A New Paradigm for Identifying Reconciliation-Scenario Altering Mutations Conferring Environmental Adaptation

Abstract

An important goal in microbial computational genomics is to identify crucial events in the evolution of a gene that severely alter the duplication, loss, and mobilization patterns of the gene within the genomes in which it disseminates. In this article, we formalize this microbiological goal as a new pattern-matching problem in the domain of gene tree and species tree reconciliation, denoted “Reconciliation-Scenario Altering Mutation (RSAM) Discovery.” We propose an $O\left(m\cdot n\cdot k\right)$ time algorithm to solve this new problem, where m and n are the number of vertices of the input gene tree and species tree, respectively, and k is a user-specified parameter that bounds from above the number of optimal solutions of interest. The algorithm first constructs a hypergraph representing the k highest scoring reconciliation scenarios between the given gene tree and species tree, and then interrogates this hypergraph for subtrees matching a prespecified RSAM pattern. Our algorithm is optimal in the sense that the number of hypernodes in the hypergraph can be lower bounded by $\Omega \left(m\cdot n\cdot k\right)$ . We implement the new algorithm as a tool, called RSAM-finder, and demonstrate its application to the identification of RSAMs in toxins and drug resistance elements across a data set spanning hundreds of species.