-based parsimony models have been among the most studied notions in computational evolution, but research in the field stagnated until some important, recent applications, such as the analysis of data from protein domains, protein networks, and genetic markers, as well as haplotyping, brought new life into this sector. The focus of this survey is to present the perfect phylogeny model and some of its generalizations. In particular, we develop the use of persistency in the perfect phylogeny model as a new promising computational approach to analyzing and reconstructing evolution. We show that, in this setting, some graphtheoretical notions can provide a characterization of the relationships between characters (or attributes), playing a crucial role in developing algorithmic solutions to the problem of reconstructing a maximum parsimony tree
Bonizzoni, P., Carrieri, A., DELLA VEDOVA, G., Dondi, R., Przytycka, T. (2014). When and How the Perfect Phylogeny Model Explains Evolution. In N. Jonoska, M. Saito (a cura di), Discrete and Topological Models in Molecular Biology (pp. 67-83). Springer [10.1007/978-3-642-40193-0_4].
When and How the Perfect Phylogeny Model Explains Evolution
BONIZZONI, PAOLA;CARRIERI, ANNA PAOLA;DELLA VEDOVA, GIANLUCA;
2014
Abstract
Character-based parsimony models have been among the most studied notions in computational evolution, but research in the field stagnated until some important, recent applications, such as the analysis of data from protein domains, protein networks, and genetic markers, as well as haplotyping, brought new life into this sector. The focus of this survey is to present the perfect phylogeny model and some of its generalizations. In particular, we develop the use of persistency in the perfect phylogeny model as a new promising computational approach to analyzing and reconstructing evolution. We show that, in this setting, some graphtheoretical notions can provide a characterization of the relationships between characters (or attributes), playing a crucial role in developing algorithmic solutions to the problem of reconstructing a maximum parsimony tree
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