Finding the most parsimonious or likely tree in a network with respect to an alignment
July 12, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Steven Kelk, Fabio Pardi, Celine Scornavacca, Leo van Iersel
arXiv ID
1707.03648
Category
q-bio.PE
Cross-listed
cs.DS
Citations
0
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Phylogenetic networks are often constructed by merging multiple conflicting phylogenetic signals into a directed acyclic graph. It is interesting to explore whether a network constructed in this way induces biologically-relevant phylogenetic signals that were not present in the input. Here we show that, given a multiple alignment A for a set of taxa X and a rooted phylogenetic network N whose leaves are labelled by X, it is NP-hard to locate the most parsimonious phylogenetic tree displayed by N (with respect to A) even when the level of N - the maximum number of reticulation nodes within a biconnected component - is 1 and A contains only 2 distinct states. (If, additionally, gaps are allowed the problem becomes APX-hard.) We also show that under the same conditions, and assuming a simple binary symmetric model of character evolution, finding the most likely tree displayed by the network is NP-hard. These negative results contrast with earlier work on parsimony in which it is shown that if A consists of a single column the problem is fixed parameter tractable in the level. We conclude with a discussion of why, despite the NP-hardness, both the parsimony and likelihood problem can likely be well-solved in practice.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-bio.PE
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Simulating COVID-19 in a University Environment
R.I.P.
π»
Ghosted
How morphological development can guide evolution
R.I.P.
π»
Ghosted
Evolutionary forces in language change
R.I.P.
π»
Ghosted
Entropy and Diversity: The Axiomatic Approach
R.I.P.
π»
Ghosted
The evolution of conditional moral assessment in indirect reciprocity
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted