Abstract
Reconstructing the evolutionary past of a family of genes is an important aspect of many genomic studies. To help with this, simple relations on a set of sequences called orthology relations may be employed. In addition to being interesting from a practical point of view they are also attractive from a theoretical perspective in that e.\,g.\,a characterization is known for when such a relation is representable by a certain type of phylogenetic tree. For an orthology relation inferred from real biological data it is however generally too much to hope for that it satisfies that characterization. Rather than trying to correct the data in some way or another which has its own drawbacks, as an alternative, we propose to represent an orthology relation $\delta$ in terms of a structure more general than a phylogenetic tree called a phylogenetic network. To compute such a network in the form of a level-1 representation for $\delta$, we formalize an orthology relation in terms of the novel concept of a symbolic 3- dissimilarity which is motivated by the biological concept of a ``cluster of orthologous groups'', or COG for short. For such maps which assign symbols rather that real values to elements, we introduce the novel {\sc Network-Popping} algorithm which has several attractive properties. In addition, we characterize an orthology relation $\delta$ on some set $X$ that has a level-1 representation in terms of eight natural properties for $\delta$ as well as in terms of level-1 representations of orthology relations on certain subsets of $X$.
Original language | English |
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Pages (from-to) | 73–103 |
Number of pages | 31 |
Journal | Algorithmica |
Volume | 80 |
Issue number | 1 |
Early online date | 9 Nov 2016 |
DOIs | |
Publication status | Published - Jan 2018 |
Keywords
- orthology relation
- phylogenetic network
- level-1 network
- trinet
- 3-dissimilarity
- COG
Profiles
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Katharina Huber
- School of Computing Sciences - Associate Professor
- Computational Biology - Member
Person: Research Group Member, Academic, Teaching & Research