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

TL;DR

This paper introduces RSAM-finder, an efficient algorithm for detecting mutations that significantly alter gene evolution patterns, aiding in understanding microbial adaptation and resistance mechanisms.

Contribution

It formalizes the RSAM discovery problem in gene and species tree reconciliation and provides an optimal $O(m\,n\,k)$ time algorithm with a practical implementation.

Findings

Successfully identified RSAMs in toxins and drug resistance elements across hundreds of species.

Demonstrated the algorithm's efficiency and effectiveness in real microbial datasets.

Provided a new computational tool for evolutionary genomics research.

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 paper, 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 $O(m\cdot n\cdot k)$ 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 pre-specified RSAM Pattern. Our algorithm is optimal in the sense that the number of hypernodes in the hypergraph can be lower bounded by $\Omega(m\cdot n\cdot k)$. 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 dataset spanning hundreds of species.