Novel Phylogenetic Network Inference by Combining Maximum Likelihood and Hidden Markov Models
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Simultaneous Identification of Duplications, Losses, and Lateral Gene Transfers
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: The evolution of viruses is very rapid and in addition to local point mutations (insertion, deletion, substitution) it also includes frequent recombinations, genome rearrangements and horizontal transfer of genetic materials (HGTS). Evolutionary analysis of viral sequences is therefore a complicated matter for two main reasons: First, due to HGTs and recombinations, the right model of evolution is a network and not a tree. Second, due to genome rearrangements, an alignment of the input sequences is not guaranteed. These facts encourage developing methods for inferring phylogenetic networks that do not require aligned sequences as input. Results: In this work, we present the first computational approach which deals with both genome rearrangements and horizontal gene transfers and does not require a multiple alignment as input. We formalize a new set of computational problems which involve analyzing such complex models of evolution. We investigate their computational complexity, and devise algorithms for solving them. Moreover, we demonstrate the viability of our methods on several synthetic datasets as well as four biological datasets. Availability: The code is available from the authors upon request. Contact: tamirtul@post.tau.ac.il Supplementary information: Supplementary data are available at Bioinformatics online.